fix : change threshold

.gitignore

@@ -3,3 +3,4 @@ wider face.v3i.yolov5pytorch
 dist
 build
 myenv
+__pycache__

app.py

@@ -42,7 +42,7 @@ def handle_video():
         image_file = request.files[f'image{i}']
         if image_file:
             filename, extension = os.path.splitext(image_file.filename)
-            image_filename = os.path.join('save/train/Gongyoo', f'image{i}{extension}')
+            image_filename = os.path.join('save/train', f'image{i}{extension}')
             image_file.save(image_filename)
             image_paths.append(image_filename)
             print(f'Image {i} saved successfully.')

createTarget.py

@@ -26,6 +26,7 @@ def extract_and_identify_faces_from_video(video_path):
             # 얼굴 이미지 추출
             face_image = frame[top:bottom, left:right]
             face_images.append(face_image)
+
             face_encodings.append(encoding)
 
     # 인식된 얼굴 분류
@@ -38,13 +39,16 @@ def extract_and_identify_faces_from_video(video_path):
                 group_encodings = [enc for _, enc in face_group]
                 avg_encoding = np.mean(group_encodings, axis=0)
                 dist = np.linalg.norm(avg_encoding - encoding)
-                if dist < 0.6:  # 같은 사람으로 판단하는 임계값
+                if dist < 0.555:  # 같은 사람으로 판단하는 임계값
                     face_group.append((face_images[idx], encoding))
                     matched = True
                     break
             if not matched:
                 identified_faces.append([(face_images[idx], encoding)])
 
+        # 얼굴 수가 많은 순서로 그룹 정렬
+        identified_faces.sort(key=lambda x: len(x), reverse=True)
+
     video_capture.release()
     print('end1')
 
@@ -77,4 +81,4 @@ def save_faces(identified_faces):
         # 모든 인물의 인코딩된 이미지를 배열에 추가
         face_base64_arrays.append(encoded_faces)
 
-    return face_base64_arrays
+    return face_base64_arrays

mosaic.py

@@ -17,9 +17,6 @@ def mosaic(video_path, image_paths):
 
     print(f"Total number of frames in the video: {total_frames}")
 
-    threshold = 0.465
-    not_threshold = 0.47
-
     faces_dir = os.path.join('tmp', 'faces')
     if not os.path.exists(faces_dir):
         os.makedirs(faces_dir)
@@ -49,6 +46,13 @@ def mosaic(video_path, image_paths):
 
         print(f"{current_frame_count}감지 시작")
 
+        if len(detections.xyxy[0]) > 2:
+            threshold = 0.27
+            not_threshold = 0.2851
+        else:
+            threshold = 0.3
+            not_threshold = 0.47
+
         for face_id in detections.xyxy[0]:
             x1, y1, x2, y2 = face_id[:4].int().tolist()
             if y2 - y1 > 50 and x2 - x1 > 50:
@@ -59,17 +63,24 @@ def mosaic(video_path, image_paths):
                     distance = result['distance']
 
                     if not_threshold >= distance >= threshold:
-                        face_filename = f"face_{face_count}.jpg"
+                        face_filename = f"D/face_{face_count}.jpg"
                         verified_str = 'Different'
-                        distance_str = '(%.4f <= %.4f)' % (distance, threshold)
+                        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)
+                        break
 
                     if distance < threshold:
+                        face_filename = f"S/face_{face_count}.jpg"
+                        verified_str = 'Same'
+                        distance_str = '(%.4f >= %.4f)' % (distance, threshold)
+                        print(face_filename, verified_str, distance_str)
+                        face_filepath = os.path.join(faces_dir, face_filename)
+                        cv2.imwrite(face_filepath, face_image)
                         break
 
                     if distance > not_threshold: