パフォーマンスの修正・Blurサイズ問題の修正
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@ -49,13 +49,12 @@ def _head_bbox_from_pose(
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cx = (kp_x1 + kp_x2) / 2.0
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cy = (kp_y1 + kp_y2) / 2.0
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# Head radius: inter-landmark span ≈ 80% of head width, so expand by ~1.25
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# Shift center upward slightly to include scalp
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r = max(span * 1.25, person_w * 0.20)
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# span はキーポイントの外接幅（≒顔幅）なので、半径 = span/2 で顔と等倍になる
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r = max(span * 0.5, person_w * 0.10)
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x1 = int(cx - r)
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y1 = int(cy - r * 1.15) # extra margin above (scalp)
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y1 = int(cy - r)
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x2 = int(cx + r)
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y2 = int(cy + r * 0.85) # less margin below (chin)
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y2 = int(cy + r)
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return x1, y1, x2 - x1, y2 - y1
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# --- Step 2: shoulder keypoints ---
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@ -71,8 +70,9 @@ def _head_bbox_from_pose(
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sh_width = abs(visible_shoulder[1][0] - visible_shoulder[0][0])
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else:
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sh_width = person_w * 0.5
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r = max(sh_width * 0.5, person_w * 0.20)
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cy = cy_sh - r * 1.3 # head center is above shoulders
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# 肩幅は顔幅の約1.5〜2倍なので、0.3倍で顔サイズに近い半径になる
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r = max(sh_width * 0.3, person_w * 0.12)
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cy = cy_sh - r * 1.3 # 頭の中心は肩より上
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x1 = int(cx - r)
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y1 = int(cy - r)
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x2 = int(cx + r)
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@ -80,7 +80,8 @@ def _head_bbox_from_pose(
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return x1, y1, x2 - x1, y2 - y1
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# --- Step 3: person bbox top ---
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r = max(person_w * 0.35, 20.0)
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# 顔幅は人物幅の約20〜30%なので、半径 = person_w * 0.15 で顔サイズに近い
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r = max(person_w * 0.15, 20.0)
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cx = (person_x1 + person_x2) / 2.0
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x1 = int(cx - r)
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y1 = int(person_y1)
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@ -446,36 +446,59 @@ def process_bake_task(task_id: str, req: BakeRequest):
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def _reader_worker():
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"""Read frames from video."""
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import time as _time
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cap = cv2.VideoCapture(req.video_path)
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if not cap.isOpened():
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error_holder["error"] = "Failed to open video in reader"
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return
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t_read_total = 0.0
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frame_count = 0
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try:
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for idx in range(total):
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if cancel_event and cancel_event.is_set():
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break
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t0 = _time.perf_counter()
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ok, frame = cap.read()
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t_read_total += _time.perf_counter() - t0
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if not ok:
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break
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read_queue.put((idx, frame))
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frame_count += 1
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except Exception as e:
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error_holder["error"] = f"Reader error: {e}"
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finally:
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cap.release()
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read_queue.put(None) # Sentinel
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if frame_count > 0:
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print(
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f"[Perf/Reader] FINAL frame={frame_count}"
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f" read_avg={t_read_total/frame_count*1000:.1f}ms"
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f" throughput≈{frame_count/max(t_read_total,1e-9):.1f}fps"
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)
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def _processor_worker():
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"""Process frames with ROI blur."""
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import time as _time
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t_wait_total = 0.0
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t_blur_total = 0.0
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t_blend_total = 0.0
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frame_count = 0
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REPORT_INTERVAL = 50
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try:
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while True:
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if cancel_event and cancel_event.is_set():
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process_queue.put(None)
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break
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t0 = _time.perf_counter()
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item = read_queue.get()
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t_wait_total += _time.perf_counter() - t0
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if item is None:
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process_queue.put(None)
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break
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@ -485,6 +508,7 @@ def process_bake_task(task_id: str, req: BakeRequest):
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if not frame_boxes:
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process_queue.put((idx, frame))
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frame_count += 1
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continue
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# 各人物ごとに個別ROIで処理（全員まとめると離れた人物間が巨大ROIになるため）
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@ -499,6 +523,7 @@ def process_bake_task(task_id: str, req: BakeRequest):
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if not valid_boxes:
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process_queue.put((idx, frame))
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frame_count += 1
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continue
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for x, y, w, h in valid_boxes:
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@ -523,7 +548,16 @@ def process_bake_task(task_id: str, req: BakeRequest):
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# ブラーはROI全体で計算（余白があるので端の精度が保証される）
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roi_src = frame[roi_y1:roi_y2, roi_x1:roi_x2]
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roi_blurred = cv2.GaussianBlur(roi_src, (blur_size, blur_size), 0)
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# ダウンサンプル→blur→アップサンプル（同等のぼかしを1/4の計算量で実現）
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t1 = _time.perf_counter()
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small_w = max(1, roi_width // 2)
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small_h = max(1, roi_height // 2)
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roi_small = cv2.resize(roi_src, (small_w, small_h), interpolation=cv2.INTER_LINEAR)
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small_blur_size = max(3, (blur_size // 2) | 1)
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roi_small_blurred = cv2.GaussianBlur(roi_small, (small_blur_size, small_blur_size), 0)
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roi_blurred = cv2.resize(roi_small_blurred, (roi_width, roi_height), interpolation=cv2.INTER_LINEAR)
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t_blur_total += _time.perf_counter() - t1
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# 合成マスクはdisplay_scaleサイズの楕円のみ（featheringなし）
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roi_mask = np.zeros((roi_height, roi_width), dtype=np.uint8)
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@ -531,18 +565,47 @@ def process_bake_task(task_id: str, req: BakeRequest):
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axes = (max(1, dw // 2), max(1, dh // 2))
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cv2.ellipse(roi_mask, center, axes, 0, 0, 360, 255, -1)
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roi_alpha = (roi_mask.astype(np.float32) / 255.0)[..., np.newaxis]
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roi_composed = roi_src.astype(np.float32) * (1.0 - roi_alpha) + roi_blurred.astype(np.float32) * roi_alpha
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frame[roi_y1:roi_y2, roi_x1:roi_x2] = np.clip(roi_composed, 0, 255).astype(np.uint8)
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# バイナリマスクなのでcopyToで高速合成（float32変換不要）
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t2 = _time.perf_counter()
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result = roi_src.copy()
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cv2.copyTo(roi_blurred, roi_mask, result)
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frame[roi_y1:roi_y2, roi_x1:roi_x2] = result
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t_blend_total += _time.perf_counter() - t2
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process_queue.put((idx, frame))
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frame_count += 1
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if frame_count % REPORT_INTERVAL == 0:
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n = max(frame_count, 1)
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fps_proc = frame_count / max(t_wait_total + t_blur_total + t_blend_total, 1e-9)
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print(
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f"[Perf/Processor] frame={frame_count}"
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f" wait={t_wait_total/n*1000:.1f}ms"
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f" blur={t_blur_total/n*1000:.1f}ms"
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f" blend={t_blend_total/n*1000:.1f}ms"
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f" ROI={roi_width}x{roi_height}"
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f" throughput≈{fps_proc:.1f}fps"
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)
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except Exception as e:
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error_holder["error"] = f"Processor error: {e}"
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process_queue.put(None)
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finally:
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if frame_count > 0:
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n = max(frame_count, 1)
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print(
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f"[Perf/Processor] FINAL frame={frame_count}"
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f" wait_avg={t_wait_total/n*1000:.1f}ms"
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f" blur_avg={t_blur_total/n*1000:.1f}ms"
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f" blend_avg={t_blend_total/n*1000:.1f}ms"
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)
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def _writer_worker():
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"""Write frames to output."""
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import time as _time
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t_wait_total = 0.0
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t_write_total = 0.0
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frame_count = 0
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writer = None
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try:
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writer = _build_video_writer(req.output_path, req.format, src_fps, src_width, src_height)
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@ -551,12 +614,18 @@ def process_bake_task(task_id: str, req: BakeRequest):
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if cancel_event and cancel_event.is_set():
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break
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t0 = _time.perf_counter()
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item = process_queue.get()
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t_wait_total += _time.perf_counter() - t0
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if item is None:
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break
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idx, frame = item
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t1 = _time.perf_counter()
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writer.write(frame)
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t_write_total += _time.perf_counter() - t1
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frame_count += 1
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with progress_lock:
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current_progress[0] = idx + 1
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@ -570,6 +639,13 @@ def process_bake_task(task_id: str, req: BakeRequest):
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writer.release()
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except Exception as e:
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print(f"[FaceMask] Writer release error: {e}")
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if frame_count > 0:
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n = max(frame_count, 1)
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print(
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f"[Perf/Writer] FINAL frame={frame_count}"
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f" wait_avg={t_wait_total/n*1000:.1f}ms"
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f" write_avg={t_write_total/n*1000:.1f}ms"
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)
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print(
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f"[FaceMask] Starting blur bake: {req.video_path} + "
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