From 204371026d16a88b12bfdd4761ed15c16c8ecc9d Mon Sep 17 00:00:00 2001 From: Alex Liberzon Date: Sun, 15 Mar 2026 14:23:05 +0200 Subject: [PATCH 1/7] explain the API --- docs/_static/native-backend-unification.svg | 56 +++++++++ docs/_static/native-stress-demo.svg | 58 +++++++++ docs/index.md | 1 + docs/native_backend_unification.md | 127 ++++++++++++++++++++ 4 files changed, 242 insertions(+) create mode 100644 docs/_static/native-backend-unification.svg create mode 100644 docs/_static/native-stress-demo.svg create mode 100644 docs/native_backend_unification.md diff --git a/docs/_static/native-backend-unification.svg b/docs/_static/native-backend-unification.svg new file mode 100644 index 0000000..97a2eb0 --- /dev/null +++ b/docs/_static/native-backend-unification.svg @@ -0,0 +1,56 @@ + + Unified openptv-python backend architecture + Diagram showing a single Python API routing work to Python, Numba, or optv backends while keeping result types stable. + + + + One user API, multiple execution paths + User scripts and batch jobs call openptv_python once; backend routing stays internal. + + + User script / batch job + import openptv_python + run the same pipeline + + + Public openptv_python API + preprocess_image() + targ_rec() + point_positions(), py_correspondences(), TrackingRun + + + Adapter layer + _native_compat.py + _native_convert.py + same Python signature + + + + + + + + Pure Python + Reference implementation + Always available + Guarantees batch execution + + + Python + Numba + Accelerated kernels + No API change + Current medium-performance path + + + Native optv + Optional dependency + Already active for preprocessing + and full-frame segmentation + + + + + + + Stable result objects and workflow semantics: the user should not need to care which backend ran. + \ No newline at end of file diff --git a/docs/_static/native-stress-demo.svg b/docs/_static/native-stress-demo.svg new file mode 100644 index 0000000..6898889 --- /dev/null +++ b/docs/_static/native-stress-demo.svg @@ -0,0 +1,58 @@ + + Native stress benchmark demo from this machine + Horizontal bar chart showing native speedup over the Python path for sequence loading, preprocessing, segmentation, stereomatching, reconstruction, multilayer reconstruction, and tracking. + + + + Local demo from tests/test_native_stress_performance.py + Measured on this machine on 2026-03-15. Bars show native speedup over the routed Python path used by each stress test. + + + + + 1x + 10x + 100x + 1,000x + 10,000x + + + + + + + Sequence params + + 2.78x + + Preprocess image + + 464.80x + + Segmentation + + 4.08x + + Stereo matching + + 7646.98x + + Reconstruction + + 1.93x + + Multilayer reconstruction + + 2.33x + + Tracking + + 6.68x + + + Interpretation + Green: general Python-versus-native stages + Blue: compiled Python plus Numba versus native + + Median timings came from the stress-suite printouts captured from this repository on this workstation. + \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 58315f4..da4681b 100644 --- a/docs/index.md +++ b/docs/index.md @@ -18,6 +18,7 @@ version is: README.md bundle_adjustment.md +native_backend_unification.md ``` # Indices and tables diff --git a/docs/native_backend_unification.md b/docs/native_backend_unification.md new file mode 100644 index 0000000..7759ac4 --- /dev/null +++ b/docs/native_backend_unification.md @@ -0,0 +1,127 @@ +# Unified Python API Across Python, Numba, and optv + +This page explains the backend model that `openptv-python` is moving toward: +one public Python API for the user, with execution routed internally to pure +Python, Python plus Numba, or native `optv` when available. + +The important user-facing contract is simple: + +- the script imports `openptv_python`, not `optv` +- the same Python calls should work with and without `optv` installed +- native acceleration is an implementation detail, not a second API surface + +![Unified API pipeline](_static/native-backend-unification.svg) + +## How the API is unified today + +The repository already has the basic adapter structure needed for a transparent +backend model: + +- optional native feature detection lives in `openptv_python/_native_compat.py` +- Python-to-native object conversion lives in `openptv_python/_native_convert.py` +- public runtime entry points keep their Python signatures and decide internally + whether to call Python or native implementations + +Today, transparent native delegation is already active for two high-value entry +points: + +- `openptv_python.image_processing.preprocess_image()` +- `openptv_python.segmentation.targ_rec()` + +The rest of the 3D-PTV pipeline is already callable from Python without native +bindings, and the stress suite shows that several later stages also have native +parity paths available: + +- reconstruction via `openptv_python.orientation.point_positions()` +- correspondence search via `openptv_python.correspondences.py_correspondences()` +- tracking orchestration via `openptv_python.tracking_run.TrackingRun` + +That split is exactly what we want for batch jobs: + +1. one Python script controls the workflow +2. pure Python remains the guaranteed fallback path +3. Numba accelerates selected kernels automatically +4. `optv` can be used underneath the same API when installed + +## Why `tests/test_native_stress_performance.py` matters + +The best executable description of this backend strategy is +`tests/test_native_stress_performance.py`. + +That test module does three useful things: + +1. it validates parity between Python and native paths for several stages +2. it benchmarks Python, compiled Python, and native implementations side by side +3. it demonstrates which stages already have a viable native provider behind the + same workflow semantics + +The benchmarked workloads in that file currently cover: + +- sequence parameter loading +- image preprocessing +- full-frame target recognition +- stereo matching / correspondences +- point reconstruction +- multilayer point reconstruction +- short-sequence tracking + +## Demo from this machine + +The figure below was generated from a real run on this machine on March 15, +2026 using: + +```bash +/home/user/Documents/GitHub/openptv-python/.venv/bin/python -m pytest -q -s tests/test_native_stress_performance.py +``` + +The chart shows native speedup over the routed Python path used by the test: + +- for preprocessing, segmentation, stereomatching, and tracking this is Python + versus native +- for reconstruction workloads this is compiled Python plus Numba versus native + +![Native stress benchmark demo](_static/native-stress-demo.svg) + +## Measured median timings + +| Workload | Python or routed Python path | Native path | Speedup shown in figure | +| --- | ---: | ---: | ---: | +| Sequence params | 0.000029 s | 0.000010 s | 2.78x | +| Preprocess image | 2.263711 s | 0.004870 s | 464.80x | +| Segmentation | 0.012110 s | 0.002967 s | 4.08x | +| Stereo matching | 23.990435 s | 0.003137 s | 7646.98x | +| Reconstruction | 0.012397 s | 0.006419 s | 1.93x | +| Multilayer reconstruction | 0.015707 s | 0.006754 s | 2.33x | +| Tracking | 2.247133 s | 0.336165 s | 6.68x | + +For the two reconstruction workloads, the same stress test also reported the +benefit of the current compiled Python path over the older per-point Python loop: + +- reconstruction: compiled Python was 63.01x faster than legacy Python +- multilayer reconstruction: compiled Python was 54.87x faster than legacy Python + +This is the key architectural message of the page: the repository does not need +two user APIs to get performance. It needs one stable Python API with multiple +internal execution paths. + +## What this means for the roadmap + +The test results support a practical backend policy: + +1. keep Python as the orchestration and scripting surface +2. preserve current public function signatures so scripts and GUI code do not + care which backend is active +3. keep the pure Python implementation as the reference behavior +4. continue using Numba for the medium-performance path +5. route additional heavy stages to `optv` or future compiled kernels behind + the same API + +In other words, users should be able to write one batch script and get: + +- correct execution with no native dependencies installed +- better throughput when Numba is active +- still better throughput when `optv` is installed and the operation has a + native provider + +That is the unifying API story already visible in the codebase and demonstrated +by the stress suite. \ No newline at end of file From 17214bb04861c25c8c7b8060f3f6a14110551fe8 Mon Sep 17 00:00:00 2001 From: Alex Liberzon Date: Sun, 15 Mar 2026 14:47:07 +0200 Subject: [PATCH 2/7] created doc page with explanation and differences in native vs compiled API --- .../_static/native-backend-rollout-status.svg | 43 +++ docs/_static/native-backend-unification.svg | 2 +- docs/_static/native-stress-demo.json | 84 +++++ docs/_static/native-stress-demo.svg | 67 +--- docs/generate_native_stress_demo.py | 294 ++++++++++++++++++ docs/native_backend_unification.md | 49 ++- 6 files changed, 475 insertions(+), 64 deletions(-) create mode 100644 docs/_static/native-backend-rollout-status.svg create mode 100644 docs/_static/native-stress-demo.json create mode 100644 docs/generate_native_stress_demo.py diff --git a/docs/_static/native-backend-rollout-status.svg b/docs/_static/native-backend-rollout-status.svg new file mode 100644 index 0000000..f5fdff7 --- /dev/null +++ b/docs/_static/native-backend-rollout-status.svg @@ -0,0 +1,43 @@ + + Rollout status for unified backend dispatch + Figure showing which 3D-PTV pipeline stages already auto-delegate to optv and which only have native parity paths today. + + + + Backend rollout status by pipeline stage + The public Python API covers the full workflow today. The distinction is whether native dispatch is already transparent in normal runtime calls. + + + Auto-delegated today + These public calls already route to optv automatically when bindings are available. + + + Image preprocessing + openptv_python.image_processing.preprocess_image() + Same signature, internal dispatch through _native_compat and _native_convert. + + + Full-frame target recognition + openptv_python.segmentation.targ_rec() + Whole-image path delegates transparently; results are converted back to Python Target objects. + + + Parity-tested, not yet transparent + These stages have native comparison or adapter paths, but routine openptv_python dispatch still uses the Python implementation by default. + + + Point reconstruction + point_positions() and multi_cam_point_positions() have native parity in the stress suite. + + + Correspondences / stereo matching + py_correspondences() is benchmarked against optv correspondences, but not auto-routed yet. + + + Tracking workflow + Python TrackingRun is compared with native optv Tracker in the stress suite, but dispatch is still separate. + + + Sequence parameter I/O + Native bindings exist and are benchmarked, but Python remains the normal entry point. + \ No newline at end of file diff --git a/docs/_static/native-backend-unification.svg b/docs/_static/native-backend-unification.svg index 97a2eb0..9b1037b 100644 --- a/docs/_static/native-backend-unification.svg +++ b/docs/_static/native-backend-unification.svg @@ -53,4 +53,4 @@ Stable result objects and workflow semantics: the user should not need to care which backend ran. - \ No newline at end of file + diff --git a/docs/_static/native-stress-demo.json b/docs/_static/native-stress-demo.json new file mode 100644 index 0000000..fef56bc --- /dev/null +++ b/docs/_static/native-stress-demo.json @@ -0,0 +1,84 @@ +{ + "generated_at": "2026-03-15T12:45:22.894671+00:00", + "python": "/home/user/Documents/GitHub/openptv-python/.venv/bin/python", + "command": [ + "/home/user/Documents/GitHub/openptv-python/.venv/bin/python", + "-m", + "pytest", + "-q", + "-s", + "tests/test_native_stress_performance.py" + ], + "results": [ + { + "key": "sequence", + "label": "Sequence params", + "python_label": "python", + "python_seconds": 2.8e-05, + "native_seconds": 1e-05, + "speedup": 2.86, + "legacy_seconds": null, + "compiled_vs_legacy": null + }, + { + "key": "preprocess", + "label": "Preprocess image", + "python_label": "python", + "python_seconds": 2.190105, + "native_seconds": 0.004607, + "speedup": 475.37, + "legacy_seconds": null, + "compiled_vs_legacy": null + }, + { + "key": "segmentation", + "label": "Segmentation", + "python_label": "python+numba", + "python_seconds": 0.005055, + "native_seconds": 0.001317, + "speedup": 3.84, + "legacy_seconds": null, + "compiled_vs_legacy": null + }, + { + "key": "stereomatching", + "label": "Stereo matching", + "python_label": "python", + "python_seconds": 14.192568, + "native_seconds": 0.002493, + "speedup": 5693.37, + "legacy_seconds": null, + "compiled_vs_legacy": null + }, + { + "key": "reconstruction", + "label": "Reconstruction", + "python_label": "compiled-python", + "python_seconds": 0.011528, + "native_seconds": 0.004852, + "speedup": 2.38, + "legacy_seconds": 0.649974, + "compiled_vs_legacy": 56.38 + }, + { + "key": "multilayer_reconstruction", + "label": "Multilayer reconstruction", + "python_label": "compiled-python", + "python_seconds": 0.009571, + "native_seconds": 0.004759, + "speedup": 2.01, + "legacy_seconds": 0.533542, + "compiled_vs_legacy": 55.74 + }, + { + "key": "tracking", + "label": "Tracking", + "python_label": "python", + "python_seconds": 1.300061, + "native_seconds": 0.144059, + "speedup": 9.02, + "legacy_seconds": null, + "compiled_vs_legacy": null + } + ] +} diff --git a/docs/_static/native-stress-demo.svg b/docs/_static/native-stress-demo.svg index 6898889..8f6c0de 100644 --- a/docs/_static/native-stress-demo.svg +++ b/docs/_static/native-stress-demo.svg @@ -1,58 +1,23 @@ - + Native stress benchmark demo from this machine - Horizontal bar chart showing native speedup over the Python path for sequence loading, preprocessing, segmentation, stereomatching, reconstruction, multilayer reconstruction, and tracking. - - + Horizontal bar chart showing native speedup over the Python path for several openptv-python workloads. + + Local demo from tests/test_native_stress_performance.py - Measured on this machine on 2026-03-15. Bars show native speedup over the routed Python path used by each stress test. + Regenerated from a fresh stress-suite run. Bars show native speedup over the routed Python path used by each test. + Generated: 2026-03-15 12:45 UTC - - + + + + 1x10x100x1,000x10,000x + Sequence params2.86xpython: 0.000028s, native: 0.000010sPreprocess image475.37xpython: 2.190105s, native: 0.004607sSegmentation3.84xpython+numba: 0.005055s, native: 0.001317sStereo matching5693.37xpython: 14.192568s, native: 0.002493sReconstruction2.38xcompiled-python: 0.011528s, native: 0.004852sMultilayer reconstruction2.01xcompiled-python: 0.009571s, native: 0.004759sTracking9.02xpython: 1.300061s, native: 0.144059s - 1x - 10x - 100x - 1,000x - 10,000x - - - - - - - Sequence params - - 2.78x - - Preprocess image - - 464.80x - - Segmentation - - 4.08x - - Stereo matching - - 7646.98x - - Reconstruction - - 1.93x - - Multilayer reconstruction - - 2.33x - - Tracking - - 6.68x - - + Interpretation - Green: general Python-versus-native stages - Blue: compiled Python plus Numba versus native + Green: general Python or Python+Numba path versus native + Blue: compiled Python path versus native - Median timings came from the stress-suite printouts captured from this repository on this workstation. - \ No newline at end of file + Machine-specific results are also saved as JSON and raw log files next to this SVG. + diff --git a/docs/generate_native_stress_demo.py b/docs/generate_native_stress_demo.py new file mode 100644 index 0000000..891d2cf --- /dev/null +++ b/docs/generate_native_stress_demo.py @@ -0,0 +1,294 @@ +"""Regenerate the native stress benchmark demo assets for the docs. + +This script reruns tests/test_native_stress_performance.py, parses the timing +summary lines printed by the test module, and writes machine-specific demo +artifacts under docs/_static/. +""" + +from __future__ import annotations + +import argparse +import json +import math +import re +import subprocess +import sys +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +from pathlib import Path + + +@dataclass +class BenchmarkResult: + """Structured benchmark summary for one workload.""" + + key: str + label: str + python_label: str + python_seconds: float + native_seconds: float + speedup: float + legacy_seconds: float | None = None + compiled_vs_legacy: float | None = None + + +LINE_PATTERNS = { + "sequence": re.compile( + r"^sequence stress benchmark: python: median=(?P[0-9.]+)s .*?" + r"native: median=(?P[0-9.]+)s .*?speedup=(?P[0-9.]+)x" + ), + "preprocess": re.compile( + r"^preprocess stress benchmark: python: median=(?P[0-9.]+)s .*?" + r"native: median=(?P[0-9.]+)s .*?speedup=(?P[0-9.]+)x" + ), + "segmentation": re.compile( + r"^segmentation stress benchmark: python\+numba: median=(?P[0-9.]+)s .*?" + r"native: median=(?P[0-9.]+)s .*?speedup=(?P[0-9.]+)x" + ), + "stereomatching": re.compile( + r"^stereomatching stress benchmark: python: median=(?P[0-9.]+)s .*?" + r"native: median=(?P[0-9.]+)s .*?speedup=(?P[0-9.]+)x" + ), + "reconstruction": re.compile( + r"^reconstruction stress benchmark: compiled-python: median=(?P[0-9.]+)s .*?" + r"legacy-python: median=(?P[0-9.]+)s .*?" + r"native: median=(?P[0-9.]+)s .*?" + r"compiled-vs-legacy=(?P[0-9.]+)x; " + r"compiled-vs-native=(?P[0-9.]+)x" + ), + "multilayer_reconstruction": re.compile( + r"^multilayer reconstruction stress benchmark: compiled-python: median=(?P[0-9.]+)s .*?" + r"legacy-python: median=(?P[0-9.]+)s .*?" + r"native: median=(?P[0-9.]+)s .*?" + r"compiled-vs-legacy=(?P[0-9.]+)x; " + r"compiled-vs-native=(?P[0-9.]+)x" + ), + "tracking": re.compile( + r"^tracking stress benchmark: python: median=(?P[0-9.]+)s .*?" + r"native: median=(?P[0-9.]+)s .*?speedup=(?P[0-9.]+)x" + ), +} + + +WORKLOAD_METADATA = { + "sequence": ("Sequence params", "python"), + "preprocess": ("Preprocess image", "python"), + "segmentation": ("Segmentation", "python+numba"), + "stereomatching": ("Stereo matching", "python"), + "reconstruction": ("Reconstruction", "compiled-python"), + "multilayer_reconstruction": ("Multilayer reconstruction", "compiled-python"), + "tracking": ("Tracking", "python"), +} + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--repo-root", + type=Path, + default=Path(__file__).resolve().parent.parent, + help="Repository root containing tests/test_native_stress_performance.py", + ) + parser.add_argument( + "--svg-output", + type=Path, + default=Path(__file__).resolve().parent / "_static" / "native-stress-demo.svg", + help="Output path for the generated SVG chart", + ) + parser.add_argument( + "--json-output", + type=Path, + default=Path(__file__).resolve().parent / "_static" / "native-stress-demo.json", + help="Output path for the parsed benchmark JSON", + ) + parser.add_argument( + "--log-output", + type=Path, + default=Path(__file__).resolve().parent / "_static" / "native-stress-demo.log", + help="Output path for the raw benchmark log", + ) + parser.add_argument( + "--python", + default=sys.executable, + help="Python interpreter used to run pytest", + ) + return parser.parse_args() + + +def run_benchmarks(repo_root: Path, python_executable: str) -> str: + command = [ + python_executable, + "-m", + "pytest", + "-q", + "-s", + "tests/test_native_stress_performance.py", + ] + completed = subprocess.run( + command, + cwd=repo_root, + capture_output=True, + text=True, + check=False, + ) + output = completed.stdout + completed.stderr + if completed.returncode != 0: + raise RuntimeError( + "stress benchmark run failed with exit code " + f"{completed.returncode}\n\n{output}" + ) + return output + + +def parse_benchmarks(output: str) -> list[BenchmarkResult]: + result_map: dict[str, BenchmarkResult] = {} + for raw_line in output.splitlines(): + line = re.sub(r"\s+", " ", raw_line.strip()) + line = line.lstrip(".").strip() + for key, pattern in LINE_PATTERNS.items(): + match = pattern.match(line) + if match is None: + continue + label, python_label = WORKLOAD_METADATA[key] + groups = match.groupdict() + result_map[key] = BenchmarkResult( + key=key, + label=label, + python_label=python_label, + python_seconds=float(groups["python"]), + native_seconds=float(groups["native"]), + speedup=float(groups["speedup"]), + legacy_seconds=( + float(groups["legacy"]) if groups.get("legacy") else None + ), + compiled_vs_legacy=( + float(groups["compiled_vs_legacy"]) + if groups.get("compiled_vs_legacy") + else None + ), + ) + break + + expected_order = list(WORKLOAD_METADATA) + missing = [key for key in expected_order if key not in result_map] + if missing: + raise RuntimeError( + "failed to parse benchmark summaries for: " + ", ".join(missing) + ) + return [result_map[key] for key in expected_order] + + +def _speedup_to_width(speedup: float, chart_width: float, max_decade: int) -> float: + if speedup <= 1.0: + return 18.0 + return max(18.0, chart_width * (math.log10(speedup) / max_decade)) + + +def render_svg(results: list[BenchmarkResult], generated_at: datetime) -> str: + width = 1180 + height = 760 + chart_left = 250 + chart_top = 180 + chart_width = 840 + row_height = 60 + max_speedup = max(result.speedup for result in results) + max_decade = max(1, math.ceil(math.log10(max_speedup))) + palette = { + "python": "#5f8f77", + "python+numba": "#5f8f77", + "compiled-python": "#4473a8", + } + + grid_lines = [] + grid_labels = [] + for decade in range(max_decade + 1): + x = chart_left + chart_width * (decade / max_decade) + grid_lines.append( + f'' + ) + label = f"{10 ** decade:,}x" + grid_labels.append( + f'{label}' + ) + + bars = [] + for index, result in enumerate(results): + y = chart_top + index * row_height + bar_y = y - 20 + bar_width = _speedup_to_width(result.speedup, chart_width, max_decade) + bar_color = palette[result.python_label] + bars.append( + f'{result.label}' + ) + bars.append( + f'' + ) + bars.append( + f'{result.speedup:.2f}x' + ) + bars.append( + f'{result.python_label}: {result.python_seconds:.6f}s, native: {result.native_seconds:.6f}s' + ) + + generated_label = generated_at.strftime("%Y-%m-%d %H:%M UTC") + return f''' + Native stress benchmark demo from this machine + Horizontal bar chart showing native speedup over the Python path for several openptv-python workloads. + + + + Local demo from tests/test_native_stress_performance.py + Regenerated from a fresh stress-suite run. Bars show native speedup over the routed Python path used by each test. + Generated: {generated_label} + + + + {''.join(grid_lines)} + {''.join(grid_labels)} + {''.join(bars)} + + + Interpretation + Green: general Python or Python+Numba path versus native + Blue: compiled Python path versus native + + Machine-specific results are also saved as JSON and raw log files next to this SVG. + +''' + + +def main() -> int: + args = parse_args() + repo_root = args.repo_root.resolve() + output = run_benchmarks(repo_root, args.python) + results = parse_benchmarks(output) + generated_at = datetime.now(timezone.utc) + + args.svg_output.parent.mkdir(parents=True, exist_ok=True) + args.svg_output.write_text(render_svg(results, generated_at), encoding="utf-8") + args.json_output.write_text( + json.dumps( + { + "generated_at": generated_at.isoformat(), + "python": args.python, + "command": [ + args.python, + "-m", + "pytest", + "-q", + "-s", + "tests/test_native_stress_performance.py", + ], + "results": [asdict(result) for result in results], + }, + indent=2, + ) + + "\n", + encoding="utf-8", + ) + args.log_output.write_text(output, encoding="utf-8") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/docs/native_backend_unification.md b/docs/native_backend_unification.md index 7759ac4..70ea7cf 100644 --- a/docs/native_backend_unification.md +++ b/docs/native_backend_unification.md @@ -39,9 +39,19 @@ parity paths available: That split is exactly what we want for batch jobs: 1. one Python script controls the workflow -2. pure Python remains the guaranteed fallback path -3. Numba accelerates selected kernels automatically -4. `optv` can be used underneath the same API when installed +1. pure Python remains the guaranteed fallback path +1. Numba accelerates selected kernels automatically +1. `optv` can be used underneath the same API when installed + +## Dispatch status by stage + +The current rollout state is easiest to read as a pipeline-status figure: + +![Backend rollout status](_static/native-backend-rollout-status.svg) + +The important distinction is not whether a native implementation exists. It is +whether the normal `openptv_python` runtime path already chooses that native +implementation transparently for the user. ## Why `tests/test_native_stress_performance.py` matters @@ -51,8 +61,8 @@ The best executable description of this backend strategy is That test module does three useful things: 1. it validates parity between Python and native paths for several stages -2. it benchmarks Python, compiled Python, and native implementations side by side -3. it demonstrates which stages already have a viable native provider behind the +1. it benchmarks Python, compiled Python, and native implementations side by side +1. it demonstrates which stages already have a viable native provider behind the same workflow semantics The benchmarked workloads in that file currently cover: @@ -67,13 +77,28 @@ The benchmarked workloads in that file currently cover: ## Demo from this machine -The figure below was generated from a real run on this machine on March 15, -2026 using: +The figure below was generated from a real run on this machine using the docs +helper script: + +```bash +/home/user/Documents/GitHub/openptv-python/.venv/bin/python docs/generate_native_stress_demo.py +``` + +That script reruns: ```bash /home/user/Documents/GitHub/openptv-python/.venv/bin/python -m pytest -q -s tests/test_native_stress_performance.py ``` +and then writes three machine-specific artifacts under `docs/_static/`: + +- `native-stress-demo.svg` +- `native-stress-demo.json` +- `native-stress-demo.log` + +Because it reruns the full stress suite, it is expected to take around a couple +of minutes on a normal developer machine. + The chart shows native speedup over the routed Python path used by the test: - for preprocessing, segmentation, stereomatching, and tracking this is Python @@ -109,11 +134,11 @@ internal execution paths. The test results support a practical backend policy: 1. keep Python as the orchestration and scripting surface -2. preserve current public function signatures so scripts and GUI code do not +1. preserve current public function signatures so scripts and GUI code do not care which backend is active -3. keep the pure Python implementation as the reference behavior -4. continue using Numba for the medium-performance path -5. route additional heavy stages to `optv` or future compiled kernels behind +1. keep the pure Python implementation as the reference behavior +1. continue using Numba for the medium-performance path +1. route additional heavy stages to `optv` or future compiled kernels behind the same API In other words, users should be able to write one batch script and get: @@ -124,4 +149,4 @@ In other words, users should be able to write one batch script and get: native provider That is the unifying API story already visible in the codebase and demonstrated -by the stress suite. \ No newline at end of file +by the stress suite. From 8360ec5e8661eb3e6228a2e465f93f70548ca2c8 Mon Sep 17 00:00:00 2001 From: Alex Liberzon Date: Sun, 15 Mar 2026 18:57:08 +0200 Subject: [PATCH 3/7] Update docs workflow and build artifacts --- .gitignore | 1 + Makefile | 9 +- README.md | 15 +- .../_static/native-backend-rollout-status.svg | 43 - docs/_static/native-backend-unification.svg | 56 - docs/_static/native-stress-demo.svg | 23 - docs/generate_native_stress_demo.py | 93 +- docs/native_backend_unification.md | 51 +- docs/render_native_stress_demo_include.py | 127 ++ uv.lock | 1190 ++++++++--------- 10 files changed, 775 insertions(+), 833 deletions(-) delete mode 100644 docs/_static/native-backend-rollout-status.svg delete mode 100644 docs/_static/native-backend-unification.svg delete mode 100644 docs/_static/native-stress-demo.svg create mode 100644 docs/render_native_stress_demo_include.py diff --git a/.gitignore b/.gitignore index 5194d8e..e8f9c7d 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ version.py # Sphinx automatic generation of API docs/README.md docs/_api/ +docs/_generated/ # Combined environments ci/combined-environment-*.yml diff --git a/Makefile b/Makefile index 2a48625..e7a5bed 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,8 @@ PROJECT := openptv_python CONDA := conda CONDAFLAGS := +UV := uv +UVFLAGS := --extra dev --upgrade COV_REPORT := html PYTHON ?= python @@ -15,11 +17,16 @@ unit-tests: type-check: $(PYTHON) -m mypy . +env-update: uv-env-update + conda-env-update: $(CONDA) install -y -c conda-forge conda-merge $(CONDA) run conda-merge environment.yml ci/environment-ci.yml > ci/combined-environment-ci.yml $(CONDA) env update $(CONDAFLAGS) -f ci/combined-environment-ci.yml +uv-env-update: + $(UV) sync $(UVFLAGS) + docker-build: docker build -t $(PROJECT) . @@ -30,6 +37,6 @@ template-update: pre-commit run --all-files cruft -c .pre-commit-config-cruft.yaml docs-build: - cp README.md docs/. && cd docs && rm -fr _api && make clean && make html + cp README.md docs/. && $(PYTHON) docs/render_native_stress_demo_include.py && cd docs && rm -fr _api && make clean && make html # DO NOT EDIT ABOVE THIS LINE, ADD COMMANDS BELOW diff --git a/README.md b/README.md index 6466639..cae31e7 100644 --- a/README.md +++ b/README.md @@ -184,17 +184,28 @@ OPENPTV_SKIP_STRESS_BENCHMARKS=1 uv run make ### Workflow for developers/contributors -For the best experience create a new conda environment (e.g. DEVELOP) with Python 3.12: +Recommended contributor workflow with `uv`: +```bash +uv venv +source .venv/bin/activate +make env-update ``` + +This keeps the local environment synced to the locked developer dependency set. + +If you prefer the conda workflow instead: + +```bash conda create -n openptv-python -c conda-forge python=3.12 conda activate openptv-python +make conda-env-update ``` Before pushing to GitHub, use the developer install above and then run the following commands: -1. Update conda environment: `make conda-env-update` or `uv venv` and `source .venv/bin/activate` followed by `uv sync --extra dev --upgrade` +1. Update the environment: `make env-update` by default, or `make conda-env-update` if you are using the conda workflow 1. If you are using pip instead of uv, install the editable developer environment: `pip install -e ".[dev]"` 1. Sync with the latest [template](https://github.com/ecmwf-projects/cookiecutter-conda-package) (optional): `make template-update` 1. Run quality assurance checks: `make qa` diff --git a/docs/_static/native-backend-rollout-status.svg b/docs/_static/native-backend-rollout-status.svg deleted file mode 100644 index f5fdff7..0000000 --- a/docs/_static/native-backend-rollout-status.svg +++ /dev/null @@ -1,43 +0,0 @@ - - Rollout status for unified backend dispatch - Figure showing which 3D-PTV pipeline stages already auto-delegate to optv and which only have native parity paths today. - - - - Backend rollout status by pipeline stage - The public Python API covers the full workflow today. The distinction is whether native dispatch is already transparent in normal runtime calls. - - - Auto-delegated today - These public calls already route to optv automatically when bindings are available. - - - Image preprocessing - openptv_python.image_processing.preprocess_image() - Same signature, internal dispatch through _native_compat and _native_convert. - - - Full-frame target recognition - openptv_python.segmentation.targ_rec() - Whole-image path delegates transparently; results are converted back to Python Target objects. - - - Parity-tested, not yet transparent - These stages have native comparison or adapter paths, but routine openptv_python dispatch still uses the Python implementation by default. - - - Point reconstruction - point_positions() and multi_cam_point_positions() have native parity in the stress suite. - - - Correspondences / stereo matching - py_correspondences() is benchmarked against optv correspondences, but not auto-routed yet. - - - Tracking workflow - Python TrackingRun is compared with native optv Tracker in the stress suite, but dispatch is still separate. - - - Sequence parameter I/O - Native bindings exist and are benchmarked, but Python remains the normal entry point. - \ No newline at end of file diff --git a/docs/_static/native-backend-unification.svg b/docs/_static/native-backend-unification.svg deleted file mode 100644 index 9b1037b..0000000 --- a/docs/_static/native-backend-unification.svg +++ /dev/null @@ -1,56 +0,0 @@ - - Unified openptv-python backend architecture - Diagram showing a single Python API routing work to Python, Numba, or optv backends while keeping result types stable. - - - - One user API, multiple execution paths - User scripts and batch jobs call openptv_python once; backend routing stays internal. - - - User script / batch job - import openptv_python - run the same pipeline - - - Public openptv_python API - preprocess_image() - targ_rec() - point_positions(), py_correspondences(), TrackingRun - - - Adapter layer - _native_compat.py - _native_convert.py - same Python signature - - - - - - - - Pure Python - Reference implementation - Always available - Guarantees batch execution - - - Python + Numba - Accelerated kernels - No API change - Current medium-performance path - - - Native optv - Optional dependency - Already active for preprocessing - and full-frame segmentation - - - - - - - Stable result objects and workflow semantics: the user should not need to care which backend ran. - diff --git a/docs/_static/native-stress-demo.svg b/docs/_static/native-stress-demo.svg deleted file mode 100644 index 8f6c0de..0000000 --- a/docs/_static/native-stress-demo.svg +++ /dev/null @@ -1,23 +0,0 @@ - - Native stress benchmark demo from this machine - Horizontal bar chart showing native speedup over the Python path for several openptv-python workloads. - - - - Local demo from tests/test_native_stress_performance.py - Regenerated from a fresh stress-suite run. Bars show native speedup over the routed Python path used by each test. - Generated: 2026-03-15 12:45 UTC - - - - - 1x10x100x1,000x10,000x - Sequence params2.86xpython: 0.000028s, native: 0.000010sPreprocess image475.37xpython: 2.190105s, native: 0.004607sSegmentation3.84xpython+numba: 0.005055s, native: 0.001317sStereo matching5693.37xpython: 14.192568s, native: 0.002493sReconstruction2.38xcompiled-python: 0.011528s, native: 0.004852sMultilayer reconstruction2.01xcompiled-python: 0.009571s, native: 0.004759sTracking9.02xpython: 1.300061s, native: 0.144059s - - - Interpretation - Green: general Python or Python+Numba path versus native - Blue: compiled Python path versus native - - Machine-specific results are also saved as JSON and raw log files next to this SVG. - diff --git a/docs/generate_native_stress_demo.py b/docs/generate_native_stress_demo.py index 891d2cf..9d93d91 100644 --- a/docs/generate_native_stress_demo.py +++ b/docs/generate_native_stress_demo.py @@ -1,4 +1,4 @@ -"""Regenerate the native stress benchmark demo assets for the docs. +"""Regenerate the native stress benchmark data assets for the docs. This script reruns tests/test_native_stress_performance.py, parses the timing summary lines printed by the test module, and writes machine-specific demo @@ -9,7 +9,6 @@ import argparse import json -import math import re import subprocess import sys @@ -89,12 +88,6 @@ def parse_args() -> argparse.Namespace: default=Path(__file__).resolve().parent.parent, help="Repository root containing tests/test_native_stress_performance.py", ) - parser.add_argument( - "--svg-output", - type=Path, - default=Path(__file__).resolve().parent / "_static" / "native-stress-demo.svg", - help="Output path for the generated SVG chart", - ) parser.add_argument( "--json-output", type=Path, @@ -178,85 +171,6 @@ def parse_benchmarks(output: str) -> list[BenchmarkResult]: return [result_map[key] for key in expected_order] -def _speedup_to_width(speedup: float, chart_width: float, max_decade: int) -> float: - if speedup <= 1.0: - return 18.0 - return max(18.0, chart_width * (math.log10(speedup) / max_decade)) - - -def render_svg(results: list[BenchmarkResult], generated_at: datetime) -> str: - width = 1180 - height = 760 - chart_left = 250 - chart_top = 180 - chart_width = 840 - row_height = 60 - max_speedup = max(result.speedup for result in results) - max_decade = max(1, math.ceil(math.log10(max_speedup))) - palette = { - "python": "#5f8f77", - "python+numba": "#5f8f77", - "compiled-python": "#4473a8", - } - - grid_lines = [] - grid_labels = [] - for decade in range(max_decade + 1): - x = chart_left + chart_width * (decade / max_decade) - grid_lines.append( - f'' - ) - label = f"{10 ** decade:,}x" - grid_labels.append( - f'{label}' - ) - - bars = [] - for index, result in enumerate(results): - y = chart_top + index * row_height - bar_y = y - 20 - bar_width = _speedup_to_width(result.speedup, chart_width, max_decade) - bar_color = palette[result.python_label] - bars.append( - f'{result.label}' - ) - bars.append( - f'' - ) - bars.append( - f'{result.speedup:.2f}x' - ) - bars.append( - f'{result.python_label}: {result.python_seconds:.6f}s, native: {result.native_seconds:.6f}s' - ) - - generated_label = generated_at.strftime("%Y-%m-%d %H:%M UTC") - return f''' - Native stress benchmark demo from this machine - Horizontal bar chart showing native speedup over the Python path for several openptv-python workloads. - - - - Local demo from tests/test_native_stress_performance.py - Regenerated from a fresh stress-suite run. Bars show native speedup over the routed Python path used by each test. - Generated: {generated_label} - - - - {''.join(grid_lines)} - {''.join(grid_labels)} - {''.join(bars)} - - - Interpretation - Green: general Python or Python+Numba path versus native - Blue: compiled Python path versus native - - Machine-specific results are also saved as JSON and raw log files next to this SVG. - -''' - - def main() -> int: args = parse_args() repo_root = args.repo_root.resolve() @@ -264,8 +178,7 @@ def main() -> int: results = parse_benchmarks(output) generated_at = datetime.now(timezone.utc) - args.svg_output.parent.mkdir(parents=True, exist_ok=True) - args.svg_output.write_text(render_svg(results, generated_at), encoding="utf-8") + args.json_output.parent.mkdir(parents=True, exist_ok=True) args.json_output.write_text( json.dumps( { @@ -291,4 +204,4 @@ def main() -> int: if __name__ == "__main__": - raise SystemExit(main()) \ No newline at end of file + raise SystemExit(main()) diff --git a/docs/native_backend_unification.md b/docs/native_backend_unification.md index 70ea7cf..5c9bd2c 100644 --- a/docs/native_backend_unification.md +++ b/docs/native_backend_unification.md @@ -10,7 +10,18 @@ The important user-facing contract is simple: - the same Python calls should work with and without `optv` installed - native acceleration is an implementation detail, not a second API surface -![Unified API pipeline](_static/native-backend-unification.svg) +The runtime flow is: + +| Layer | Responsibility | +| --- | --- | +| User script | Imports `openptv_python` and calls the normal Python API. | +| Public API | Keeps the stable Python function signatures used by scripts and GUI code. | +| Dispatch layer | Chooses between pure Python, Python plus Numba, or native `optv` based on availability and the specific operation. | +| Backend implementation | Executes the selected kernel while preserving the same user-facing behavior. | + +In other words, the user writes one Python workflow and the repository decides +internally whether the current operation should run through the fallback Python +path, an accelerated Numba path, or a native `optv` path. ## How the API is unified today @@ -45,9 +56,16 @@ That split is exactly what we want for batch jobs: ## Dispatch status by stage -The current rollout state is easiest to read as a pipeline-status figure: +The current rollout state is clearer as a status table: -![Backend rollout status](_static/native-backend-rollout-status.svg) +| Stage | Public entry point | Current status | Notes | +| --- | --- | --- | --- | +| Image preprocessing | `openptv_python.image_processing.preprocess_image()` | Auto-delegated today | Same Python signature; dispatches internally through native compatibility and conversion helpers when `optv` is available. | +| Full-frame target recognition | `openptv_python.segmentation.targ_rec()` | Auto-delegated today | Whole-image path routes transparently to native and converts results back into Python `Target` objects. | +| Point reconstruction | `openptv_python.orientation.point_positions()` and `multi_cam_point_positions()` | Parity-tested, not yet transparent | Native parity exists in the stress suite, but routine runtime dispatch still uses the Python implementation by default. | +| Correspondences / stereo matching | `openptv_python.correspondences.py_correspondences()` | Parity-tested, not yet transparent | Benchmarked against `optv` correspondences, but not auto-routed yet. | +| Tracking workflow | `openptv_python.tracking_run.TrackingRun` | Parity-tested, not yet transparent | Python workflow is compared with native `optv` `Tracker`, but dispatch remains separate today. | +| Sequence parameter I/O | sequence parameter loading path | Parity-tested, not yet transparent | Native bindings exist and are benchmarked, but Python remains the default runtime entry point. | The important distinction is not whether a native implementation exists. It is whether the normal `openptv_python` runtime path already chooses that native @@ -77,7 +95,7 @@ The benchmarked workloads in that file currently cover: ## Demo from this machine -The figure below was generated from a real run on this machine using the docs +The timings below were generated from a real run on this machine using the docs helper script: ```bash @@ -90,40 +108,27 @@ That script reruns: /home/user/Documents/GitHub/openptv-python/.venv/bin/python -m pytest -q -s tests/test_native_stress_performance.py ``` -and then writes three machine-specific artifacts under `docs/_static/`: +and then writes two machine-specific artifacts under `docs/_static/`: -- `native-stress-demo.svg` - `native-stress-demo.json` - `native-stress-demo.log` Because it reruns the full stress suite, it is expected to take around a couple of minutes on a normal developer machine. -The chart shows native speedup over the routed Python path used by the test: +The reported speedups compare native execution against the routed Python path +used by the test: - for preprocessing, segmentation, stereomatching, and tracking this is Python versus native - for reconstruction workloads this is compiled Python plus Numba versus native -![Native stress benchmark demo](_static/native-stress-demo.svg) +The parsed machine-specific results are summarized in the next section. ## Measured median timings -| Workload | Python or routed Python path | Native path | Speedup shown in figure | -| --- | ---: | ---: | ---: | -| Sequence params | 0.000029 s | 0.000010 s | 2.78x | -| Preprocess image | 2.263711 s | 0.004870 s | 464.80x | -| Segmentation | 0.012110 s | 0.002967 s | 4.08x | -| Stereo matching | 23.990435 s | 0.003137 s | 7646.98x | -| Reconstruction | 0.012397 s | 0.006419 s | 1.93x | -| Multilayer reconstruction | 0.015707 s | 0.006754 s | 2.33x | -| Tracking | 2.247133 s | 0.336165 s | 6.68x | - -For the two reconstruction workloads, the same stress test also reported the -benefit of the current compiled Python path over the older per-point Python loop: - -- reconstruction: compiled Python was 63.01x faster than legacy Python -- multilayer reconstruction: compiled Python was 54.87x faster than legacy Python +```{include} _generated/native-stress-demo.mdinc +``` This is the key architectural message of the page: the repository does not need two user APIs to get performance. It needs one stable Python API with multiple diff --git a/docs/render_native_stress_demo_include.py b/docs/render_native_stress_demo_include.py new file mode 100644 index 0000000..ee7b993 --- /dev/null +++ b/docs/render_native_stress_demo_include.py @@ -0,0 +1,127 @@ +"""Render a Markdown include from docs/_static/native-stress-demo.json.""" + +from __future__ import annotations + +import argparse +import json +from datetime import datetime, timezone +from pathlib import Path + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--json-input", + type=Path, + default=Path(__file__).resolve().parent / "_static" / "native-stress-demo.json", + help="Path to the generated native stress benchmark JSON file.", + ) + parser.add_argument( + "--markdown-output", + type=Path, + default=( + Path(__file__).resolve().parent / "_generated" / "native-stress-demo.mdinc" + ), + help="Path for the generated Markdown include.", + ) + return parser.parse_args() + + +def _format_timestamp(timestamp: str) -> str: + return ( + datetime.fromisoformat(timestamp) + .astimezone(timezone.utc) + .strftime("%Y-%m-%d %H:%M UTC") + ) + + +def _format_seconds(value: float) -> str: + return f"{value:.6f} s" + + +def _format_speedup(value: float) -> str: + return f"{value:.2f}x" + + +def render_markdown(payload: dict) -> str: + generated_label = _format_timestamp(payload["generated_at"]) + results = payload["results"] + result_by_key = {result["key"]: result for result in results} + + lines = [ + "", + "", + ( + "Latest machine-specific data was read from " + "`docs/_static/native-stress-demo.json` " + f"generated at `{generated_label}`." + ), + "", + "The machine-specific headline from this run is straightforward:", + "", + ( + "- native `optv` is dramatically faster for the heaviest matching " + "workload " + f"(`Stereo matching`, about `{_format_speedup(result_by_key['stereomatching']['speedup'])}`)" + ), + ( + "- preprocessing also shows a large native win " + f"(`{_format_speedup(result_by_key['preprocess']['speedup'])}`)" + ), + ( + "- reconstruction already benefits substantially from the compiled " + "Python path, so the additional native gain there is smaller " + f"(`{_format_speedup(result_by_key['reconstruction']['speedup'])}` " + f"and `{_format_speedup(result_by_key['multilayer_reconstruction']['speedup'])}`)" + ), + ( + "- tracking still benefits materially from native execution " + f"(`{_format_speedup(result_by_key['tracking']['speedup'])}`)" + ), + "", + "| Workload | Python or routed Python path | Native path | Native speedup |", + "| --- | ---: | ---: | ---: |", + ] + + for result in results: + lines.append( + "| " + f"{result['label']} | " + f"{_format_seconds(result['python_seconds'])} | " + f"{_format_seconds(result['native_seconds'])} | " + f"{_format_speedup(result['speedup'])} |" + ) + + compiled_results = [ + result for result in results if result.get("compiled_vs_legacy") is not None + ] + if compiled_results: + lines.extend( + [ + "", + "For the reconstruction workloads, the same stress test also reported", + "the benefit of the current compiled Python path over the older", + "per-point Python loop:", + "", + ] + ) + for result in compiled_results: + lines.append( + f"- {result['label'].lower()}: compiled Python was " + f"{_format_speedup(result['compiled_vs_legacy'])} faster than legacy Python" + ) + + lines.append("") + return "\n".join(lines) + + +def main() -> int: + args = parse_args() + payload = json.loads(args.json_input.read_text(encoding="utf-8")) + args.markdown_output.parent.mkdir(parents=True, exist_ok=True) + args.markdown_output.write_text(render_markdown(payload), encoding="utf-8") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/uv.lock b/uv.lock index a96057f..739dc03 100644 --- a/uv.lock +++ b/uv.lock @@ -1,954 +1,954 @@ -requires-python = ">=3.12, <3.14" -revision = 3 version = 1 +revision = 3 +requires-python = 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scripts/fix_pyside6_traitsui_auto.sh | 54 + scripts/generate_ground_truth_points.py | 41 + scripts/install_pyptv.bat | 229 +++ scripts/legacy_parameters_to_yaml.py | 44 + scripts/run_docker_tests.sh | 4 + scripts/run_headless_tests.sh | 4 + scripts/run_pyptv.sh | 23 + scripts/run_tests.sh | 5 + scripts/standalone_calibration.py | 141 ++ scripts/standalone_dumbbell_calibration.py | 77 + scripts/verify_environment.py | 59 + scripts/visualize_cameras.py | 122 ++ {openptv_python => src}/__init__.py | 0 {openptv_python => src}/_native_compat.py | 0 {openptv_python => src}/_native_convert.py | 0 {openptv_python => src}/calibration.py | 0 .../calibration_compare.py | 0 {openptv_python => src}/constants.py | 0 {openptv_python => src}/correspondences.py | 51 +- .../demo_bundle_adjustment.py | 0 {openptv_python => src}/epi.py | 0 {openptv_python => src}/find_candidate.py | 0 .../generate_synthetic_cavity_case.py | 0 {openptv_python => src}/image_processing.py | 0 {openptv_python => 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=> src/notebooks}/manual_selection.ipynb (100%) rename {openptv_python => src}/orientation.py (100%) rename {openptv_python => src}/parameters.py (97%) rename {openptv_python => src}/ptv.py.bck (100%) rename {openptv_python => src}/ray_tracing.py (100%) rename {openptv_python => src}/segmentation.py (100%) rename {openptv_python => src}/sortgrid.py (100%) rename {openptv_python => src}/track.py (100%) rename {openptv_python => src}/tracking_frame_buf.py (97%) rename {openptv_python => src}/tracking_run.py (100%) rename {openptv_python => src}/trafo.py (100%) rename {openptv_python => src}/vec_utils.py (100%) diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml new file mode 100644 index 0000000..7f0f9cf --- /dev/null +++ b/.github/workflows/publish-to-pypi.yml @@ -0,0 +1,110 @@ +name: Publish Python Package to PyPI + +# This workflow uses GitHub's OIDC trusted publishing to authenticate with PyPI. +# For this to work, you must configure trusted publishers on PyPI/TestPyPI: +# 1. Go to https://test.pypi.org/ or https://pypi.org/ +# 2. Navigate to your project settings → Publishing +# 3. Add a new publisher with these details: +# - Owner: alexlib +# - Repository: openptv-python +# - Workflow: publish-to-pypi.yml +# - Environment: testpypi (for TestPyPI) or pypi (for PyPI) +# +# TROUBLESHOOTING: +# If you see "invalid-publisher" error, it means the trusted publisher is not configured +# correctly on PyPI/TestPyPI. The configuration MUST match these exact values: +# - Repository owner: alexlib +# - Repository name: openptv-python +# - Workflow filename: publish-to-pypi.yml (must be exact match) +# - Environment name: testpypi or pypi (must match exactly) +# +# For detailed setup instructions, see DEPLOYMENT.md + +on: + release: + types: [published] + workflow_dispatch: + inputs: + deploy_target: + description: 'Deploy to PyPI or TestPyPI' + required: true + default: 'testpypi' + type: choice + options: + - pypi + - testpypi + +jobs: + build: + name: Build distribution + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install build dependencies + run: | + python -m pip install --upgrade pip + pip install build twine + + - name: Build package + run: python -m build + + - name: Check distribution + run: twine check dist/* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + name: python-package-distributions + path: dist/ + + publish-to-pypi: + name: Publish to PyPI + if: github.event_name == 'release' || (github.event_name == 'workflow_dispatch' && github.event.inputs.deploy_target == 'pypi') + needs: [build] + runs-on: ubuntu-latest + environment: + name: pypi # This must match the environment name in PyPI's trusted publisher config + url: https://pypi.org/p/openptv-python + permissions: + id-token: write # Required for trusted publishing via OIDC + + steps: + - name: Download distributions + uses: actions/download-artifact@v4 + with: + name: python-package-distributions + path: dist/ + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + + publish-to-testpypi: + name: Publish to TestPyPI + if: github.event_name == 'workflow_dispatch' && github.event.inputs.deploy_target == 'testpypi' + needs: [build] + runs-on: ubuntu-latest + environment: + name: testpypi # This must match the environment name in TestPyPI's trusted publisher config + url: https://test.pypi.org/p/openptv-python + permissions: + id-token: write # Required for trusted publishing via OIDC + + steps: + - name: Download distributions + uses: actions/download-artifact@v4 + with: + name: python-package-distributions + path: dist/ + + - name: Publish to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ + diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml new file mode 100644 index 0000000..4c7ebf5 --- /dev/null +++ b/.github/workflows/python-app.yml @@ -0,0 +1,24 @@ +name: Python application + +on: + push: + branches: [ main, simple_yaml_with_tests ] + pull_request: + branches: [ main, simple_yaml_with_tests ] + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.11' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install -e ".[dev]" + - name: Run tests + run: | + make unit-tests diff --git a/.gitignore b/.gitignore index e8f9c7d..9a658e2 100644 --- a/.gitignore +++ b/.gitignore @@ -474,3 +474,68 @@ $RECYCLE.BIN/ .cruft.json /.tmp /tmp + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +*.pyc + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + + + + diff --git a/README.md b/README.md index cae31e7..3a8e682 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,8 @@ execution modes behind that API: use, so the Python implementation still benefits from acceleration. - Native `optv` bindings: selected operations reuse the native OpenPTV implementation when the `optv` package is available. +- PyPTV GUI: packaged in the same repository and installed as an optional + application package. At the moment, automatic native delegation is implemented for image preprocessing and full-frame target recognition. The rest of the library keeps @@ -21,15 +23,11 @@ use. ## How this is started -This work started from the https://github.com/OpenPTV/openptv/tree/pure_python branch. It's a long-standing idea to convert all the C code to Python and now it's possible with ChatGPT to save -a lot of typing time. - -This repo is created using a *cookiecutter* and the rest of the readme describes the way to work with -this structure +This work started from the https://github.com/OpenPTV/openptv/tree/pure_python branch. It now serves as the single repository for the Python core library and the GUI layer. ## Supported Python Versions -The project currently supports Python `>=3.12,<3.14`. +The project currently supports Python `>=3.11,<3.14`. ## Installation @@ -55,6 +53,12 @@ platform and Python version, install the optional extra: uv sync --extra native ``` +If you also want the GUI application, install the GUI extra: + +```bash +uv sync --extra gui +``` + #### Alternative: pip ```bash @@ -69,6 +73,12 @@ Optional native bindings: pip install ".[native]" ``` +Optional GUI application: + +```bash +pip install ".[gui]" +``` + ### Developer install #### Recommended: uv @@ -92,8 +102,9 @@ pip install -e ".[dev]" - The default install contains the runtime dependencies only. - The optional `native` extra adds `optv` bindings for automatic native delegation on supported platforms. -- The optional `dev` extra adds test, docs, typing, and pre-commit tooling for - contributors. +- The optional `gui` extra adds the PyPTV GUI and its runtime dependencies. +- The optional `dev` extra adds test, docs, typing, pre-commit, and GUI + tooling for contributors. - The public API stays the same regardless of which backend extras are installed. diff --git a/conda.recipe/conda_build_config.yaml b/conda.recipe/conda_build_config.yaml new file mode 100644 index 0000000..c289ef9 --- /dev/null +++ b/conda.recipe/conda_build_config.yaml @@ -0,0 +1,9 @@ +python: + - 3.6 + - 3.7 + - 3.8 + - 3.9 + - 3.10 + - 3.11 + - 3.12 + - 3.13 diff --git a/conda.recipe/meta.yaml b/conda.recipe/meta.yaml new file mode 100644 index 0000000..aed2931 --- /dev/null +++ b/conda.recipe/meta.yaml @@ -0,0 +1,47 @@ +package: + name: pyptv + version: 0.2.9 + +source: + path: .. + +build: + # If the installation is complex, or different between Unix and Windows, use + # separate bld.bat and build.sh files instead of this key. Add the line + # skip: True # [py>27] + # "skip: True # [not win]" to limit to Windows. + script: '{{ PYTHON }} -m pip install . --no-deps --ignore-installed --no-cache-dir -vvv --index-url https://pypi.fury.io/pyptv --extra-index-url https://pypi.org/simple' + # skip: true # [(win and vc<14) or py<35] + +requirements: + host: + - {{ compiler('c') }} + - python {{ python }} + - swig + - pip + - pyyaml + - setuptools + build: + - python {{ python }} + - setuptools + - numpy ==1.26.4 + - optv ==0.3.0 + - cython + run: + - python + - numpy ==1.26.4 + - optv ==0.3.0 + - cython + - numba + - scipy + - pyyaml + - nose + +about: + home: https://github.com/alexlib/pyptv + summary: Python GUI for the OpenPTV library `liboptv` + license: MIT + license_family: MIT + license_file: LICENSE.txt + doc_url: http://openptv-python.readthedocs.io + dev_url: http://www.openptv.net diff --git a/docs/pyptv/README.md b/docs/pyptv/README.md new file mode 100644 index 0000000..b227ac3 --- /dev/null +++ b/docs/pyptv/README.md @@ -0,0 +1,216 @@ +# PyPTV is the GUI and batch processing software for 3D Particle Tracking Velocimetry (PTV) + +### Using PyPTV + +# PyPTV Documentation Index + +Welcome to the PyPTV documentation! This index provides an organized overview of all available guides and resources. Use this page as your starting point for learning, troubleshooting, and reference. + +## Getting Started +- [Installation Guide](installation.md) +- [Windows Installation Guide](windows-installation.md) +- [Quick Start Guide](quick-start.md) + +## Core Usage +- [Running the GUI](running-gui.md) +- [YAML Parameters Reference](yaml-parameters.md) +- [Parameter Migration Guide](parameter-migration.md) +- [Calibration Guide](calibration.md) +- [Examples and Workflows](examples.md) + +## Advanced Features +- [Splitter Mode Guide](splitter-mode.md) +- [Plugins System Guide](plugins.md) + +## System Administration +- [Logging Guide](LOGGING_GUIDE.md) +- [Environment Guide](PYPTV_ENVIRONMENT_GUIDE.md) + +## Additional Resources +- [Test Cavity Example](examples.md#test-cavity) +- [Parameter Migration FAQ](parameter-migration.md#common-migration-issues) + +--- + +**How to use this documentation:** +- Click any link above to jump to the relevant guide. +- Use your browser's search to find keywords or topics. +- For troubleshooting, check the FAQ sections in each guide. +- For community help, visit [GitHub Issues](https://github.com/openptv/pyptv/issues) or [Discussions](https://github.com/openptv/pyptv/discussions). + +--- + +*Documentation last updated: August 2025 for PyPTV 2025* + +Welcome to PyPTV - the open-source 3D Particle Tracking Velocimetry software. + +## Table of Contents + +### Getting Started +- [📦 Installation Guide](installation.md) - Install PyPTV on Linux/macOS +- [🪟 Windows Installation](windows-installation.md) - Special instructions for Windows users +- [🚀 Quick Start](quick-start.md) - Get up and running with your first experiment + +### Using PyPTV +- [💻 Running the GUI](running-gui.md) - Launch and use the PyPTV graphical interface +- [� YAML Parameters Reference](yaml-parameters.md) - Complete parameter documentation +- [📹 Calibration Guide](calibration.md) - Camera calibration procedures and best practices +- [� Parameter Migration](parameter-migration.md) - Convert legacy formats to modern YAML +- [� Examples and Workflows](examples.md) - Practical examples using test_cavity dataset + +### Additional Resources +- [📋 Logging Guide](LOGGING_GUIDE.md) - Understanding PyPTV's logging system +- [🐍 Environment Guide](PYPTV_ENVIRONMENT_GUIDE.md) - Python environment management + +## What is PyPTV? + +PyPTV is a Python-based implementation of 3D Particle Tracking Velocimetry (PTV), enabling you to: + +- **Track particles in 3D space** from multiple camera views +- **Measure fluid velocities** in experimental setups +- **Calibrate camera systems** for accurate 3D reconstruction +- **Process image sequences** with customizable algorithms +- **Export tracking data** for further analysis + +## Key Features + +✅ **Modern YAML Configuration** - Single-file parameter management +✅ **Graphical User Interface** - Intuitive operation and visualization +✅ **Multi-Camera Support** - 2-4 camera systems with flexible setup +✅ **Plugin Architecture** - Extend functionality with custom algorithms +✅ **Cross-Platform** - Runs on Linux, macOS, and Windows +✅ **Open Source** - MIT license with active community development + +## System Requirements + +- **Operating System**: Linux (Ubuntu/Debian recommended), macOS, or Windows 10/11 +- **Python**: 3.11 or newer +- **Memory**: 8GB RAM minimum (16GB+ recommended for large datasets) +- **Storage**: 2GB free space (plus space for your experimental data) + +## Quick Installation + +For most users, follow these steps: + +```bash +# Clone the repository +git clone https://github.com/openptv/pyptv +cd pyptv + +# Run the installation script (Linux/macOS) +./install_pyptv.sh + +# Or use conda directly +conda env create -f environment.yml +conda activate pyptv +pip install -e . +``` + +For detailed installation instructions, see the [Installation Guide](installation.md). + +## Testing: Headless vs GUI + +PyPTV separates tests into two categories: + +- **Headless tests** (no GUI): Located in `tests/`. These run in CI (GitHub Actions) and Docker, and do not require a display. +- **GUI-dependent tests**: Located in `tests_gui/`. These require a display and are run locally or with Xvfb. + +To run all tests locally: +```bash +bash run_tests.sh +``` +To run only headless tests (recommended for CI/Docker): +```bash +bash run_headless_tests.sh +``` + +## Environment Setup + +PyPTV uses a modern `environment.yml` and `requirements-dev.txt` for reproducible environments. Most dependencies are installed via conda, but some (e.g., `optv`, `rembg`, `flowtracks`) are installed via pip in the conda environment. + +See [PYPTV_ENVIRONMENT_GUIDE.md](PYPTV_ENVIRONMENT_GUIDE.md) for details. + +## Docker Usage + +For headless testing and reproducible builds, you can use Docker: +```bash +docker build -t pyptv-test . +docker run --rm pyptv-test +``` +This runs only headless tests in a minimal environment, mimicking CI. + +## Getting Help + +- 📖 **Documentation**: You're reading it! Start with [Quick Start](quick-start.md) +- 🐛 **Issues**: Report bugs on [GitHub Issues](https://github.com/openptv/pyptv/issues) +- 💬 **Discussions**: Join the [GitHub Discussions](https://github.com/openptv/pyptv/discussions) +- 📧 **Contact**: Reach out to the development team + +## Contributing + +PyPTV is an open-source project and welcomes contributions! See our contributing guidelines for more information. + +--- + +*Ready to get started? Begin with the [Installation Guide](installation.md) or jump to [Quick Start](quick-start.md) if you already have PyPTV installed.* + +## Complete Documentation Overview + +The PyPTV documentation is organized into the following sections: + +### 1. Getting Started +- **[Installation Guide](installation.md)** - Complete setup for Linux/macOS +- **[Windows Installation](windows-installation.md)** - Windows-specific installation +- **[Quick Start](quick-start.md)** - 10-minute tutorial using test_cavity + +### 2. Running PyPTV +- **[Running the GUI](running-gui.md)** - Launch and use the graphical interface + +### 3. Parameter Management +- **[Parameter Migration](parameter-migration.md)** - Convert from legacy .par files to YAML +- **[YAML Parameters Reference](yaml-parameters.md)** - Complete parameter documentation + +### 4. Camera Calibration +- **[Calibration Guide](calibration.md)** - Step-by-step calibration procedures + +### 5. Specialized Features +- **[Splitter Mode](splitter-mode.md)** - Beam splitter stereo camera systems +- **[Plugins System](plugins.md)** - Custom tracking and sequence processing + +### 6. Examples and Workflows +- **[Examples and Workflows](examples.md)** - Practical examples with test_cavity + +### 7. System Administration +- **[Logging Guide](LOGGING_GUIDE.md)** - Understanding PyPTV's logging +- **[Environment Guide](PYPTV_ENVIRONMENT_GUIDE.md)** - Python environment management + +## Key Improvements + +This documentation has been completely restructured to provide: + +✅ **Modern YAML Focus** - All examples use the current YAML parameter system +✅ **Correct num_cams Usage** - No references to obsolete `n_img` field +✅ **test_cavity Reference** - Consistent examples using the included test dataset +✅ **Modular Structure** - Each topic in its own focused guide +✅ **Practical Workflows** - Step-by-step procedures for common tasks +✅ **Cross-Referenced** - Links between related topics +✅ **Up-to-Date** - Reflects current PyPTV 2025 functionality + +## Quick Navigation + +| I want to... | Go to... | +|---------------|----------| +| Install PyPTV | [Installation Guide](installation.md) or [Windows Install](windows-installation.md) | +| Get started quickly | [Quick Start Guide](quick-start.md) | +| Run the software | [Running the GUI](running-gui.md) | +| Convert old parameters | [Parameter Migration](parameter-migration.md) | +| Understand YAML format | [YAML Parameters Reference](yaml-parameters.md) | +| Calibrate cameras | [Calibration Guide](calibration.md) | +| See examples | [Examples and Workflows](examples.md) | +| Use splitter cameras | [Splitter Mode](splitter-mode.md) | +| Create custom plugins | [Plugins System](plugins.md) | +| Troubleshoot issues | Check individual guides for troubleshooting sections | + +--- + +*Documentation last updated: July 2025 for PyPTV 2025* diff --git a/docs/pyptv/calibration.md b/docs/pyptv/calibration.md new file mode 100644 index 0000000..c7ca207 --- /dev/null +++ b/docs/pyptv/calibration.md @@ -0,0 +1,279 @@ +# Calibration Guide + +This guide covers camera calibration in PyPTV, from basic concepts to advanced techniques. + +## Overview + +Camera calibration is the process of determining the intrinsic and extrinsic parameters of your camera system. This is essential for accurate 3D particle tracking. + +## Environment Setup and Testing + +PyPTV uses a modern conda environment (`environment.yml`) and separates tests into headless (`tests/`) and GUI (`tests_gui/`) categories. See the README for details. + +## Prerequisites + +Before starting calibration: + +1. **Calibration Target**: You need a calibration target with known 3D coordinates +2. **Camera Images**: High-quality images of the calibration target from all cameras +3. **Parameter File**: A properly configured YAML parameter file + +## Basic Calibration Workflow + +### 1. Prepare Calibration Images + +Place calibration images in your `cal/` directory: + +``` +your_experiment/ +├── parameters_Run1.yaml +├── cal/ +│ ├── cam1.tif +│ ├── cam2.tif +│ ├── cam3.tif +│ ├── cam4.tif +│ └── target_coordinates.txt +└── img/ + └── ... +``` + +### 2. Configure Calibration Parameters + +In your YAML file, set up the calibration section: + +```yaml +num_cams: 4 + +cal_ori: + chfield: 0 + fixp_name: cal/target_coordinates.txt + img_cal_name: + - cal/cam1.tif + - cal/cam2.tif + - cal/cam3.tif + - cal/cam4.tif + img_ori: [] # Will be filled during calibration + pair_flag: false + tiff_flag: true + cal_splitter: false +``` + +### 3. Define Target Coordinates + +Create a target coordinate file (`cal/target_coordinates.txt`) with known 3D points: + +``` +# point_id X Y Z +1 -25.0 -25.0 0.0 +2 25.0 -25.0 0.0 +3 25.0 25.0 0.0 +4 -25.0 25.0 0.0 +``` + +### 4. Run Calibration in GUI + +1. **Open PyPTV GUI** + ```bash + python -m pyptv + ``` + +2. **Load Your Experiment** + - File → Open Experiment + - Select your parameter YAML file + +3. **Open Calibration Window** + - Tools → Calibration + - Or click the "Calibration" button + +4. **Detect Calibration Points** + - Click "Detect points" for each camera + - Verify detection quality in the image display + - Manually correct points if needed + +5. **Manual Orientation (if needed)** + - Click "Manual orient" if automatic detection fails + - Manually click on known calibration points + - Follow the on-screen prompts + +6. **Run Calibration** + - Click "Calibration" to calculate camera parameters + - Check the calibration residuals in the output + +7. **Save Results** + - Calibration parameters are automatically saved to `.ori` files + - Updated parameters are saved to your YAML file + +## Advanced Calibration Features + +### Dumbbell Calibration Parameters + +PyPTV supports dumbbell-based calibration to refine camera extrinsics. The dumbbell length is specified in millimeters. + +Key parameters in the `dumbbell` section: + +- `dumbbell_scale`: Expected dumbbell length (mm). Measure the physical dumbbell length and enter it here. +- `dumbbell_penalty_weight`: Weight of the length constraint relative to ray convergence. Start at 0.1-1.0 and increase only if length drift is visible. +- `dumbbell_eps`: Frame filter threshold (mm). Frames with reconstructed length deviating by more than this are excluded. Typical range: 3-10. +- `dumbbell_step`: Frame stride. Use 1 for full data; increase to 2-5 to speed up or reduce correlated frames. +- `dumbbell_fixed_camera`: Camera index to fix (1-based). Set to 0 for automatic selection (most valid detections). +- `dumbbell_niter`: Legacy parameter (not used by the current least-squares solver). +- `dumbbell_gradient_descent`: Legacy parameter (not used by the current least-squares solver). + +Suggested starting values: + +```yaml +dumbbell: + dumbbell_scale: 25.0 + dumbbell_penalty_weight: 0.5 + dumbbell_eps: 5.0 + dumbbell_step: 1 + dumbbell_fixed_camera: 0 +``` + +Tuning tips: + +- If many frames are filtered, increase `dumbbell_eps`. +- If the length drifts but convergence is stable, increase `dumbbell_penalty_weight`. +- If optimization becomes unstable or flat, reduce `dumbbell_penalty_weight` and remove outlier frames. + +### Multi-Plane Calibration + +For improved accuracy with large measurement volumes: + +```yaml +multi_planes: + n_planes: 3 + plane_name: + - img/calib_a_cam + - img/calib_b_cam + - img/calib_c_cam +``` + +### Calibration with Splitter + +For splitter-based stereo systems: + +```yaml +cal_ori: + cal_splitter: true + # Additional splitter-specific parameters +``` + +### Manual Orientation Points + +You can specify manual orientation points in the YAML: + +```yaml +man_ori: + nr: [3, 5, 72, 73, 3, 5, 72, 73, 1, 5, 71, 73, 1, 5, 71, 73] + +man_ori_coordinates: + camera_0: + point_1: {x: 1009.0, y: 608.0} + point_2: {x: 979.0, y: 335.0} + # ... more points + camera_1: + point_1: {x: 1002.0, y: 609.0} + # ... more points +``` + +## Calibration Quality Assessment + +### Residual Analysis + +Good calibration typically shows: +- **RMS residuals < 0.5 pixels** for each camera +- **Consistent residuals** across all cameras +- **No systematic patterns** in residual distribution + +### Visual Inspection + +Check calibration quality by: +1. Examining the 3D visualization of calibrated cameras +2. Verifying that detected points align with known target geometry +3. Testing 3D reconstruction with known test points + +## Troubleshooting Calibration Issues + +### Common Problems + +**Problem**: Points not detected automatically +**Solution**: +- Adjust detection parameters in `detect_plate` section +- Use manual point picking +- Improve image quality/contrast + +**Problem**: High calibration residuals +**Solution**: +- Check target coordinate file accuracy +- Verify image quality and focus +- Ensure stable camera mounting +- Re-examine manual point selections + +**Problem**: Inconsistent results between cameras +**Solution**: +- Check that all cameras use the same coordinate system +- Verify synchronization between cameras +- Examine individual camera calibrations + +### Detection Parameters + +Fine-tune detection in the `detect_plate` section: + +```yaml +detect_plate: + gvth_1: 40 # Threshold for camera 1 + gvth_2: 40 # Threshold for camera 2 + gvth_3: 40 # Threshold for camera 3 + gvth_4: 40 # Threshold for camera 4 + min_npix: 25 # Minimum pixel count + max_npix: 400 # Maximum pixel count + size_cross: 3 # Cross correlation size + sum_grey: 100 # Minimum sum of grey values + tol_dis: 500 # Distance tolerance +``` + +## Best Practices + +### Target Design +- Use high-contrast markers (black dots on white background) +- Ensure markers are clearly visible from all camera angles +- Include sufficient markers for robust calibration (>10 points) +- Distribute markers throughout the measurement volume + +### Image Quality +- Use adequate lighting to avoid shadows +- Ensure all cameras are in focus +- Minimize motion blur during image capture +- Use appropriate exposure settings + +### Camera Setup +- Mount cameras rigidly to prevent movement +- Choose camera positions that minimize occlusion +- Ensure good coverage of the measurement volume +- Avoid extreme viewing angles + +## File Outputs + +Successful calibration generates: + +``` +cal/ +├── cam1.tif.ori # Camera 1 calibration parameters +├── cam2.tif.ori # Camera 2 calibration parameters +├── cam3.tif.ori # Camera 3 calibration parameters +├── cam4.tif.ori # Camera 4 calibration parameters +├── cam1.tif.addpar # Additional parameters (distortion, etc.) +├── cam2.tif.addpar +├── cam3.tif.addpar +└── cam4.tif.addpar +``` + +These files contain the intrinsic and extrinsic camera parameters needed for 3D reconstruction. + +## See Also + +- [Quick Start Guide](quick-start.md) +- [YAML Parameters Guide](yaml-parameters.md) +- [Examples](examples.md) +- [GUI Usage Guide](running-gui.md) diff --git a/docs/pyptv/examples.md b/docs/pyptv/examples.md new file mode 100644 index 0000000..25865d7 --- /dev/null +++ b/docs/pyptv/examples.md @@ -0,0 +1,367 @@ +# Examples and Workflows + +This guide provides practical examples and common workflows for using PyPTV effectively. + +## Test Cavity Example + +The test_cavity example is included with PyPTV and demonstrates a complete 4-camera PTV setup. + +## Environment Setup and Testing + +PyPTV uses a modern conda environment (`environment.yml`) and separates tests into headless (`tests/`) and GUI (`tests_gui/`) categories. See the README for details. + +### Location and Setup + +```bash +cd tests/test_cavity +ls -la +``` + +You'll find: +``` +test_cavity/ +├── parameters_Run1.yaml # Main parameter file +├── cal/ # Calibration data +│ ├── cam1.tif - cam4.tif # Calibration images +│ ├── *.ori # Calibration results +│ ├── *.addpar # Additional parameters +│ └── target_on_a_side.txt # Target coordinates +├── img/ # Image sequence +│ ├── cam1.10001 - cam1.10004 +│ ├── cam2.10001 - cam2.10004 +│ ├── cam3.10001 - cam3.10004 +│ └── cam4.10001 - cam4.10004 +└── plugins/ # Example plugins + ├── ext_sequence_*.py + └── ext_tracker_*.py +``` + +### Running the Test Cavity Example + +1. **Navigate to the test directory** + ```bash + cd tests/test_cavity + ``` + +2. **Start PyPTV GUI** + ```bash + python -m pyptv + ``` + +3. **Load the experiment** + - File → Open Experiment + - Select `parameters_Run1.yaml` + +4. **Explore the setup** + - View calibration images: Tools → Calibration + - Check detection: Tools → Detection + - Run tracking: Process → Track Particles + +### Key Learning Points + +The test_cavity example demonstrates: + +- **4-camera setup** with proper calibration +- **Correct YAML structure** with `num_cams: 4` +- **Plugin system** usage +- **Complete workflow** from calibration to tracking + +## Common Workflows + +### Workflow 1: New Experiment Setup + +Starting a new PTV experiment from scratch. + +#### Step 1: Create Directory Structure + +```bash +mkdir my_experiment +cd my_experiment + +# Create subdirectories +mkdir cal img results + +# Copy template from test_cavity +cp tests/test_cavity/parameters_Run1.yaml parameters_my_experiment.yaml +``` + +#### Step 2: Modify Parameters + +Edit `parameters_my_experiment.yaml`: + +```yaml +num_cams: 3 # Adjust for your camera count + +sequence: + base_name: + - img/cam1.%d + - img/cam2.%d + - img/cam3.%d + first: 1 + last: 100 + +cal_ori: + img_cal_name: + - cal/cam1_cal.tif + - cal/cam2_cal.tif + - cal/cam3_cal.tif + fixp_name: cal/my_target.txt +``` + +#### Step 3: Add Your Data + +```bash +# Copy calibration images +cp /path/to/calibration/cam1.tif cal/cam1_cal.tif +cp /path/to/calibration/cam2.tif cal/cam2_cal.tif +cp /path/to/calibration/cam3.tif cal/cam3_cal.tif + +# Copy image sequence +cp /path/to/sequence/cam1_* img/ +cp /path/to/sequence/cam2_* img/ +cp /path/to/sequence/cam3_* img/ + +# Create target coordinate file +cat > cal/my_target.txt << EOF +# X Y Z ID +-30.0 -30.0 0.0 1 + 30.0 -30.0 0.0 2 + 30.0 30.0 0.0 3 +-30.0 30.0 0.0 4 +EOF +``` + +#### Step 4: Run Calibration + +1. Open PyPTV GUI +2. Load your parameter file +3. Tools → Calibration +4. Detect calibration points +5. Run calibration +6. Check residuals + +#### Step 5: Process Sequence + +1. Tools → Detection (test on single frame) +2. Process → Correspondences +3. Process → Track Particles +4. Analyze results + +### Workflow 2: Parameter Optimization + +Optimizing parameters for better tracking results. + +#### Detection Optimization + +Start with conservative detection parameters: + +```yaml +detect_plate: + gvth_1: 50 # Start higher, reduce if too few particles + gvth_2: 50 + gvth_3: 50 + min_npix: 20 # Minimum particle size + max_npix: 200 # Maximum particle size +``` + +Test detection on a representative frame: +1. Tools → Detection +2. Adjust thresholds in real-time +3. Save optimized values to YAML + +#### Tracking Optimization + +Adjust tracking parameters based on your flow: + +```yaml +track: + # For slow flows + dvxmax: 5.0 + dvxmin: -5.0 + dvymax: 5.0 + dvymin: -5.0 + dvzmax: 2.0 + dvzmin: -2.0 + + # For fast flows + dvxmax: 50.0 + dvxmin: -50.0 + # ... etc +``` + +### Workflow 3: Multi-Plane Calibration + +For large measurement volumes or improved accuracy. + +#### Setup Multi-Plane Configuration + +```yaml +multi_planes: + n_planes: 3 + plane_name: + - cal/plane_front + - cal/plane_middle + - cal/plane_back +``` + +#### Calibration Process + +1. Take calibration images at multiple Z positions +2. Configure plane parameters +3. Run calibration for each plane +4. Combine results for improved 3D accuracy + +### Workflow 4: Using Plugins + +PyPTV supports plugins for extended functionality. + +#### Available Plugins + +Check available plugins in your parameter file: + +```yaml +plugins: + available_tracking: + - default + - ext_tracker_splitter # For splitter systems + available_sequence: + - default + - ext_sequence_rembg # Background removal + - ext_sequence_contour # Contour detection + selected_tracking: default + selected_sequence: default +``` + +#### Background Removal Plugin + +To use background removal: + +1. Install dependencies: + ```bash + pip install rembg[cpu] # or rembg[gpu] + ``` + +2. Enable in parameters: + ```yaml + plugins: + selected_sequence: ext_sequence_rembg + ``` + +3. The plugin will automatically remove backgrounds during processing + +#### Splitter System Plugin + +For splitter-based stereo systems: + +```yaml +plugins: + selected_tracking: ext_tracker_splitter + +ptv: + splitter: true + +cal_ori: + cal_splitter: true +``` + +## Troubleshooting Common Issues + +### Issue: Poor Calibration Quality + +**Symptoms**: High residuals, tracking errors + +**Solutions**: +1. Check target coordinate file accuracy +2. Improve calibration image quality +3. Use more calibration points +4. Verify camera stability + +### Issue: Few or No Particles Detected + +**Symptoms**: Empty detection results + +**Solutions**: +1. Lower detection thresholds +2. Check image contrast +3. Verify image file paths +4. Adjust min/max pixel counts + +### Issue: Poor Tracking Performance + +**Symptoms**: Broken trajectories, false matches + +**Solutions**: +1. Optimize detection parameters first +2. Adjust velocity limits +3. Check correspondence criteria +4. Improve temporal resolution + +### Issue: Memory or Performance Problems + +**Symptoms**: Slow processing, crashes + +**Solutions**: +1. Process smaller batches +2. Reduce image resolution if possible +3. Use efficient file formats +4. Close unnecessary applications + +## Data Analysis Examples + +### Basic Trajectory Analysis + +After tracking, analyze results with Python: + +```python +import numpy as np +import matplotlib.pyplot as plt + +# Load tracking results (format depends on output) +# trajectories = load_trajectories('results/trajectories.txt') + +# Example analysis +# velocities = compute_velocities(trajectories) +# plot_velocity_field(velocities) +``` + +### Statistical Analysis + +```python +# Compute flow statistics +# mean_velocity = np.mean(velocities, axis=0) +# velocity_fluctuations = velocities - mean_velocity +# turbulent_intensity = np.std(velocity_fluctuations, axis=0) +``` + +## Best Practices Summary + +### Experimental Setup +- Use stable camera mounts +- Ensure good lighting and contrast +- Take high-quality calibration images +- Include sufficient calibration points + +### Parameter Configuration +- Start with test_cavity as template +- Use only `num_cams`, never `n_img` +- Test parameters on single frames first +- Document parameter changes + +### Processing Workflow +- Always calibrate first +- Validate detection on test frames +- Process in small batches initially +- Monitor intermediate results + +### Data Management +- Use consistent file naming +- Backup original data +- Document processing parameters +- Archive final results + +## See Also + +- [Quick Start Guide](quick-start.md) +- [Calibration Guide](calibration.md) +- [YAML Parameters Guide](yaml-parameters.md) +- [GUI Usage Guide](running-gui.md) diff --git a/docs/pyptv/index.md b/docs/pyptv/index.md new file mode 100644 index 0000000..fe52d4e --- /dev/null +++ b/docs/pyptv/index.md @@ -0,0 +1,48 @@ +# PyPTV Documentation Index + +Welcome to the PyPTV documentation! This index provides an organized overview of all available guides and resources. Use this page as your starting point for learning, troubleshooting, and reference. + +## Getting Started +- [Installation Guide](installation.md) +- [Windows Installation Guide](windows-installation.md) +- [Quick Start Guide](quick-start.md) + +## Core Usage +- [Running the GUI](running-gui.md) +- [YAML Parameters Reference](yaml-parameters.md) +- [Parameter Migration Guide](parameter-migration.md) +- [Calibration Guide](calibration.md) +- [Examples and Workflows](examples.md) + +## Advanced Features +- [Splitter Mode Guide](splitter-mode.md) +- [Plugins System Guide](plugins.md) + + +## Additional Resources +- [Test Cavity Example](examples.md#test-cavity) +- [Parameter Migration FAQ](parameter-migration.md#common-migration-issues) + +--- + +**How to use this documentation:** +- Click any link above to jump to the relevant guide. +- Use your browser's search to find keywords or topics. +- For troubleshooting, check the FAQ sections in each guide. +- For community help, visit [GitHub Issues](https://github.com/openptv/pyptv/issues) or [Discussions](https://github.com/openptv/pyptv/discussions). + + +## Video Tutorials + +Explore our YouTube video tutorials for step-by-step demonstrations: + +- [PyPTV installation on Linux](https://youtu.be/mlpaJ64t3Kw) +- [Installation on Windows](https://youtu.be/kWREmrGa7Z8) +- [Using Splitter Mode](https://youtu.be/FYZGZlL1yqs?si=4shcdja9CK3SEXGJ) +- [Using rembg plugin](https://youtu.be/vzlX2dIkhjw) + +Find more videos on our [PyPTV YouTube Channel](https://www.youtube.com/@openptv1113). + +--- + +*Documentation last updated: August 2025 for PyPTV 2025* diff --git a/docs/pyptv/installation.md b/docs/pyptv/installation.md new file mode 100644 index 0000000..c5ce20c --- /dev/null +++ b/docs/pyptv/installation.md @@ -0,0 +1,218 @@ +# Installation Guide + +This guide covers installing PyPTV on Linux and macOS systems. + +> 📝 **Windows Users**: See the [Windows Installation Guide](windows-installation.md) for platform-specific instructions. + +## Prerequisites + +Before installing PyPTV, ensure you have: + +- **Operating System**: Linux (Ubuntu 20.04+ or equivalent) or macOS 10.15+ +- **Conda**: [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/products/distribution) +- **Git**: For cloning the repository +- **Compiler**: GCC (Linux) or Xcode Command Line Tools (macOS) + +### System Dependencies + +#### Ubuntu/Debian +```bash +sudo apt update +sudo apt install -y build-essential cmake git pkg-config +sudo apt install -y libhdf5-dev +``` + +#### Fedora/RHEL/CentOS +```bash +sudo dnf install -y gcc gcc-c++ cmake git pkg-config +sudo dnf install -y hdf5-devel +``` + +#### macOS +```bash +# Install Xcode Command Line Tools +xcode-select --install + +# Install Homebrew (if not already installed) +/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" + +# Install dependencies +brew install cmake pkg-config hdf5 +``` + +## Installation Methods + +### Method 1: Automated Installation (Recommended) + +The easiest way to install PyPTV is using the provided installation script: + +```bash +# 1. Clone the repository +git clone https://github.com/openptv/pyptv.git +cd pyptv + +# 2. Run the installation script +./install_pyptv.sh + +# 3. Activate the environment +conda activate pyptv +``` + +The script will: +- Create a conda environment named "pyptv" with Python 3.11 +- Install all required dependencies +- Build and install OpenPTV (liboptv) +- Install PyPTV in development mode + +### Method 2: Manual Installation + +If you prefer manual control or need to customize the installation: + +```bash +# 1. Clone the repository +git clone https://github.com/openptv/pyptv.git +cd pyptv + +# 2. Create conda environment +conda env create -f environment.yml +conda activate pyptv + +# 3. Install PyPTV +pip install -e . +``` + +### Method 3: Development Installation + +For developers who want to contribute to PyPTV: + +```bash +# 1. Fork and clone your fork +git clone https://github.com/yourusername/pyptv.git +cd pyptv + +# 2. Create development environment +conda env create -f environment.yml +conda activate pyptv + +# 3. Install in development mode with test dependencies +pip install -e ".[dev,test]" + +# 4. Install pre-commit hooks +pre-commit install +``` + +## Verification + +Test your installation by running: + +```bash +# Activate the environment +conda activate pyptv + +# Test basic import +python -c "import pyptv; print('PyPTV installed successfully!')" + +# Launch the GUI (should open without errors) +python -m pyptv.pyptv_gui + +# Run the test suite +pytest tests/ + +## Testing: Headless vs GUI + +PyPTV separates tests into two categories: + +- **Headless tests** (no GUI): Located in `tests/`. These run in CI (GitHub Actions) and Docker, and do not require a display. +- **GUI-dependent tests**: Located in `tests_gui/`. These require a display and are run locally or with Xvfb. + +To run all tests locally: +```bash +bash run_tests.sh +``` +To run only headless tests (recommended for CI/Docker): +```bash +bash run_headless_tests.sh +``` + +## Docker Usage + +For headless testing and reproducible builds, you can use Docker: +```bash +docker build -t pyptv-test . +docker run --rm pyptv-test +``` +This runs only headless tests in a minimal environment, mimicking CI. +``` + +## Common Installation Issues + +### Issue: "liboptv not found" +**Solution**: The OpenPTV library needs to be built and installed. Try: +```bash +conda activate pyptv +cd pyptv +./install_pyptv.sh +``` + + +### Issue: "HDF5 headers not found" +**Solution**: Install HDF5 development packages: +```bash +# Ubuntu/Debian +sudo apt install libhdf5-dev + +# macOS +brew install hdf5 +``` + +### Issue: Permission errors during compilation +**Solution**: Ensure you have write permissions and try: +```bash +# Clean previous builds +rm -rf build/ dist/ *.egg-info/ +./install_pyptv.sh +``` + +## Environment Management + +### Activating PyPTV +Every time you want to use PyPTV: +```bash +conda activate pyptv +``` + +### Updating PyPTV +To get the latest changes: +```bash +conda activate pyptv +cd pyptv +git pull origin main +pip install -e . +``` + +### Removing PyPTV +To completely remove PyPTV: +```bash +conda env remove -n pyptv +rm -rf pyptv/ # Remove the source directory +``` + +## Next Steps + +Once PyPTV is installed: + +1. **Test with Example Data**: Follow the [Quick Start Guide](quick-start.md) +2. **Set Up Your Experiment**: Learn about [parameter configuration](parameter-migration.md) +3. **Launch the GUI**: See [Running the GUI](running-gui.md) + +## Getting Help + +If you encounter installation issues: + +- Check the [GitHub Issues](https://github.com/openptv/pyptv/issues) for similar problems +- Create a new issue with your system details and error messages +- Join the [GitHub Discussions](https://github.com/openptv/pyptv/discussions) for community help + +--- + +**Next**: [Quick Start Guide](quick-start.md) or [Windows Installation](windows-installation.md) diff --git a/docs/pyptv/parameter-migration.md b/docs/pyptv/parameter-migration.md new file mode 100644 index 0000000..8c072cb --- /dev/null +++ b/docs/pyptv/parameter-migration.md @@ -0,0 +1,222 @@ +# Parameter Migration Guide + +This guide helps you migrate from older PyPTV parameter formats to the current YAML-based system. + +## Overview + +PyPTV has undergone significant improvements in its parameter management system. This guide will help you understand and migrate to the current format. + +## Environment Setup and Testing + +PyPTV uses a modern conda environment (`environment.yml`) and separates tests into headless (`tests/`) and GUI (`tests_gui/`) categories. See the README for details. + +> **Important**: Always use `num_cams` for camera count. Do not use legacy fields like `n_img`. + +## Current YAML Structure + +The current parameter system uses a single YAML file with the following top-level structure: + +```yaml +num_cams: 4 # Number of cameras (global setting) + +cal_ori: + # Calibration and orientation parameters + +criteria: + # Tracking criteria parameters + +detect_plate: + # Detection parameters + +ptv: + # Main PTV processing parameters + +sequence: + # Image sequence parameters + +track: + # Tracking algorithm parameters + +plugins: + # Plugin configuration +``` + +## Key Changes from Legacy Formats + +### 1. Camera Count Management + +**Old system:** Used `n_img` in various parameter sections +**New system:** Uses single global `num_cams` field + +```yaml +# ✅ Correct - current format +num_cams: 4 + +# ❌ Incorrect - legacy format +ptv: + n_img: 4 +``` + +### 2. Parameter Organization + +Parameters are now organized into logical groups rather than scattered across multiple files. + +### 3. Manual Orientation Format + +The `man_ori` section now uses a flattened array format: + +```yaml +man_ori: + nr: [3, 5, 72, 73, 3, 5, 72, 73, 1, 5, 71, 73, 1, 5, 71, 73] +``` + +## Migration Steps + +### From Old PyPTV Installations + +1. **Backup your existing parameters** + ```bash + cp -r your_project/parameters your_project/parameters_backup + ``` + +2. **Use the GUI to load and save parameters** + - Open PyPTV GUI + - Load your old parameter files + - Save as new YAML format using "Save Parameters" + +3. **Verify the migration** + - Check that `num_cams` is set correctly at the top level + - Ensure no `n_img` fields remain in the YAML + - Test calibration and tracking workflows + +### Step-by-step: Migrating from Parameter Directories to YAML + +**1. Locate your legacy parameter files:** + - Typical files: `ptv_par.txt`, `criterium.txt`, `detect_plate.txt`, `track.txt`, etc. + - These are usually in a `parameters/` or project root directory. + +**2. Open PyPTV GUI:** + - Launch with `python -m pyptv.pyptv_gui` + - Use `File → Load Legacy` to select your old parameter directory. + +**3. Save as YAML:** + - After loading, use `File → Save Parameters` to export all settings to a single YAML file (e.g., `parameters_Run1.yaml`). + +**4. Check and edit YAML:** + - Open the YAML file in a text editor. + - Ensure `num_cams` is present and correct. + - Update any file paths to be relative to your experiment directory. + - Remove any legacy fields (e.g., `n_img`). + +**5. Validate in GUI:** + - Reload the YAML in the GUI and check that all dialogs open and parameters are correct. + +**6. Use the YAML in Python:** + - You can now use the YAML file for all PyPTV workflows, including headless and batch processing. + +#### Using YAML Parameters in Python + +You can load and use YAML parameters in Python via two main interfaces: + +**A. Using the `Experiment` class:** +```python +from pyptv.experiment import Experiment +exp = Experiment('parameters_Run1.yaml') +# Access parameters: +print(exp.cpar) # ControlParams object +print(exp.spar) # SequenceParams object +print(exp.vpar) # VolumeParams object +print(exp.tpar) # TargetParams object +print(exp.cals) # List of Calibration objects +``` + +**B. Using the `ParameterManager` directly:** +```python +from pyptv.parameter_manager import ParameterManager +pm = ParameterManager('parameters_Run1.yaml') +# Access raw parameter dictionary: +params = pm.parameters +num_cams = pm.num_cams +# Use helper functions to populate objects: +from pyptv.ptv import _populate_cpar, _populate_spar +cpar = _populate_cpar(params['ptv'], num_cams) +spar = _populate_spar(params['sequence'], num_cams) + + +**Tip:** For most workflows, use the `Experiment` class for convenience. For advanced or custom workflows, use `ParameterManager` and the population functions. + +**Summary:** +- Migrate all legacy parameter files to a single YAML using the GUI. +- Always use `num_cams` for camera count. +- Use the YAML file in Python via `Experiment` or `ParameterManager`. +### From Manual Parameter Files + +If you have manually created parameter files: + +1. Start with the test_cavity example as a template +2. Copy the structure from `tests/test_cavity/parameters_Run1.yaml` +3. Update paths and values to match your experiment + +## Common Migration Issues + +### Issue 1: Multiple Camera Count Fields + +**Problem:** Old files may have `n_img` in multiple sections +**Solution:** Remove all `n_img` fields and use only the global `num_cams` + +### Issue 2: Incorrect File Paths + +**Problem:** Relative paths may not work with new structure +**Solution:** Use paths relative to your experiment directory + +### Issue 3: Missing Parameter Groups + +**Problem:** New YAML structure requires all parameter groups +**Solution:** Use the test_cavity example to ensure all sections are present + +## Validation + +After migration, validate your parameters: + +1. Load the YAML file in PyPTV GUI +2. Check the "Edit Parameters" dialogs work correctly +3. Run a test calibration to ensure all parameters are read properly +4. Verify tracking parameters are applied correctly + +## Example Migration + +From this legacy structure: +``` +project/ +├── ptv_par.txt +├── criterium.txt +├── detect_plate.txt +└── track.txt +``` + +To this modern structure: +``` +project/ +├── parameters_Run1.yaml +├── cal/ +│ ├── cam1.tif +│ └── ... +└── img/ + ├── cam1.10001 + └── ... +``` + +## Getting Help + +If you encounter issues during migration: + +1. Check the test_cavity example for reference +2. Use the PyPTV GUI parameter editors to understand the expected format +3. Consult the [YAML Parameters Guide](yaml-parameters.md) for detailed field descriptions +4. Ask for help on the PyPTV community forums or GitHub issues + +## See Also + +- [YAML Parameters Guide](yaml-parameters.md) +- [Quick Start Guide](quick-start.md) +- [Test Cavity Example](examples.md#test-cavity) diff --git a/docs/pyptv/plugins.md b/docs/pyptv/plugins.md new file mode 100644 index 0000000..4e41fba --- /dev/null +++ b/docs/pyptv/plugins.md @@ -0,0 +1,460 @@ +# Plugins System Guide + +PyPTV features an extensible plugin system that allows you to customize tracking algorithms and sequence processing without modifying the core code. + +## Overview + +The plugin system provides two main extension points: + +## Environment Setup and Testing + +PyPTV uses a modern conda environment (`environment.yml`) and separates tests into headless (`tests/`) and GUI (`tests_gui/`) categories. See the README for details. + +1. **Tracking Plugins** - Custom particle tracking algorithms +2. **Sequence Plugins** - Custom image sequence preprocessing + +Plugins are Python files that implement specific interfaces and can be selected via the YAML configuration. + +## Plugin Configuration + +### Available vs Selected Plugins + +In your YAML configuration: + +```yaml +plugins: + available_tracking: # List of available tracking plugins + - default + - ext_tracker_splitter + - my_custom_tracker + selected_tracking: default # Currently active tracking plugin + + available_sequence: # List of available sequence plugins + - default + - ext_sequence_rembg + - ext_sequence_contour + - my_custom_sequence + selected_sequence: default # Currently active sequence plugin +``` + +### Plugin Directory + +Place custom plugins in the `plugins/` directory of your experiment: + +``` +my_experiment/ +├── parameters_Run1.yaml +├── plugins/ +│ ├── my_custom_tracker.py +│ ├── my_custom_sequence.py +│ └── __init__.py +├── cal/ +└── img/ +``` + +## Tracking Plugins + +Tracking plugins customize how particles are tracked between frames. + +### Plugin Interface + +Create a tracking plugin by implementing the required functions: + +```python +# plugins/my_custom_tracker.py + +def default_tracking(exp, step, num_cams): + """ + Custom tracking algorithm + + Args: + exp: Experiment object + step: Current time step + num_cams: Number of cameras + + Returns: + Number of tracked particles + """ + + # Your custom tracking logic here + # Access experiment data via exp object + # Return number of successfully tracked particles + + return num_tracked + + +# Optional: initialization function +def initialize_tracking(exp): + """Initialize tracking plugin with experiment data""" + pass + +# Optional: cleanup function +def finalize_tracking(exp): + """Clean up after tracking is complete""" + pass +``` + +### Example: Velocity-Based Tracker + +```python +# plugins/velocity_tracker.py + +import numpy as np +from optv.tracking_framebuf import TargetArray + +def default_tracking(exp, step, num_cams): + """Tracking based on velocity prediction""" + + # Get current and previous particles + current_targets = exp.current_step_targets + previous_targets = exp.previous_step_targets + + if previous_targets is None: + return len(current_targets) + + # Predict positions based on velocity + predicted_positions = predict_next_positions(previous_targets) + + # Match current particles to predictions + matches = match_particles(current_targets, predicted_positions) + + # Update particle trajectories + update_trajectories(exp, matches) + + return len(matches) + +def predict_next_positions(targets): + """Predict next positions based on velocity""" + positions = [] + for target in targets: + # Simple linear prediction + next_x = target.x + target.vx + next_y = target.y + target.vy + next_z = target.z + target.vz + positions.append((next_x, next_y, next_z)) + return positions + +def match_particles(current, predicted): + """Match current particles to predicted positions""" + # Implement matching algorithm + # Return list of (current_particle, predicted_particle) pairs + pass +``` + +### Built-in Tracking Plugins + +PyPTV includes several built-in tracking plugins: + +#### default +Standard PTV tracking algorithm using the OpenPTV libraries. + +#### ext_tracker_splitter +Specialized tracking for splitter-based stereo systems. + +```python +# Automatically enabled when splitter mode is active +plugins: + selected_tracking: ext_tracker_splitter + +ptv: + splitter: true +``` + +## Sequence Plugins + +Sequence plugins preprocess images before particle detection. + +### Plugin Interface + +```python +# plugins/my_sequence_plugin.py + +def sequence_preprocess(image_data, frame_number, camera_id): + """ + Preprocess image data + + Args: + image_data: Raw image array + frame_number: Current frame number + camera_id: Camera identifier (0, 1, 2, ...) + + Returns: + Processed image array + """ + + # Your preprocessing logic here + processed_image = apply_preprocessing(image_data) + + return processed_image +``` + +### Example: Background Subtraction + +```python +# plugins/background_subtraction.py + +import numpy as np +import cv2 + +# Global background storage +background_models = {} + +def sequence_preprocess(image_data, frame_number, camera_id): + """Background subtraction preprocessing""" + + # Initialize background model for this camera + if camera_id not in background_models: + background_models[camera_id] = cv2.createBackgroundSubtractorMOG2() + + # Apply background subtraction + bg_model = background_models[camera_id] + foreground_mask = bg_model.apply(image_data) + + # Apply mask to original image + processed_image = cv2.bitwise_and(image_data, image_data, mask=foreground_mask) + + return processed_image +``` + +### Built-in Sequence Plugins + +#### default +No preprocessing - passes images through unchanged. + +#### ext_sequence_rembg +Background removal using the `rembg` library. + +```bash +# Install rembg first +pip install rembg[cpu] # or rembg[gpu] +``` + +```yaml +plugins: + selected_sequence: ext_sequence_rembg +``` + +#### ext_sequence_contour +Contour-based preprocessing for improved particle detection. + +#### ext_sequence_rembg_contour +Combines background removal with contour detection. + +## Advanced Plugin Development + +### Accessing Experiment Data + +Plugins have access to the full experiment object: + +```python +def default_tracking(exp, step, num_cams): + # Access parameters + detect_params = exp.pm.get_parameter('detect_plate') + track_params = exp.pm.get_parameter('track') + + # Access calibration data + calibration = exp.calibration + + # Access current tracking data + current_targets = exp.current_step_targets + + # Access file paths + working_dir = exp.working_directory +``` + +### State Management + +Maintain state between plugin calls: + +```python +# Global state storage +plugin_state = {} + +def default_tracking(exp, step, num_cams): + # Initialize state if needed + if 'initialized' not in plugin_state: + plugin_state['particle_histories'] = {} + plugin_state['initialized'] = True + + # Use state data + histories = plugin_state['particle_histories'] + + # Update state + histories[step] = current_tracking_data +``` + +### Error Handling + +Implement robust error handling: + +```python +def sequence_preprocess(image_data, frame_number, camera_id): + try: + # Main processing + result = process_image(image_data) + return result + + except Exception as e: + # Log error and return original image + print(f"Plugin error on frame {frame_number}, camera {camera_id}: {e}") + return image_data +``` + +## Plugin Testing + +### Unit Testing + +Create tests for your plugins: + +```python +# test_my_plugin.py + +import unittest +import numpy as np +from plugins.my_custom_tracker import default_tracking + +class TestCustomTracker(unittest.TestCase): + + def setUp(self): + # Create mock experiment object + self.exp = create_mock_experiment() + + def test_tracking_basic(self): + # Test basic tracking functionality + result = default_tracking(self.exp, step=1, num_cams=4) + self.assertIsInstance(result, int) + self.assertGreaterEqual(result, 0) +``` + +### Integration Testing + +Test plugins with real data: + +```python +# Test with test_cavity dataset +def test_with_real_data(): + exp = Experiment('tests/test_cavity/parameters_Run1.yaml') + + # Enable your plugin + exp.pm.set_parameter('plugins', { + 'selected_tracking': 'my_custom_tracker' + }) + + # Run a few frames + for step in range(1, 5): + result = run_tracking_step(exp, step) + assert result > 0 +``` + +## Plugin Examples + +### Particle Size Filter + +```python +# plugins/size_filter.py + +def sequence_preprocess(image_data, frame_number, camera_id): + """Filter particles by size""" + + # Apply morphological operations to remove small noise + kernel = np.ones((3,3), np.uint8) + + # Remove small particles + opened = cv2.morphologyEx(image_data, cv2.MORPH_OPEN, kernel) + + # Remove holes in particles + closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel) + + return closed +``` + +### Multi-Exposure Fusion + +```python +# plugins/hdr_fusion.py + +exposure_buffers = {} + +def sequence_preprocess(image_data, frame_number, camera_id): + """Fuse multiple exposures for better dynamic range""" + + # Store multiple exposures + if camera_id not in exposure_buffers: + exposure_buffers[camera_id] = [] + + exposure_buffers[camera_id].append(image_data) + + # Fuse when we have enough exposures + if len(exposure_buffers[camera_id]) >= 3: + fused = fuse_exposures(exposure_buffers[camera_id]) + exposure_buffers[camera_id] = [] # Reset buffer + return fused + else: + return image_data # Return single exposure for now +``` + +## Best Practices + +### Plugin Design +- Keep plugins focused on a single task +- Handle errors gracefully +- Document plugin parameters and behavior +- Test with various datasets + +### Performance +- Minimize memory allocation in tracking plugins +- Use efficient image processing operations +- Consider parallel processing for independent operations +- Profile plugin performance with real data + +### Compatibility +- Follow the standard plugin interface +- Test with different PyPTV versions +- Document plugin dependencies +- Provide fallback behavior when possible + +## Debugging Plugins + +### Logging + +Add logging to your plugins: + +```python +import logging + +logger = logging.getLogger(__name__) + +def default_tracking(exp, step, num_cams): + logger.info(f"Starting tracking for step {step}") + + try: + result = perform_tracking() + logger.debug(f"Tracked {result} particles") + return result + except Exception as e: + logger.error(f"Tracking failed: {e}") + raise +``` + +### Visual Debugging + +Create debug visualizations: + +```python +def sequence_preprocess(image_data, frame_number, camera_id): + processed = apply_processing(image_data) + + # Save debug images + if DEBUG_MODE: + cv2.imwrite(f'debug/frame_{frame_number}_cam_{camera_id}_original.png', image_data) + cv2.imwrite(f'debug/frame_{frame_number}_cam_{camera_id}_processed.png', processed) + + return processed +``` + +## See Also + +- [Examples and Workflows](examples.md) +- [YAML Parameters Reference](yaml-parameters.md) +- [Splitter Mode Guide](splitter-mode.md) +- [Calibration Guide](calibration.md) diff --git a/docs/pyptv/quick-start.md b/docs/pyptv/quick-start.md new file mode 100644 index 0000000..51eda96 --- /dev/null +++ b/docs/pyptv/quick-start.md @@ -0,0 +1,206 @@ +# Quick Start Guide + +Get up and running with PyPTV using the included test dataset in under 10 minutes. + +## Prerequisites + + +## Environment Setup and Testing + +PyPTV uses a modern conda environment (`environment.yml`) and separates tests into headless (`tests/`) and GUI (`tests_gui/`) categories. See the README for details. + +## Overview + +This guide walks you through: +1. **Loading test data** - Use the included test_cavity experiment +2. **Running the GUI** - Launch and navigate the PyPTV interface +3. **Viewing calibration** - Check camera calibration +4. **Processing images** - Detect and track particles +5. **Exporting results** - Save tracking data + +## Step 1: Activate PyPTV + +Open your terminal (or Anaconda Prompt on Windows) and activate the PyPTV environment: + +```bash +conda activate pyptv +cd /path/to/pyptv # Navigate to your PyPTV installation +``` + +## Step 2: Launch the GUI + +Start the PyPTV graphical interface: + +```bash +python -m pyptv.pyptv_gui +``` + +The main PyPTV window should open with a parameter tree on the left and camera views on the right. + +## Step 3: Load Test Data + +The test_cavity experiment is included with PyPTV and provides a complete working example. + +1. **Load the experiment**: + - In the GUI, go to **File → Load Experiment** + - Navigate to `tests/test_cavity/` + - Select `parameters_Run1.yaml` + - Click **Open** + +2. **Verify loading**: + - The parameter tree should now show "Run1" with expandable sections + - You should see 4 camera tabs at the bottom + - Status bar should show "Experiment loaded successfully" + +## Step 4: Initialize Parameters + +1. **Load images and parameters**: + - Click **"Load images/parameters"** button + - This reads all configuration and prepares the cameras + - Camera views should show calibration images + +2. **Check the setup**: + - **Camera count**: 4 cameras (cam1, cam2, cam3, cam4) + - **Image format**: TIFF calibration images + - **Parameters**: Detection thresholds, tracking parameters loaded + +## Step 5: Explore Calibration + +The test_cavity experiment comes with pre-calculated camera calibrations: + +1. **View calibration**: + - Go to **Calibration → Open Calibration** (or click the calibration button) + - The calibration GUI opens showing camera positions and target images + +2. **Check calibration quality**: + - Click **"Load images/parameters"** in calibration GUI + - Click **"Show initial guess"** to see projected calibration points + - Observe how well points align with detected features + +## Step 6: Detect Particles + +Return to the main GUI and detect particles in the sequence: + +1. **Go to Sequence Processing**: + - In the main GUI, ensure parameters are loaded + - Click **"Detection"** button + - This detects particles in all camera views for the current frame + +2. **Review detection results**: + - Blue crosses appear on detected particles + - Check all 4 camera views for reasonable particle detection + - Particles should be clearly marked on the images + +## Step 7: Find Correspondences + +Find matching particles across cameras: + +1. **Run correspondence**: + - Click **"Correspondences"** button + - This matches particles between camera views + - Look for colored lines connecting corresponding particles + +2. **Check results**: + - Good correspondences show consistent particle matches + - Status bar shows number of correspondences found + +## Step 8: Determine 3D Positions + +Calculate 3D particle positions: + +1. **Run determination**: + - Click **"Determination"** button + - This triangulates 3D positions from 2D correspondences + - Results are saved to files + +2. **View output files**: + - Check the experiment directory for result files + - Look for files like `rt_is.XXXXX` with 3D positions + +## Step 9: Process Sequence (Optional) + +For multiple frames: + +1. **Set frame range**: + - Adjust sequence parameters if needed + - Set first and last frame numbers + +2. **Run sequence**: + - Click **"Sequence"** button + - This processes the entire image sequence + - Progress is shown in the status bar + +## Understanding the Test Data + +The test_cavity experiment includes: + +### Directory Structure +``` +test_cavity/ +├── parameters_Run1.yaml # Main parameter file +├── cal/ # Calibration data +│ ├── cam1.tif # Calibration images +│ ├── cam1.tif.ori # Camera orientations +│ ├── cam1.tif.addpar # Additional parameters +│ └── target_on_a_side.txt # Calibration target coordinates +├── img/ # Image sequences +│ ├── cam1.10000 # Frame images +│ ├── cam1.10001 +│ └── ... +└── plugins/ # Custom processing plugins +``` + +### Key Parameters +- **4 cameras** in a stereo configuration +- **Calibration target** with known 3D coordinates +- **Particle detection** tuned for dark particles on bright background +- **Tracking parameters** set for moderate particle velocities + +## Typical Results + +After processing, you should see: +- **~20-50 particles** detected per camera per frame +- **~10-30 correspondences** per frame +- **3D positions** with coordinate accuracy of ~0.1 mm +- **Tracking data** suitable for velocity analysis + +## Next Steps + +Now that you've successfully run the test case: + +1. **Learn calibration**: Follow the [Calibration Guide](calibration.md) +2. **Set up your own experiment**: See [Parameter Migration](parameter-migration.md) +3. **Explore plugins**: Check out the [Plugins Guide](plugins.md) +4. **Use advanced features**: Try [Splitter Mode](splitter-mode.md) + +## Common Issues + +### "No images found" +- **Check file paths** in the YAML parameter file +- **Verify image format** (should match what's in img/ directory) + +### "Calibration failed" +- **Calibration files missing** - check cal/ directory +- **Try the calibration GUI** to debug calibration issues + +### "No particles detected" +- **Adjust detection thresholds** in detect_plate parameters +- **Check image quality** - particles should be clearly visible + +### "Poor correspondences" +- **Check calibration quality** first +- **Adjust correspondence tolerances** in criteria parameters + +## Performance Tips + +- **RAM usage**: Large image sequences require significant memory +- **Disk space**: Allow ~1GB per 1000 frames for results +- **Processing time**: Expect ~1-10 seconds per frame depending on particle count + +--- + +**Success!** You've completed your first PyPTV analysis. Ready to set up your own experiment? See [Parameter Migration](parameter-migration.md) to convert your existing setup. + +--- + +**Next**: [Running the GUI](running-gui.md) or [Calibration Guide](calibration.md) diff --git a/docs/pyptv/running-gui.md b/docs/pyptv/running-gui.md new file mode 100644 index 0000000..2df6546 --- /dev/null +++ b/docs/pyptv/running-gui.md @@ -0,0 +1,340 @@ +# Running the GUI + +Learn how to use the PyPTV graphical user interface for particle tracking analysis. + +## Launching PyPTV + +### Command Line Launch +```bash +# Activate environment +conda activate pyptv + +# Launch GUI from any directory +python -m pyptv.pyptv_gui + + +## GUI Requirements and Testing + +The PyPTV GUI requires a display (X11 on Linux/macOS, or native on Windows). GUI-dependent tests are located in `tests_gui/` and are not run in CI or Docker. Run these tests locally or with Xvfb if needed. + +For headless testing, see the main README and installation guide. + +# Or from PyPTV source directory +cd pyptv +python -m pyptv.pyptv_gui +``` + +### From Python Script +```python +from pyptv.pyptv_gui import MainGUI +from pathlib import Path + +# Launch with specific experiment +experiment_path = Path("path/to/your/experiment") +gui = MainGUI(experiment_path, Path.cwd()) +gui.configure_traits() +``` + +## GUI Overview + +The PyPTV interface consists of several main areas: + +### 1. Parameter Tree (Left Panel) +- **Experiment structure** with parameter sets (Run1, Run2, etc.) +- **Right-click menus** for parameter management +- **Expandable sections** showing parameter groups + +### 2. Camera Views (Center/Right) +- **Tabbed interface** for multiple cameras +- **Image display** with zoom and pan +- **Overlay graphics** for particles, correspondences, and trajectories + +### 3. Control Buttons (Top) +- **Processing buttons** for detection, correspondence, tracking +- **Parameter editing** and calibration access +- **Sequence processing** controls + +### 4. Status Bar (Bottom) +- **Progress indicators** during processing +- **File information** and current frame +- **Error messages** and warnings + +## Main Workflow + +### 1. Load Experiment + +**Option A: Load Existing YAML** +``` +File → Load Experiment → Select parameters.yaml +``` + +**Option B: Load Legacy Parameters** +``` +File → Load Legacy → Select parameters/ folder +# Automatically converts to YAML format +``` + +**Option C: Create New Experiment** +``` +File → New Experiment → Choose directory +# Creates basic parameter structure +``` + +### 2. Initialize Parameters + +After loading an experiment: + +1. **Load images/parameters** button + - Reads all configuration files + - Loads calibration data + - Prepares camera views + +2. **Verify setup**: + - Check parameter tree is populated + - Ensure camera tabs are visible + - Confirm calibration images load + +### 3. Single Frame Processing + +Process one frame to test setup: + +1. **Detection**: + ``` + Click "Detection" button + → Blue crosses mark detected particles + ``` + +2. **Correspondences**: + ``` + Click "Correspondences" button + → Colored lines connect matching particles + ``` + +3. **Determination**: + ``` + Click "Determination" button + → Calculates 3D positions + ``` + +### 4. Sequence Processing + +Process multiple frames: + +1. **Set frame range** in sequence parameters +2. **Click "Sequence" button** +3. **Monitor progress** in status bar +4. **Check output files** in experiment directory + +## Parameter Management + +### Editing Parameters + +**Method 1: Right-click in Parameter Tree** +``` +Right-click parameter set → "Edit Parameters" +→ Opens parameter editing dialog +``` + +**Method 2: Direct File Editing** +``` +Edit parameters.yaml in text editor +→ Reload experiment in GUI +``` + +**Method 3: Calibration-specific** +``` +Calibration → Open Calibration +→ Specialized calibration interface +``` + +### Parameter Sets + +Create multiple parameter configurations: + +1. **Add new set**: + ``` + Right-click in parameter tree → "Add Parameter Set" + → Enter name (e.g., "HighSpeed", "LowLight") + ``` + +2. **Switch between sets**: + ``` + Right-click parameter set → "Set as Active" + ``` + +3. **Copy settings**: + ``` + Right-click → "Duplicate Parameter Set" + ``` + +### Parameter Sections + +Key parameter groups: + +| Section | Purpose | Key Settings | +|---------|---------|--------------| +| **ptv** | General PTV settings | Image names, camera count, preprocessing | +| **detect_plate** | Particle detection | Gray thresholds, size limits | +| **criteria** | Correspondence matching | Search tolerances, minimum matches | +| **track** | Particle tracking | Velocity limits, trajectory linking | +| **cal_ori** | Calibration | Camera files, calibration images | + +## Camera Views + +### Navigation +- **Zoom**: Mouse wheel or zoom tools +- **Pan**: Click and drag +- **Reset view**: Right-click → "Reset zoom" + +### Overlays +- **Blue crosses**: Detected particles +- **Colored lines**: Correspondences between cameras +- **Numbers**: Particle IDs or point numbers +- **Trajectories**: Particle paths over time + +### Camera-specific Operations +- **Right-click particle**: Delete detection +- **Left-click**: Add manual detection (in calibration mode) +- **Tab switching**: Move between camera views + +## Processing Controls + +### Detection Settings +``` +detect_plate: + gvth_1: 80 # Primary detection threshold + gvth_2: 40 # Secondary threshold + min_npix: 5 # Minimum particle size + max_npix: 100 # Maximum particle size +``` + +### Correspondence Settings +``` +criteria: + eps0: 0.2 # Search radius (mm) + corrmin: 2 # Minimum cameras for correspondence + cn: 0.02 # Additional tolerance +``` + +### Tracking Settings +``` +track: + dvxmin: -50.0 # Velocity limits (mm/frame) + dvxmax: 50.0 + dvymin: -50.0 + dvymax: 50.0 +``` + +## File Management + +### Input Files +- **Image sequences**: `img/cam1.XXXXX`, `img/cam2.XXXXX`, etc. +- **Calibration images**: `cal/cam1.tif`, `cal/cam2.tif`, etc. +- **Parameter file**: `parameters.yaml` + +### Output Files +- **Detection results**: `cam1_targets`, `cam2_targets`, etc. +- **3D positions**: `rt_is.XXXXX` files +- **Tracking data**: `ptv_is.XXXXX` files +- **Calibration**: Updated `.ori` and `.addpar` files + +## Advanced Features + +### Plugin Integration +``` +Right-click parameter tree → "Configure Plugins" +→ Select tracking and sequence plugins +``` + +### Batch Processing +```python +# Script for multiple experiments +for experiment in experiment_list: + gui.load_experiment(experiment) + gui.process_sequence() + gui.export_results() +``` + +### Custom Visualization +```python +# Add custom overlays +def custom_overlay(camera_view, data): + camera_view.plot_custom_data(data) +``` + +## Troubleshooting + +### Common Issues + +**"Images not found"** +- Check file paths in parameters.yaml +- Verify image naming convention +- Ensure correct working directory + +**"Calibration errors"** +- Open calibration GUI to debug +- Check .ori and .addpar files exist +- Verify calibration target coordinates + +**"No particles detected"** +- Adjust detection thresholds +- Check image contrast and quality +- Try preprocessing options + +**"Poor correspondences"** +- Improve calibration accuracy +- Adjust search tolerances +- Check camera synchronization + +### Performance Tips + +- **Memory usage**: Close unused camera tabs +- **Processing speed**: Reduce image resolution if possible +- **Disk I/O**: Use SSD for image sequences +- **Parallel processing**: Enable multi-threading in plugins + +### Debugging Mode + +Enable verbose output: +```bash +python -m pyptv.pyptv_gui --debug +``` + +Check log files: +```bash +tail -f pyptv.log +``` + +## Keyboard Shortcuts + +| Key | Action | +|-----|--------| +| `Ctrl+O` | Open experiment | +| `Ctrl+S` | Save parameters | +| `F5` | Refresh/reload | +| `Space` | Process next frame | +| `Esc` | Cancel current operation | + +## Best Practices + +### Workflow Organization +1. **Test single frame** before sequence processing +2. **Save parameter changes** before major operations +3. **Back up original parameters** before modifications +4. **Use descriptive parameter set names** + +### Data Management +- Keep experiment folders organized +- Use consistent naming conventions +- Document parameter changes +- Archive completed experiments + +### Quality Control +- Regularly check calibration accuracy +- Monitor particle detection quality +- Validate tracking results +- Compare with expected physical behavior + +--- + +**Next**: Learn about [Camera Calibration](calibration.md) or [Parameter Migration](parameter-migration.md) diff --git a/docs/pyptv/splitter-mode.md b/docs/pyptv/splitter-mode.md new file mode 100644 index 0000000..ca76aa0 --- /dev/null +++ b/docs/pyptv/splitter-mode.md @@ -0,0 +1,262 @@ +# Splitter Mode Guide + +This guide covers PyPTV's splitter mode functionality for stereo camera systems using beam splitters. + +## Overview + +Splitter mode is designed for stereo PTV systems where a single camera is split using a beam splitter to create two views of the same region. This technique is commonly used to achieve stereo vision with a single camera sensor. + +## Environment Setup and Testing + +PyPTV uses a modern conda environment (`environment.yml`) and separates tests into headless (`tests/`) and GUI (`tests_gui/`) categories. See the README for details. + +## When to Use Splitter Mode + +Use splitter mode when: +- You have a beam splitter-based stereo system +- Single camera sensor captures multiple views +- Views are arranged side-by-side or top-bottom on the sensor +- You need stereo 3D tracking with limited camera hardware + +## Configuration + +### Basic Splitter Setup + +Enable splitter mode in your YAML configuration: + +```yaml +num_cams: 4 # Even though it's one physical camera + +ptv: + splitter: true + imx: 512 # Half width - will be width of splitted + imy: 512 # Half height - will be height of splitted + img_name: img/unsplitted_%d.tif + +cal_ori: + cal_splitter: true + img_cal_name: + - cal/unsplitted.tif # unsplitted image + +plugins: + selected_tracking: ext_tracker_splitter + available_tracking: + - default + - ext_tracker_splitter +``` + +### Image Processing + +In splitter mode, PyPTV automatically: +1. **Splits images** into left and right portions +2. **Processes each portion** as a separate camera view +3. **Applies stereo matching** between the split views +4. **Reconstructs 3D positions** using the stereo geometry + +### Splitter Geometry +So far it's fixed into 4, but probably can work for 2 + +## Calibration with Splitter + +Profidve the unsplitted image and check in the GUI option +the splitter will work automatically + + +### Calibration Process + +1. **Capture calibration image** with target visible in both views +2. **Split the image** manually or use PyPTV's splitter tools +3. **Run calibration** on each split view separately +4. **Verify stereo geometry** by checking calibration residuals + +### Manual Splitting + +If needed, manually split calibration images: + +Look into the plugins/ folder there is an example of manual splitting but this obsolete now. + + +## Processing Workflow + +### 1. Image Sequence Setup + +Configure image sequence for splitter processing: + +```yaml +sequence: + base_name: + - img/splitter.%d # Single image file per frame + first: 1 + last: 100 + +# Or for pre-split images: +sequence: + base_name: + - img/left.%d # Left view sequence + - img/right.%d # Right view sequence + first: 1 + last: 100 +``` + +### 2. Detection Parameters + +Tune detection for each split view: + +```yaml +detect_plate: + gvth_1: 40 # Threshold for left view + gvth_2: 45 # Threshold for right view (may differ) + min_npix: 20 + max_npix: 200 +``` + +### 3. Stereo Matching + +Configure stereo correspondence: + +```yaml +criteria: + corrmin: 50.0 # Higher threshold for stereo matching + cn: 0.01 # Tighter correspondence tolerance + eps0: 0.1 # Smaller search window +``` + +## Plugin System + +### Splitter Tracking Plugin + +The `ext_tracker_splitter` plugin provides specialized functionality: + +```python +# Example plugin functionality (simplified) +class SplitterTracker: + def process_frame(self, image): + # Split image into left and right views + left_view, right_view = self.split_image(image) + + # Detect particles in each view + left_particles = self.detect_particles(left_view) + right_particles = self.detect_particles(right_view) + + # Perform stereo matching + matched_pairs = self.stereo_match(left_particles, right_particles) + + # Reconstruct 3D positions + positions_3d = self.reconstruct_3d(matched_pairs) + + return positions_3d +``` + +### Custom Splitter Plugins + +Create custom plugins for specialized splitter setups: + +```python +# plugins/my_splitter_plugin.py +def my_splitter_sequence(frame_data): + """Custom sequence processing for specific splitter setup""" + + # Custom splitting logic + left_view = extract_left_view(frame_data) + right_view = extract_right_view(frame_data) + + # Apply custom preprocessing + left_processed = preprocess_view(left_view) + right_processed = preprocess_view(right_view) + + return [left_processed, right_processed] +``` + +## Troubleshooting + +### Common Issues + +**Issue**: Poor stereo matching between split views +**Solution**: +- Check calibration quality for both views +- Verify splitting geometry is correct +- Adjust correspondence criteria +- Ensure good overlap between views + +**Issue**: Inconsistent detection between views +**Solution**: +- Use different detection thresholds for each view +- Check illumination uniformity +- Verify image splitting is consistent + +**Issue**: Calibration residuals too high +**Solution**: +- Ensure calibration target is visible in both views +- Check that split views don't have distortion artifacts +- Use more calibration points +- Verify beam splitter optical quality + +### Validation + +Test your splitter setup: + +1. **Split View Alignment**: Verify views are properly aligned +2. **Stereo Geometry**: Check calibration produces reasonable camera positions +3. **3D Reconstruction**: Test with known 3D points +4. **Temporal Consistency**: Verify tracking works across frames + +## Best Practices + +### Hardware Setup +- Use high-quality beam splitters to minimize distortion +- Ensure uniform illumination across both views +- Mount beam splitter rigidly to prevent movement +- Use appropriate filters if needed for contrast + +### Software Configuration +- Start with the test_cavity example as template +- Use conservative detection parameters initially +- Validate calibration thoroughly before tracking +- Monitor stereo matching quality + +### Data Processing +- Process test sequences before full datasets +- Check 3D reconstruction accuracy with known objects +- Validate temporal tracking consistency +- Export data in appropriate formats for analysis + +## Advanced Features + +### Multi-Frame Splitter + +For time-resolved measurements: + +```yaml +sequence: + base_name: + - img/splitter_early.%d + - img/splitter_late.%d # Different timing + first: 1 + last: 100 +``` + +### Splitter with Multiple Cameras + +Combine splitter mode with multi-camera setups: + +```yaml +num_cams: 4 # 2 physical cameras, each with splitter + +ptv: + splitter: true + +# Configure as 4 logical cameras +sequence: + base_name: + - img/cam1_left.%d + - img/cam1_right.%d + - img/cam2_left.%d + - img/cam2_right.%d +``` + +## See Also + +- [Calibration Guide](calibration.md) +- [YAML Parameters Reference](yaml-parameters.md) +- [Examples and Workflows](examples.md) +- [Plugin Development Guide](plugins.md) diff --git a/docs/pyptv/windows-installation.md b/docs/pyptv/windows-installation.md new file mode 100644 index 0000000..85740b3 --- /dev/null +++ b/docs/pyptv/windows-installation.md @@ -0,0 +1,230 @@ +# Windows Installation Guide + +This guide provides step-by-step instructions for installing PyPTV on Windows 10/11. + +> ⚠️ **Note**: Windows installation requires additional steps compared to Linux/macOS due to compiler requirements. + +## Prerequisites + +### Required Software + +1. **Miniconda or Anaconda** + - Download from [miniconda.com](https://docs.conda.io/en/latest/miniconda.html) + - Choose the Python 3.x version for Windows + +2. **Git for Windows** + - Download from [git-scm.com](https://git-scm.com/download/win) + - Install with default settings + +3. **Microsoft Visual Studio Build Tools** + - Download [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2022) + - Install "C++ build tools" workload + - **Alternative**: Install Visual Studio Community (includes build tools) + +### System Requirements + +- Windows 10 (1909 or later) or Windows 11 +- 8GB RAM minimum (16GB+ recommended) +- 5GB free disk space +- Administrator privileges for installation + +## Installation Steps + +### Step 1: Install Miniconda + +1. Download Miniconda from the official website +2. Run the installer as Administrator +3. Choose "Add Miniconda to PATH" during installation +4. Complete the installation and restart your computer + +### Step 2: Install Git and Build Tools + +1. Install Git for Windows with default settings +2. Install Visual Studio Build Tools: + - Run the installer as Administrator + - Select "C++ build tools" workload + - Include "MSVC v143 - VS 2022 C++ x64/x86 build tools" + - Include "Windows 10/11 SDK" + +### Step 3: Set Up PyPTV + +Open **Anaconda Prompt** (or Command Prompt) as Administrator: + +```cmd +# 1. Clone the repository +git clone https://github.com/openptv/pyptv.git +cd pyptv + +# 2. Create conda environment +conda env create -f environment.yml + +# 3. Activate the environment +conda activate pyptv + +# 4. Install additional Windows dependencies +conda install -c conda-forge cmake ninja + +# 5. Install PyPTV +pip install -e . +``` + +### Step 4: Windows-Specific Setup + +For Windows, you may need to set environment variables: + +```cmd +# Set up compiler environment (if needed) +call "C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\VC\Auxiliary\Build\vcvars64.bat" + +# Install PyPTV with explicit compiler +pip install -e . --global-option build_ext --global-option --compiler=msvc +``` + +## Alternative Installation Methods + +### Method 1: Using Windows Subsystem for Linux (WSL) + +If you have WSL2 installed, you can follow the Linux installation guide: + +```bash +# In WSL2 terminal +git clone https://github.com/openptv/pyptv.git +cd pyptv +./install_pyptv.sh +``` + +Note: GUI applications require X11 forwarding setup. + +### Method 2: Using Pre-built Binaries (When Available) + +Check the [releases page](https://github.com/openptv/pyptv/releases) for Windows binaries: + +```cmd +# Download and extract the release +# Follow included instructions +``` + +## Testing Installation + +Verify your installation: + +```cmd +# Activate the environment +conda activate pyptv + +# Test import +python -c "import pyptv; print('PyPTV installed successfully!')" + +# Launch GUI (should open without errors) +python -m pyptv.pyptv_gui +``` + +## Common Windows Issues + +### Issue: "Microsoft Visual C++ 14.0 is required" +**Solution**: Install Visual Studio Build Tools as described above. + +### Issue: "cmake not found" +**Solution**: Install cmake via conda: +```cmd +conda activate pyptv +conda install -c conda-forge cmake +``` + +### Issue: "Failed to build optv" +**Solution**: Ensure Visual Studio Build Tools are properly installed: +```cmd +# Verify compiler +where cl +# Should show path to Microsoft C++ compiler + +# Reinstall with verbose output +pip install -e . -v +``` + +### Issue: "Permission denied" errors +**Solution**: Run Anaconda Prompt as Administrator: +- Right-click "Anaconda Prompt" +- Select "Run as administrator" +- Retry installation + +### Issue: Long path names causing errors +**Solution**: Enable long paths in Windows: +1. Open Group Policy Editor (`gpedit.msc`) +2. Navigate to: Computer Configuration → Administrative Templates → System → Filesystem +3. Enable "Enable Win32 long paths" + + +## Environment Management + +### Daily Usage +Always activate the PyPTV environment before use: +```cmd +conda activate pyptv +python -m pyptv.pyptv_gui +``` + +### Creating Desktop Shortcut + +Create a batch file `PyPTV.bat`: +```batch +@echo off +call conda activate pyptv +python -m pyptv.pyptv_gui +pause +``` + +Save it to your desktop and double-click to launch PyPTV. + +### Updating PyPTV + +```cmd +conda activate pyptv +cd pyptv +git pull origin main +pip install -e . +``` + +## Troubleshooting + +### Performance Issues +- Ensure Windows Defender excludes the PyPTV directory +- Close unnecessary background applications +- Consider using SSD storage for image sequences + +### Display Issues +- Update graphics drivers +- Try different display scaling settings +- Ensure sufficient graphics memory + +### File Path Issues +- Avoid spaces in file paths +- Use forward slashes (/) in Python scripts +- Keep experiment directories close to drive root + +## Next Steps + +Once PyPTV is installed on Windows: + +1. **Test Installation**: Follow the [Quick Start Guide](quick-start.md) +2. **Set Up Data**: Learn about [parameter configuration](parameter-migration.md) +3. **Start Tracking**: See [Running the GUI](running-gui.md) + +## Windows-Specific Tips + +- **File Organization**: Keep experiment folders in `C:\PyPTV\experiments\` for shorter paths +- **Antivirus**: Add PyPTV directories to antivirus exclusions +- **Updates**: Windows may reset some settings after major updates +- **Backup**: Regularly backup your experiment parameters + +## Getting Help + +For Windows-specific issues: + +- Check [Windows-tagged issues](https://github.com/openptv/pyptv/issues?q=label%3Awindows) on GitHub +- Include Windows version and Python version in bug reports +- Share the full error message and installation log + +--- + +**Next**: [Quick Start Guide](quick-start.md) or back to [Main Installation Guide](installation.md) diff --git a/docs/pyptv/yaml-parameters.md b/docs/pyptv/yaml-parameters.md new file mode 100644 index 0000000..6f35701 --- /dev/null +++ b/docs/pyptv/yaml-parameters.md @@ -0,0 +1,385 @@ +# YAML Parameters Reference + +This guide provides a comprehensive reference for all parameters in PyPTV's YAML configuration system. + +## Overview + +PyPTV uses a single YAML file to store all experiment parameters. The file is organized into logical sections, each controlling different aspects of the PTV workflow. + +## Environment Setup and Testing + +PyPTV uses a modern conda environment (`environment.yml`) and separates tests into headless (`tests/`) and GUI (`tests_gui/`) categories. See the README for details. + +> **Important**: Always use `num_cams` for camera count. Do not use legacy fields like `n_img`. + +## File Structure + +```yaml +num_cams: 4 # Global camera count + +cal_ori: + # Calibration parameters + +criteria: + # Correspondence criteria + +detect_plate: + # Detection parameters + +dumbbell: + # Dumbbell tracking parameters + +examine: + # Examination settings + +man_ori: + # Manual orientation + +multi_planes: + # Multi-plane calibration + +orient: + # Orientation parameters + +pft_version: + # Version settings + +ptv: + # Main PTV parameters + +sequence: + # Image sequence settings + +shaking: + # Shaking correction + +sortgrid: + # Grid sorting + +targ_rec: + # Target recognition + +track: + # Tracking parameters + +masking: + # Image masking + +unsharp_mask: + # Unsharp mask filter + +plugins: + # Plugin configuration + +man_ori_coordinates: + # Manual orientation coordinates +``` + +## Global Parameters + +### num_cams +**Type**: Integer +**Description**: Number of cameras in the system +**Example**: `num_cams: 4` + +> **Important**: This is the master camera count. Do not use `n_img` anywhere in the YAML file. + +## Calibration Parameters (cal_ori) + +Controls camera calibration and orientation setup. + +```yaml +cal_ori: + chfield: 0 # Change field flag + fixp_name: cal/target.txt # Fixed point file path + img_cal_name: # Calibration image paths + - cal/cam1.tif + - cal/cam2.tif + - cal/cam3.tif + - cal/cam4.tif + img_ori: # Orientation file paths (auto-generated) + - cal/cam1.tif.ori + - cal/cam2.tif.ori + - cal/cam3.tif.ori + - cal/cam4.tif.ori + pair_flag: false # Pair calibration flag + tiff_flag: true # TIFF format flag + cal_splitter: false # Splitter calibration mode +``` + +### Field Descriptions + +- **chfield**: Field change flag (0 = no change, 1 = change) +- **fixp_name**: Path to file containing fixed 3D calibration points +- **img_cal_name**: List of calibration image file paths for each camera +- **img_ori**: List of orientation file paths (automatically populated) +- **pair_flag**: Enable pair-wise calibration +- **tiff_flag**: Use TIFF image format +- **cal_splitter**: Enable splitter-based calibration + +## Correspondence Criteria (criteria) + +Defines criteria for stereo matching and correspondence. + +```yaml +criteria: + X_lay: [-40, 40] # X layer bounds [min, max] + Zmax_lay: [25, 25] # Maximum Z bounds per layer + Zmin_lay: [-20, -20] # Minimum Z bounds per layer + cn: 0.02 # Correspondence tolerance + cnx: 0.02 # X correspondence tolerance + cny: 0.02 # Y correspondence tolerance + corrmin: 33.0 # Minimum correlation value + csumg: 0.02 # Sum of grey value tolerance + eps0: 0.2 # Initial epsilon value +``` + +## Detection Parameters (detect_plate) + +Controls particle detection on each camera. + +```yaml +detect_plate: + gvth_1: 40 # Grey value threshold camera 1 + gvth_2: 40 # Grey value threshold camera 2 + gvth_3: 40 # Grey value threshold camera 3 + gvth_4: 40 # Grey value threshold camera 4 + max_npix: 400 # Maximum pixel count + max_npix_x: 50 # Maximum pixels in X + max_npix_y: 50 # Maximum pixels in Y + min_npix: 25 # Minimum pixel count + min_npix_x: 5 # Minimum pixels in X + min_npix_y: 5 # Minimum pixels in Y + size_cross: 3 # Cross correlation size + sum_grey: 100 # Minimum sum of grey values + tol_dis: 500 # Distance tolerance +``` + +## PTV Main Parameters (ptv) + +Core PTV processing parameters. + +```yaml +ptv: + allcam_flag: false # All cameras flag + chfield: 0 # Change field flag + hp_flag: true # High pass filter flag + img_cal: # Calibration images + - cal/cam1.tif + - cal/cam2.tif + - cal/cam3.tif + - cal/cam4.tif + img_name: # Current frame images + - img/cam1.10002 + - img/cam2.10002 + - img/cam3.10002 + - img/cam4.10002 + imx: 1280 # Image width in pixels + imy: 1024 # Image height in pixels + mmp_d: 6.0 # Glass thickness (mm) + mmp_n1: 1.0 # Refractive index air + mmp_n2: 1.33 # Refractive index water + mmp_n3: 1.46 # Refractive index glass + pix_x: 0.012 # Pixel size X (mm) + pix_y: 0.012 # Pixel size Y (mm) + tiff_flag: true # TIFF format flag + splitter: false # Splitter mode flag +``` + +## Sequence Parameters (sequence) + +Defines image sequence for processing. + +```yaml +sequence: + base_name: # Base filename patterns + - img/cam1.%d + - img/cam2.%d + - img/cam3.%d + - img/cam4.%d + first: 10001 # First frame number + last: 10004 # Last frame number +``` + +## Tracking Parameters (track) + +Controls particle tracking algorithm. + +```yaml +track: + angle: 100.0 # Maximum angle change (degrees) + dacc: 2.8 # Acceleration tolerance + dvxmax: 15.5 # Maximum velocity X + dvxmin: -15.5 # Minimum velocity X + dvymax: 15.5 # Maximum velocity Y + dvymin: -15.5 # Minimum velocity Y + dvzmax: 15.5 # Maximum velocity Z + dvzmin: -15.5 # Minimum velocity Z + flagNewParticles: true # Allow new particles +``` + +## Target Recognition (targ_rec) + +Parameters for target/particle recognition. + +```yaml +targ_rec: + cr_sz: 2 # Cross size + disco: 100 # Discontinuity threshold + gvthres: # Grey value thresholds per camera + - 9 + - 9 + - 9 + - 11 + nnmax: 500 # Maximum neighbors + nnmin: 4 # Minimum neighbors + nxmax: 100 # Maximum X extent + nxmin: 2 # Minimum X extent + nymax: 100 # Maximum Y extent + nymin: 2 # Minimum Y extent + sumg_min: 150 # Minimum sum of grey values +``` + +## Plugin Configuration (plugins) + +Manages available and selected plugins. + +```yaml +plugins: + available_tracking: # Available tracking plugins + - default + - ext_tracker_splitter + available_sequence: # Available sequence plugins + - default + - ext_sequence_rembg + - ext_sequence_contour + selected_tracking: default # Selected tracking plugin + selected_sequence: default # Selected sequence plugin +``` + +## Manual Orientation (man_ori) + +Manual orientation setup for calibration. + +```yaml +man_ori: + nr: [3, 5, 72, 73, 3, 5, 72, 73, 1, 5, 71, 73, 1, 5, 71, 73] +``` + +The `nr` array contains point IDs for manual orientation, flattened across all cameras. + +## Manual Orientation Coordinates (man_ori_coordinates) + +Pixel coordinates for manual orientation points. + +```yaml +man_ori_coordinates: + camera_0: + point_1: {x: 1009.0, y: 608.0} + point_2: {x: 979.0, y: 335.0} + point_3: {x: 246.0, y: 620.0} + point_4: {x: 235.0, y: 344.0} + camera_1: + point_1: {x: 1002.0, y: 609.0} + # ... more points +``` + +## Optional Parameters + +### Masking (masking) + +Image masking configuration. + +```yaml +masking: + mask_flag: false # Enable masking + mask_base_name: '' # Mask file base name +``` + +### Unsharp Mask (unsharp_mask) + +Unsharp mask filter settings. + +```yaml +unsharp_mask: + flag: false # Enable unsharp mask + size: 3 # Kernel size + strength: 1.0 # Filter strength +``` + +### Dumbbell Tracking (dumbbell) + +Specialized dumbbell particle tracking. + +```yaml +dumbbell: + dumbbell_eps: 3.0 # Epsilon parameter + dumbbell_gradient_descent: 0.05 # Gradient descent step + dumbbell_niter: 500 # Number of iterations + dumbbell_penalty_weight: 1.0 # Penalty weight + dumbbell_scale: 25.0 # Scale factor + dumbbell_step: 1 # Step size +``` + +## Common Parameter Patterns + +### Camera-Specific Arrays + +Many parameters are arrays with one value per camera: + +```yaml +# For 4 cameras, provide 4 values +gvthres: [9, 9, 9, 11] +img_cal_name: + - cal/cam1.tif + - cal/cam2.tif + - cal/cam3.tif + - cal/cam4.tif +``` + +### File Paths + +Use paths relative to the parameter file location: + +```yaml +# Correct - relative paths +fixp_name: cal/target.txt +img_name: + - img/cam1.10002 + +# Avoid - absolute paths (not portable) +# fixp_name: /full/path/to/target.txt +``` + +### Boolean Flags + +Use lowercase true/false: + +```yaml +tiff_flag: true +pair_flag: false +``` + +## Validation + +To validate your parameter file: + +1. Load it in the PyPTV GUI +2. Check that all parameter dialogs open without errors +3. Verify camera count matches your hardware +4. Ensure all file paths exist and are accessible + +## Migration Notes + +When migrating from older formats: + +- Remove any `n_img` fields - use only `num_cams` +- Ensure all camera arrays have exactly `num_cams` elements +- Flatten `man_ori.nr` array if it was nested +- Convert boolean values to lowercase + +## See Also + +- [Parameter Migration Guide](parameter-migration.md) +- [Calibration Guide](calibration.md) +- [Quick Start Guide](quick-start.md) diff --git a/pyproject.toml b/pyproject.toml index 1a8218b..3dacf25 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ classifiers = [ "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python", - "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Topic :: Scientific/Engineering" @@ -25,27 +25,36 @@ dynamic = ["version"] license = {file = "LICENSE"} name = "openptv-python" readme = "README.md" -requires-python = ">=3.12,<3.14" +requires-python = ">=3.11,<3.14" + +[project.scripts] +pyptv = "pyptv.pyptv_gui:main" [project.optional-dependencies] -dev = [ - "mypy>=1.19.1", - "myst-parser>=5.0.0", - "optv>=0.3.2", - "pre-commit>=4.5.1", - "pydata-sphinx-theme>=0.16.1", - "pytest>=9.0.2", - "pytest-cov>=7.0.0", - "sphinx>=9.1.0", - "sphinx-autoapi>=3.7.0", - "types-pyyaml>=6.0.12.20250915" -] docs = [ "myst-parser>=5.0.0", "pydata-sphinx-theme>=0.16.1", - "sphinx>=9.1.0", + "sphinx>=8.0.0,<9.1.0", "sphinx-autoapi>=3.7.0" ] +gui = [ + "optv>=0.3.2", + "traits>=6.4.0", + "traitsui>=7.4.0", + "enable>=6.1.0", + "chaco>=6.1.0", + "PySide6>=6.0.0", + "scikit-image>=0.20.0", + "pandas>=2.0.0", + "matplotlib>=3.7.0", + "tables>=3.8.0", + "tqdm>=4.65.0", + "imagecodecs>=2023.1.23", + "flowtracks>=0.3.0", + "Pygments>=2.15.0", + "pyparsing>=3.0.0", + "marimo>=0.19.10" +] native = [ "optv>=0.3.2" ] @@ -53,6 +62,33 @@ test = [ "pytest>=9.0.2", "pytest-cov>=7.0.0" ] +dev = [ + "mypy>=1.19.1", + "myst-parser>=5.0.0", + "optv>=0.3.2", + "pre-commit>=4.5.1", + "pydata-sphinx-theme>=0.16.1", + "pytest>=9.0.2", + "pytest-cov>=7.0.0", + "sphinx>=8.0.0,<9.1.0", + "sphinx-autoapi>=3.7.0", + "types-pyyaml>=6.0.12.20250915", + "traits>=6.4.0", + "traitsui>=7.4.0", + "enable>=6.1.0", + "chaco>=6.1.0", + "PySide6>=6.0.0", + "scikit-image>=0.20.0", + "pandas>=2.0.0", + "matplotlib>=3.7.0", + "tables>=3.8.0", + "tqdm>=4.65.0", + "imagecodecs>=2023.1.23", + "flowtracks>=0.3.0", + "Pygments>=2.15.0", + "pyparsing>=3.0.0", + "marimo>=0.19.10" +] [tool.coverage.run] branch = true @@ -89,11 +125,15 @@ max-line-length = 110 convention = "numpy" [tool.setuptools] -packages = ["openptv_python"] +packages = ["openptv_python", "pyptv"] + +[tool.setuptools.package-dir] +openptv_python = "src" +pyptv = "src/pyptv/src" [tool.setuptools_scm] fallback_version = "999" -write_to = "openptv_python/version.py" +write_to = "src/version.py" write_to_template = ''' # Do not change! Do not track in version control! __version__ = "{version}" diff --git a/scripts/build_wheels.sh b/scripts/build_wheels.sh new file mode 100755 index 0000000..6f88e2e --- /dev/null +++ b/scripts/build_wheels.sh @@ -0,0 +1,43 @@ +#!/bin/bash +# Build binary wheels for pyptv +# pyptv is pure Python, so a single universal wheel works for all Python versions + +set -e + +echo "=== Building pyptv wheels ===" + +# Ensure we're in the right directory +cd "$(dirname "$0")" + +# Clean previous builds +rm -rf dist/ build/ *.egg-info + +# Build the wheel and source distribution +echo "Building wheel and source distribution..." +uv build + +# Copy wheels to wheels/ folder +echo "Copying wheel to wheels/ folder..." +mkdir -p wheels/ +cp dist/*.whl wheels/ + +# List built distributions +echo "" +echo "=== Built distributions ===" +ls -lh dist/ + +echo "" +echo "=== Wheels folder ===" +ls -lh wheels/ + +echo "" +echo "✓ Build complete!" +echo "" +echo "To install the wheel:" +echo " uv venv --python 3.11" +echo " source .venv/bin/activate" +echo " uv pip install wheels/optv-0.3.2-*.whl" +echo " uv pip install dist/pyptv-*.whl" +echo "" +echo "Or install in editable mode:" +echo " uv pip install -e ." diff --git a/scripts/clean.sh b/scripts/clean.sh new file mode 100755 index 0000000..97b6f00 --- /dev/null +++ b/scripts/clean.sh @@ -0,0 +1,4 @@ +#!/bin/bash +rm -rf build/ +rm -rf dist/ +rm -rf *.egg-info/ \ No newline at end of file diff --git a/scripts/fix_opengl.sh b/scripts/fix_opengl.sh new file mode 100755 index 0000000..9773558 --- /dev/null +++ b/scripts/fix_opengl.sh @@ -0,0 +1,24 @@ +#!/bin/bash +# Script to fix OpenGL driver issues for pyptv + +set -e # Exit on error + +echo "=== Installing Mesa OpenGL libraries and drivers ===" + +# Install Mesa OpenGL libraries and drivers +sudo apt-get update +sudo apt-get install -y \ + libgl1-mesa-glx \ + libgl1-mesa-dri \ + mesa-utils \ + mesa-utils-extra \ + libglx-mesa0 \ + libegl-mesa0 \ + libglu1-mesa + +echo "" +echo "=== Installation complete! ===" +echo "Try running pyptv again. If you still encounter OpenGL errors, you may need to:" +echo "1. Update your graphics drivers" +echo "2. Set the QT_XCB_GL_INTEGRATION environment variable: export QT_XCB_GL_INTEGRATION=none" +echo "3. Or try running with software rendering: export LIBGL_ALWAYS_SOFTWARE=1" diff --git a/scripts/fix_pyside6_traitsui.sh b/scripts/fix_pyside6_traitsui.sh new file mode 100755 index 0000000..9367ddf --- /dev/null +++ b/scripts/fix_pyside6_traitsui.sh @@ -0,0 +1,31 @@ +#!/bin/bash +# Script to fix PySide6 and TraitsUI compatibility issues + +set -e # Exit on error + +echo "=== Fixing PySide6 and TraitsUI compatibility issues ===" + +# Check if conda is installed +if ! command -v conda &> /dev/null; then + echo "Conda is required but not found. Please install Miniconda or Anaconda first." + exit 1 +fi + +# Define a function to run commands in the conda environment +run_in_conda() { + # This is a workaround since 'conda activate' doesn't work in scripts + bash -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate pyptv && $1" +} + +# Uninstall problematic packages +echo "=== Uninstalling problematic packages ===" +run_in_conda "pip uninstall -y PySide6 traitsui pyface" + +# Install compatible versions +echo "=== Installing compatible versions ===" +run_in_conda "pip install traitsui==7.4.3 pyface==7.4.2 PySide6==6.4.0.1" + +echo "" +echo "=== Fix completed! ===" +echo "The compatibility issues between PySide6 and TraitsUI should now be resolved." +echo "Try running pyptv again." diff --git a/scripts/fix_pyside6_traitsui_auto.sh b/scripts/fix_pyside6_traitsui_auto.sh new file mode 100755 index 0000000..5ed37e0 --- /dev/null +++ b/scripts/fix_pyside6_traitsui_auto.sh @@ -0,0 +1,54 @@ +#!/bin/bash +# Script to fix PySide6 and TraitsUI compatibility issues by trying multiple versions + +set -e # Exit on error + +echo "=== Fixing PySide6 and TraitsUI compatibility issues ===" + +# Check if conda is installed +if ! command -v conda &> /dev/null; then + echo "Conda is required but not found. Please install Miniconda or Anaconda first." + exit 1 +fi + +# Define a function to run commands in the conda environment +run_in_conda() { + # This is a workaround since 'conda activate' doesn't work in scripts + bash -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate pyptv && $1" +} + +# Uninstall problematic packages +echo "=== Uninstalling problematic packages ===" +run_in_conda "pip uninstall -y PySide6 traitsui pyface" + +# Install traitsui and pyface +echo "=== Installing traitsui and pyface ===" +run_in_conda "pip install traitsui==7.4.3 pyface==7.4.2" + +# Try different versions of PySide6 until one works +echo "=== Trying different versions of PySide6 ===" +VERSIONS=("6.4.0.1" "6.4.1" "6.4.2" "6.4.3" "6.5.0" "6.5.1") +SUCCESS=false + +for VERSION in "${VERSIONS[@]}"; do + echo "Trying PySide6 version $VERSION..." + if run_in_conda "pip install PySide6==$VERSION" > /dev/null 2>&1; then + echo "Successfully installed PySide6 version $VERSION" + SUCCESS=true + break + else + echo "Failed to install PySide6 version $VERSION, trying next version..." + fi +done + +if [ "$SUCCESS" = true ]; then + echo "" + echo "=== Fix completed! ===" + echo "The compatibility issues between PySide6 and TraitsUI should now be resolved." + echo "Try running pyptv again." +else + echo "" + echo "=== Fix failed! ===" + echo "Could not find a compatible version of PySide6." + echo "Please try manually installing a different version of PySide6." +fi diff --git a/scripts/generate_ground_truth_points.py b/scripts/generate_ground_truth_points.py new file mode 100644 index 0000000..9b0e088 --- /dev/null +++ b/scripts/generate_ground_truth_points.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""Generate synthetic calibration correspondences from ground-truth OpenPTV calibration. + +This script reads a PyPTV/OpenPTV YAML, loads the referenced `.ori/.addpar` files +(ground truth), projects the 3D calibration points into each camera, and writes +an `.npz` that can be fed into `scripts/standalone_calibration.py`. + +By default, it reads XYZ from `cal_ori.fixp_name`. + +Example: + ./.venv/bin/python scripts/generate_ground_truth_points.py \ + tests/test_cavity/parameters_Run1.yaml out_points.npz --noise-sigma-px 0.2 +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +from pyptv.ground_truth import generate_ground_truth, save_ground_truth_npz + + +def _parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("yaml", type=Path, help="Path to parameters_*.yaml") + p.add_argument("out", type=Path, help="Output .npz path") + p.add_argument("--noise-sigma-px", type=float, default=0.0, help="Gaussian noise sigma in pixels") + p.add_argument("--seed", type=int, default=0, help="RNG seed") + return p.parse_args() + + +def main() -> int: + ns = _parse_args() + gt = generate_ground_truth(ns.yaml, noise_sigma_px=float(ns.noise_sigma_px), seed=int(ns.seed)) + save_ground_truth_npz(ns.out, gt) + print(f"Wrote {ns.out} with xy shape {gt.xy.shape} and xyz shape {gt.xyz.shape}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/install_pyptv.bat b/scripts/install_pyptv.bat new file mode 100644 index 0000000..f1c94b2 --- /dev/null +++ b/scripts/install_pyptv.bat @@ -0,0 +1,229 @@ +@echo off +REM Script to install pyptv locally on Windows +REM Tested with Wine on Linux to ensure compatibility + +setlocal enabledelayedexpansion + +REM Create a log file +set LOG_FILE=%~dp0install_pyptv.log +echo PyPTV Installation Log > %LOG_FILE% +echo Started at: %date% %time% >> %LOG_FILE% +echo. >> %LOG_FILE% + +echo === Setting up pyptv local environment === +echo === Setting up pyptv local environment === >> %LOG_FILE% + +REM Check if conda is installed +where conda >nul 2>&1 +if %ERRORLEVEL% NEQ 0 ( + echo Conda is required but not found. Please install Miniconda or Anaconda first. + exit /b 1 +) + +REM Check if git is installed +where git >nul 2>&1 +if %ERRORLEVEL% NEQ 0 ( + echo Git is required but not found. Please install Git for Windows first. + exit /b 1 +) + +REM Check if Visual Studio Build Tools are installed (needed for compiling C extensions) +where cl >nul 2>&1 +if %ERRORLEVEL% NEQ 0 ( + echo Visual Studio Build Tools are required but not found. + echo Please install Visual Studio Build Tools with C++ development components. + echo You can download it from: https://visualstudio.microsoft.com/visual-cpp-build-tools/ + exit /b 1 +) + +REM Check if CMake is installed +where cmake >nul 2>&1 +if %ERRORLEVEL% NEQ 0 ( + echo CMake is required but not found. Please install CMake first. + echo You can download it from: https://cmake.org/download/ + exit /b 1 +) + +REM Create and activate conda environment +set ENV_NAME=pyptv +set PYTHON_VERSION=3.11 + +echo === Creating conda environment '%ENV_NAME%' with Python %PYTHON_VERSION% === +call conda create -n %ENV_NAME% python=%PYTHON_VERSION% -y +if %ERRORLEVEL% NEQ 0 ( + echo Failed to create conda environment. + exit /b 1 +) + +REM Install Python dependencies +echo === Installing Python dependencies === +call conda activate %ENV_NAME% && ^ +pip install setuptools "numpy>=1.26.4,<2.7" matplotlib pytest flake8 tqdm cython pyyaml build +if %ERRORLEVEL% NEQ 0 ( + echo Failed to install Python dependencies. + exit /b 1 +) + +REM Install specific versions of traitsui and PySide6 that work together +echo === Installing compatible UI dependencies === +call conda activate %ENV_NAME% && ^ +pip install traitsui==7.4.3 pyface==7.4.2 PySide6==6.4.0.1 +if %ERRORLEVEL% NEQ 0 ( + echo Failed to install UI dependencies. + exit /b 1 +) + +REM Get the current directory +set REPO_DIR=%CD% + +REM Install optv from local wheel (NumPy 2 compatible) +echo === Installing optv from local wheel === +if exist "wheels\optv-0.3.2-cp311-cp311-linux_x86_64.whl" ( + call conda activate %ENV_NAME% && pip install wheels\optv-0.3.2-cp311-cp311-linux_x86_64.whl +) else ( + echo WARNING: Local optv wheel not found, will build from source + REM Clone and build OpenPTV + echo === Building OpenPTV === + if not exist "openptv" ( + call conda activate %ENV_NAME% && ^ + git clone https://github.com/openptv/openptv + if %ERRORLEVEL% NEQ 0 ( + echo Failed to clone OpenPTV repository. + exit /b 1 + ) + ) + REM Build and install Python bindings + echo === Building and installing OpenPTV Python bindings === + call conda activate %ENV_NAME% && ^ + cd %REPO_DIR%\openptv\py_bind && ^ + python setup.py prepare && ^ + python -m build --wheel --outdir dist\ && ^ + pip install dist\*.whl --force-reinstall + if %ERRORLEVEL% NEQ 0 ( + echo Failed to build and install OpenPTV Python bindings. + exit /b 1 + ) + cd %REPO_DIR% +) + +REM Install pyptv from local wheel or repository +echo === Installing pyptv === +if exist "wheels\pyptv-*.whl" ( + call conda activate %ENV_NAME% && pip install wheels\pyptv-*.whl + echo Installed pyptv from wheel +) else ( + call conda activate %ENV_NAME% && pip install -e . + echo Installed pyptv in editable mode +) +if %ERRORLEVEL% NEQ 0 ( + echo Failed to install pyptv. + exit /b 1 +) + +REM Set up test data +echo === Setting up test data === +if not exist "test_cavity" ( + call conda activate %ENV_NAME% && ^ + git clone https://github.com/openptv/test_cavity + if %ERRORLEVEL% NEQ 0 ( + echo Failed to clone test_cavity repository. + exit /b 1 + ) +) + +REM Verify installation +echo === Verifying installation === +call conda activate %ENV_NAME% && ^ +python -c "import pyptv; print(f'PyPTV version: {pyptv.__version__}'); import optv; print(f'OpenPTV version: {optv.__version__}')" +if %ERRORLEVEL% NEQ 0 ( + echo Failed to verify installation. + exit /b 1 +) + +REM Create a version check script if it doesn't exist +if not exist "check_version.py" ( + echo Creating version check script... + echo #!/usr/bin/env python > check_version.py + echo """>> check_version.py + echo Script to check the installed version of pyptv and warn if it's not the expected version.>> check_version.py + echo """>> check_version.py + echo import sys>> check_version.py + echo import importlib.metadata>> check_version.py + echo.>> check_version.py + echo EXPECTED_VERSION = "0.4.3" # The version in the local repository>> check_version.py + echo.>> check_version.py + echo def check_version():>> check_version.py + echo """Check if the installed version matches the expected version.""">> check_version.py + echo try:>> check_version.py + echo installed_version = importlib.metadata.version("pyptv")>> check_version.py + echo print(f"Installed pyptv version: {installed_version}")>> check_version.py + echo.>> check_version.py + echo if installed_version != EXPECTED_VERSION:>> check_version.py + echo print(f"\nWARNING: The installed version ({installed_version}) does not match ">> check_version.py + echo f"the expected version ({EXPECTED_VERSION}).")>> check_version.py + echo print("\nPossible reasons:")>> check_version.py + echo.>> check_version.py + echo if installed_version == "0.4.2":>> check_version.py + echo print("- You installed from PyPI, which has version 0.4.2")>> check_version.py + echo print("- To install the development version (0.4.3), run:")>> check_version.py + echo print(" pip install -e /path/to/pyptv/repository")>> check_version.py + echo else:>> check_version.py + echo print("- You might have a different version installed")>> check_version.py + echo print("- Check your installation source")>> check_version.py + echo.>> check_version.py + echo return False>> check_version.py + echo else:>> check_version.py + echo print(f"Version check passed: {installed_version}")>> check_version.py + echo return True>> check_version.py + echo except importlib.metadata.PackageNotFoundError:>> check_version.py + echo print("ERROR: pyptv is not installed.")>> check_version.py + echo return False>> check_version.py + echo.>> check_version.py + echo if __name__ == "__main__":>> check_version.py + echo success = check_version()>> check_version.py + echo sys.exit(0 if success else 1)>> check_version.py +) + +REM Check if the installed version matches the expected version +echo === Checking version === +call conda activate %ENV_NAME% && ^ +python %REPO_DIR%\check_version.py +if %ERRORLEVEL% NEQ 0 ( + echo Version check failed. + exit /b 1 +) + +echo. +echo === Installation complete! === +echo To activate the environment, run: conda activate %ENV_NAME% +echo To run pyptv with test_cavity data, run: pyptv %REPO_DIR%\test_cavity +echo. +echo Note: If you encounter OpenGL errors, try setting these environment variables: +echo set LIBGL_ALWAYS_SOFTWARE=1 +echo set QT_QPA_PLATFORM=windows +echo. + +REM Create a run script for convenience +echo @echo off > run_pyptv.bat +echo REM Script to run pyptv with OpenGL workarounds >> run_pyptv.bat +echo. >> run_pyptv.bat +echo REM Set environment variables to work around OpenGL issues >> run_pyptv.bat +echo set LIBGL_ALWAYS_SOFTWARE=1 >> run_pyptv.bat +echo set QT_QPA_PLATFORM=windows >> run_pyptv.bat +echo. >> run_pyptv.bat +echo REM Activate conda environment and run pyptv >> run_pyptv.bat +echo call conda activate %ENV_NAME% ^&^& pyptv test_cavity >> run_pyptv.bat +echo. >> run_pyptv.bat +echo pause >> run_pyptv.bat + +echo Created run_pyptv.bat for easy launching of pyptv. + +REM Log completion +echo. >> %LOG_FILE% +echo Installation completed successfully at: %date% %time% >> %LOG_FILE% +echo Log file created at: %LOG_FILE% + +REM Return to original directory +cd %REPO_DIR% + +endlocal diff --git a/scripts/legacy_parameters_to_yaml.py b/scripts/legacy_parameters_to_yaml.py new file mode 100644 index 0000000..1b8a560 --- /dev/null +++ b/scripts/legacy_parameters_to_yaml.py @@ -0,0 +1,44 @@ +import sys +import os +from pathlib import Path +from pyptv.experiment import Experiment + +def main(): + if len(sys.argv) != 2: + print("Usage: python legacy_parameters_to_yaml.py ") + sys.exit(1) + + directory_path = sys.argv[1] + if not os.path.isdir(directory_path): + print(f"Error: {directory_path} is not a valid directory.") + sys.exit(1) + + # Initialize Experiment + exp = Experiment() + exp.populate_runs(Path(directory_path)) + + # Prepare list of YAML files + yaml_files = [] + # List all YAML files in the directory + for file in os.listdir(directory_path): + if file.endswith(".yaml") or file.endswith(".yml"): + yaml_files.append(file) # Store without extension + + # List all parameter names in the experiment + param_names = [param.yaml_path.name for param in exp.paramsets] + + print(yaml_files) + print(param_names) + + + # Compare parameter names to YAML files (without extension) + # yaml_basenames = [os.path.splitext(f)[0] for f in yaml_files] + missing_in_yaml = [p for p in param_names if p not in yaml_files] + extra_yaml = [y for y in yaml_files if y not in param_names] + + print("\nParameters missing YAML files:", missing_in_yaml) + print("YAML files without matching parameters:", extra_yaml) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scripts/run_docker_tests.sh b/scripts/run_docker_tests.sh new file mode 100755 index 0000000..4b811eb --- /dev/null +++ b/scripts/run_docker_tests.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +# Clean up any existing containers/images and run tests in one line +docker stop $(docker ps -a | grep pyptv-test | awk '{print $1}') 2>/dev/null; docker rm $(docker ps -a | grep pyptv-test | awk '{print $1}') 2>/dev/null; docker rmi pyptv-test 2>/dev/null; docker image prune -f && docker build --no-cache -t pyptv-test -f Dockerfile.test . && docker run --rm pyptv-test diff --git a/scripts/run_headless_tests.sh b/scripts/run_headless_tests.sh new file mode 100755 index 0000000..886b8d6 --- /dev/null +++ b/scripts/run_headless_tests.sh @@ -0,0 +1,4 @@ +#!/bin/bash +# Run only headless (non-GUI) tests +cd "$(dirname "$0")" +pytest tests/ "$@" diff --git a/scripts/run_pyptv.sh b/scripts/run_pyptv.sh new file mode 100755 index 0000000..d18d899 --- /dev/null +++ b/scripts/run_pyptv.sh @@ -0,0 +1,23 @@ +#!/bin/bash +# Script to run pyptv with OpenGL workarounds + +# Activate conda environment +source $(conda info --base)/etc/profile.d/conda.sh +conda activate pyptv + +# Set environment variables to work around OpenGL issues +export LIBGL_ALWAYS_SOFTWARE=1 +export QT_XCB_GL_INTEGRATION=none +export QT_QPA_PLATFORM=xcb + +# Run pyptv with the test_cavity data +echo "Running pyptv with OpenGL workarounds..." +pyptv tests/test_cavity + +# If pyptv exits with an error, try with different settings +if [ $? -ne 0 ]; then + echo "First attempt failed, trying with different settings..." + unset QT_XCB_GL_INTEGRATION + export QT_QPA_PLATFORM=offscreen + pyptv test_cavity +fi diff --git a/scripts/run_tests.sh b/scripts/run_tests.sh new file mode 100755 index 0000000..db8cd16 --- /dev/null +++ b/scripts/run_tests.sh @@ -0,0 +1,5 @@ +#!/bin/bash +# Run all tests (headless and GUI) locally +cd "$(dirname "$0")" +pytest tests/ "$@" +pytest tests_gui/ "$@" diff --git a/scripts/standalone_calibration.py b/scripts/standalone_calibration.py new file mode 100644 index 0000000..baf90d2 --- /dev/null +++ b/scripts/standalone_calibration.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +"""Standalone OpenPTV calibration runner. + +Purpose +- Load PyPTV/OpenPTV parameters from a YAML file. +- Accept externally-generated correspondences: + - 3D calibration points XYZ (metric units, shape: (N, 3)) + - 2D detected image points per camera xy (pixel units, shape: (C, N, 2)) + where point index i in XY corresponds to point index i in XYZ. +- Run calibration per camera: + 1) Optional external (6DOF) calibration using 4 point pairs (to get a robust initial guess) + 2) Full calibration (bundle-like refinement) using OpenPTV's `full_calibration` +- Write OpenPTV calibration files (`.ori` + `.addpar`) for each camera. + +This script is intended for closed-loop testing (ground truth -> synthetic detections -> calibration). + +Input format (.npz) +- Required: + - `xyz` or `XYZ`: float array (N, 3) + - One of: + - `xy`: float array (C, N, 2) + - `xy_cam0`, `xy_cam1`, ... arrays each (N, 2) +- Optional: + - `pnr`: int array (N,) mapping each provided 2D point to an index in XYZ. + If absent, assumes pnr = [0..N-1]. + +Examples +- Run using orient flags from YAML and initialize with first four points: + python scripts/standalone_calibration.py /path/to/parameters_run.yaml points.npz \ + --init-external first4 --flags-from-yaml --write + +- Run without external initialization (requires decent initial .ori/.addpar): + python scripts/standalone_calibration.py params.yaml points.npz --flags cc xh yh k1 k2 --write +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path +from typing import Iterable, Sequence + +import numpy as np + +# Allow running from a repo checkout without installation +_REPO_ROOT = Path(__file__).resolve().parents[1] +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) + +from pyptv.standalone_calibration import ( + NAMES as _NAMES, + get_flags_from_yaml as _get_flags_from_yaml, + load_parameter_manager as _load_pm, + load_points_npz as _load_points_npz, + run_standalone_calibration, +) + + +def _parse_args(argv: Sequence[str]) -> argparse.Namespace: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("yaml", type=Path, help="Path to parameters_*.yaml") + p.add_argument("npz", type=Path, help="Path to .npz containing xyz and xy arrays") + + flags_group = p.add_mutually_exclusive_group() + flags_group.add_argument( + "--flags-from-yaml", + action="store_true", + help="Use 'orient' section flags from YAML (cc/xh/...)", + ) + flags_group.add_argument( + "--flags", + nargs="+", + default=None, + help=f"Explicit flags to optimize. Allowed: {', '.join(_NAMES)}", + ) + + p.add_argument( + "--init-external", + choices=["first4"], + default="first4", + help="How to choose 4 points for external_calibration init (default: first4). Use --no-init-external to skip.", + ) + p.add_argument( + "--no-init-external", + action="store_true", + help="Skip external_calibration init step (requires decent initial .ori/.addpar).", + ) + + p.add_argument( + "--write", + action="store_true", + help="Write output .ori/.addpar to paths in cal_ori.img_ori", + ) + + return p.parse_args(argv) + + +def main(argv: Sequence[str] | None = None) -> int: + ns = _parse_args(argv or sys.argv[1:]) + + yaml_path: Path = ns.yaml.resolve() + npz_path: Path = ns.npz.resolve() + + if not yaml_path.exists(): + raise FileNotFoundError(yaml_path) + if not npz_path.exists(): + raise FileNotFoundError(npz_path) + + pm = _load_pm(yaml_path) + num_cams = int(pm.num_cams) + + xyz, xy, pnr = _load_points_npz(npz_path, num_cams=num_cams) + + if ns.flags_from_yaml: + flags = _get_flags_from_yaml(pm) + elif ns.flags is not None: + unknown = [f for f in ns.flags if f not in _NAMES] + if unknown: + raise ValueError(f"Unknown flags: {unknown}. Allowed: {_NAMES}") + flags = list(ns.flags) + else: + # Default: mimic GUI behavior (YAML-driven) if present, else optimize nothing. + flags = _get_flags_from_yaml(pm) + + init_external = None if ns.no_init_external else ns.init_external + + run_standalone_calibration( + yaml_path, + xyz, + xy, + pnr, + flags=flags, + init_external=init_external, + write=bool(ns.write), + ) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/standalone_dumbbell_calibration.py b/scripts/standalone_dumbbell_calibration.py new file mode 100644 index 0000000..a67e086 --- /dev/null +++ b/scripts/standalone_dumbbell_calibration.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +"""Standalone dumbbell calibration runner (headless). + +This script runs dumbbell calibration using existing target files. + +Prerequisites +- You must already have per-frame target files on disk in the format written by + `pyptv.ptv.write_targets()` (e.g. `cam1.0001_targets`). +- For each frame used, each camera must contain exactly 2 targets (the dumbbell endpoints). + +Typical workflow +1) Run detection on a dumbbell sequence and write target files. +2) Run this script to optimize camera extrinsics. + +Example + ./.venv/bin/python scripts/standalone_dumbbell_calibration.py \ + tests/test_cavity/parameters_Run1.yaml --maxiter 500 --write + +Note +- This optimizes only camera position+angles. +- Intrinsics/distortions are left as-is. +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +from pyptv.standalone_dumbbell_calibration import run_dumbbell_calibration + + +def _parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("yaml", type=Path, help="Path to parameters_*.yaml") + p.add_argument("--step", type=int, default=None, help="Frame step (default: dumbbell.dumbbell_step or 1)") + p.add_argument("--fixed-cams", nargs="*", type=int, default=[], help="0-based camera indices to keep fixed") + p.add_argument("--maxiter", type=int, default=1000, help="SciPy least_squares max function evaluations") + p.add_argument("--write", action="store_true", help="Write updated .ori/.addpar") + p.add_argument("--no-write", action="store_true", help="Do not write outputs") + return p.parse_args() + + +def main() -> int: + ns = _parse_args() + write = bool(ns.write) and not bool(ns.no_write) + + result = run_dumbbell_calibration( + ns.yaml, + step=ns.step, + fixed_cams=ns.fixed_cams, + maxiter=ns.maxiter, + write=write, + ) + + print( + "Dumbbell calibration result: " + f"success={result.success} fun_initial={result.fun_initial:.6g} fun_final={result.fun_final:.6g} " + f"frames_used={result.n_frames_used}/{result.n_frames_total} msg={result.message}" + ) + + # SciPy occasionally reports `success=False` with messages like + # "Desired error not necessarily achieved due to precision loss." even when + # the objective has converged to a very small value. Treat that as a + # successful run when the objective improved substantially. + if result.success: + return 0 + + improved = result.fun_final < result.fun_initial + converged_enough = result.fun_final < 1e-2 + if improved and converged_enough: + return 0 + + return 2 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/verify_environment.py b/scripts/verify_environment.py new file mode 100644 index 0000000..bbb0bf5 --- /dev/null +++ b/scripts/verify_environment.py @@ -0,0 +1,59 @@ +""" +Verification script to check numpy and optv compatibility +""" + +import sys +from packaging import version +import numpy as np +import optv +import pyptv + + +def verify_environment(): + print("Environment Verification Report") + print("-" * 30) + print(f"Python version: {sys.version}") + + # Check NumPy version + min_numpy = "2.0.0" + max_numpy = "2.7" + np_version = np.__version__ + if not (version.parse(min_numpy) <= version.parse(np_version) < version.parse(max_numpy)): + print( + f"WARNING: NumPy version out of range. Required: >={min_numpy} <{max_numpy}, Found: {np_version}" + ) + else: + print(f"NumPy version: {np_version} (OK)") + + # Check OpenPTV version + required_optv = "0.3.0" + if optv.__version__ != required_optv: + print( + f"WARNING: OpenPTV version mismatch. Required: {required_optv}, Found: {optv.__version__}" + ) + else: + print(f"OpenPTV version: {optv.__version__} (OK)") + + print(f"PyPTV version: {pyptv.__version__}") + + # Verify numpy functionality + try: + # Test basic numpy operations used by PyPTV + test_arr = np.zeros((10, 10)) + test_arr[5:8, 5:8] = 1 + print("\nNumPy array operations: OK") + except Exception as e: + print(f"\nNumPy array operations failed: {str(e)}") + + # Verify optv compatibility + try: + from optv.calibration import Calibration + + Calibration() + print("OpenPTV calibration module: OK") + except Exception as e: + print(f"OpenPTV calibration module error: {str(e)}") + + +if __name__ == "__main__": + verify_environment() diff --git a/scripts/visualize_cameras.py b/scripts/visualize_cameras.py new file mode 100644 index 0000000..cc3067e --- /dev/null +++ b/scripts/visualize_cameras.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python3 +"""Visualize camera poses from .ori/.addpar files. + +Usage: + python scripts/visualize_cameras.py path/to/parameters.yaml +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import numpy as np + +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D # noqa: F401 + +from optv.calibration import Calibration + +from pyptv.parameter_manager import ParameterManager + + +def _load_calibrations(yaml_path: Path) -> list[Calibration]: + pm = ParameterManager() + pm.from_yaml(yaml_path) + + cal_ori = pm.get_parameter("cal_ori") + if not isinstance(cal_ori, dict): + raise KeyError("Missing cal_ori section in YAML") + + img_ori = cal_ori.get("img_ori") + if not img_ori: + raise ValueError("cal_ori.img_ori is empty") + + base_dir = yaml_path.parent + calibs = [] + for ori in img_ori: + ori_path = Path(ori) + if not ori_path.is_absolute(): + ori_path = (base_dir / ori_path).resolve() + addpar_path = Path(str(ori_path).replace(".ori", ".addpar")) + cal = Calibration() + cal.from_file(str(ori_path), str(addpar_path)) + calibs.append(cal) + + return calibs + + +def _set_axes_equal(ax, points: np.ndarray) -> None: + mins = points.min(axis=0) + maxs = points.max(axis=0) + centers = (mins + maxs) * 0.5 + max_range = (maxs - mins).max() * 0.5 + + ax.set_xlim(centers[0] - max_range, centers[0] + max_range) + ax.set_ylim(centers[1] - max_range, centers[1] + max_range) + ax.set_zlim(centers[2] - max_range, centers[2] + max_range) + + +def _parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("yaml", type=Path, help="Path to parameters YAML") + p.add_argument("--axis-length", type=float, default=50.0, help="Axis length in world units") + p.add_argument("--save", type=Path, default=None, help="Save figure to file") + p.add_argument("--no-show", action="store_true", help="Do not display the figure") + return p.parse_args() + + +def main() -> int: + args = _parse_args() + yaml_path = args.yaml.resolve() + + calibs = _load_calibrations(yaml_path) + + fig = plt.figure(figsize=(9, 7)) + ax = fig.add_subplot(111, projection="3d") + + # World axes at origin + axis_len = float(args.axis_length) + origin = np.zeros(3) + ax.scatter([0], [0], [0], color="k", s=30) + ax.quiver(*origin, axis_len, 0, 0, color="r", linewidth=1.5) + ax.quiver(*origin, 0, axis_len, 0, color="g", linewidth=1.5) + ax.quiver(*origin, 0, 0, axis_len, color="b", linewidth=1.5) + + points = [origin] + + for idx, cal in enumerate(calibs): + pos = np.asarray(cal.get_pos(), dtype=float) + rot = np.asarray(cal.get_rotation_matrix(), dtype=float) + + # Camera axes in world coordinates + cam_axes = rot.T + + ax.scatter([pos[0]], [pos[1]], [pos[2]], color="k", s=20) + ax.text(pos[0], pos[1], pos[2], f"Cam {idx + 1}") + + ax.quiver(*pos, *(cam_axes[:, 0] * axis_len), color="r", linewidth=1.0) + ax.quiver(*pos, *(cam_axes[:, 1] * axis_len), color="g", linewidth=1.0) + ax.quiver(*pos, *(cam_axes[:, 2] * axis_len), color="b", linewidth=1.0) + + points.append(pos) + + points = np.vstack(points) + _set_axes_equal(ax, points) + + ax.set_xlabel("X") + ax.set_ylabel("Y") + ax.set_zlabel("Z") + ax.set_title("Camera Poses from .ori Files") + + if args.save is not None: + fig.savefig(args.save, dpi=200, bbox_inches="tight") + + if not args.no_show: + plt.show() + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/openptv_python/__init__.py b/src/__init__.py similarity index 100% rename from openptv_python/__init__.py rename to src/__init__.py diff --git a/openptv_python/_native_compat.py b/src/_native_compat.py similarity index 100% rename from openptv_python/_native_compat.py rename to src/_native_compat.py diff --git a/openptv_python/_native_convert.py b/src/_native_convert.py similarity index 100% rename from openptv_python/_native_convert.py rename to src/_native_convert.py diff --git a/openptv_python/calibration.py b/src/calibration.py similarity index 100% rename from openptv_python/calibration.py rename to src/calibration.py diff --git a/openptv_python/calibration_compare.py b/src/calibration_compare.py similarity index 100% rename from openptv_python/calibration_compare.py rename to src/calibration_compare.py diff --git a/openptv_python/constants.py b/src/constants.py similarity index 100% rename from openptv_python/constants.py rename to src/constants.py diff --git a/openptv_python/correspondences.py b/src/correspondences.py similarity index 94% rename from openptv_python/correspondences.py rename to src/correspondences.py index 0bcb08f..589f328 100644 --- a/openptv_python/correspondences.py +++ b/src/correspondences.py @@ -15,7 +15,14 @@ from .epi import epi_mm from .find_candidate import find_candidate from .parameters import ControlPar, VolumePar -from .tracking_frame_buf import Frame, Target, n_tupel_dtype +from .tracking_frame_buf import ( + Frame, + Target, + get_by_pnrs as _get_by_pnrs, + match_coords as _match_coords, + matched_coords_as_arrays as _matched_coords_as_arrays, + n_tupel_dtype, +) Correspond_dtype = np.dtype( [ @@ -28,6 +35,48 @@ ) +class MatchedCoords: + """Compatibility wrapper for x-sorted matched 2D coordinates. + + The legacy PyPTV GUI expects a class-like object with ``as_arrays()`` and + ``get_by_pnrs()`` methods. The core implementation uses a NumPy recarray, + so this wrapper keeps the public API stable while delegating the actual + work to :func:`tracking_frame_buf.match_coords`. + """ + + def __init__( + self, + targs: List[Target], + cpar: ControlPar, + cal: Calibration, + tol: float = 1e-5, + reset_numbers: bool = False, + ) -> None: + self._coords = _match_coords(targs, cpar, cal, tol=tol, reset_numbers=reset_numbers) + + def as_arrays(self) -> Tuple[np.ndarray, np.ndarray]: + """Return position and target-number arrays.""" + return _matched_coords_as_arrays(self._coords) + + def get_by_pnrs(self, pnrs: np.ndarray) -> np.ndarray: + """Return flat positions for the provided target numbers.""" + return _get_by_pnrs(self._coords, pnrs) + + @property + def coords(self) -> np.recarray: + """Expose the underlying recarray for low-level consumers.""" + return self._coords + + def __len__(self) -> int: + return len(self._coords) + + def __iter__(self): + return iter(self._coords) + + def __getitem__(self, item): + return self._coords[item] + + def safely_allocate_target_usage_marks( num_cams: int, nmax: int = NMAX ) -> np.ndarray: # num_cams x nmax instead of List[List[int]]: diff --git a/openptv_python/demo_bundle_adjustment.py b/src/demo_bundle_adjustment.py similarity index 100% rename from openptv_python/demo_bundle_adjustment.py rename to src/demo_bundle_adjustment.py diff --git a/openptv_python/epi.py b/src/epi.py similarity index 100% rename from openptv_python/epi.py rename to src/epi.py diff --git a/openptv_python/find_candidate.py b/src/find_candidate.py similarity index 100% rename from openptv_python/find_candidate.py rename to src/find_candidate.py diff --git a/openptv_python/generate_synthetic_cavity_case.py b/src/generate_synthetic_cavity_case.py similarity index 100% rename from openptv_python/generate_synthetic_cavity_case.py rename to src/generate_synthetic_cavity_case.py diff --git a/openptv_python/image_processing.py b/src/image_processing.py similarity index 100% rename from openptv_python/image_processing.py rename to src/image_processing.py diff --git a/openptv_python/imgcoord.py b/src/imgcoord.py similarity index 100% rename from openptv_python/imgcoord.py rename to src/imgcoord.py diff --git a/openptv_python/multimed.py b/src/multimed.py similarity index 100% rename from openptv_python/multimed.py rename to src/multimed.py diff --git a/openptv_python/gui/manual_selection.ipynb b/src/notebooks/manual_selection.ipynb similarity index 100% rename from openptv_python/gui/manual_selection.ipynb rename to src/notebooks/manual_selection.ipynb diff --git a/openptv_python/orientation.py b/src/orientation.py similarity index 100% rename from openptv_python/orientation.py rename to src/orientation.py diff --git a/openptv_python/parameters.py b/src/parameters.py similarity index 97% rename from openptv_python/parameters.py rename to src/parameters.py index d9062e6..a5c4e17 100644 --- a/openptv_python/parameters.py +++ b/src/parameters.py @@ -503,6 +503,24 @@ def get_hp_flag(self): """Return high pass flag.""" return self.hp_flag + def set_allCam_flag(self, all_cam_flag): + """Set the all-camera flag using the legacy PyPTV name.""" + self.all_cam_flag = all_cam_flag + + def set_tiff_flag(self, tiff_flag): + """Set the TIFF flag.""" + self.tiff_flag = tiff_flag + + def set_cal_img_base_name(self, cam_index: int, base_name: str): + """Set the calibration image base name for one camera.""" + while len(self.cal_img_base_name) <= cam_index: + self.cal_img_base_name.append("") + self.cal_img_base_name[cam_index] = base_name + + def get_cal_img_base_name(self, cam_index: int = 0): + """Return the calibration image base name for one camera.""" + return self.cal_img_base_name[cam_index] + def get_allCam_flag(self): """Return allCam flag.""" return self.all_cam_flag diff --git a/openptv_python/ptv.py.bck b/src/ptv.py.bck similarity index 100% rename from openptv_python/ptv.py.bck rename to src/ptv.py.bck diff --git a/openptv_python/ray_tracing.py b/src/ray_tracing.py similarity index 100% rename from openptv_python/ray_tracing.py rename to src/ray_tracing.py diff --git a/openptv_python/segmentation.py b/src/segmentation.py similarity index 100% rename from openptv_python/segmentation.py rename to src/segmentation.py diff --git a/openptv_python/sortgrid.py b/src/sortgrid.py similarity index 100% rename from openptv_python/sortgrid.py rename to src/sortgrid.py diff --git a/openptv_python/track.py b/src/track.py similarity index 100% rename from openptv_python/track.py rename to src/track.py diff --git a/openptv_python/tracking_frame_buf.py b/src/tracking_frame_buf.py similarity index 97% rename from openptv_python/tracking_frame_buf.py rename to src/tracking_frame_buf.py index 3539887..fd57557 100644 --- a/openptv_python/tracking_frame_buf.py +++ b/src/tracking_frame_buf.py @@ -135,31 +135,31 @@ def sort_target_y(targets: List[Target]) -> List[Target]: return sorted(targets, key=lambda t: t.y) -# class TargetArray(list): -# """Target array class.""" +class TargetArray(list): + """Target array class.""" -# def __init__(self, num_targets: int = 0): -# super().__init__(Target() for item in range(num_targets)) + def __init__(self, num_targets: int = 0): + super().__init__(Target() for _ in range(num_targets)) -# def __setitem__(self, index, item): -# super().__setitem__(index, item) + def __setitem__(self, index, item): + super().__setitem__(index, item) -# def insert(self, index, item): -# super().insert(index, item) + def insert(self, index, item): + super().insert(index, item) -# def append(self, item): -# super().append(str(item)) + def append(self, item): + super().append(item) -# def extend(self, other): -# if isinstance(other, type(self)): -# super().extend(other) -# else: -# super().extend(item for item in other) + def extend(self, other): + if isinstance(other, type(self)): + super().extend(other) + else: + super().extend(item for item in other) -# @property -# def num_targs(self): -# """Return the number of targets in the list.""" -# return len(self) + @property + def num_targs(self): + """Return the number of targets in the list.""" + return len(self) def read_targets(file_base: str, frame_num: int) -> List[Target]: diff --git a/openptv_python/tracking_run.py b/src/tracking_run.py similarity index 100% rename from openptv_python/tracking_run.py rename to src/tracking_run.py diff --git a/openptv_python/trafo.py b/src/trafo.py similarity index 100% rename from openptv_python/trafo.py rename to src/trafo.py diff --git a/openptv_python/vec_utils.py b/src/vec_utils.py similarity index 100% rename from openptv_python/vec_utils.py rename to src/vec_utils.py diff --git a/uv.lock b/uv.lock index 739dc03..e8dab93 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,11 @@ version = 1 revision = 3 -requires-python = ">=3.12, <3.14" +requires-python = ">=3.11, <3.14" +resolution-markers = [ + "sys_platform == 'win32'", + "sys_platform == 'emscripten'", + 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self.mmlut_data = mmlut_data @classmethod - def from_file(cls, ori_file: Path, add_file: Path | None): + def from_file(cls, ori_file: Path | str | bytes, add_file: Path | str | bytes | None): """ Read exterior and interior orientation, and if available, parameters for distortion corrections. @@ -143,6 +144,9 @@ def from_file(cls, ori_file: Path, add_file: Path | None): ------- - ext_par, int_par, glass, addp: Calibration object parts without multimedia lookup table. """ + ori_file = Path(os.fsdecode(os.fspath(ori_file))) + add_file = None if add_file is None else Path(os.fsdecode(os.fspath(add_file))) + if not ori_file.exists(): raise IOError(f"File {ori_file} does not exist") diff --git a/src/parameters.py b/src/parameters.py index a5c4e17..f637356 100644 --- a/src/parameters.py +++ b/src/parameters.py @@ -138,9 +138,19 @@ class SequencePar(Parameters): # 'last': self.last, # } - def set_img_base_name(self, new_name: List[str]): - """Set the image base name for each camera.""" - self.img_base_name[:] = new_name + def set_img_base_name(self, new_name, value: str | None = None): + """Set the image base name for each camera. + + Accepts either a full list of names or a legacy (index, value) call. + """ + if value is None: + self.img_base_name[:] = list(new_name) + return + + index = int(new_name) + while len(self.img_base_name) <= index: + self.img_base_name.append("") + self.img_base_name[index] = value def get_img_base_name(self, icam: int = 0): """Get the image base name for each camera.""" @@ -239,38 +249,74 @@ def get_dvxmin(self): """Return the minimum velocity in x direction.""" return self.dvxmin + def set_dvxmin(self, value): + """Set the minimum velocity in x direction.""" + self.dvxmin = value + def get_dvxmax(self): """Return the maximum velocity in x direction.""" return self.dvxmax + def set_dvxmax(self, value): + """Set the maximum velocity in x direction.""" + self.dvxmax = value + def get_dvymin(self): """Return the minimum velocity in y direction.""" return self.dvymin + def set_dvymin(self, value): + """Set the minimum velocity in y direction.""" + self.dvymin = value + def get_dvymax(self): """Return the maximum velocity in y direction.""" return self.dvymax + def set_dvymax(self, value): + """Set the maximum velocity in y direction.""" + self.dvymax = value + def get_dvz_min(self): """Return the minimum velocity in z direction.""" return self.dvzmin + def set_dvzmin(self, value): + """Set the minimum velocity in z direction.""" + self.dvzmin = value + def get_dvz_max(self): """Return the minimum velocity in z direction.""" return self.dvzmax + def set_dvzmax(self, value): + """Set the maximum velocity in z direction.""" + self.dvzmax = value + def get_dangle(self): """Return the maximum angle.""" return self.dangle + def set_dangle(self, value): + """Set the maximum angle.""" + self.dangle = value + def get_dacc(self): """Return the maximum acceleration.""" return self.dacc + def set_dacc(self, value): + """Set the maximum acceleration.""" + self.dacc = value + def get_add(self): """Return the adding new particles parameter.""" return self.add + def set_add(self, value): + """Set the adding new particles parameter.""" + self.add = value + def get_dsumg(self): """Return the maximum sum of the gradient.""" return self.dsumg @@ -369,10 +415,22 @@ def set_z_min_lay(self, z_min_lay: list[float]) -> None: """Set the minimum z coordinate of the layers.""" self.z_min_lay = z_min_lay + def set_Zmin_lay(self, z_min_lay: list[float]) -> None: + """Legacy alias for set_z_min_lay().""" + self.set_z_min_lay(z_min_lay) + def set_z_max_lay(self, z_max_lay: list[float]) -> None: """Set the maximum z coordinate of the layers.""" self.z_max_lay = z_max_lay + def set_Zmax_lay(self, z_max_lay: list[float]) -> None: + """Legacy alias for set_z_max_lay().""" + self.set_z_max_lay(z_max_lay) + + def set_X_lay(self, x_lay: list[float]) -> None: + """Legacy setter for x-layer bounds.""" + self.x_lay = x_lay + def set_cn(self, cn: float) -> None: """Set the refractive index.""" self.cn = cn @@ -381,6 +439,10 @@ def set_cnx(self, cnx: float) -> None: """Set the refractive index in x direction.""" self.cnx = cnx + def set_cny(self, cny: float) -> None: + """Set the refractive index in y direction.""" + self.cny = cny + def set_csumg(self, csumg: float) -> None: """Set the maximum sum of the gradient.""" self.csumg = csumg @@ -393,6 +455,18 @@ def set_corrmin(self, corrmin: float): """Set the minimum correlation value of all criteria.""" self.corrmin = corrmin + def get_X_lay(self) -> list[float]: + """Legacy getter for x-layer bounds.""" + return self.x_lay + + def get_Zmin_lay(self) -> list[float]: + """Legacy getter for z min layer bounds.""" + return self.z_min_lay + + def get_Zmax_lay(self) -> list[float]: + """Legacy getter for z max layer bounds.""" + return self.z_max_lay + @classmethod def from_file(cls, filename: Path): """Read volume parameters from file. @@ -689,22 +763,46 @@ def get_grey_thresholds(self): """Return the grey thresholds.""" return self.gvthresh + def set_grey_thresholds(self, thresholds: list[int]) -> None: + """Set the grey thresholds.""" + self.gvthresh = list(thresholds) + def get_pixel_count_bounds(self): """Return the pixel count bounds.""" return (self.nnmin, self.nnmax) + def set_pixel_count_bounds(self, bounds: tuple[int, int]) -> None: + """Set the pixel count bounds.""" + self.nnmin, self.nnmax = bounds + def get_xsize_bounds(self): """Return the xsize bounds.""" return (self.nxmin, self.nxmax) + def set_xsize_bounds(self, bounds: tuple[int, int]) -> None: + """Set the xsize bounds.""" + self.nxmin, self.nxmax = bounds + def get_ysize_bounds(self): """Return the ysize bounds.""" return (self.nymin, self.nymax) + def set_ysize_bounds(self, bounds: tuple[int, int]) -> None: + """Set the ysize bounds.""" + self.nymin, self.nymax = bounds + def get_min_sum_grey(self): """Return the sum grey bounds.""" return self.sumg_min + def set_min_sum_grey(self, value: int) -> None: + """Set the minimum sum grey value.""" + self.sumg_min = value + + def set_max_discontinuity(self, value: int) -> None: + """Set the maximum discontinuity.""" + self.discont = value + def read_target_par(filename: Path) -> TargetPar: """Read target parameters from file and returns target_par object.""" diff --git a/src/pyptv b/src/pyptv new file mode 160000 index 0000000..bf69c60 --- /dev/null +++ b/src/pyptv @@ -0,0 +1 @@ +Subproject commit bf69c604ebf34c45c94c28782a36bafd50bd36de From cbbac56bcc28dad7a0f18ca00def0acc688c83bb Mon Sep 17 00:00:00 2001 From: Alex Liberzon Date: Wed, 18 Mar 2026 10:58:04 +0200 Subject: [PATCH 7/7] Update pyptv pointer --- src/pyptv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pyptv b/src/pyptv index bf69c60..6ab8697 160000 --- a/src/pyptv +++ b/src/pyptv @@ -1 +1 @@ -Subproject commit bf69c604ebf34c45c94c28782a36bafd50bd36de +Subproject commit 6ab8697a1cd601c1bd9726bb7bbd08a7647b830d