feat : mosaic_frame 구현

frame을 3개 단위로 짤라 매칭, frame에서 인식한 얼굴 개수를 이용하여 다음 frame에 재사용

mosaic_frame.py

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+import cv2
+import os
+from deepface import DeepFace
+import torch
+
+
+def mosaic(video_path, image_paths):
+    model_name = "Facenet"
+
+    output_video_path = os.path.join('tmp', 'output2.mp4')
+    cap = cv2.VideoCapture(video_path)
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    out = cv2.VideoWriter(output_video_path, cv2.VideoWriter.fourcc(*'mp4v'), fps, (frame_width, frame_height))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # 동영상의 전체 프레임 수를 얻음
+
+    print(f"Total number of frames in the video: {total_frames}")
+
+    threshold = 0.458
+    not_threshold = 0.47
+
+    faces_dir = os.path.join('tmp', 'faces')
+    if not os.path.exists(faces_dir):
+        os.makedirs(faces_dir)
+
+    face_count = 0
+    current_frame_count = 0
+
+    model = torch.hub.load('ultralytics/yolov5', 'custom', path='macWideface.pt', force_reload=True)
+
+    embedding_list = []
+    for image_path in image_paths:
+        embedding_result = DeepFace.create_verification_result(
+            img1_path=image_path,
+            detector_backend='retinaface',
+            model_name=model_name,
+            enforce_detection=False
+        )
+        embedding_list.append(embedding_result["embeddings"][0])
+
+    before_num_faces = 0
+    before_match_face = []
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        detections = model(frame)
+
+        if before_num_faces == len(detections.xyxy[0]) and current_frame_count % 3 != 0 and before_match_face:
+            print(f"{current_frame_count}감지 중")
+            for index, face_id in enumerate(detections.xyxy[0]):
+                if index in before_match_face:
+                    break
+                else:
+                    x1, y1, x2, y2 = face_id[:4].int().tolist()
+                    if y2 - y1 > 50 and x2 - x1 > 50:
+                        face_image = frame[y1:y2, x1:x2]
+                        face = cv2.resize(face_image, (10, 10))
+                        face = cv2.resize(face, (x2 - x1, y2 - y1), interpolation=cv2.INTER_AREA)
+                        frame[y1:y2, x1:x2] = face
+                        face_count += 1
+            current_frame_count += 1
+            out.write(frame)
+        else:
+            before_match_face.clear()
+            before_num_faces = len(detections.xyxy[0])
+            print(f"{current_frame_count}다시 감지 중")
+            for index, face_id in enumerate(detections.xyxy[0]):
+                x1, y1, x2, y2 = face_id[:4].int().tolist()
+                if y2 - y1 > 50 and x2 - x1 > 50:
+                    face_image = frame[y1:y2, x1:x2]
+                    for ref_face in embedding_list:
+                        result = DeepFace.verify(img1_path=face_image, img2_path=ref_face, model_name=model_name,
+                                                 detector_backend='retinaface', enforce_detection=False)
+                        distance = result['distance']
+
+                        if not_threshold >= distance >= threshold:
+                            face_filename = f"face_{face_count}.jpg"
+                            verified_str = 'Different'
+                            distance_str = '(%.4f <= %.4f)' % (distance, threshold)
+                            print(face_filename, verified_str, distance_str)
+                            face = cv2.resize(face_image, (10, 10))
+                            face = cv2.resize(face, (x2 - x1, y2 - y1), interpolation=cv2.INTER_AREA)
+                            frame[y1:y2, x1:x2] = face
+                            face_filepath = os.path.join(faces_dir, face_filename)
+                            cv2.imwrite(face_filepath, face_image)
+
+                        if distance < threshold:
+                            before_match_face.append(index)
+                            break
+
+                        if distance > not_threshold:
+                            face = cv2.resize(face_image, (10, 10))
+                            face = cv2.resize(face, (x2 - x1, y2 - y1), interpolation=cv2.INTER_AREA)
+                            frame[y1:y2, x1:x2] = face
+                            break
+
+                    face_count += 1
+            current_frame_count += 1
+            out.write(frame)
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+    return output_video_path