{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Full Pipeline Tutorial\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tlancaster6/AquaCal/blob/main/docs/tutorials/01_full_pipeline.ipynb)\n", "\n", "This tutorial demonstrates the complete AquaCal calibration pipeline from start to finish. You'll learn how to:\n", "\n", "- Load synthetic or real calibration data\n", "- Run all four calibration stages (or load a previous calibration)\n", "- Visualize camera rig geometry and calibration quality\n", "- Diagnose calibration results with reprojection and 3D error analysis\n", "\n", "**Prerequisites:** Basic Python and OpenCV knowledge. Familiarity with camera calibration concepts is helpful but not required." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local environment — skipping install.\n" ] } ], "source": [ "# Colab setup installs AquaCal from PyPI if running on Google Colab.\n", "# When running locally, this cell is a no-op.\n", "import sys\n", "\n", "if \"google.colab\" in sys.modules:\n", " print(\"Colab detected — installing AquaCal...\")\n", " !pip install -q aquacal\n", " print(\"Done.\")\n", "else:\n", " print(\"Local environment — skipping install.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Source Selection\n", "\n", "Choose your data source:\n", "\n", "- **`synthetic-small`** (recommended for first run): 4 cameras, 20 frames, ideal conditions (no noise). Fast, no download required. Uses the same \"ideal\" scenario as the small preset in the [synthetic validation tutorial](02_synthetic_validation.ipynb).\n", "- **`synthetic-large`**: 12 cameras matching the real hardware rig, 30 frames, 0.5 px noise. Slower but produces compelling diagnostics. Uses the same \"realistic\" scenario as the large preset in the [synthetic validation tutorial](02_synthetic_validation.ipynb).\n", "- **`zenodo`**: Downloads a real hardware dataset from Zenodo (~164 MB, 13 cameras) and runs the full calibration pipeline from scratch. A reference calibration is included for comparison but is not used. Requires internet on first run. Note that your calibration will probably be worse than the reference calibration, as the zenodo dataset only contains a few calibration images per camera. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "DATA_SOURCE = \"zenodo\" # Options: \"synthetic-small\", \"synthetic-large\", \"zenodo\" " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup and Imports\n", "\n", "We'll import the necessary modules and configure matplotlib for inline plotting." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imports complete!\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from pathlib import Path\n", "\n", "from aquacal.calibration import calibrate_from_detections\n", "from aquacal.core.board import BoardGeometry\n", "from aquacal.datasets import create_scenario, generate_synthetic_detections, load_example\n", "from aquacal.validation.diagnostics import plot_camera_rig, plot_per_camera_error, plot_error_distribution\n", "from aquacal.validation.reprojection import compute_reprojection_errors\n", "\n", "# Configure matplotlib\n", "plt.rcParams['figure.figsize'] = (10, 6)\n", "plt.rcParams['font.size'] = 10\n", "\n", "OUTPUT_DIR = Path(\"output\")\n", "\n", "print(\"Imports complete!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data and Calibrate\n", "\n", "This cell loads data and produces a `CalibrationResult` — the central object for all\n", "subsequent analysis. The path differs by data source:\n", "\n", "- **Synthetic**: generate a scenario with `create_scenario()`, produce detections, run Stages 2-3\n", "- **Zenodo (real rig)**: download the dataset and run the complete pipeline (Stages 1-4, ~15-20 min)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading real-rig dataset from Zenodo (first run only)...\n", "Running full calibration pipeline (Stages 1-4, ~15-20 min)...\n", "============================================================\n", "AquaCal Calibration Pipeline\n", "============================================================\n", "\n", "[Stage 1] Intrinsic calibration (in-air)...\n", " Calibrating e3v8250 (1/13)...\n", " Calibrating e3v829d (2/13)...\n", " Calibrating e3v82e0 (3/13)...\n", " Calibrating e3v82f9 (4/13)...\n", " Calibrating e3v831e (5/13)...\n", " Calibrating e3v832e (6/13)...\n", " Calibrating e3v8334 (7/13)...\n", " Calibrating e3v83e9 (8/13)...\n", " Calibrating e3v83eb (9/13)...\n", " Calibrating e3v83ee (10/13)...\n", " Calibrating e3v83ef (11/13)...\n", " Calibrating e3v83f0 (12/13)...\n", " Calibrating e3v83f1 (13/13)...\n", " e3v8250: RMS 0.503 px\n", " e3v829d: RMS 0.376 px\n", " e3v82e0: RMS 0.479 px\n", " e3v82f9: RMS 0.276 px\n", " e3v831e: RMS 0.570 px\n", " e3v832e: RMS 0.470 px\n", " e3v8334: RMS 0.386 px\n", " e3v83e9: RMS 0.420 px\n", " e3v83eb: RMS 0.477 px\n", " e3v83ee: RMS 0.346 px\n", " e3v83ef: RMS 0.388 px\n", " e3v83f0: RMS 0.397 px\n", " e3v83f1: RMS 0.474 px\n", " Calibrated 13 cameras\n", "\n", "[Detection] Detecting ChArUco in underwater videos...\n", " Frame 6/60 (10%)\n", " Frame 12/60 (20%)\n", " Frame 18/60 (30%)\n", " Frame 24/60 (40%)\n", " Frame 30/60 (50%)\n", " Frame 36/60 (60%)\n", " Frame 42/60 (70%)\n", " Frame 48/60 (80%)\n", " Frame 54/60 (90%)\n", " Frame 60/60 (100%)\n", " Found 60 usable frames\n", "\n", "[Split] Holdout fraction: 0.2\n", " Calibration frames: 48\n", " Validation frames: 12\n", "\n", "[Stage 2] Extrinsic initialization...\n", " Located e3v829d (1/12)\n", " Located e3v82e0 (2/12)\n", " Located e3v82f9 (3/12)\n", " Located e3v832e (4/12)\n", " Located e3v83ee (5/12)\n", " Located e3v83ef (6/12)\n", " Located e3v8334 (7/12)\n", " Located e3v83e9 (8/12)\n", " Located e3v831e (9/12)\n", " Located e3v83f0 (10/12)\n", " Located e3v83f1 (11/12)\n", " Located e3v83eb (12/12)\n", " Averaging poses...\n", " Initialized 12 camera poses\n", " Saved calibration_initial.json\n", " Saved camera_rig_initial.png\n", "\n", "[Stage 3] Interface and pose optimization...\n", " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", " 0 1 1.5503e+06 5.45e+06 \n", " 1 7 1.2613e+06 2.89e+05 2.05e-02 6.07e+06 \n", " 2 8 1.1763e+06 8.50e+04 2.02e-02 5.74e+06 \n", " 3 9 1.0564e+06 1.20e+05 2.02e-02 6.07e+06 \n", " 4 10 1.0444e+06 1.20e+04 2.04e-02 5.83e+06 \n", " 5 11 9.8221e+05 6.22e+04 5.14e-03 5.59e+06 \n", " 6 12 8.9709e+05 8.51e+04 1.01e-02 1.39e+06 \n", " 7 13 7.9306e+05 1.04e+05 2.00e-02 1.25e+06 \n", " 8 14 6.1630e+05 1.77e+05 4.09e-02 8.72e+05 \n", " 9 15 3.5992e+05 2.56e+05 8.16e-02 1.96e+06 \n", " 10 16 2.4690e+05 1.13e+05 1.65e-01 3.15e+06 \n", " 11 18 1.8335e+05 6.35e+04 4.13e-02 2.69e+06 \n", " 12 19 1.5567e+05 2.77e+04 4.17e-02 2.97e+06 \n", " 13 20 1.3240e+05 2.33e+04 4.18e-02 9.70e+05 \n", " 14 21 1.0263e+05 2.98e+04 8.33e-02 2.22e+05 \n", " 15 22 6.3717e+04 3.89e+04 1.67e-01 5.74e+05 \n", " 16 23 3.6187e+04 2.75e+04 3.34e-01 2.28e+06 \n", " 17 24 3.5563e+04 6.24e+02 6.68e-01 3.89e+06 \n", " 18 25 2.0151e+04 1.54e+04 1.66e-01 3.34e+06 \n", " 19 26 1.7254e+04 2.90e+03 1.65e-01 2.91e+06 \n", " 20 27 1.4147e+04 3.11e+03 4.17e-02 1.87e+06 \n", " 21 28 1.1949e+04 2.20e+03 4.12e-02 6.72e+05 \n", " 22 29 1.1462e+04 4.87e+02 4.12e-02 1.21e+04 \n", " 23 30 1.1208e+04 2.54e+02 8.24e-02 4.64e+04 \n", " 24 31 1.0903e+04 3.05e+02 1.65e-01 1.81e+05 \n", " 25 32 1.0771e+04 1.32e+02 1.77e-01 1.80e+05 \n", " 26 33 1.0701e+04 7.06e+01 2.73e-02 4.26e+03 \n", " 27 34 1.0700e+04 6.54e-01 1.05e-02 5.62e+02 \n", " 28 35 1.0700e+04 1.42e-03 1.46e-04 8.19e-01 \n", " 29 36 1.0700e+04 1.33e-06 1.25e-05 4.34e-02 \n", "`ftol` termination condition is satisfied.\n", "Function evaluations 36, initial cost 1.5503e+06, final cost 1.0700e+04, first-order optimality 4.34e-02.\n", " Stage 3 RMS: 0.848 pixels (401.3s)\n", " Estimated reference camera tilt: 2.76 degrees\n", " Water surface Z: 1.0127 m\n", " Camera heights above water (h_c):\n", " e3v829d: cam_z=0.0000 h_c=1.0127\n", " e3v82e0: cam_z=-0.0122 h_c=1.0249\n", " e3v82f9: cam_z=-0.0041 h_c=1.0168\n", " e3v831e: cam_z=-0.0160 h_c=1.0287\n", " e3v832e: cam_z=0.0082 h_c=1.0045\n", " e3v8334: cam_z=0.0010 h_c=1.0117\n", " e3v83e9: cam_z=0.0298 h_c=0.9829\n", " e3v83eb: cam_z=0.0111 h_c=1.0016\n", " e3v83ee: cam_z=-0.0159 h_c=1.0286\n", " e3v83ef: cam_z=0.0128 h_c=0.9999\n", " e3v83f0: cam_z=0.0027 h_c=1.0100\n", " e3v83f1: cam_z=-0.0477 h_c=1.0603\n", " Camera height spread: 0.0774 m\n", "\n", "[Stage 4] Joint refinement with intrinsics...\n", " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", " 0 1 1.0700e+04 1.27e+05 \n", " 1 5 1.0176e+04 5.24e+02 3.11e+00 9.39e+04 \n", " 2 7 9.9669e+03 2.10e+02 1.54e+00 7.86e+04 \n", " 3 8 9.6440e+03 3.23e+02 3.01e+00 4.99e+04 \n", " 4 11 9.6117e+03 3.23e+01 3.74e-01 4.71e+04 \n", " 5 12 9.5521e+03 5.96e+01 7.38e-01 4.05e+04 \n", " 6 15 9.5451e+03 7.00e+00 9.21e-02 3.97e+04 \n", " 7 17 9.5416e+03 3.46e+00 4.60e-02 3.93e+04 \n", " 8 19 9.5399e+03 1.72e+00 2.30e-02 3.91e+04 \n", " 9 20 9.5365e+03 3.43e+00 4.59e-02 3.87e+04 \n", " 10 24 9.5364e+03 1.07e-01 1.44e-03 3.87e+04 \n", " 11 26 9.5363e+03 5.34e-02 7.18e-04 3.87e+04 \n", " 12 28 9.5363e+03 2.67e-02 3.59e-04 3.87e+04 \n", " 13 30 9.5363e+03 1.33e-02 1.79e-04 3.87e+04 \n", " 14 31 9.5362e+03 2.67e-02 3.59e-04 3.87e+04 \n", " 15 34 9.5362e+03 3.33e-03 4.49e-05 3.87e+04 \n", "`xtol` termination condition is satisfied.\n", "Function evaluations 34, initial cost 1.0700e+04, final cost 9.5362e+03, first-order optimality 3.87e+04.\n", " Stage 4 RMS: 0.807 pixels (518.6s)\n", " Water surface Z (after refinement): 1.0197 m\n", " Camera heights above water (h_c):\n", " e3v829d: cam_z=0.0000 h_c=1.0197\n", " e3v82e0: cam_z=-0.0117 h_c=1.0314\n", " e3v82f9: cam_z=-0.0043 h_c=1.0240\n", " e3v831e: cam_z=-0.0165 h_c=1.0362\n", " e3v832e: cam_z=0.0084 h_c=1.0112\n", " e3v8334: cam_z=0.0016 h_c=1.0181\n", " e3v83e9: cam_z=0.0305 h_c=0.9892\n", " e3v83eb: cam_z=0.0108 h_c=1.0089\n", " e3v83ee: cam_z=-0.0162 h_c=1.0359\n", " e3v83ef: cam_z=0.0122 h_c=1.0075\n", " e3v83f0: cam_z=0.0033 h_c=1.0164\n", " e3v83f1: cam_z=-0.0485 h_c=1.0682\n", " Camera height spread: 0.0790 m\n", "\n", "[Stage 3b] Registering 1 auxiliary camera(s)...\n", " e3v8250: 42 frames, 3584 corners\n", " Iteration Total nfev Cost Cost reduction Step norm Optimality \n", " 0 1 4.0486e+04 1.43e+06 \n", " 1 5 2.7207e+04 1.33e+04 3.39e-02 2.21e+06 \n", " 2 7 1.5954e+04 1.13e+04 8.47e-03 8.78e+05 \n", " 3 8 1.3168e+04 2.79e+03 8.47e-03 4.97e+05 \n", " 4 9 1.1852e+04 1.32e+03 8.47e-03 7.84e+04 \n", " 5 10 1.0214e+04 1.64e+03 1.69e-02 6.63e+04 \n", " 6 11 7.1604e+03 3.05e+03 3.39e-02 7.35e+04 \n", " 7 12 3.1551e+03 4.01e+03 6.78e-02 6.52e+04 \n", " 8 13 3.0852e+03 6.99e+01 6.30e-03 1.27e+04 \n", " 9 14 3.0849e+03 3.16e-01 2.96e-04 1.25e+02 \n", " 10 15 3.0849e+03 2.04e-05 1.26e-06 4.41e-02 \n", "`ftol` termination condition is satisfied.\n", "Function evaluations 15, initial cost 4.0486e+04, final cost 3.0849e+03, first-order optimality 4.41e-02.\n", " e3v8250: RMS 1.22 px, interface_d=1.0197m\n", "\n", "[Validation] Estimating board poses for held-out frames...\n", " Estimated 12 validation frame poses\n", "\n", "[Validation] Computing errors on held-out data...\n", " Primary cameras:\n", " Reprojection RMS: 0.934 pixels\n", " 3D distance error: MAE 0.24 mm, RMSE 0.44 mm (0.4% of square size)\n", " Auxiliary cameras:\n", " e3v8250: RMS 25.991 pixels\n", "\n", "[Diagnostics] Generating report...\n", " Saved diagnostics to output\n", "\n", "[Save] Saving calibration result...\n", " Saved to output\\calibration.json\n", "\n", "============================================================\n", "Calibration complete!\n", " Primary cameras:\n", " Reprojection RMS: 0.934 pixels\n", " 3D error: MAE 0.24 mm, RMSE 0.44 mm (0.4%)\n", " Auxiliary cameras:\n", " e3v8250: RMS 25.991 pixels\n", "============================================================\n", "\n", "Calibration complete!\n", " Cameras: 13\n", " RMS: 0.934 px\n", " 3D error: 0.24 mm (mean)\n" ] } ], "source": [ "# These will be set by the data-source branch below:\n", "# result — CalibrationResult (always)\n", "# scenario — SyntheticScenario with ground truth (synthetic only, else None)\n", "# detections — DetectionResult (synthetic only, else None)\n", "# board — BoardGeometry (always)\n", "\n", "scenario = None\n", "detections = None\n", "board_poses = None\n", "reprojection_result = None\n", "pipeline_output_dir = None\n", "\n", "if DATA_SOURCE in (\"synthetic-small\", \"synthetic-large\"):\n", " # --- Create scenario ---\n", " # \"synthetic-small\" uses create_scenario(\"ideal\"): 4 cameras, 20 frames, 0 noise\n", " # \"synthetic-large\" uses create_scenario(\"realistic\"): 12 cameras, 30 frames, 0.5px noise\n", " scenario_name = \"ideal\" if DATA_SOURCE == \"synthetic-small\" else \"realistic\"\n", " scenario = create_scenario(scenario_name, seed=42)\n", " print(f\"Created scenario: {scenario.description}\")\n", " print(f\" Cameras: {len(scenario.intrinsics)}, Frames: {len(scenario.board_poses)}, Noise: {scenario.noise_std} px\")\n", "\n", " board = BoardGeometry(scenario.board_config)\n", "\n", " # --- Generate detections ---\n", " print(\"Generating synthetic detections...\")\n", " detections = generate_synthetic_detections(\n", " intrinsics=scenario.intrinsics,\n", " extrinsics=scenario.extrinsics,\n", " water_zs=scenario.water_zs,\n", " board=board,\n", " board_poses=scenario.board_poses,\n", " noise_std=scenario.noise_std,\n", " seed=42,\n", " )\n", "\n", " # --- Run Stages 2-3 ---\n", " print(\"Running calibration (Stages 2-3)...\")\n", " result, board_poses = calibrate_from_detections(\n", " detections, scenario.intrinsics, board,\n", " )\n", "\n", " # Compute detailed reprojection errors for later analysis\n", " reprojection_result = compute_reprojection_errors(result, detections, board_poses)\n", " print(f\"\\nCalibration complete! RMS: {result.diagnostics.reprojection_error_rms:.3f} px\")\n", "\n", "elif DATA_SOURCE == \"zenodo\":\n", " import os\n", " from aquacal.calibration.pipeline import run_calibration\n", "\n", " print(\"Downloading real-rig dataset from Zenodo (first run only)...\")\n", " dataset = load_example(\"real-rig\")\n", " config_path = dataset.cache_path / \"config.yaml\"\n", "\n", " # chdir so relative paths in config.yaml resolve correctly\n", " original_cwd = os.getcwd()\n", " os.chdir(dataset.cache_path)\n", "\n", " print(\"Running full calibration pipeline (Stages 1-4, ~15-20 min)...\")\n", " result = run_calibration(str(config_path), verbose=True)\n", "\n", " os.chdir(original_cwd)\n", "\n", " pipeline_output_dir = dataset.cache_path / \"output\"\n", " board = BoardGeometry(result.board)\n", " print(f\"\\nCalibration complete!\")\n", " print(f\" Cameras: {len(result.cameras)}\")\n", " print(f\" RMS: {result.diagnostics.reprojection_error_rms:.3f} px\")\n", " print(f\" 3D error: {result.diagnostics.validation_3d_error_mean * 1000:.2f} mm (mean)\")\n", "\n", "else:\n", " raise ValueError(f\"Unknown DATA_SOURCE: {DATA_SOURCE!r}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploring the Result\n", "\n", "Every calibration produces a `CalibrationResult` containing per-camera parameters,\n", "interface geometry, and diagnostic metrics." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cameras (13): ['e3v8250', 'e3v829d', 'e3v82e0', 'e3v82f9', 'e3v831e', 'e3v832e', 'e3v8334', 'e3v83e9', 'e3v83eb', 'e3v83ee', 'e3v83ef', 'e3v83f0', 'e3v83f1']\n", "\n", "Board: 12x9, 60.0 mm squares\n", "\n", "Interface: n_air=1.0, n_water=1.333\n", "\n", "Diagnostics:\n", " Reprojection RMS: 0.934 px\n", " 3D error mean: 0.24 mm\n", " 3D error std: 0.37 mm\n", "\n", "Water surface Z per camera:\n", " e3v8250: water_z = 1.0197 m, C = [-0.334, 0.576, 0.015], h_c = 1.0046 m\n", " e3v829d: water_z = 1.0197 m, C = [0.000, 0.000, 0.000], h_c = 1.0197 m\n", " e3v82e0: water_z = 1.0197 m, C = [-0.334, -0.060, -0.012], h_c = 1.0314 m\n", " e3v82f9: water_z = 1.0197 m, C = [0.344, 0.568, -0.004], h_c = 1.0240 m\n", " e3v831e: water_z = 1.0197 m, C = [-0.889, 0.280, -0.016], h_c = 1.0362 m\n", " e3v832e: water_z = 1.0197 m, C = [0.210, 0.238, 0.008], h_c = 1.0112 m\n", " e3v8334: water_z = 1.0197 m, C = [-0.659, 0.007, 0.002], h_c = 1.0181 m\n", " e3v83e9: water_z = 1.0197 m, C = [-0.330, 1.197, 0.031], h_c = 0.9892 m\n", " e3v83eb: water_z = 1.0197 m, C = [-0.876, 0.887, 0.011], h_c = 1.0089 m\n", " e3v83ee: water_z = 1.0197 m, C = [0.016, 1.152, -0.016], h_c = 1.0359 m\n", " e3v83ef: water_z = 1.0197 m, C = [0.236, 0.865, 0.012], h_c = 1.0075 m\n", " e3v83f0: water_z = 1.0197 m, C = [-1.000, 0.570, 0.003], h_c = 1.0164 m\n", " e3v83f1: water_z = 1.0197 m, C = [-0.664, 1.155, -0.049], h_c = 1.0682 m\n" ] } ], "source": [ "cam_names_sorted = sorted(result.cameras.keys())\n", "print(f\"Cameras ({len(cam_names_sorted)}): {cam_names_sorted}\")\n", "\n", "print(f\"\\nBoard: {result.board.squares_x}x{result.board.squares_y}, \"\n", " f\"{result.board.square_size * 1000:.1f} mm squares\")\n", "\n", "print(f\"\\nInterface: n_air={result.interface.n_air}, n_water={result.interface.n_water}\")\n", "\n", "print(f\"\\nDiagnostics:\")\n", "print(f\" Reprojection RMS: {result.diagnostics.reprojection_error_rms:.3f} px\")\n", "if result.diagnostics.validation_3d_error_mean > 0:\n", " print(f\" 3D error mean: {result.diagnostics.validation_3d_error_mean * 1000:.2f} mm\")\n", " print(f\" 3D error std: {result.diagnostics.validation_3d_error_std * 1000:.2f} mm\")\n", "\n", "print(f\"\\nWater surface Z per camera:\")\n", "for cam in cam_names_sorted:\n", " cc = result.cameras[cam]\n", " C = cc.extrinsics.C\n", " h_c = cc.water_z - C[2]\n", " print(f\" {cam}: water_z = {cc.water_z:.4f} m, \"\n", " f\"C = [{C[0]:.3f}, {C[1]:.3f}, {C[2]:.3f}], h_c = {h_c:.4f} m\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Camera Rig Geometry\n", "\n", "A 3D plot of the camera positions and orientations from the calibration result.\n", "The water surface plane is shown at the estimated Z-coordinate." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "fig = plot_camera_rig(result, title=\"Camera Rig Geometry\")\n", "plt.show()\n", "plt.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Intrinsic Parameters\n", "\n", "Focal lengths and principal points for each camera. For synthetic data these are\n", "ground truth values passed through to calibration; for real data they come from\n", "Stage 1 (in-air) calibration." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_intrinsics_summary(result):\n", " \"\"\"Side-by-side focal length bar chart and principal point scatter.\"\"\"\n", " cam_names = sorted(result.cameras.keys())\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n", "\n", " fx_vals = [result.cameras[c].intrinsics.K[0, 0] for c in cam_names]\n", " cx_vals = [result.cameras[c].intrinsics.K[0, 2] for c in cam_names]\n", " cy_vals = [result.cameras[c].intrinsics.K[1, 2] for c in cam_names]\n", "\n", " x = np.arange(len(cam_names))\n", " ax1.bar(x, fx_vals, color='steelblue', alpha=0.7)\n", " ax1.set_xlabel('Camera')\n", " ax1.set_ylabel('Focal Length (pixels)')\n", " ax1.set_title('Focal Lengths (fx)')\n", " ax1.set_xticks(x)\n", " ax1.set_xticklabels(cam_names, rotation=45, ha='right')\n", " ax1.grid(axis='y', alpha=0.3)\n", "\n", " ax2.scatter(cx_vals, cy_vals, s=100, color='steelblue', alpha=0.7)\n", " for i, cam in enumerate(cam_names):\n", " ax2.annotate(cam, (cx_vals[i], cy_vals[i]), xytext=(5, 5), textcoords='offset points')\n", " ax2.set_xlabel('cx (pixels)')\n", " ax2.set_ylabel('cy (pixels)')\n", " ax2.set_title('Principal Points')\n", " ax2.grid(alpha=0.3)\n", "\n", " fig.tight_layout()\n", " return fig\n", "\n", "fig = plot_intrinsics_summary(result)\n", "plt.show()\n", "plt.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Diagnostics\n", "\n", "### Per-Camera Reprojection Error\n", "\n", "A bar chart of per-camera RMS errors quickly reveals whether any camera is performing\n", "worse than the others. Cameras with high error often have poor intrinsic calibration\n", "or insufficient board observations." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Cameras with error > 2x the average may need re-calibration.\n" ] } ], "source": [ "fig = plot_per_camera_error(result)\n", "plt.show()\n", "plt.close()\n", "\n", "print(\"Cameras with error > 2x the average may need re-calibration.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reprojection Error Distribution\n", "\n", "The histogram shows the overall distribution of per-corner errors across all cameras\n", "and frames. A well-calibrated rig produces a tight distribution centered near zero.\n", "\n", "For synthetic data, residuals come from recomputing projections against detections.\n", "For the Zenodo dataset, residuals are loaded from the saved calibration file\n", "(computed on the validation holdout set during the original pipeline run)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error statistics:\n", " Primary cameras:\n", " N: 4,451\n", " Mean: 0.700 px\n", " Median: 0.563 px\n", " 95th percentile: 1.642 px\n", " Max: 13.175 px\n", " Auxiliary cameras:\n", " N: 908\n", " Mean: 17.718 px\n", " Median: 10.700 px\n", " 95th percentile: 58.351 px\n", " Max: 97.466 px\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Identify auxiliary cameras (if any) for split display\n", "aux_cameras = {name for name, cc in result.cameras.items() if cc.is_auxiliary}\n", "\n", "if reprojection_result is not None:\n", " fig = plot_error_distribution(reprojection_result, auxiliary_cameras=aux_cameras or None)\n", " plt.show()\n", " plt.close()\n", "elif result.diagnostics.per_corner_residuals is not None:\n", " # Build a ReprojectionErrors from the saved residuals (e.g. Zenodo reference calibration)\n", " from aquacal.validation.reprojection import ReprojectionErrors\n", "\n", " camera_labels = (\n", " np.array(result.diagnostics.per_corner_camera_labels, dtype=object)\n", " if result.diagnostics.per_corner_camera_labels is not None\n", " else None\n", " )\n", " stored = ReprojectionErrors(\n", " rms=result.diagnostics.reprojection_error_rms,\n", " per_camera=result.diagnostics.reprojection_error_per_camera,\n", " per_frame=result.diagnostics.per_frame_errors or {},\n", " residuals=result.diagnostics.per_corner_residuals,\n", " num_observations=len(result.diagnostics.per_corner_residuals),\n", " camera_labels=camera_labels,\n", " )\n", " fig = plot_error_distribution(stored, auxiliary_cameras=aux_cameras or None)\n", " plt.show()\n", " plt.close()\n", "else:\n", " print(\"Skipped — per-corner residuals not available for this data source.\")\n", " print(f\"Overall RMS from calibration: {result.diagnostics.reprojection_error_rms:.3f} px\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interface Distance Recovery (Synthetic Only)\n", "\n", "The water surface Z-coordinate (`water_z`) is the key refractive parameter.\n", "Comparing estimated values to ground truth tells you how well Stage 3 converged." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skipped — ground truth not available for real data.\n", "Estimated water_z values are shown in the result summary above.\n" ] } ], "source": [ "def plot_interface_recovery(result, scenario):\n", " \"\"\"Grouped bar chart comparing estimated vs ground-truth water_z.\"\"\"\n", " cam_names = sorted(result.cameras.keys())\n", " estimated = [result.cameras[c].water_z for c in cam_names]\n", " ground_truth = [scenario.water_zs[c] for c in cam_names]\n", "\n", " fig, ax = plt.subplots(figsize=(10, 5))\n", " x = np.arange(len(cam_names))\n", " width = 0.35\n", "\n", " ax.bar(x - width / 2, estimated, width, label='Estimated', color='steelblue', alpha=0.8)\n", " ax.bar(x + width / 2, ground_truth, width, label='Ground Truth', color='orange', alpha=0.8)\n", "\n", " ax.set_xlabel('Camera')\n", " ax.set_ylabel('Interface Distance (m)')\n", " ax.set_title('Interface Distance Recovery')\n", " ax.set_xticks(x)\n", " ax.set_xticklabels(cam_names, rotation=45, ha='right')\n", " ax.legend()\n", " ax.grid(axis='y', alpha=0.3)\n", " fig.tight_layout()\n", "\n", " mean_err = np.mean([abs(e - g) for e, g in zip(estimated, ground_truth)])\n", " print(f\"Mean absolute interface distance error: {mean_err * 1000:.2f} mm\")\n", " return fig\n", "\n", "if scenario is not None:\n", " fig = plot_interface_recovery(result, scenario)\n", " plt.show()\n", " plt.close()\n", "else:\n", " print(\"Skipped — ground truth not available for real data.\")\n", " print(\"Estimated water_z values are shown in the result summary above.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3D Reconstruction Error\n", "\n", "Reprojection error measures 2D fit quality, but 3D reconstruction error is the ultimate\n", "accuracy metric. We triangulate board corners from multiple cameras and compare the\n", "recovered inter-corner distances to the known board geometry." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3D Reconstruction Quality (from pipeline spatial measurements):\n", " Signed mean error: 0.006 mm\n", " RMSE: 0.441 mm\n", " Measurements: 1817\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spatial_measurements = None\n", "\n", "if detections is not None:\n", " from aquacal.validation.reconstruction import compute_3d_distance_errors\n", "\n", " dist_errors = compute_3d_distance_errors(\n", " calibration=result,\n", " detections=detections,\n", " board=board,\n", " include_per_pair=False,\n", " include_spatial=True,\n", " )\n", "\n", " print(\"3D Reconstruction Quality:\")\n", " print(f\" Signed mean error: {dist_errors.signed_mean * 1000:.3f} mm\")\n", " print(f\" RMSE: {dist_errors.rmse * 1000:.3f} mm\")\n", " print(f\" Comparisons: {dist_errors.num_comparisons}\")\n", "\n", " if dist_errors.spatial is not None:\n", " spatial_measurements = dist_errors.spatial\n", " fig, ax = plt.subplots(figsize=(8, 5))\n", " ax.hist(\n", " spatial_measurements.signed_errors * 1000,\n", " bins=30, color='steelblue', alpha=0.7, edgecolor='black',\n", " )\n", " ax.axvline(0, color='black', linestyle='--', alpha=0.5)\n", " ax.axvline(\n", " dist_errors.signed_mean * 1000, color='red', linestyle='--',\n", " label=f'Mean: {dist_errors.signed_mean * 1000:.2f} mm',\n", " )\n", " ax.set_xlabel('Signed Distance Error (mm)')\n", " ax.set_ylabel('Frequency')\n", " ax.set_title('3D Reconstruction Error Distribution')\n", " ax.legend()\n", " ax.grid(axis='y', alpha=0.3)\n", " plt.tight_layout()\n", " plt.show()\n", " plt.close()\n", "\n", "elif pipeline_output_dir is not None:\n", " spatial_csv = pipeline_output_dir / \"spatial_measurements.csv\"\n", " if spatial_csv.exists():\n", " from aquacal.validation.reconstruction import load_spatial_measurements\n", "\n", " spatial_measurements = load_spatial_measurements(spatial_csv)\n", " print(\"3D Reconstruction Quality (from pipeline spatial measurements):\")\n", " print(f\" Signed mean error: {np.mean(spatial_measurements.signed_errors) * 1000:.3f} mm\")\n", " print(f\" RMSE: {np.sqrt(np.mean(spatial_measurements.signed_errors**2)) * 1000:.3f} mm\")\n", " print(f\" Measurements: {len(spatial_measurements.signed_errors)}\")\n", "\n", " fig, ax = plt.subplots(figsize=(8, 5))\n", " ax.hist(\n", " spatial_measurements.signed_errors * 1000,\n", " bins=30, color='steelblue', alpha=0.7, edgecolor='black',\n", " )\n", " ax.axvline(0, color='black', linestyle='--', alpha=0.5)\n", " mean_err = np.mean(spatial_measurements.signed_errors) * 1000\n", " ax.axvline(\n", " mean_err, color='red', linestyle='--',\n", " label=f'Mean: {mean_err:.2f} mm',\n", " )\n", " ax.set_xlabel('Signed Distance Error (mm)')\n", " ax.set_ylabel('Frequency')\n", " ax.set_title('3D Reconstruction Error Distribution')\n", " ax.legend()\n", " ax.grid(axis='y', alpha=0.3)\n", " plt.tight_layout()\n", " plt.show()\n", " plt.close()\n", " else:\n", " print(\"3D Reconstruction Quality (from calibration diagnostics):\")\n", " print(f\" Mean error: {result.diagnostics.validation_3d_error_mean * 1000:.2f} mm\")\n", " print(f\" Std: {result.diagnostics.validation_3d_error_std * 1000:.2f} mm\")\n", " print(f\" (spatial_measurements.csv not found — histogram not available)\")\n", "else:\n", " print(\"3D Reconstruction Quality (from calibration diagnostics):\")\n", " print(f\" Mean error: {result.diagnostics.validation_3d_error_mean * 1000:.2f} mm\")\n", " print(f\" Std: {result.diagnostics.validation_3d_error_std * 1000:.2f} mm\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Common Issues Checklist\n", "\n", "| Symptom | Likely Cause | Fix |\n", "|---------|-------------|-----|\n", "| High error for one camera | Poor intrinsic calibration | Re-calibrate intrinsics with more frames, check for motion blur |\n", "| High error in image corners | Distortion model insufficient | Try rational model (8 coefficients) |\n", "| Interface distance not converging | Initial estimate too far from truth | Provide better `initial_water_zs` in config |\n", "| Interface distances differ between cameras | Degenerate board poses | Ensure board is visible at varied angles and depths |\n", "| High 3D reconstruction error | Systematic bias | Check that `n_water` is correct for your water type |\n", "| Reprojection < 1px but 3D error high | Overfitting to 2D | Verify interface distance initialization, add validation frames |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving and Loading Results\n", "\n", "AquaCal provides utilities to save and load calibration results in JSON format." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calibration results can be saved to JSON for later use.\n", "See aquacal.io.serialization.save_calibration() and load_calibration().\n" ] } ], "source": [ "# Save calibration result\n", "# save_calibration(result, \"output/calibration.json\")\n", "\n", "# Load it back later\n", "# loaded = load_calibration(\"output/calibration.json\")\n", "\n", "print(\"Calibration results can be saved to JSON for later use.\")\n", "print(\"See aquacal.io.serialization.save_calibration() and load_calibration().\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exported Data\n", "\n", "The CSVs below capture the data behind all plots in this notebook, so downstream\n", "consumers can reproduce figures without re-running the calibration." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wrote output\\camera_parameters.csv (13 rows)\n", "Wrote output\\reprojection_residuals.csv (5359 rows)\n", "Wrote output\\reconstruction_errors.csv (1817 rows)\n" ] } ], "source": [ "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n", "cam_names = sorted(result.cameras.keys())\n", "\n", "# --- Camera parameters (backs rig geometry, intrinsics, and interface plots) ---\n", "rows_cameras = []\n", "for cam in cam_names:\n", " cc = result.cameras[cam]\n", " C = cc.extrinsics.C\n", " K = cc.intrinsics.K\n", " rows_cameras.append({\n", " \"camera\": cam,\n", " \"x_m\": C[0], \"y_m\": C[1], \"z_m\": C[2],\n", " \"fx_px\": K[0, 0], \"fy_px\": K[1, 1],\n", " \"cx_px\": K[0, 2], \"cy_px\": K[1, 2],\n", " \"water_z_m\": cc.water_z,\n", " \"h_c_m\": cc.water_z - C[2],\n", " \"reprojection_rms_px\": result.diagnostics.reprojection_error_per_camera.get(cam, float(\"nan\")),\n", " })\n", " # Add ground-truth water_z if available\n", " if scenario is not None:\n", " rows_cameras[-1][\"gt_water_z_m\"] = scenario.water_zs[cam]\n", "\n", "df_cameras = pd.DataFrame(rows_cameras)\n", "path_cameras = OUTPUT_DIR / \"camera_parameters.csv\"\n", "df_cameras.to_csv(path_cameras, index=False)\n", "print(f\"Wrote {path_cameras} ({len(df_cameras)} rows)\")\n", "\n", "# --- Reprojection residuals (backs error distribution histogram) ---\n", "# Available from live computation (synthetic) or saved calibration (Zenodo)\n", "residuals_array = None\n", "camera_labels = None\n", "if reprojection_result is not None:\n", " residuals_array = reprojection_result.residuals\n", " camera_labels = reprojection_result.camera_labels\n", "elif result.diagnostics.per_corner_residuals is not None:\n", " residuals_array = result.diagnostics.per_corner_residuals\n", " if result.diagnostics.per_corner_camera_labels is not None:\n", " camera_labels = np.array(result.diagnostics.per_corner_camera_labels, dtype=object)\n", "\n", "if residuals_array is not None:\n", " df_dict = {\n", " \"residual_x_px\": residuals_array[:, 0],\n", " \"residual_y_px\": residuals_array[:, 1],\n", " }\n", " if camera_labels is not None and len(camera_labels) == len(residuals_array):\n", " aux_set = {name for name, cc in result.cameras.items() if cc.is_auxiliary}\n", " df_dict[\"camera\"] = camera_labels\n", " df_dict[\"is_auxiliary\"] = [lbl in aux_set for lbl in camera_labels]\n", " df_residuals = pd.DataFrame(df_dict)\n", " path_residuals = OUTPUT_DIR / \"reprojection_residuals.csv\"\n", " df_residuals.to_csv(path_residuals, index=False)\n", " print(f\"Wrote {path_residuals} ({len(df_residuals)} rows)\")\n", "\n", "# --- 3D reconstruction errors (backs error histogram) ---\n", "if spatial_measurements is not None:\n", " df_3d = pd.DataFrame({\n", " \"x_m\": spatial_measurements.positions[:, 0],\n", " \"y_m\": spatial_measurements.positions[:, 1],\n", " \"z_m\": spatial_measurements.positions[:, 2],\n", " \"signed_error_m\": spatial_measurements.signed_errors,\n", " \"frame_idx\": spatial_measurements.frame_indices,\n", " })\n", " path_3d = OUTPUT_DIR / \"reconstruction_errors.csv\"\n", " df_3d.to_csv(path_3d, index=False)\n", " print(f\"Wrote {path_3d} ({len(df_3d)} rows)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "In this tutorial you learned how to:\n", "\n", "1. Load calibration data (synthetic or real hardware from Zenodo)\n", "2. Run the calibration pipeline (Stages 2-3 for synthetic, Stages 1-4 for real data)\n", "3. Inspect camera rig geometry, intrinsic parameters, and interface distances\n", "4. Diagnose calibration quality with per-camera, distribution, and 3D error analysis\n", "5. Save and load calibration results\n", "\n", "**Next:** Explore [why refractive calibration matters](02_synthetic_validation.ipynb) with\n", "controlled experiments comparing refractive vs non-refractive models.\n", "\n", "- [User Guide](../guide/index.md): Comprehensive documentation on calibration theory and best practices" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 4 }