Update app.py

app.py

@@ -84,31 +84,6 @@ def crop_center(frame):
     cropped = keras.ops.squeeze(cropped)
     return cropped
 
-
-# Following method is modified from this tutorial:
-# https://www.tensorflow.org/hub/tutorials/action_recognition_with_tf_hub
-def load_video(path, max_frames=0, offload_to_cpu=False):
-    cap = cv2.VideoCapture(path)
-    frames = []
-    try:
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            frame = frame[:, :, [2, 1, 0]]
-            frame = crop_center(frame)
-            if offload_to_cpu and keras.backend.backend() == "torch":
-                frame = frame.to("cpu")
-            frames.append(frame)
-
-            if len(frames) == max_frames:
-                break
-    finally:
-        cap.release()
-    if offload_to_cpu and keras.backend.backend() == "torch":
-        return np.array([frame.to("cpu").numpy() for frame in frames])
-    return np.array(frames)
-
 def build_feature_extractor():
     feature_extractor = DenseNet121(
         weights="imagenet",
@@ -188,7 +163,29 @@ with open(labels_file_path, "r") as file:
 #print("Tableau recréé à partir du fichier :")
 #print(class_labels)
 
+#read video
+def load_video(path, max_frames=0, offload_to_cpu=False):
+    cap = cv2.VideoCapture(path)
+    frames = []
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frame = frame[:, :, [2, 1, 0]]
+            frame = crop_center(frame)
+            if offload_to_cpu and keras.backend.backend() == "torch":
+                frame = frame.to("cpu")
+            frames.append(frame)
 
+            if len(frames) == max_frames:
+                break
+    finally:
+        cap.release()
+    if offload_to_cpu and keras.backend.backend() == "torch":
+        return np.array([frame.to("cpu").numpy() for frame in frames])
+    return np.array(frames)
+    
 # test on video from val dataset
 def prepare_single_video(frames):
     frame_features = np.zeros(shape=(1, MAX_SEQ_LENGTH, NUM_FEATURES), dtype="float32")