Update ProcessVideo.py

ProcessVideo.py

@@ -1,57 +1,77 @@
 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 numpy as np
+import time
+from FeatureExtraction import FeatureExtractor
+from Prediction import predict_fight
 
+def process_video(video_path, sequence_length=40, threshold=0.8, output_frame_rate=30, debug=False):
+    try:
+        start_time = time.time()
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            raise ValueError("Could not open video file")
+        
+        all_frames = []
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            all_frames.append(frame)
+        cap.release()
+        total_frames = len(all_frames)
+        
+        feature_extractor = FeatureExtractor(img_shape=(224, 224), channels=3, seq_length=sequence_length)
+        
+        predictions_list = []
+        predictions = []
+        for i in range(0, total_frames, sequence_length):
+            frames_buffer = all_frames[i:i + sequence_length]
+            if len(frames_buffer) < sequence_length:
+                frames_buffer += [frames_buffer[-1]] * (sequence_length - len(frames_buffer))
+            
+            fight_detected, fight_prob = predict_fight(frames_buffer, threshold, feature_extractor)
+            predictions.append(fight_detected)
+            predictions_list.append({
+                'chunk_start_frame': i,
+                'chunk_end_frame': i + sequence_length - 1,
+                'fight_probability': float(fight_prob),
+                'fight_detected': bool(fight_detected)
+            })
+        
+        output_video_path = "output_labeled.mp4"
+        height, width, _ = all_frames[0].shape
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_video_path, fourcc, output_frame_rate, (width, height))
+        
+        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 _ in range(sequence_length):
+                if frame_idx >= total_frames:
+                    break
+                frame = all_frames[frame_idx].copy()
+                cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
+                out.write(frame)
+                frame_idx += 1
+        out.release()
+        
+        processing_time = time.time() - start_time
+        json_response = {
+            'output_video_path': output_video_path,
+            'total_frames': total_frames,
+            'sequence_length': sequence_length,
+            'threshold': threshold,
+            'output_frame_rate': output_frame_rate,
+            'processing_time_seconds': processing_time,
+            'predictions': predictions_list,
+            'error': None
+        }
+        return output_video_path, json_response
+    except Exception as e:
+        error_message = f"Error processing video: {str(e)}"
+        json_response = {
+            'output_video_path': None,
+            'error': error_message
+        }
+        return None, json_response