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ProcessVideo.py

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+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
+

deployement.py

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+
+import cv2
+import numpy as np
+import gradio as gr
+from ProcessVideo import process_video
+
+# Gradio function to handle the video upload and processing
+def gradio_video_processing(video):
+    video_path = video.name  # Gradio provides the uploaded video file path
+    output_video_path = process_video(video_path)  # Process the video and get the output path
+    return output_video_path
+
+# Gradio Interface
+video_input = gr.inputs.Video(label="Upload a Video")
+video_output = gr.outputs.Video(label="Processed Video with Labels")
+
+# Create and launch Gradio interface
+gr.Interface(fn=gradio_video_processing, inputs=video_input, outputs=video_output, title="Fight Detection in Video").launch()