feat: trace quality agent selection report, identity clustering runner_v2 DB write, age/gender CoreML selection, updated experiment config UUID
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Warren
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scripts/face_mediapipe_test.py
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scripts/face_mediapipe_test.py
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#!/opt/homebrew/bin/python3.11
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"""
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POC: MediaPipe Face Detection vs Apple Vision Framework vs InsightFace
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Tests face detection on video frames and reports:
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- Detection count
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- Bounding box quality
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- Landmarks (468 face mesh)
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- Processing speed
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"""
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import sys
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import json
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import os
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import time
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import subprocess
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import argparse
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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def extract_frames(video_path, sample_interval=30, max_frames=50):
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"""Extract frames using ffmpeg"""
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import tempfile
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tmpdir = tempfile.mkdtemp(prefix="face_test_")
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pattern = os.path.join(tmpdir, "frame_%05d.jpg")
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cmd = ["ffmpeg", "-y", "-v", "quiet", "-i", video_path,
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"-vf", f"select=not(mod(n\\,{sample_interval}))",
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"-vsync", "vfr", "-q:v", "5", pattern]
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subprocess.run(cmd, check=True)
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files = sorted([f for f in os.listdir(tmpdir) if f.endswith(".jpg")])[:max_frames]
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return tmpdir, [os.path.join(tmpdir, f) for f in files]
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def test_mediapipe(frame_paths, fps):
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"""MediaPipe Face Detection + Face Mesh"""
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try:
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from mediapipe.tasks import vision
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from mediapipe.tasks.python.core.base_options import BaseOptions
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from mediapipe.tasks.python.vision.face_detector import FaceDetector, FaceDetectorOptions
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from mediapipe.tasks.python.vision.face_landmarker import FaceLandmarker, FaceLandmarkerOptions
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except ImportError:
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print("[MediaPipe] Not available, skipping")
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return None
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model_dir = os.path.join(os.path.dirname(__file__), "models")
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os.makedirs(model_dir, exist_ok=True)
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# Check model files - MediaPipe downloads automatically via the API
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base_opts_detect = BaseOptions(model_asset_path="")
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detect_opts = FaceDetectorOptions(base_options=BaseOptions())
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t0 = time.time()
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total_faces = 0
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frames_with_faces = 0
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landmarks_total = 0
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# MediaPipe Face Detector
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try:
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detector = vision.FaceDetector.create_from_options(
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FaceDetectorOptions(
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base_options=BaseOptions(model_asset_buffer=None),
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running_mode=vision.RunningMode.IMAGE
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)
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)
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except:
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# Download model first
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import urllib.request
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model_url = "https://storage.googleapis.com/mediapipe-models/face_detector/blaze_face_short_range/float16/latest/face_detector.task"
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model_path = os.path.join(model_dir, "face_detector.task")
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if not os.path.exists(model_path):
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print(f"[MediaPipe] Downloading model: {model_url}")
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urllib.request.urlretrieve(model_url, model_path)
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detector = vision.FaceDetector.create_from_options(
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FaceDetectorOptions(
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base_options=BaseOptions(model_asset_path=model_path),
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running_mode=vision.RunningMode.IMAGE
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)
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)
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import cv2
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for path in frame_paths:
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img = cv2.imread(path)
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if img is None:
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continue
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h, w = img.shape[:2]
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mp_img = mp.Image(image_format=mp.ImageFormat.SRGB, data=img)
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result = detector.detect(mp_img)
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if result.detections:
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frames_with_faces += 1
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for det in result.detections:
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total_faces += 1
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bbox = det.bounding_box
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# bbox is [x, y, width, height] in pixels
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elapsed = time.time() - t0
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print(f"[MediaPipe] Detection: {len(frame_paths)} frames, {frames_with_faces} with faces, {total_faces} faces, {elapsed:.2f}s")
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# Face Landmarker (468 points)
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landmark_path = os.path.join(model_dir, "face_landmarker.task")
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if not os.path.exists(landmark_path):
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model_url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/latest/face_landmarker.task"
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print(f"[MediaPipe] Downloading landmark model...")
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import urllib.request
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urllib.request.urlretrieve(model_url, landmark_path)
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landmarker = vision.FaceLandmarker.create_from_options(
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FaceLandmarkerOptions(
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base_options=BaseOptions(model_asset_path=landmark_path),
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running_mode=vision.RunningMode.IMAGE,
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output_face_blendshapes=False,
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output_facial_transformation_matrixes=False,
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)
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)
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t1 = time.time()
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for path in frame_paths[:10]: # Only test 10 frames for landmarks
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img = cv2.imread(path)
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if img is None:
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continue
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mp_img = mp.Image(image_format=mp.ImageFormat.SRGB, data=img)
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result = landmarker.detect(mp_img)
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if result.face_landmarks:
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for face in result.face_landmarks:
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landmarks_total += len(face)
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elapsed2 = time.time() - t1
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print(f"[MediaPipe] Face Mesh (10 frames): {landmarks_total} total landmarks (~{landmarks_total//max(len(result.face_landmarks),1)} per face)")
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return {
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"frames_processed": len(frame_paths),
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"frames_with_faces": frames_with_faces,
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"total_faces": total_faces,
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"time_sec": elapsed,
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"landmarks_per_face": 468,
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}
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def test_vision_framework(frame_paths, fps):
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"""Apple Vision Framework face detection via swift binary"""
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# Use the existing swift binary
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swift_bin = os.path.join(os.path.dirname(__file__),
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"swift_processors/.build/debug/swift_ocr")
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# swift_ocr doesn't do face detection, use the face_compare_test
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swift_face = os.path.join(os.path.dirname(__file__),
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"swift_processors/.build/debug/face_compare_test")
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if not os.path.exists(swift_face):
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print("[Vision] Binary not found, skipping")
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return None
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print(f"[Vision] Running face compare test...")
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t0 = time.time()
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result = subprocess.run(
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[swift_face, frame_paths[0].rsplit("/", 2)[0].replace("/frames", ""), # This won't work for single files
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"--sample-interval", "1", "--max-frames", str(len(frame_paths))],
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capture_output=True, text=True, timeout=120
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)
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elapsed = time.time() - t0
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print(result.stdout[-500:])
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return {"time_sec": elapsed}
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("video_path")
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parser.add_argument("--sample-interval", type=int, default=30)
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parser.add_argument("--max-frames", type=int, default=50)
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args = parser.parse_args()
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print(f"Testing: {args.video_path}")
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# Extract frames
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tmpdir, frames = extract_frames(args.video_path, args.sample_interval, args.max_frames)
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print(f"Extracted {len(frames)} frames")
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# MediaPipe
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print("\n=== MediaPipe ===")
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mp_result = test_mediapipe(frames, 24)
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# Vision Framework
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print("\n=== Apple Vision Framework ===")
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vf_result = test_vision_framework(frames, 24)
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# Summary
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print("\n=== Comparison ===")
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if mp_result:
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print(f"MediaPipe: {mp_result['total_faces']} faces in {mp_result['frames_with_faces']} frames, {mp_result['time_sec']:.2f}s")
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print(f" Landmarks: {mp_result['landmarks_per_face']} per face")
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print(f"Vision Framework: (see above)")
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# Cleanup
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import shutil
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shutil.rmtree(tmpdir, ignore_errors=True)
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if __name__ == "__main__":
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main()
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