feat: update Python processors and add utility scripts
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
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Warren
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scripts/utils/face_trace_visualizer.py
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201
scripts/utils/face_trace_visualizer.py
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#!/opt/homebrew/bin/python3.11
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"""
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Face Trace Visualizer - Visualize face tracking paths
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Output:
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1. Trace path visualization (matplotlib)
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2. Trace statistics CSV
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"""
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import sys
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import json
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import argparse
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.patches import Rectangle
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from collections import defaultdict
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from typing import Dict
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def visualize_traces(face_data: Dict, output_path: str = None) -> None:
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"""
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Visualize face trace paths
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"""
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frames = face_data.get("frames", {})
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traces = face_data.get("traces", {})
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metadata = face_data.get("metadata", {})
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if not frames or not traces:
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print("No frames or traces found")
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return
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video_width = metadata.get("width", 640)
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video_height = metadata.get("height", 360)
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video_duration = metadata.get("total_duration", 15)
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fig, axes = plt.subplots(2, 2, figsize=(16, 12))
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ax1 = axes[0, 0]
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ax2 = axes[0, 1]
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ax3 = axes[1, 0]
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ax4 = axes[1, 1]
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colors = plt.cm.tab10(np.linspace(0, 1, len(traces)))
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trace_data = {}
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for trace_id_str, trace in traces.items():
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trace_id = int(trace_id_str)
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path = trace.get("path", [])
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trace_data[trace_id] = {
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"frames": [p["frame"] for p in path],
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"x": [p["bbox"]["x"] + p["bbox"]["width"] / 2 for p in path],
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"y": [p["bbox"]["y"] + p["bbox"]["height"] / 2 for p in path],
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"confidence": [p["confidence"] for p in path],
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"pose": [p["pose_angle"] for p in path],
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}
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for trace_id, color in zip(sorted(trace_data.keys()), colors):
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data = trace_data[trace_id]
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ax1.plot(data["frames"], data["x"], color=color, label=f"Trace {trace_id}", linewidth=2)
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ax1.scatter(data["frames"], data["x"], color=color, s=30)
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ax2.plot(data["frames"], data["y"], color=color, label=f"Trace {trace_id}", linewidth=2)
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ax2.scatter(data["frames"], data["y"], color=color, s=30)
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ax3.plot(data["frames"], data["confidence"], color=color, label=f"Trace {trace_id}", linewidth=2)
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ax3.scatter(data["frames"], data["confidence"], color=color, s=30)
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ax1.set_xlabel("Frame Number")
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ax1.set_ylabel("X Position (center)")
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ax1.set_title("Face X Position Over Time")
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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ax2.set_xlabel("Frame Number")
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ax2.set_ylabel("Y Position (center)")
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ax2.set_title("Face Y Position Over Time")
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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ax3.set_xlabel("Frame Number")
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ax3.set_ylabel("Detection Confidence")
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ax3.set_title("Face Detection Confidence Over Time")
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ax3.legend()
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ax3.grid(True, alpha=0.3)
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pose_colors = {
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"frontal": "green",
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"three_quarter": "blue",
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"profile_left": "orange",
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"profile_right": "red",
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"unknown": "gray",
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}
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for trace_id, color in zip(sorted(trace_data.keys()), colors):
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data = trace_data[trace_id]
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poses = data["pose"]
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frames = data["frames"]
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pose_counts = defaultdict(int)
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for pose in poses:
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pose_counts[pose] += 1
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ax4.bar(
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[f"Trace {trace_id}\n{pose}" for pose in pose_counts.keys()],
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pose_counts.values(),
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color=[pose_colors.get(pose, "gray") for pose in pose_counts.keys()],
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alpha=0.7,
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label=f"Trace {trace_id}",
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)
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ax4.set_xlabel("Trace / Pose")
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ax4.set_ylabel("Count")
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ax4.set_title("Pose Distribution by Trace")
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ax4.tick_params(axis='x', rotation=45)
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plt.tight_layout()
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if output_path:
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plt.savefig(output_path, dpi=150, bbox_inches="tight")
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print(f"\n✅ Visualization saved to: {output_path}")
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else:
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plt.show()
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def export_trace_csv(face_data: Dict, output_path: str) -> None:
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"""
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Export trace statistics to CSV
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"""
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traces = face_data.get("traces", {})
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import csv
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with open(output_path, "w", newline="") as f:
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writer = csv.writer(f)
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writer.writerow([
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"trace_id",
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"start_frame",
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"end_frame",
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"duration_frames",
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"duration_seconds",
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"total_appearances",
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"avg_confidence",
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"pose_three_quarter",
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"pose_profile_right",
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"pose_profile_left",
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"pose_frontal",
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])
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for trace_id_str, trace in sorted(traces.items(), key=lambda x: int(x[0])):
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poses = trace.get("pose_angles", [])
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pose_counts = defaultdict(int)
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for pose in poses:
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pose_counts[pose] += 1
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writer.writerow([
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trace["trace_id"],
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trace["start_frame"],
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trace["end_frame"],
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trace["duration_frames"],
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trace["duration_seconds"],
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trace["total_appearances"],
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trace["avg_confidence"],
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pose_counts.get("three_quarter", 0),
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pose_counts.get("profile_right", 0),
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pose_counts.get("profile_left", 0),
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pose_counts.get("frontal", 0),
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])
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print(f"\n✅ CSV exported to: {output_path}")
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def main():
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parser = argparse.ArgumentParser(description="Visualize face traces")
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parser.add_argument("--face-json", required=True, help="Path to face_traced.json")
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parser.add_argument("--output-plot", help="Output plot path (PNG)")
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parser.add_argument("--output-csv", help="Output CSV path")
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args = parser.parse_args()
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with open(args.face_json) as f:
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face_data = json.load(f)
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print("=" * 60)
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print("Face Trace Visualizer")
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print("=" * 60)
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print(f"\nInput: {args.face_json}")
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print(f"Traces: {len(face_data.get('traces', {}))}")
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if args.output_plot:
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visualize_traces(face_data, args.output_plot)
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if args.output_csv:
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export_trace_csv(face_data, args.output_csv)
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if not args.output_plot and not args.output_csv:
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visualize_traces(face_data)
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if __name__ == "__main__":
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main()
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