feat: media API (video/bbox/thumbnail), UUID unification, dot matrix text, portal fixes, API dictionary V1.3
This commit is contained in:
Warren
@@ -18,6 +18,7 @@ Configuration:
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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 argparse
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import signal
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import subprocess
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@@ -179,7 +180,11 @@ def run_asr(video_path, output_path, uuid: str = ""):
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if publisher:
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publisher.info("asr", "Loading Whisper model...")
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sys.stderr.write(f"[ASR] Loading Whisper model {MODEL_SIZE}...\n")
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sys.stderr.flush()
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model = WhisperModel(MODEL_SIZE, device="cpu", compute_type="int8")
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sys.stderr.write(f"[ASR] Model loaded\n")
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sys.stderr.flush()
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if publisher:
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publisher.info("asr", f"Transcribing: {video_path}")
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@@ -189,10 +194,13 @@ def run_asr(video_path, output_path, uuid: str = ""):
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if cut_scenes:
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# 分段處理：對每個場景萃取音訊並轉錄
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sys.stderr.write(f"[ASR] Starting segmented transcription for {len(cut_scenes)} scenes\n")
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sys.stderr.flush()
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import subprocess
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import tempfile
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import json
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temp_dir = tempfile.mkdtemp(prefix="asr_cut_")
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sys.stderr.write(f"[ASR] Temp dir: {temp_dir}\n")
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sys.stderr.flush()
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transcript_language = None
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# 建立 scene lookup: 給定時間點，找是哪個 scene
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@@ -204,29 +212,79 @@ def run_asr(video_path, output_path, uuid: str = ""):
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# 逐段處理，每段結果即時寫入 .asr.tmp
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tmp_path = output_path + ".tmp"
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err_path = output_path + ".err"
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all_segments = []
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# Resume: 若 executor 將 .tmp rename 成 .err，先救回
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if not os.path.exists(tmp_path) and os.path.exists(err_path) and os.path.getsize(err_path) > 10:
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try:
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os.rename(err_path, tmp_path)
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sys.stderr.write(f"[ASR] Recovered .err → .tmp for resume ({os.path.getsize(tmp_path)} bytes)\n")
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sys.stderr.flush()
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except Exception as e:
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sys.stderr.write(f"[ASR] Failed to recover .err: {e}\n")
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sys.stderr.flush()
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# Resume: 若已有 .asr.tmp，載入已完成的 segments 並跳過已處理的 scenes
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resume_from_scene = 0
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if os.path.exists(tmp_path) and os.path.getsize(tmp_path) > 10:
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try:
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with open(tmp_path) as f:
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existing = json.load(f)
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all_segments = existing.get("segments", [])
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if all_segments:
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# 找出最後一個 segment 的 end_time，決定 resume 起點
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last_end = max(s.get("end", 0) for s in all_segments)
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# 找出最後完成的 scene_idx（場景 end_time > last_end）
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for i, (st, et) in enumerate(cut_scenes):
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if et > last_end:
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resume_from_scene = i
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break
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else:
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resume_from_scene = len(cut_scenes) # 全部完成
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# 繼承 language
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if existing.get("language"):
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transcript_language = existing["language"]
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sys.stderr.write(f"[ASR] Resume from scene {resume_from_scene}/{len(cut_scenes)} "
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f"(last segment end={last_end:.1f}s, {len(all_segments)} existing segments)\n")
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sys.stderr.flush()
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except Exception as e:
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sys.stderr.write(f"[ASR] Failed to load tmp for resume: {e}, starting fresh\n")
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sys.stderr.flush()
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all_segments = []
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for idx, (start_t, end_t) in enumerate(cut_scenes):
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if idx < resume_from_scene:
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continue # 跳過已處理的 scenes
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seg_wav = os.path.join(temp_dir, f"seg_{idx:04d}.wav")
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sys.stderr.write(f"[ASR] Scene {idx}: {start_t:.1f}-{end_t:.1f}s\n")
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sys.stderr.flush()
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# 用 ffmpeg 萃取出該段音訊
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t0 = time.time()
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cmd = ["ffmpeg", "-y", "-v", "quiet", "-i", video_path,
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"-ss", str(start_t), "-to", str(end_t),
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"-ar", "16000", "-ac", "1", seg_wav]
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subprocess.run(cmd, check=False, capture_output=True)
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sys.stderr.write(f"[ASR] Scene {idx}: ffmpeg took {time.time()-t0:.1f}s\n")
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sys.stderr.flush()
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if not os.path.exists(seg_wav) or os.path.getsize(seg_wav) < 100:
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continue # 跳過空音訊
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sys.stderr.write(f"[ASR] Scene {idx}: empty audio, skipping\n")
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sys.stderr.flush()
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continue
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try:
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t1 = time.time()
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seg_result, seg_info = model.transcribe(
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seg_wav, beam_size=5,
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vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=500, speech_pad_ms=200),
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)
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if transcript_language is None:
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transcript_language = seg_info.language
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sys.stderr.write(f"[ASR] Scene {idx}: transcribe took {time.time()-t1:.1f}s, language={seg_info.language}\n")
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sys.stderr.flush()
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scene_segments = []
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seg_language = seg_info.language if seg_info else transcript_language
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for segment in seg_result:
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seg_start = start_t + segment.start
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seg_end = start_t + segment.end
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@@ -236,6 +294,7 @@ def run_asr(video_path, output_path, uuid: str = ""):
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"end": seg_end,
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"text": segment.text.strip(),
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"scene_number": scene_idx + 1,
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"language": seg_language,
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})
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total_segments += 1
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552
scripts/face_processor.py
Executable file → Normal file
552
scripts/face_processor.py
Executable file → Normal file
@@ -1,341 +1,283 @@
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#!/opt/homebrew/bin/python3.11
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"""
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Face Processor - Face Detection & Demographics with Resume Support
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Uses InsightFace for detection, age, gender, and embedding extraction.
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Face Processor V2 - Apple Vision detection + CoreML FaceNet embedding
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IMPORTANT: InsightFace is REQUIRED. No Haar fallback.
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- InsightFace provides 512-dim ArcFace embedding for identity matching
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- Haar Cascade cannot generate embedding, only detection
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- If InsightFace fails, processor will ERROR and exit
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Flow:
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1. swift_face (Vision/ANE) → bbox + pose per frame
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2. cv2 opens video, crops faces from bbox
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3. CoreML FaceNet → 512D embedding per face
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4. Output face.json in standard format
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Resume Feature:
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- Auto-detect existing results and resume from last frame
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- Auto-save at configurable intervals (default: 30 seconds)
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- Ctrl+C gracefully saves and exits
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Replaces face_processor.py (no more InsightFace CPU detection).
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Detection cost: near-zero CPU (Vision ANE)
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Embedding cost: near-zero CPU (CoreML ANE)
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"""
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import sys
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import os
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import json
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import argparse
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import os
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import subprocess
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import time
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from typing import Optional, Dict
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import cv2
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import numpy as np
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from pathlib import Path
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# CoreML
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import coremltools as ct
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from resume_framework import ResumeFramework, format_time, print_progress
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from utils.pose_analyzer import calculate_pose_angle_v2
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "debug", "swift_face")
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FACENET_PATH = os.path.join(SCRIPT_DIR, "..", "models", "facenet512.mlpackage")
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def process_face(
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video_path: str,
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output_path: str,
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uuid: str = "",
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auto_save_interval: int = 30,
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auto_save_frames: int = 300,
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force_restart: bool = False,
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sample_interval: int = 30,
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):
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"""Process video for face detection and demographics analysis with resume support"""
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framework = ResumeFramework(
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output_path=output_path,
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processor_name="face",
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uuid=uuid,
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auto_save_interval=auto_save_interval,
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auto_save_frames=auto_save_frames,
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force_restart=force_restart,
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)
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framework.publish_info("FACE_START")
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try:
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import cv2
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import numpy as np
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import insightface
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except ImportError as e:
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error_msg = f"Missing dependency: {e.name}"
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framework.publish_error(error_msg)
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result = {
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"metadata": {"status": "error", "error": error_msg},
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"frames": {},
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}
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with open(output_path, "w") as f:
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json.dump(result, f, indent=2)
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return result
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app = None
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try:
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framework.publish_info("LOADING_INSIGHTFACE")
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app = insightface.app.FaceAnalysis(
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name="buffalo_l", providers=["CPUExecutionProvider"]
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)
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app.prepare(ctx_id=0, det_size=(320, 320))
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framework.publish_info("INSIGHTFACE_LOADED")
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except Exception as e:
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error_msg = f"InsightFace failed to load (REQUIRED): {e}"
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framework.publish_error(error_msg)
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result = {
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"metadata": {"status": "error", "error": error_msg},
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"frames": {},
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}
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with open(output_path, "w") as f:
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json.dump(result, f, indent=2)
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return result
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framework.publish_info("PROCESSING_VIDEO")
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(f"Error: Cannot open video: {video_path}")
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return {"metadata": {"status": "error"}, "frames": {}}
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fps = cap.get(cv2.CAP_PROP_FPS)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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total_duration = total_frames / fps if fps > 0 else 0
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cap.release()
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framework.publish_info(f"fps={fps}, frames={total_frames}")
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existing_data, last_checkpoint = framework.load_existing_data()
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resume_mode = existing_data is not None and last_checkpoint > 0 and not force_restart
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if resume_mode:
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print(f"\nFound existing data: {output_path}")
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print(f"Last processed frame: {last_checkpoint}")
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print(f"Will resume from frame {last_checkpoint + 1}")
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if resume_mode and existing_data:
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face_data = existing_data
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frame_count = last_checkpoint
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processed_frames = set(int(k) for k in existing_data.get("frames", {}).keys())
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cap = cv2.VideoCapture(video_path)
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cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count)
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# Pose angle classification from roll/yaw
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def classify_pose(roll: float, yaw: float) -> str:
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"""Convert roll/yaw to pose angle label"""
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abs_yaw = abs(yaw)
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abs_roll = abs(roll)
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if abs_yaw < 15 and abs_roll < 15:
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return "frontal"
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elif abs_yaw > 30:
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return "profile_right" if yaw > 0 else "profile_left"
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else:
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face_data = {
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"metadata": framework.init_metadata(
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video_path=video_path,
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fps=fps,
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width=width,
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height=height,
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total_frames=total_frames,
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total_duration=total_duration,
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extra={
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"sample_interval": sample_interval,
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"detection_method": "insightface",
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},
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),
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"frames": {},
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}
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frame_count = 0
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processed_frames = set()
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cap = cv2.VideoCapture(video_path)
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return "three_quarter"
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framework.set_data(face_data)
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start_time = time.time()
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framework.last_save_time = start_time
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class FaceProcessorVision:
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def __init__(self, video_path: str, output_path: str, uuid: str = "",
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sample_interval: int = 30):
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self.video_path = video_path
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self.output_path = output_path
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self.uuid = uuid
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self.sample_interval = sample_interval
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print(f"\nProcessing video: {total_frames} frames @ {fps:.2f} fps")
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print(f"Auto-save every {auto_save_interval}s or {auto_save_frames} frames")
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print(f"Resume from frame {frame_count + 1 if resume_mode else 1}")
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print("Detection method: InsightFace (REQUIRED)")
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print()
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# Load CoreML FaceNet
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self.coreml_model = None
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facenet = os.path.normpath(FACENET_PATH)
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if os.path.exists(facenet):
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try:
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self.coreml_model = ct.models.MLModel(facenet)
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print(f"[FACE_V2] CoreML FaceNet loaded: {facenet}")
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except Exception as e:
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print(f"[FACE_V2] CoreML load failed: {e}")
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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self.video = None
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self.fps = 30.0
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self.total_frames = 0
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self.width = 0
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self.height = 0
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frame_count += 1
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current_time = (frame_count - 1) / fps if fps > 0 else 0
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if frame_count in processed_frames:
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continue
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if frame_count % sample_interval != 0:
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continue
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face_list = []
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def open_video(self):
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self.video = cv2.VideoCapture(self.video_path)
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if not self.video.isOpened():
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raise RuntimeError(f"Cannot open: {self.video_path}")
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self.fps = self.video.get(cv2.CAP_PROP_FPS)
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self.total_frames = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))
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self.width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT))
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print(f"[FACE_V2] Video: {self.width}x{self.height}, {self.fps:.1f}fps, {self.total_frames}f")
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def extract_face_embedding(self, face_img: np.ndarray) -> Optional[list]:
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"""Run CoreML FaceNet on cropped face"""
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if self.coreml_model is None:
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return None
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try:
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faces = app.get(frame)
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for face in faces:
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bbox = face.bbox.astype(int)
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bx, by, bw, bh = (
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bbox[0],
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bbox[1],
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bbox[2] - bbox[0],
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bbox[3] - bbox[1],
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)
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age = int(face.age) if hasattr(face, "age") else None
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gender_val = face.gender if hasattr(face, "gender") else None
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gender = (
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"female"
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if gender_val == 0
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else ("male" if gender_val == 1 else None)
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)
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embedding = None
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if hasattr(face, "embedding"):
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embedding = face.embedding.tolist()
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landmarks = None
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if hasattr(face, "kps"):
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landmarks = face.kps.tolist()
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elif hasattr(face, "landmark_3d_68"):
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landmarks = face.landmark_3d_68.tolist()
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pose_angle = None
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if landmarks and len(landmarks) >= 5:
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try:
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pose_result = calculate_pose_angle_v2(landmarks)
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pose_angle = {
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"angle": pose_result.get("angle", "unknown"),
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"confidence": pose_result.get("confidence", 0.0),
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"pitch": pose_result.get("pitch", "neutral"),
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"features": pose_result.get("features", {}),
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}
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except Exception:
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pass
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face_list.append(
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{
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"x": int(bx),
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"y": int(by),
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"width": int(bw),
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"height": int(bh),
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"confidence": float(face.det_score)
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if hasattr(face, "det_score")
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else 0.9,
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"embedding": embedding,
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"landmarks": landmarks,
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"pose_angle": pose_angle,
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"attributes": {"age": age, "gender": gender},
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}
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)
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# Resize to 160x160
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resized = cv2.resize(face_img, (160, 160))
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# Convert HWC to CHW and normalize to [-1, 1]
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normalized = (resized.astype(np.float32) / 127.5) - 1.0
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normalized = np.transpose(normalized, (2, 0, 1)) # HWC -> CHW
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# Add batch dim: (1, 3, 160, 160)
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input_array = np.expand_dims(normalized, axis=0)
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result = self.coreml_model.predict({"input": input_array})
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# Find output key (var_xxx)
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emb_key = [k for k in result.keys() if k.startswith("var_")][0]
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emb = result[emb_key].flatten().tolist()
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return emb
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except Exception as e:
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print(f"[ERROR] Frame processing error: {e}")
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print(f"[FACE_V2] Embedding error: {e}")
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return None
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if face_list:
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face_data["frames"][str(frame_count)] = {
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"frame_number": frame_count,
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"time_seconds": round(current_time, 3),
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"time_formatted": format_time(current_time),
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||||
"faces": face_list,
|
||||
}
|
||||
processed_frames.add(frame_count)
|
||||
def process_with_swift(self) -> Dict:
|
||||
"""Step 1: Run swift_face to get bbox + pose"""
|
||||
print(f"[FACE_V2] Step 1: Vision detection...")
|
||||
|
||||
if frame_count % 500 == 0:
|
||||
elapsed = time.time() - start_time
|
||||
print_progress(frame_count, total_frames, elapsed, f"{len(face_list)} faces")
|
||||
framework.publish_progress(frame_count, total_frames, f"frame {frame_count}")
|
||||
# Build swift_face if needed
|
||||
if not os.path.exists(SWIFT_BIN):
|
||||
build_dir = os.path.join(SCRIPT_DIR, "swift_processors")
|
||||
print(f"[FACE_V2] Building swift_face in {build_dir}...")
|
||||
subprocess.run(
|
||||
["swift", "build", "-c", "debug", "--product", "swift_face"],
|
||||
cwd=build_dir, check=True
|
||||
)
|
||||
|
||||
if framework.should_auto_save(frame_count):
|
||||
framework.save_progress(frame_count, silent=True)
|
||||
swift_out = self.output_path.replace(".json", "_detect.json")
|
||||
cmd = [
|
||||
SWIFT_BIN,
|
||||
self.video_path,
|
||||
swift_out,
|
||||
"--sample-interval", str(self.sample_interval),
|
||||
]
|
||||
if self.uuid:
|
||||
cmd.extend(["--uuid", self.uuid])
|
||||
|
||||
cap.release()
|
||||
print(f"[FACE_V2] Running: {' '.join(cmd)}")
|
||||
t0 = time.time()
|
||||
subprocess.run(cmd, check=True)
|
||||
elapsed = time.time() - t0
|
||||
print(f"[FACE_V2] Detection done in {elapsed:.1f}s")
|
||||
|
||||
total_processed = len(processed_frames)
|
||||
with open(swift_out) as f:
|
||||
return json.load(f)
|
||||
|
||||
framework.finalize(
|
||||
total_processed=total_processed,
|
||||
extra_metadata={
|
||||
"sample_interval": sample_interval,
|
||||
"detection_method": "insightface",
|
||||
},
|
||||
def embed_and_save(self, detection_data: Dict):
|
||||
"""Step 2: Crop faces + CoreML embedding + save face.json"""
|
||||
print(f"[FACE_V2] Step 2: CoreML embedding...")
|
||||
|
||||
frames = detection_data.get("frames", [])
|
||||
self.open_video()
|
||||
|
||||
face_data = {
|
||||
"metadata": {
|
||||
"video_path": os.path.abspath(self.video_path),
|
||||
"fps": self.fps, "width": self.width, "height": self.height,
|
||||
"sample_interval": self.sample_interval,
|
||||
"detection_method": "apple_vision",
|
||||
"embedding_method": "coreml_facenet",
|
||||
"status": "in_progress",
|
||||
"total_frames": self.total_frames,
|
||||
},
|
||||
"frames": {}
|
||||
}
|
||||
|
||||
t0 = time.time()
|
||||
embed_count = 0
|
||||
|
||||
for frame_info in frames:
|
||||
frame_num = frame_info["frame"]
|
||||
faces = []
|
||||
for face in frame_info.get("faces", []):
|
||||
bb = face["bbox"]
|
||||
x, y, w, h = bb["x"], bb["y"], bb["width"], bb["height"]
|
||||
|
||||
if w <= 10 or h <= 10:
|
||||
continue # skip tiny faces
|
||||
|
||||
# Seek to frame and read
|
||||
self.video.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
|
||||
ret, frame = self.video.read()
|
||||
if not ret:
|
||||
continue
|
||||
|
||||
# Crop face
|
||||
x1, y1 = max(0, x), max(0, y)
|
||||
x2, y2 = min(self.width, x + w), min(self.height, y + h)
|
||||
if x2 <= x1 or y2 <= y1:
|
||||
continue
|
||||
face_img = frame[y1:y2, x1:x2]
|
||||
if face_img.size == 0:
|
||||
continue
|
||||
|
||||
# CoreML embedding
|
||||
emb = self.extract_face_embedding(face_img)
|
||||
if emb is not None:
|
||||
embed_count += 1
|
||||
|
||||
# Pose classification
|
||||
pose_info = face.get("pose", {})
|
||||
pose_angle = classify_pose(
|
||||
pose_info.get("roll", 0),
|
||||
pose_info.get("yaw", 0)
|
||||
)
|
||||
|
||||
faces.append({
|
||||
"x": x, "y": y, "width": w, "height": h,
|
||||
"confidence": face.get("confidence", 0.5),
|
||||
"embedding": emb,
|
||||
"pose_angle": {
|
||||
"angle": pose_angle,
|
||||
"roll": pose_info.get("roll", 0),
|
||||
"yaw": pose_info.get("yaw", 0),
|
||||
"pitch": pose_info.get("pitch", 0),
|
||||
},
|
||||
"lips": face.get("lips"),
|
||||
"landmarks": None,
|
||||
"attributes": None,
|
||||
})
|
||||
|
||||
if faces:
|
||||
face_data["frames"][str(frame_num)] = {
|
||||
"frame_number": frame_num,
|
||||
"time_seconds": frame_info.get("timestamp", frame_num / self.fps),
|
||||
"time_formatted": f"{frame_num / self.fps:.1f}s",
|
||||
"faces": faces,
|
||||
}
|
||||
|
||||
if len(face_data["frames"]) % 100 == 0:
|
||||
elapsed = time.time() - t0
|
||||
print(f"[FACE_V2] {len(face_data['frames'])} frames, {embed_count} embeddings, {elapsed:.0f}s")
|
||||
|
||||
self.video.release()
|
||||
|
||||
# Finalize
|
||||
face_data["metadata"]["status"] = "completed"
|
||||
face_data["metadata"]["total_embeddings"] = embed_count
|
||||
face_data["metadata"]["embedder"] = "coreml_facenet"
|
||||
|
||||
# Convert dict frames to list for Rust FaceResult format
|
||||
frames_list = []
|
||||
for fnum_str, fdata in sorted(face_data["frames"].items(), key=lambda x: int(x[0])):
|
||||
frames_list.append({
|
||||
"frame": int(fnum_str),
|
||||
"timestamp": fdata["time_seconds"],
|
||||
"faces": fdata["faces"],
|
||||
})
|
||||
|
||||
output = {
|
||||
"frame_count": len(frames_list),
|
||||
"fps": self.fps,
|
||||
"frames": frames_list,
|
||||
}
|
||||
|
||||
with open(self.output_path, "w") as f:
|
||||
json.dump(output, f, indent=2, ensure_ascii=False)
|
||||
|
||||
elapsed = time.time() - t0
|
||||
print(f"[FACE_V2] Done: {len(frames_list)} frames, {embed_count} embeddings, {elapsed:.0f}s")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Apple Vision Face Processor V2")
|
||||
parser.add_argument("video_path", help="Video file path")
|
||||
parser.add_argument("output_path", help="Output JSON path")
|
||||
parser.add_argument("--uuid", "-u", default="")
|
||||
parser.add_argument("--sample-interval", type=int, default=30)
|
||||
parser.add_argument("--force", action="store_true")
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.force and os.path.exists(args.output_path):
|
||||
os.remove(args.output_path)
|
||||
|
||||
processor = FaceProcessorVision(
|
||||
args.video_path, args.output_path,
|
||||
args.uuid, args.sample_interval
|
||||
)
|
||||
|
||||
print(f"\nFace detection completed: {total_processed} frames processed")
|
||||
print(f"Frames with faces: {len(face_data['frames'])}")
|
||||
# Step 1: Vision detection (bbox + pose via ANE)
|
||||
detection = processor.process_with_swift()
|
||||
|
||||
return face_data
|
||||
# Step 2: CoreML embedding + save
|
||||
processor.embed_and_save(detection)
|
||||
|
||||
|
||||
def _convert_to_face_result(face_data: dict) -> dict:
|
||||
"""Convert ResumeFramework output to FaceResult format expected by Rust."""
|
||||
metadata = face_data.get("metadata", {})
|
||||
raw_frames = face_data.get("frames", {})
|
||||
fps = metadata.get("fps", 30.0)
|
||||
frames = []
|
||||
for frame_key in sorted(raw_frames.keys(), key=lambda k: int(k)):
|
||||
f = raw_frames[frame_key]
|
||||
faces = []
|
||||
for raw_face in f.get("faces", []):
|
||||
pose = raw_face.get("pose_angle")
|
||||
attributes = raw_face.get("attributes", {})
|
||||
face = {
|
||||
"face_id": None,
|
||||
"x": raw_face["x"],
|
||||
"y": raw_face["y"],
|
||||
"width": raw_face["width"],
|
||||
"height": raw_face["height"],
|
||||
"confidence": raw_face.get("confidence", 0.0),
|
||||
"embedding": raw_face.get("embedding"),
|
||||
"landmarks": raw_face.get("landmarks"),
|
||||
"attributes": {
|
||||
"age": attributes.get("age") if attributes else None,
|
||||
"gender": attributes.get("gender") if attributes else None,
|
||||
},
|
||||
}
|
||||
faces.append(face)
|
||||
frames.append({
|
||||
"frame": f["frame_number"],
|
||||
"timestamp": f["time_seconds"],
|
||||
"faces": faces,
|
||||
})
|
||||
return {
|
||||
"frame_count": len(frames),
|
||||
"fps": fps,
|
||||
"frames": frames,
|
||||
}
|
||||
# Clean up temp detection file
|
||||
swift_out = args.output_path.replace(".json", "_detect.json")
|
||||
if os.path.exists(swift_out):
|
||||
os.remove(swift_out)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Face Detection & Demographics with Resume Support")
|
||||
parser.add_argument("video_path", help="Path to video file")
|
||||
parser.add_argument("output_path", help="Output JSON path")
|
||||
parser.add_argument("--uuid", "-u", help="UUID for Redis progress", default="")
|
||||
parser.add_argument(
|
||||
"--auto-save-interval",
|
||||
"-a",
|
||||
help="Auto-save interval in seconds",
|
||||
type=int,
|
||||
default=30,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--auto-save-frames",
|
||||
"-f",
|
||||
help="Auto-save interval in frames",
|
||||
type=int,
|
||||
default=300,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--force-restart",
|
||||
"-r",
|
||||
help="Force restart (ignore existing data)",
|
||||
action="store_true",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--sample-interval",
|
||||
"-s",
|
||||
help="Frame sample interval",
|
||||
type=int,
|
||||
default=5,
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
result = process_face(
|
||||
args.video_path,
|
||||
args.output_path,
|
||||
args.uuid,
|
||||
args.auto_save_interval,
|
||||
args.auto_save_frames,
|
||||
args.force_restart,
|
||||
args.sample_interval,
|
||||
)
|
||||
face_result = _convert_to_face_result(result)
|
||||
with open(args.output_path, "w") as f:
|
||||
json.dump(face_result, f, indent=2)
|
||||
main()
|
||||
|
||||
@@ -1,243 +1,89 @@
|
||||
#!/opt/homebrew/bin/python3.11
|
||||
"""
|
||||
OCR Processor - Text Recognition with Resume Support
|
||||
Uses EasyOCR (local model)
|
||||
|
||||
Resume Feature:
|
||||
- Auto-detect existing results and resume from last frame
|
||||
- Auto-save at configurable intervals (default: 30 seconds)
|
||||
- Ctrl+C gracefully saves and exits
|
||||
OCR Processor Wrapper
|
||||
Calls Swift Vision Framework OCR (swift_ocr) with fallback to PaddleOCR.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import argparse
|
||||
import os
|
||||
import time
|
||||
import subprocess
|
||||
import argparse
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from resume_framework import ResumeFramework, format_time, print_progress
|
||||
|
||||
SWIFT_OCR_PATH = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)),
|
||||
"swift_processors/.build/debug/swift_ocr"
|
||||
)
|
||||
SWIFT_OCR_ALT = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)),
|
||||
"swift_processors/.build/arm64-apple-macosx/debug/swift_ocr"
|
||||
)
|
||||
|
||||
|
||||
def process_ocr(
|
||||
video_path: str,
|
||||
output_path: str,
|
||||
uuid: str = "",
|
||||
auto_save_interval: int = 30,
|
||||
auto_save_frames: int = 300,
|
||||
force_restart: bool = False,
|
||||
sample_interval: int = 30,
|
||||
):
|
||||
"""Process video for OCR using EasyOCR with resume support"""
|
||||
recognition_level: str = "accurate",
|
||||
) -> dict:
|
||||
swift_bin = SWIFT_OCR_PATH
|
||||
if not os.path.exists(swift_bin):
|
||||
swift_bin = SWIFT_OCR_ALT
|
||||
|
||||
framework = ResumeFramework(
|
||||
output_path=output_path,
|
||||
processor_name="ocr",
|
||||
uuid=uuid,
|
||||
auto_save_interval=auto_save_interval,
|
||||
auto_save_frames=auto_save_frames,
|
||||
force_restart=force_restart,
|
||||
if not os.path.exists(swift_bin):
|
||||
print("[OCR] Swift binary not found, using PaddleOCR", file=sys.stderr)
|
||||
return _fallback(video_path, output_path, uuid, sample_interval)
|
||||
|
||||
cmd = [swift_bin, video_path, output_path,
|
||||
"--sample-interval", str(sample_interval),
|
||||
"--recognition-level", recognition_level,
|
||||
"--uuid", uuid]
|
||||
|
||||
print(f"[OCR] Running Swift OCR", file=sys.stderr)
|
||||
result = subprocess.run(cmd, capture_output=True, text=True, timeout=7200)
|
||||
|
||||
if result.stdout:
|
||||
print(result.stdout.strip(), file=sys.stderr)
|
||||
if result.stderr:
|
||||
print(result.stderr.strip(), file=sys.stderr)
|
||||
|
||||
if result.returncode != 0 or not os.path.exists(output_path):
|
||||
print(f"[OCR] Swift OCR failed, falling back to PaddleOCR", file=sys.stderr)
|
||||
return _fallback(video_path, output_path, uuid, sample_interval)
|
||||
|
||||
with open(output_path) as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def _fallback(video_path, output_path, uuid, sample_interval):
|
||||
"""Fallback to original PaddleOCR implementation"""
|
||||
import importlib
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"paddle_ocr",
|
||||
os.path.join(os.path.dirname(__file__), "ocr_paddle.py")
|
||||
)
|
||||
|
||||
framework.publish_info("OCR_START")
|
||||
|
||||
try:
|
||||
import easyocr
|
||||
except ImportError:
|
||||
framework.publish_error("easyocr not installed")
|
||||
result = {
|
||||
"metadata": {"status": "error", "error": "easyocr not installed"},
|
||||
"frames": {},
|
||||
}
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
framework.publish_progress(0, 0, "0 frames")
|
||||
return result
|
||||
|
||||
framework.publish_info("OCR_LOADING_MODEL")
|
||||
|
||||
reader = easyocr.Reader(["en"], gpu=False, verbose=False)
|
||||
|
||||
framework.publish_info("OCR_MODEL_LOADED")
|
||||
|
||||
import cv2
|
||||
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
|
||||
if not cap.isOpened():
|
||||
print(f"Error: Cannot open video: {video_path}")
|
||||
return {"metadata": {"status": "error"}, "frames": {}}
|
||||
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
total_duration = total_frames / fps if fps > 0 else 0
|
||||
cap.release()
|
||||
|
||||
framework.publish_info(f"fps={fps}, frames={total_frames}")
|
||||
|
||||
existing_data, last_checkpoint = framework.load_existing_data()
|
||||
resume_mode = existing_data is not None and last_checkpoint > 0 and not force_restart
|
||||
|
||||
if resume_mode:
|
||||
print(f"\nFound existing data: {output_path}")
|
||||
print(f"Last processed frame: {last_checkpoint}")
|
||||
print(f"Will resume from frame {last_checkpoint + 1}")
|
||||
|
||||
if resume_mode and existing_data:
|
||||
ocr_data = existing_data
|
||||
frame_count = last_checkpoint
|
||||
processed_frames = set(int(k) for k in existing_data.get("frames", {}).keys())
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count)
|
||||
else:
|
||||
ocr_data = {
|
||||
"metadata": framework.init_metadata(
|
||||
video_path=video_path,
|
||||
fps=fps,
|
||||
width=width,
|
||||
height=height,
|
||||
total_frames=total_frames,
|
||||
total_duration=total_duration,
|
||||
extra={"sample_interval": sample_interval},
|
||||
),
|
||||
"frames": {},
|
||||
}
|
||||
frame_count = 0
|
||||
processed_frames = set()
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
|
||||
framework.set_data(ocr_data)
|
||||
|
||||
start_time = time.time()
|
||||
framework.last_save_time = start_time
|
||||
|
||||
print(f"\nProcessing video: {total_frames} frames @ {fps:.2f} fps")
|
||||
print(f"Auto-save every {auto_save_interval}s or {auto_save_frames} frames")
|
||||
print(f"Resume from frame {frame_count + 1 if resume_mode else 1}")
|
||||
print()
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
frame_count += 1
|
||||
current_time = (frame_count - 1) / fps if fps > 0 else 0
|
||||
|
||||
if frame_count in processed_frames:
|
||||
continue
|
||||
|
||||
if frame_count % sample_interval != 0:
|
||||
continue
|
||||
|
||||
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
try:
|
||||
detections = reader.readtext(
|
||||
frame_rgb, text_threshold=0.5, low_text=0.3, link_threshold=0.3
|
||||
)
|
||||
except Exception as e:
|
||||
framework.publish_error(f"Frame {frame_count}: {e}")
|
||||
detections = []
|
||||
|
||||
texts = []
|
||||
for detection in detections:
|
||||
det: tuple = tuple(detection)
|
||||
bbox = list(det[0])
|
||||
text: str = str(det[1])
|
||||
confidence: float = float(det[2])
|
||||
|
||||
x = int(min(float(p[0]) for p in bbox))
|
||||
y = int(min(float(p[1]) for p in bbox))
|
||||
w = int(max(float(p[0]) for p in bbox) - x)
|
||||
h = int(max(float(p[1]) for p in bbox) - y)
|
||||
|
||||
if text.strip():
|
||||
texts.append(
|
||||
{
|
||||
"text": text,
|
||||
"x": x,
|
||||
"y": y,
|
||||
"width": w,
|
||||
"height": h,
|
||||
"confidence": confidence,
|
||||
}
|
||||
)
|
||||
|
||||
if texts:
|
||||
ocr_data["frames"][str(frame_count)] = {
|
||||
"frame_number": frame_count,
|
||||
"time_seconds": round(current_time, 3),
|
||||
"time_formatted": format_time(current_time),
|
||||
"texts": texts,
|
||||
}
|
||||
processed_frames.add(frame_count)
|
||||
|
||||
if frame_count % 500 == 0:
|
||||
elapsed = time.time() - start_time
|
||||
print_progress(frame_count, total_frames, elapsed, f"{len(texts)} texts")
|
||||
framework.publish_progress(frame_count, total_frames, f"frame {frame_count}")
|
||||
|
||||
if framework.should_auto_save(frame_count):
|
||||
framework.save_progress(frame_count, silent=True)
|
||||
|
||||
cap.release()
|
||||
|
||||
total_processed = len(processed_frames)
|
||||
|
||||
framework.finalize(
|
||||
total_processed=total_processed,
|
||||
extra_metadata={"sample_interval": sample_interval},
|
||||
)
|
||||
|
||||
print(f"\nOCR completed: {total_processed} frames processed")
|
||||
print(f"Frames with text: {len(ocr_data['frames'])}")
|
||||
|
||||
return ocr_data
|
||||
if spec is None:
|
||||
print("[OCR] No fallback available, returning empty result", file=sys.stderr)
|
||||
return {"frame_count": 0, "fps": 0, "frames": []}
|
||||
paddle = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(paddle)
|
||||
return paddle.process_ocr(video_path, output_path, uuid, sample_interval=sample_interval)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="OCR Text Recognition with Resume Support")
|
||||
parser.add_argument("video_path", help="Path to video file")
|
||||
parser.add_argument("output_path", help="Output JSON path")
|
||||
parser.add_argument("--uuid", "-u", help="UUID for Redis progress", default="")
|
||||
parser.add_argument(
|
||||
"--auto-save-interval",
|
||||
"-a",
|
||||
help="Auto-save interval in seconds",
|
||||
type=int,
|
||||
default=30,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--auto-save-frames",
|
||||
"-f",
|
||||
help="Auto-save interval in frames",
|
||||
type=int,
|
||||
default=300,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--force-restart",
|
||||
"-r",
|
||||
help="Force restart (ignore existing data)",
|
||||
action="store_true",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--sample-interval",
|
||||
"-s",
|
||||
help="Frame sample interval",
|
||||
type=int,
|
||||
default=30,
|
||||
)
|
||||
parser = argparse.ArgumentParser(description="OCR Processor (Swift Vision)")
|
||||
parser.add_argument("video_path")
|
||||
parser.add_argument("output_path")
|
||||
parser.add_argument("--uuid", "-u", default="")
|
||||
parser.add_argument("--sample-interval", type=int, default=30)
|
||||
parser.add_argument("--recognition-level", choices=["fast", "accurate"], default="accurate")
|
||||
args = parser.parse_args()
|
||||
|
||||
process_ocr(
|
||||
args.video_path,
|
||||
args.output_path,
|
||||
args.uuid,
|
||||
args.auto_save_interval,
|
||||
args.auto_save_frames,
|
||||
args.force_restart,
|
||||
args.sample_interval,
|
||||
)
|
||||
result = process_ocr(args.video_path, args.output_path, args.uuid,
|
||||
args.sample_interval, args.recognition_level)
|
||||
|
||||
with open(args.output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
print(f"OCR: {len(result.get('frames', []))} frames with text")
|
||||
|
||||
@@ -1,255 +1,119 @@
|
||||
#!/opt/homebrew/bin/python3.11
|
||||
"""
|
||||
Pose Processor - Pose Estimation with Resume Support
|
||||
Uses YOLOv8 Pose via ultralytics (local model)
|
||||
|
||||
Resume Feature:
|
||||
- Auto-detect existing results and resume from last frame
|
||||
- Auto-save at configurable intervals (default: 30 seconds)
|
||||
- Ctrl+C gracefully saves and exits
|
||||
|
||||
Note: YOLOv8 Pose uses stream mode which is optimized for video processing.
|
||||
For resume support, we need to process frames manually with OpenCV.
|
||||
Pose Processor Wrapper
|
||||
Calls Swift Vision Framework pose (swift_pose) with fallback to YOLOv8 Pose.
|
||||
Uses VNDetectHumanBodyPoseRequest with ANE acceleration.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import argparse
|
||||
import os
|
||||
import time
|
||||
import subprocess
|
||||
import argparse
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from resume_framework import ResumeFramework, format_time, print_progress
|
||||
|
||||
|
||||
KEYPOINT_NAMES = [
|
||||
"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",
|
||||
]
|
||||
SWIFT_POSE_PATH = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)),
|
||||
"swift_processors/.build/debug/swift_pose"
|
||||
)
|
||||
SWIFT_POSE_ALT = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)),
|
||||
"swift_processors/.build/arm64-apple-macosx/debug/swift_pose"
|
||||
)
|
||||
|
||||
|
||||
def process_pose(
|
||||
video_path: str,
|
||||
output_path: str,
|
||||
uuid: str = "",
|
||||
auto_save_interval: int = 30,
|
||||
auto_save_frames: int = 300,
|
||||
force_restart: bool = False,
|
||||
):
|
||||
"""Process video for pose estimation using YOLOv8 Pose with resume support"""
|
||||
sample_interval: int = 30,
|
||||
) -> dict:
|
||||
swift_bin = SWIFT_POSE_PATH
|
||||
if not os.path.exists(swift_bin):
|
||||
swift_bin = SWIFT_POSE_ALT
|
||||
|
||||
framework = ResumeFramework(
|
||||
output_path=output_path,
|
||||
processor_name="pose",
|
||||
uuid=uuid,
|
||||
auto_save_interval=auto_save_interval,
|
||||
auto_save_frames=auto_save_frames,
|
||||
force_restart=force_restart,
|
||||
)
|
||||
if not os.path.exists(swift_bin):
|
||||
print("[Pose] Swift binary not found, using YOLOv8 fallback", file=sys.stderr)
|
||||
return _fallback(video_path, output_path, uuid, sample_interval)
|
||||
|
||||
framework.publish_info("POSE_START")
|
||||
cmd = [swift_bin, video_path, output_path,
|
||||
"--sample-interval", str(sample_interval),
|
||||
"--uuid", uuid]
|
||||
|
||||
try:
|
||||
from ultralytics import YOLO
|
||||
except ImportError:
|
||||
framework.publish_error("ultralytics not installed")
|
||||
result = {
|
||||
"metadata": {"status": "error", "error": "ultralytics not installed"},
|
||||
"frames": {},
|
||||
}
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
return result
|
||||
print(f"[Pose] Running Swift Pose (Vision Framework)", file=sys.stderr)
|
||||
result = subprocess.run(cmd, capture_output=True, text=True, timeout=7200)
|
||||
|
||||
framework.publish_info("POSE_LOADING_MODEL")
|
||||
if result.stdout:
|
||||
for line in result.stdout.strip().split("\n"):
|
||||
print(f" {line}", file=sys.stderr)
|
||||
if result.stderr:
|
||||
for line in result.stderr.strip().split("\n"):
|
||||
print(f" {line}", file=sys.stderr)
|
||||
|
||||
model = YOLO("yolov8n-pose.pt")
|
||||
if result.returncode != 0 or not os.path.exists(output_path):
|
||||
print(f"[Pose] Swift Pose failed, falling back to YOLOv8", file=sys.stderr)
|
||||
return _fallback(video_path, output_path, uuid, sample_interval)
|
||||
|
||||
with open(output_path) as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def _fallback(video_path, output_path, uuid, sample_interval):
|
||||
"""Fallback to YOLOv8 Pose"""
|
||||
from ultralytics import YOLO
|
||||
import cv2
|
||||
|
||||
model = YOLO("yolov8n-pose.pt")
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
|
||||
if not cap.isOpened():
|
||||
print(f"Error: Cannot open video: {video_path}")
|
||||
return {"metadata": {"status": "error"}, "frames": {}}
|
||||
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
total_duration = total_frames / fps if fps > 0 else 0
|
||||
cap.release()
|
||||
|
||||
framework.publish_info(f"fps={fps}, frames={total_frames}")
|
||||
|
||||
existing_data, last_checkpoint = framework.load_existing_data()
|
||||
resume_mode = existing_data is not None and last_checkpoint > 0 and not force_restart
|
||||
|
||||
if resume_mode:
|
||||
print(f"\nFound existing data: {output_path}")
|
||||
print(f"Last processed frame: {last_checkpoint}")
|
||||
print(f"Will resume from frame {last_checkpoint + 1}")
|
||||
|
||||
if resume_mode and existing_data:
|
||||
pose_data = existing_data
|
||||
frame_count = last_checkpoint
|
||||
processed_frames = set(int(k) for k in existing_data.get("frames", {}).keys())
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count)
|
||||
else:
|
||||
pose_data = {
|
||||
"metadata": framework.init_metadata(
|
||||
video_path=video_path,
|
||||
fps=fps,
|
||||
width=width,
|
||||
height=height,
|
||||
total_frames=total_frames,
|
||||
total_duration=total_duration,
|
||||
extra={"model": "yolov8n-pose"},
|
||||
),
|
||||
"frames": {},
|
||||
}
|
||||
frame_count = 0
|
||||
processed_frames = set()
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
|
||||
framework.set_data(pose_data)
|
||||
|
||||
start_time = time.time()
|
||||
framework.last_save_time = start_time
|
||||
|
||||
print(f"\nProcessing video: {total_frames} frames @ {fps:.2f} fps")
|
||||
print(f"Auto-save every {auto_save_interval}s or {auto_save_frames} frames")
|
||||
print(f"Resume from frame {frame_count + 1 if resume_mode else 1}")
|
||||
print()
|
||||
|
||||
while True:
|
||||
frame_count = 0
|
||||
frames = []
|
||||
while cap.isOpened():
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
if frame_count % sample_interval == 0:
|
||||
ts = frame_count / fps if fps > 0 else 0
|
||||
results = model(frame, verbose=False, device="cpu")
|
||||
persons = []
|
||||
for r in results:
|
||||
if r.keypoints is None:
|
||||
continue
|
||||
for kp_data in r.keypoints:
|
||||
kps = kp_data.xy[0].cpu().numpy() if hasattr(kp_data, 'xy') else []
|
||||
confs = kp_data.conf[0].cpu().numpy() if hasattr(kp_data, 'conf') else []
|
||||
keypoints = []
|
||||
names = ["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"]
|
||||
for j, name in enumerate(names):
|
||||
if j < len(kps):
|
||||
x, y = float(kps[j][0]), float(kps[j][1])
|
||||
c = float(confs[j]) if j < len(confs) else 0
|
||||
keypoints.append({"name": name, "x": x, "y": y, "confidence": c})
|
||||
if keypoints:
|
||||
xs = [k["x"] for k in keypoints if k["confidence"] > 0.1]
|
||||
ys = [k["y"] for k in keypoints if k["confidence"] > 0.1]
|
||||
bbox = {"x": int(min(xs)), "y": int(min(ys)), "width": int(max(xs)-min(xs)), "height": int(max(ys)-min(ys))} if xs else {"x": 0, "y": 0, "width": 0, "height": 0}
|
||||
persons.append({"keypoints": keypoints, "bbox": bbox})
|
||||
if persons:
|
||||
frames.append({"frame": frame_count, "timestamp": ts, "persons": persons})
|
||||
frame_count += 1
|
||||
current_time = (frame_count - 1) / fps if fps > 0 else 0
|
||||
|
||||
if frame_count in processed_frames:
|
||||
continue
|
||||
|
||||
results = model(frame, conf=0.5, verbose=False, pose=True)
|
||||
result = results[0]
|
||||
|
||||
persons = []
|
||||
|
||||
if result.keypoints is not None:
|
||||
for person in result.keypoints:
|
||||
keypoints = []
|
||||
|
||||
for i, kp in enumerate(person):
|
||||
if len(kp) >= 3:
|
||||
keypoints.append(
|
||||
{
|
||||
"name": KEYPOINT_NAMES[i]
|
||||
if i < len(KEYPOINT_NAMES)
|
||||
else f"kp_{i}",
|
||||
"x": float(kp[0]),
|
||||
"y": float(kp[1]),
|
||||
"confidence": float(kp[2]),
|
||||
}
|
||||
)
|
||||
|
||||
valid_kps = [kp for kp in keypoints if kp["confidence"] > 0.3]
|
||||
if valid_kps:
|
||||
xs = [kp["x"] for kp in valid_kps]
|
||||
ys = [kp["y"] for kp in valid_kps]
|
||||
bbox = {
|
||||
"x": int(min(xs)),
|
||||
"y": int(min(ys)),
|
||||
"width": int(max(xs) - min(xs)),
|
||||
"height": int(max(ys) - min(ys)),
|
||||
}
|
||||
else:
|
||||
bbox = {"x": 0, "y": 0, "width": 0, "height": 0}
|
||||
|
||||
persons.append({"keypoints": keypoints, "bbox": bbox})
|
||||
|
||||
if persons or frame_count % 30 == 0:
|
||||
pose_data["frames"][str(frame_count)] = {
|
||||
"frame_number": frame_count,
|
||||
"time_seconds": round(current_time, 3),
|
||||
"time_formatted": format_time(current_time),
|
||||
"persons": persons,
|
||||
}
|
||||
processed_frames.add(frame_count)
|
||||
|
||||
if frame_count % 500 == 0:
|
||||
elapsed = time.time() - start_time
|
||||
print_progress(frame_count, total_frames, elapsed, f"{len(persons)} persons")
|
||||
framework.publish_progress(frame_count, total_frames, f"frame {frame_count}")
|
||||
|
||||
if framework.should_auto_save(frame_count):
|
||||
framework.save_progress(frame_count, silent=True)
|
||||
|
||||
cap.release()
|
||||
|
||||
total_processed = len(processed_frames)
|
||||
|
||||
framework.finalize(
|
||||
total_processed=total_processed,
|
||||
extra_metadata={"model": "yolov8n-pose"},
|
||||
)
|
||||
|
||||
print(f"\nPose estimation completed: {total_processed} frames processed")
|
||||
print(f"Frames with poses: {len([f for f in pose_data['frames'].values() if f['persons']])}")
|
||||
|
||||
return pose_data
|
||||
result = {"frame_count": len(frames), "fps": fps, "frames": frames}
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
return result
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Pose Estimation with Resume Support")
|
||||
parser.add_argument("video_path", help="Path to video file")
|
||||
parser.add_argument("output_path", help="Output JSON path")
|
||||
parser.add_argument("--uuid", "-u", help="UUID for Redis progress", default="")
|
||||
parser.add_argument(
|
||||
"--auto-save-interval",
|
||||
"-a",
|
||||
help="Auto-save interval in seconds",
|
||||
type=int,
|
||||
default=30,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--auto-save-frames",
|
||||
"-f",
|
||||
help="Auto-save interval in frames",
|
||||
type=int,
|
||||
default=300,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--force-restart",
|
||||
"-r",
|
||||
help="Force restart (ignore existing data)",
|
||||
action="store_true",
|
||||
)
|
||||
parser = argparse.ArgumentParser(description="Pose Processor (Swift Vision)")
|
||||
parser.add_argument("video_path")
|
||||
parser.add_argument("output_path")
|
||||
parser.add_argument("--uuid", "-u", default="")
|
||||
parser.add_argument("--sample-interval", type=int, default=30)
|
||||
args = parser.parse_args()
|
||||
|
||||
process_pose(
|
||||
args.video_path,
|
||||
args.output_path,
|
||||
args.uuid,
|
||||
args.auto_save_interval,
|
||||
args.auto_save_frames,
|
||||
args.force_restart,
|
||||
)
|
||||
result = process_pose(args.video_path, args.output_path, args.uuid, args.sample_interval)
|
||||
with open(args.output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
print(f"Pose: {len(result.get('frames', []))} frames with poses")
|
||||
|
||||
@@ -1 +1 @@
|
||||
7861
|
||||
16433
|
||||
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@@ -534,8 +534,8 @@ relocations:
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@@ -1942,9 +1942,9 @@ relocations:
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@@ -2407,7 +2407,7 @@ relocations:
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@@ -2504,8 +2504,8 @@ relocations:
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@@ -3353,7 +3353,7 @@ relocations:
|
||||
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@@ -3648,12 +3648,12 @@ relocations:
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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@@ -5014,187 +5014,192 @@ relocations:
|
||||
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|
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|
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|
||||
- { offset: 0x1E8E11, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACPxycfCTW', symObjAddr: 0xA970, symBinAddr: 0x1000E7094, symSize: 0x40 }
|
||||
- { offset: 0x1E8E25, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP8containsySb7ElementQzFTW', symObjAddr: 0xA9B0, symBinAddr: 0x1000E70D4, symSize: 0x38 }
|
||||
- { offset: 0x1E8E39, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP5unionyxxnFTW', symObjAddr: 0xA9E8, symBinAddr: 0x1000E710C, symSize: 0x3C }
|
||||
- { offset: 0x1E8E4D, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP12intersectionyxxFTW', symObjAddr: 0xAA24, symBinAddr: 0x1000E7148, symSize: 0x3C }
|
||||
- { offset: 0x1E8E61, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP19symmetricDifferenceyxxnFTW', symObjAddr: 0xAA60, symBinAddr: 0x1000E7184, symSize: 0x3C }
|
||||
- { offset: 0x1E8E80, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP6insertySb8inserted_7ElementQz17memberAfterInserttAHnFTW', symObjAddr: 0xAA9C, symBinAddr: 0x1000E71C0, symSize: 0x4C }
|
||||
- { offset: 0x1E8EAA, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP6removey7ElementQzSgAGFTW', symObjAddr: 0xACCC, symBinAddr: 0x1000E720C, symSize: 0x3C }
|
||||
- { offset: 0x1E8EBE, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP6update4with7ElementQzSgAHn_tFTW', symObjAddr: 0xAD08, symBinAddr: 0x1000E7248, symSize: 0x3C }
|
||||
- { offset: 0x1E8ED2, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP9formUnionyyxnFTW', symObjAddr: 0xAD44, symBinAddr: 0x1000E7284, symSize: 0x40 }
|
||||
- { offset: 0x1E8EE6, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP16formIntersectionyyxFTW', symObjAddr: 0xAD84, symBinAddr: 0x1000E72C4, symSize: 0x40 }
|
||||
- { offset: 0x1E8EFA, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP23formSymmetricDifferenceyyxnFTW', symObjAddr: 0xADC4, symBinAddr: 0x1000E7304, symSize: 0x40 }
|
||||
- { offset: 0x1E8F0E, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP11subtractingyxxFTW', symObjAddr: 0xAE04, symBinAddr: 0x1000E7344, symSize: 0x14 }
|
||||
- { offset: 0x1E8F22, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP8isSubset2ofSbx_tFTW', symObjAddr: 0xAE18, symBinAddr: 0x1000E7358, symSize: 0x18 }
|
||||
- { offset: 0x1E8F36, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP10isDisjoint4withSbx_tFTW', symObjAddr: 0xAE30, symBinAddr: 0x1000E7370, symSize: 0x18 }
|
||||
- { offset: 0x1E8F4A, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP10isSuperset2ofSbx_tFTW', symObjAddr: 0xAE48, symBinAddr: 0x1000E7388, symSize: 0x18 }
|
||||
- { offset: 0x1E8F5E, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP7isEmptySbvgTW', symObjAddr: 0xAE60, symBinAddr: 0x1000E73A0, symSize: 0x18 }
|
||||
- { offset: 0x1E8F72, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACPyxqd__ncSTRd__7ElementQyd__AERtzlufCTW', symObjAddr: 0xAE78, symBinAddr: 0x1000E73B8, symSize: 0x38 }
|
||||
- { offset: 0x1E8F86, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs10SetAlgebraSCsACP8subtractyyxFTW', symObjAddr: 0xAEB0, symBinAddr: 0x1000E73F0, symSize: 0x14 }
|
||||
- { offset: 0x1E8FA5, size: 0x8, addend: 0x0, symName: '_$sSo37SFSpeechRecognizerAuthorizationStatusVSYSCSY8rawValuexSg03RawF0Qz_tcfCTW', symObjAddr: 0xAEC4, symBinAddr: 0x1000E7404, symSize: 0x30 }
|
||||
- { offset: 0x1E8FC4, size: 0x8, addend: 0x0, symName: '_$sSo37SFSpeechRecognizerAuthorizationStatusV8rawValueABSgSi_tcfC', symObjAddr: 0xAEF4, symBinAddr: 0x1000E7434, symSize: 0x24 }
|
||||
- { offset: 0x1E8FF5, size: 0x8, addend: 0x0, symName: '_$sSo37SFSpeechRecognizerAuthorizationStatusVSYSCSY8rawValue03RawF0QzvgTW', symObjAddr: 0xAF18, symBinAddr: 0x1000E7458, symSize: 0x2C }
|
||||
- { offset: 0x1E9009, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs25ExpressibleByArrayLiteralSCsACP05arrayF0x0eF7ElementQzd_tcfCTW', symObjAddr: 0xAF8C, symBinAddr: 0x1000E74CC, symSize: 0x3C }
|
||||
- { offset: 0x1E901D, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVs9OptionSetSCsACP8rawValuex03RawF0Qz_tcfCTW', symObjAddr: 0xAFC8, symBinAddr: 0x1000E7508, symSize: 0x2C }
|
||||
- { offset: 0x1E9031, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVSYSCSY8rawValuexSg03RawD0Qz_tcfCTW', symObjAddr: 0xAFF4, symBinAddr: 0x1000E7534, symSize: 0x30 }
|
||||
- { offset: 0x1E9045, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsVSYSCSY8rawValue03RawD0QzvgTW', symObjAddr: 0xB024, symBinAddr: 0x1000E7564, symSize: 0x2C }
|
||||
- { offset: 0x1E9059, size: 0x8, addend: 0x0, symName: '_$sSo20NSJSONWritingOptionsV8rawValueSuvg', symObjAddr: 0xB494, symBinAddr: 0x1000E7970, symSize: 0x4 }
|
||||
...
|
||||
|
||||
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@@ -26,5 +26,89 @@ let package = Package(
|
||||
path: ".",
|
||||
sources: ["asrx_swift.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "speaker_test",
|
||||
dependencies: [],
|
||||
path: ".",
|
||||
sources: ["speaker_test.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "speaker_meta_test",
|
||||
dependencies: [],
|
||||
path: ".",
|
||||
sources: ["speaker_meta_test.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "face_vision_test",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["face_vision_test.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "vision_object_test",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["vision_object_test.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "swift_cut_test",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["swift_cut_test.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "vision_ocr_test",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["vision_ocr_test.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "swift_ocr",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["swift_ocr.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "face_compare_test",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["face_compare_test.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "pose_benchmark",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["pose_benchmark.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "swift_pose",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["swift_pose.swift"]
|
||||
),
|
||||
.executableTarget(
|
||||
name: "swift_face",
|
||||
dependencies: [
|
||||
.product(name: "ArgumentParser", package: "swift-argument-parser"),
|
||||
],
|
||||
path: ".",
|
||||
sources: ["swift_face.swift"]
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -11,6 +11,69 @@ import ArgumentParser
|
||||
/// - 支援即時與批次辨識
|
||||
/// - 語言支援依作業系統版本
|
||||
|
||||
/// 合併逐字 segments 成句子
|
||||
/// 當相鄰 segment 間隔 < 0.5s 時合併，間隔 >= 0.5s 時視為句子邊界
|
||||
func mergeWordSegments(_ segments: [[String: Any]]) -> [[String: Any]] {
|
||||
let gapThreshold: TimeInterval = 0.5
|
||||
var merged: [[String: Any]] = []
|
||||
var current: [String: Any]? = nil
|
||||
|
||||
for seg in segments {
|
||||
guard let start = seg["start"] as? TimeInterval,
|
||||
let end = seg["end"] as? TimeInterval,
|
||||
let text = seg["text"] as? String,
|
||||
let conf = seg["confidence"] as? Float else {
|
||||
continue
|
||||
}
|
||||
|
||||
if var cur = current {
|
||||
let curEnd = cur["end"] as? TimeInterval ?? 0
|
||||
let gap = start - curEnd
|
||||
|
||||
if gap < gapThreshold {
|
||||
// 合併到當前句子
|
||||
let curText = cur["text"] as? String ?? ""
|
||||
let curConf = cur["confidence"] as? Float ?? 0
|
||||
let wordCount = cur["_wordCount"] as? Int ?? 0
|
||||
cur["text"] = curText + (curText.hasSuffix(" ") ? "" : " ") + text
|
||||
cur["end"] = end
|
||||
// 用 confidence 加權平均（依字數）
|
||||
let totalWords = wordCount + 1
|
||||
cur["confidence"] = (curConf * Float(wordCount) + conf) / Float(totalWords)
|
||||
cur["_wordCount"] = totalWords
|
||||
current = cur
|
||||
} else {
|
||||
// 句子邊界：儲存當前，開始新的
|
||||
cur.removeValue(forKey: "_wordCount")
|
||||
merged.append(cur)
|
||||
current = [
|
||||
"start": start,
|
||||
"end": end,
|
||||
"text": text,
|
||||
"confidence": conf,
|
||||
"_wordCount": 1
|
||||
]
|
||||
}
|
||||
} else {
|
||||
current = [
|
||||
"start": start,
|
||||
"end": end,
|
||||
"text": text,
|
||||
"confidence": conf,
|
||||
"_wordCount": 1
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
if let cur = current {
|
||||
var finalCur = cur
|
||||
finalCur.removeValue(forKey: "_wordCount")
|
||||
merged.append(finalCur)
|
||||
}
|
||||
|
||||
return merged
|
||||
}
|
||||
|
||||
@main
|
||||
struct ASRSwift: ParsableCommand {
|
||||
@Argument(help: "音訊/影片檔案路徑")
|
||||
@@ -137,6 +200,13 @@ struct ASRSwift: ParsableCommand {
|
||||
allSegments.append(seg)
|
||||
}
|
||||
|
||||
// 合併逐字 segments 成句子
|
||||
if !allSegments.isEmpty {
|
||||
let beforeCount = allSegments.count
|
||||
allSegments = mergeWordSegments(allSegments)
|
||||
print("[ASR_Swift] Merged segments: \(beforeCount) → \(allSegments.count)")
|
||||
}
|
||||
|
||||
// 若無 segment，用整個文字建立一個
|
||||
if allSegments.isEmpty {
|
||||
let fullText = result.bestTranscription.formattedString
|
||||
@@ -155,7 +225,10 @@ struct ASRSwift: ParsableCommand {
|
||||
}
|
||||
|
||||
// RunLoop 是必要的 — Speech Framework 需要 main runloop 觸發 callback
|
||||
RunLoop.current.run(until: Date(timeIntervalSinceNow: 120))
|
||||
// 使用 semaphore 等待完成，避免固定 timeout
|
||||
while semaphore.wait(timeout: .now()) == .timedOut {
|
||||
RunLoop.current.run(mode: .default, before: Date(timeIntervalSinceNow: 0.1))
|
||||
}
|
||||
task.cancel()
|
||||
|
||||
// 3. 輸出 JSON
|
||||
|
||||
@@ -268,8 +268,21 @@ def process_yolo(
|
||||
if publisher:
|
||||
publisher.info("yolo", "YOLO_LOADING_MODEL")
|
||||
|
||||
# Load YOLOv8 model
|
||||
model = YOLO("yolov8n.pt")
|
||||
# Load YOLO model (prefer CoreML for ANE acceleration, fallback to PyTorch)
|
||||
model_path_mlpackage = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)), "..", "yolov5nu.mlpackage"
|
||||
)
|
||||
model_path_pt = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)), "..", "yolov5nu.pt"
|
||||
)
|
||||
if os.path.exists(model_path_mlpackage):
|
||||
model = YOLO(model_path_mlpackage)
|
||||
print("YOLO: CoreML model loaded (4.5x ANE acceleration)")
|
||||
elif os.path.exists(model_path_pt):
|
||||
model = YOLO(model_path_pt)
|
||||
print("YOLO: PyTorch model loaded")
|
||||
else:
|
||||
model = YOLO("yolov5nu.pt") # will auto-download
|
||||
|
||||
# Get video info
|
||||
import cv2
|
||||
|
||||
Reference in New Issue
Block a user