Update ProcessVideo.py

ProcessVideo.py

@@ -1,57 +1,71 @@
-import cv2
-from Prediction import predict_fight            # Assuming you have this
-
-# Process video: read, predict, and output labeled video
-def process_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    sequence_length = 40  # Number of frames for one prediction
-    all_frames = []
-    predictions = []
-    
-    # Step 1: Read all frames from the video
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        all_frames.append(frame)
-    cap.release()
-
-    # Step 2: Process frames in chunks of 40 to make predictions
-    for i in range(0, len(all_frames), sequence_length):
-        frames_buffer = all_frames[i:i + sequence_length]  # Get a batch of 40 frames
-
-        # If the number of frames is less than 40 at the end, pad it with the last frame
-        if len(frames_buffer) < sequence_length:
-            frames_buffer += [frames_buffer[-1]] * (sequence_length - len(frames_buffer))
-
-        # Perform the prediction on the current batch of frames
-        fight_detected = predict_fight(frames_buffer)
-
-        # Store the prediction for this batch
-        predictions.append(fight_detected)
-
-    # Step 3: Create output video with labels
-    output_video_path = "output_labeled.mp4"
-    height, width, _ = all_frames[0].shape
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(output_video_path, fourcc, 30, (width, height))  # Adjust frame rate if needed
-
-    frame_idx = 0
-    for pred in predictions:
-        label = "Violence Detected!" if pred else "No Violence"
-        color = (0, 0, 255) if pred else (0, 255, 0)
-
-        # For the next 40 frames, show the same label
-        for _ in range(sequence_length):
-            if frame_idx >= len(all_frames):
-                break  
-
-            frame = all_frames[frame_idx]
-            cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
-            out.write(frame)
-            frame_idx += 1
-
-    out.release()
-
-    return output_video_path
-
+import cv2
+from Prediction import predict_fight            # Assuming you have this
+
+# Process video: read, predict, and output labeled video
+def process_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    sequence_length = 40  # Number of frames for one prediction
+    all_frames = []
+    predictions = []
+    
+    # Step 1: Read all frames from the video
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        all_frames.append(frame)
+    cap.release()
+
+    # Step 2: Process frames in chunks of 40 to make predictions
+    for i in range(0, len(all_frames), sequence_length):
+        frames_buffer = all_frames[i:i + sequence_length]  # Get a batch of 40 frames
+
+        # If the number of frames is less than 40 at the end, pad it with the last frame
+        if len(frames_buffer) < sequence_length:
+            frames_buffer += [frames_buffer[-1]] * (sequence_length - len(frames_buffer))
+
+        # Perform the prediction on the current batch of frames
+        fight_detected = predict_fight(frames_buffer)
+
+        # Store the prediction for this batch
+        predictions.append(fight_detected)
+
+    # Step 3: Create output video with labels
+    output_video_path = "output_labeled.mp4"
+    height, width, _ = all_frames[0].shape
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_video_path, fourcc, 30, (width, height))  # Adjust frame rate if needed
+
+    frame_idx = 0
+    for pred in predictions:
+        label = "Violence Detected!" if pred else "No Violence"
+        color = (0, 0, 255) if pred else (0, 255, 0)
+
+        # For the next 40 frames, show the same label
+        for _ in range(sequence_length):
+            if frame_idx >= len(all_frames):
+                break  
+
+            frame = all_frames[frame_idx]
+            
+            # Increase the font size
+            font_scale = 1.5  # You can adjust this for size
+            thickness = 3     # You can adjust this for thickness
+
+            # Get the text size for centering
+            text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)[0]
+        
+            # Calculate the position to center the text
+             text_x = (width - text_size[0]) // 2
+            text_y = (height + text_size[1]) // 2  # Adjust for vertical centering
+
+            # Add label to the frame in the center
+            cv2.putText(frame, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, thickness)
+
+            out.write(frame)
+            frame_idx += 1
+
+    out.release()
+
+    return output_video_path
+