Update model_3.py

model_3.py

@@ -20,4 +20,53 @@ else:
 PATH_TO_CKPT = os.path.join(MODEL_DIR, GRAPH_NAME)
 PATH_TO_LABELS = os.path.join(MODEL_DIR, LABELMAP_NAME)
 
-with open(PATH_TO_LABELS, '
+with open(PATH_TO_LABELS, 'r') as f:
+    labels = [line.strip() for line in f.readlines()]
+
+if labels[0] == '???':
+    del(labels[0])
+
+interpreter = Interpreter(model_path=PATH_TO_CKPT)
+interpreter.allocate_tensors()
+
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+height = input_details[0]['shape'][1]
+width = input_details[0]['shape'][2]
+floating_model = (input_details[0]['dtype'] == np.float32)
+
+def detect_image(input_image):
+    image = np.array(input_image)
+    resized_image = cv2.resize(image, (640, 640))
+    result_image = perform_detection(resized_image, interpreter, labels, input_details, output_details, height, width, floating_model)
+    return Image.fromarray(result_image)
+
+def detect_video(input_video):
+    cap = cv2.VideoCapture(input_video)
+    frames = []
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        resized_frame = cv2.resize(frame, (640, 640))
+        result_frame = perform_detection(resized_frame, interpreter, labels, input_details, output_details, height, width, floating_model)
+        frames.append(result_frame)
+
+    cap.release()
+
+    if not frames:
+        raise ValueError("No frames were read from the video.")
+
+    height, width, layers = frames[0].shape
+    size = (width, height)
+    output_video_path = "result_" + os.path.basename(input_video)
+    out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 15, size)
+
+    for frame in frames:
+        out.write(frame)
+
+    out.release()
+
+    return output_video_path