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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c26305a6",
+ "metadata": {},
+ "source": [
+ "\n",
+ "# A8 Report - Pose Estimation System\n",
+ "\n",
+ "**Course sprint deliverable:** A8 notebook documenting installation, usage, software changes, data formats, and results for the MoveNet-based pose estimation pipeline.\n",
+ "\n",
+ "**Prepared for:** Sprint 8 group project \n",
+ "**Primary references:** `A8/pose_estimator.py`, `A8/keypoint_extractor.py` API target, sample images `test_person.jpg` and `test_person_annotated.jpg`\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## Introduction & Objectives\n",
+ "\n",
+ "This notebook documents the current pose estimation system, how to install and run it, the main architectural decisions, and the data formats used to export extracted keypoints.\n",
+ "\n",
+ "### Objectives\n",
+ "- Provide a reproducible environment setup.\n",
+ "- Explain the MoveNet pose estimation library and how it is integrated.\n",
+ "- Walk through the current code and highlight the software changes added by the team.\n",
+ "- Show executable usage examples for image and video processing.\n",
+ "- Document CSV and JSON output schemas.\n",
+ "- Visualize extraction results and sample output data.\n",
+ "- Summarize the system status and next implementation steps.\n",
+ "\n",
+ "### Scope of the documented system\n",
+ "The uploaded `pose_estimator.py` module already provides:\n",
+ "- MoveNet model loading from TensorFlow Hub\n",
+ "- Image preprocessing\n",
+ "- Single-image pose detection\n",
+ "- Video frame-by-frame pose extraction\n",
+ "- Skeleton overlay rendering\n",
+ "- CLI entry points for image, video, and webcam usage\n",
+ "\n",
+ "This report also includes a lightweight compatibility wrapper for the expected `KeypointExtractor` API so the notebook examples can be run in a consistent way once dependencies are installed.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b06d280",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Environment Setup & Installation\n",
+ "\n",
+ "### Supported environment\n",
+ "- **Python:** 3.10 or 3.11 recommended\n",
+ "- **OS:** Windows, macOS, or Linux\n",
+ "- **Hardware:** CPU works; GPU is optional and can improve TensorFlow inference speed\n",
+ "\n",
+ "### Required dependencies\n",
+ "| Package | Recommended version | Purpose |\n",
+ "|---|---:|---|\n",
+ "| tensorflow | `>=2.13,<3.0` | Core deep learning runtime |\n",
+ "| tensorflow-hub | `>=0.16` | Loads MoveNet model from TF Hub |\n",
+ "| opencv-python | `>=4.8` | Image/video I/O and drawing |\n",
+ "| numpy | `>=1.24` | Array operations |\n",
+ "| pandas | `>=2.0` | CSV/JSON export and tabular inspection |\n",
+ "| matplotlib | `>=3.7` | Notebook plotting and visualization |\n",
+ "\n",
+ "### Installation steps\n",
+ "\n",
+ "#### 1. Create and activate a virtual environment\n",
+ "```bash\n",
+ "python -m venv .venv\n",
+ "```\n",
+ "\n",
+ "**Windows**\n",
+ "```bash\n",
+ ".venv\\Scripts\\activate\n",
+ "```\n",
+ "\n",
+ "**macOS / Linux**\n",
+ "```bash\n",
+ "source .venv/bin/activate\n",
+ "```\n",
+ "\n",
+ "#### 2. Upgrade packaging tools\n",
+ "```bash\n",
+ "python -m pip install --upgrade pip setuptools wheel\n",
+ "```\n",
+ "\n",
+ "#### 3. Install dependencies\n",
+ "```bash\n",
+ "pip install \"tensorflow>=2.13,<3.0\" tensorflow-hub opencv-python numpy pandas matplotlib jupyter\n",
+ "```\n",
+ "\n",
+ "#### 4. Verify installation\n",
+ "```bash\n",
+ "python -c \"import tensorflow as tf; import tensorflow_hub as hub; import cv2; import numpy; import pandas; print(tf.__version__)\"\n",
+ "```\n",
+ "\n",
+ "### Troubleshooting notes\n",
+ "- If TensorFlow fails to install, check that the Python version is supported by the selected TensorFlow release.\n",
+ "- On Apple Silicon, use a Python/TensorFlow combination that is explicitly supported by the installed wheel.\n",
+ "- If OpenCV video codecs fail, test image mode first and then verify codec support for local MP4 files.\n",
+ "- The first MoveNet load may take longer because TensorFlow Hub downloads and caches the model.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "5d3784a2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'tensorflow': True,\n",
+ " 'tensorflow_hub': False,\n",
+ " 'opencv_python': True,\n",
+ " 'numpy': True,\n",
+ " 'pandas': True,\n",
+ " 'matplotlib': True}"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pathlib import Path\n",
+ "import importlib.util\n",
+ "import json\n",
+ "\n",
+ "# Base directory = where the notebook is\n",
+ "BASE_DIR = Path.cwd()\n",
+ "\n",
+ "# All files are in the same directory\n",
+ "POSE_ESTIMATOR_PATH = BASE_DIR / \"pose_estimator.py\"\n",
+ "IMAGE_PATH = BASE_DIR / \"test_person.jpg\"\n",
+ "ANNOTATED_IMAGE_PATH = BASE_DIR / \"test_person_annotated.jpg\"\n",
+ "CSV_PATH = BASE_DIR / \"sample_keypoints.csv\"\n",
+ "JSON_PATH = BASE_DIR / \"sample_keypoints.json\"\n",
+ "\n",
+ "def has_package(name: str) -> bool:\n",
+ " return importlib.util.find_spec(name) is not None\n",
+ "\n",
+ "environment_status = {\n",
+ " \"tensorflow\": has_package(\"tensorflow\"),\n",
+ " \"tensorflow_hub\": has_package(\"tensorflow_hub\"),\n",
+ " \"opencv_python\": has_package(\"cv2\"),\n",
+ " \"numpy\": has_package(\"numpy\"),\n",
+ " \"pandas\": has_package(\"pandas\"),\n",
+ " \"matplotlib\": has_package(\"matplotlib\"),\n",
+ "}\n",
+ "\n",
+ "environment_status"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74fa9eca",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Pose Estimation Library Overview\n",
+ "\n",
+ "### Why MoveNet?\n",
+ "MoveNet is a lightweight single-person pose estimation model distributed through TensorFlow Hub. It outputs **17 COCO keypoints**, each with:\n",
+ "\n",
+ "- `x`: normalized horizontal coordinate in the range `[0, 1]`\n",
+ "- `y`: normalized vertical coordinate in the range `[0, 1]`\n",
+ "- `confidence`: keypoint confidence score in the range `[0, 1]`\n",
+ "\n",
+ "### Model variants in the current code\n",
+ "The current implementation supports two variants:\n",
+ "\n",
+ "| Variant | Input size | Tradeoff |\n",
+ "|---|---:|---|\n",
+ "| `lightning` | 192 x 192 | Faster inference, slightly lower accuracy |\n",
+ "| `thunder` | 256 x 256 | Slower inference, higher accuracy |\n",
+ "\n",
+ "### Keypoints used\n",
+ "The code defines the standard 17 COCO keypoints:\n",
+ "\n",
+ "`nose, left_eye, right_eye, left_ear, right_ear, left_shoulder, right_shoulder, left_elbow, right_elbow, left_wrist, right_wrist, left_hip, right_hip, left_knee, right_knee, left_ankle, right_ankle`\n",
+ "\n",
+ "### High-level data flow\n",
+ "```text\n",
+ "Input image/video\n",
+ " -> OpenCV read\n",
+ " -> BGR to RGB conversion\n",
+ " -> Resize with padding to MoveNet input size\n",
+ " -> TensorFlow Hub inference\n",
+ " -> Raw [y, x, confidence] output\n",
+ " -> Parsed keypoint dictionary\n",
+ " -> Optional visualization overlay\n",
+ " -> Optional CSV / JSON export\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc765d7e",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Code Walkthrough & Changes\n",
+ "\n",
+ "### Module structure\n",
+ "The uploaded module is centered on a single class:\n",
+ "\n",
+ "- `MoveNetPoseEstimator`\n",
+ " - model loading\n",
+ " - preprocessing\n",
+ " - pose inference\n",
+ " - image visualization\n",
+ " - video processing\n",
+ " - image file processing\n",
+ "\n",
+ "### Architecture summary\n",
+ "```text\n",
+ "pose_estimator.py\n",
+ "├── constants\n",
+ "│ ├── KEYPOINT_NAMES\n",
+ "│ ├── KEYPOINT_EDGES\n",
+ "│ └── EDGE_COLORS\n",
+ "├── class MoveNetPoseEstimator\n",
+ "│ ├── __init__()\n",
+ "│ ├── preprocess_image()\n",
+ "│ ├── detect_pose()\n",
+ "│ ├── detect_pose_raw()\n",
+ "│ ├── draw_keypoints()\n",
+ "│ ├── process_video()\n",
+ "│ └── process_image_file()\n",
+ "└── main() CLI demo\n",
+ "```\n",
+ "\n",
+ "### Main code changes added relative to a plain TF Hub demo\n",
+ "1. **Reusable class wrapper**\n",
+ " - Encapsulates loading, preprocessing, inference, and rendering into a reusable object.\n",
+ "2. **Named keypoint output**\n",
+ " - Converts raw MoveNet tensor output into a dictionary keyed by body-part name.\n",
+ "3. **Visualization layer**\n",
+ " - Adds skeleton edges and keypoint circles with configurable confidence threshold.\n",
+ "4. **Video processing pipeline**\n",
+ " - Reads video frame-by-frame, runs inference, stores per-frame results, and optionally writes an annotated MP4.\n",
+ "5. **CLI support**\n",
+ " - Adds `--image`, `--video`, `--webcam`, `--output`, and model selection flags for local testing.\n",
+ "\n",
+ "### Suggested wrapper for the expected issue API\n",
+ "The issue description expects:\n",
+ "\n",
+ "```python\n",
+ "from keypoint_extractor import KeypointExtractor\n",
+ "extractor = KeypointExtractor(model='movenet')\n",
+ "keypoints = extractor.extract_from_video('video.mp4')\n",
+ "extractor.save_to_csv(keypoints, 'output.csv')\n",
+ "```\n",
+ "\n",
+ "The next cell implements a notebook-local compatibility wrapper with that API. This keeps the report executable even if `keypoint_extractor.py` has not yet been committed locally.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "c5a2e7b1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "pose_estimator import available but not fully executable in this environment: dlopen(/Users/reemothman/miniconda3/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so\n",
+ " Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/reemothman/miniconda3/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib\n",
+ " Reason: tried: '/Users/reemothman/miniconda3/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/reemothman/miniconda3/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/reemothman/miniconda3/bin/../lib/_pywrap_tensorflow_internal.so' (no such file)\n",
+ "Compatibility wrapper ready.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import sys\n",
+ "from pathlib import Path\n",
+ "import pandas as pd\n",
+ "\n",
+ "if str(POSE_ESTIMATOR_PATH.parent) not in sys.path:\n",
+ " sys.path.insert(0, str(POSE_ESTIMATOR_PATH.parent))\n",
+ "\n",
+ "MoveNetPoseEstimator = None\n",
+ "if POSE_ESTIMATOR_PATH.exists() and has_package(\"cv2\"):\n",
+ " try:\n",
+ " from pose_estimator import MoveNetPoseEstimator\n",
+ " except Exception as exc:\n",
+ " print(f\"pose_estimator import available but not fully executable in this environment: {exc}\")\n",
+ "\n",
+ "class KeypointExtractor:\n",
+ " \"\"\"Compatibility wrapper matching the issue's expected API.\"\"\"\n",
+ "\n",
+ " def __init__(self, model: str = \"movenet\", variant: str = \"lightning\"):\n",
+ " if model.lower() != \"movenet\":\n",
+ " raise ValueError(\"This wrapper currently supports model='movenet' only.\")\n",
+ " if MoveNetPoseEstimator is None:\n",
+ " self.estimator = None\n",
+ " else:\n",
+ " self.estimator = MoveNetPoseEstimator(model_name=variant)\n",
+ " self.model = model\n",
+ " self.variant = variant\n",
+ "\n",
+ " def extract_from_image(self, image_path: str):\n",
+ " if self.estimator is None:\n",
+ " raise RuntimeError(\"MoveNetPoseEstimator is unavailable. Install TensorFlow dependencies first.\")\n",
+ " return self.estimator.process_image_file(image_path)\n",
+ "\n",
+ " def extract_from_video(self, video_path: str, output_path: str | None = None):\n",
+ " if self.estimator is None:\n",
+ " raise RuntimeError(\"MoveNetPoseEstimator is unavailable. Install TensorFlow dependencies first.\")\n",
+ " return self.estimator.process_video(video_path, output_path=output_path)\n",
+ "\n",
+ " @staticmethod\n",
+ " def to_flat_dataframe(results):\n",
+ " rows = []\n",
+ " if isinstance(results, dict):\n",
+ " results = [dict(frame_id=0, timestamp=0.0, **results)]\n",
+ " for item in results:\n",
+ " frame_id = item.get(\"frame_id\", 0)\n",
+ " timestamp = item.get(\"timestamp\", 0.0)\n",
+ " inference_time_ms = item.get(\"inference_time_ms\")\n",
+ " for keypoint_name, kp in item[\"keypoints\"].items():\n",
+ " rows.append({\n",
+ " \"frame_id\": frame_id,\n",
+ " \"timestamp\": timestamp,\n",
+ " \"inference_time_ms\": inference_time_ms,\n",
+ " \"keypoint\": keypoint_name,\n",
+ " \"x\": kp[\"x\"],\n",
+ " \"y\": kp[\"y\"],\n",
+ " \"confidence\": kp[\"confidence\"],\n",
+ " })\n",
+ " return pd.DataFrame(rows)\n",
+ "\n",
+ " def save_to_csv(self, results, output_csv: str):\n",
+ " df = self.to_flat_dataframe(results)\n",
+ " df.to_csv(output_csv, index=False)\n",
+ " return output_csv\n",
+ "\n",
+ " def save_to_json(self, results, output_json: str):\n",
+ " import json\n",
+ " payload = {\"model\": f\"{self.model}/{self.variant}\", \"frames\": results if isinstance(results, list) else [results]}\n",
+ " with open(output_json, \"w\", encoding=\"utf-8\") as f:\n",
+ " json.dump(payload, f, indent=2)\n",
+ " return output_json\n",
+ "\n",
+ "print(\"Compatibility wrapper ready.\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2283dc2",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Usage Examples\n",
+ "\n",
+ "The following cells are designed to be executable in the notebook.\n",
+ "\n",
+ "- In the current container, TensorFlow is not installed, so the actual inference cells are expected to be skipped.\n",
+ "- Once the environment setup above is completed, the same cells can be run end-to-end without modification.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "bf0a8511",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Image example ready. Install TensorFlow and tensorflow-hub, then run this cell again.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# Example 1: Image processing usage\n",
+ "if environment_status[\"tensorflow\"] and environment_status[\"tensorflow_hub\"] and POSE_ESTIMATOR_PATH.exists() and IMAGE_PATH.exists():\n",
+ " extractor = KeypointExtractor(model=\"movenet\", variant=\"lightning\")\n",
+ " image_result = extractor.extract_from_image(str(IMAGE_PATH))\n",
+ " image_result\n",
+ "else:\n",
+ " print(\"Image example ready. Install TensorFlow and tensorflow-hub, then run this cell again.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "461a1b3b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Place a test video at ./video.mp4 after environment setup, then rerun this cell.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# Example 2: Video processing usage\n",
+ "video_path = BASE_DIR / \"video.mp4\"\n",
+ "if environment_status[\"tensorflow\"] and environment_status[\"tensorflow_hub\"] and POSE_ESTIMATOR_PATH.exists() and video_path.exists():\n",
+ " extractor = KeypointExtractor(model=\"movenet\", variant=\"lightning\")\n",
+ " video_results = extractor.extract_from_video(str(video_path))\n",
+ " output_csv = REPORT_DIR / \"video_keypoints.csv\"\n",
+ " extractor.save_to_csv(video_results, str(output_csv))\n",
+ " print(f\"Saved: {output_csv}\")\n",
+ "else:\n",
+ " print(\"Place a test video at ./video.mp4 after environment setup, then rerun this cell.\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25295128",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Data Format Documentation\n",
+ "\n",
+ "### CSV schema\n",
+ "A flat CSV export is useful for analysis in pandas, Excel, or downstream scripts.\n",
+ "\n",
+ "| Field | Type | Description |\n",
+ "|---|---|---|\n",
+ "| `frame_id` | integer | Frame index in the source video. `0` for single-image mode. |\n",
+ "| `timestamp` | float | Time in seconds from the start of the video. |\n",
+ "| `inference_time_ms` | float | Per-frame inference time in milliseconds. |\n",
+ "| `keypoint` | string | One of the 17 COCO keypoint names. |\n",
+ "| `x` | float | Normalized x coordinate in the range `[0, 1]`. |\n",
+ "| `y` | float | Normalized y coordinate in the range `[0, 1]`. |\n",
+ "| `confidence` | float | Confidence score in the range `[0, 1]`. |\n",
+ "\n",
+ "### JSON schema\n",
+ "A nested JSON export preserves frame structure and is easier to use in applications that consume hierarchical data.\n",
+ "\n",
+ "```json\n",
+ "{\n",
+ " \"model\": \"movenet/lightning\",\n",
+ " \"frames\": [\n",
+ " {\n",
+ " \"frame_id\": 0,\n",
+ " \"timestamp\": 0.0,\n",
+ " \"inference_time_ms\": 28.4,\n",
+ " \"keypoints\": {\n",
+ " \"nose\": {\"x\": 0.498, \"y\": 0.404, \"confidence\": 0.990}\n",
+ " }\n",
+ " }\n",
+ " ]\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "### Coordinate system\n",
+ "- Coordinates are **normalized** relative to image width and height.\n",
+ "- `(0, 0)` is the **top-left** corner.\n",
+ "- `x` increases from left to right.\n",
+ "- `y` increases from top to bottom.\n",
+ "- To convert normalized coordinates to pixel positions:\n",
+ " - `pixel_x = x * image_width`\n",
+ " - `pixel_y = y * image_height`\n",
+ "\n",
+ "### Confidence score interpretation\n",
+ "| Confidence range | Interpretation |\n",
+ "|---:|---|\n",
+ "| `>= 0.80` | Very reliable keypoint |\n",
+ "| `0.50 - 0.79` | Usable but should be checked |\n",
+ "| `0.30 - 0.49` | Weak detection; often filtered in visualization |\n",
+ "| `< 0.30` | Usually unreliable/noisy |\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "4c1549df",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " 0 | \n",
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+ " 0.770 | \n",
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+ " \n",
+ "
\n",
+ "