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5fc297d 8c64eae 5fc297d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import gradio as gr
import tensorflow as tf
import numpy as np
import cv2
import os
# Load model
model = tf.keras.models.load_model('best_model.keras')
# Video processor class
class SingleVideoProcessor:
def __init__(self, video_path, max_frames=50, frame_size=(128, 128), frame_interval=3):
self.video_path = video_path
self.max_frames = max_frames
self.frame_size = frame_size
self.frame_interval = frame_interval
def process(self):
frames = self.load_video_frames()
frames = self.resize_frames(frames)
frames = self.pad_video_frames(frames)
return np.array(frames, dtype=np.float32)
def load_video_frames(self):
frames = []
cap = cv2.VideoCapture(self.video_path)
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_count += 1
if frame_count % self.frame_interval == 0:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
cap.release()
return frames
def resize_frames(self, frames):
return [cv2.resize(frame, self.frame_size) for frame in frames]
def pad_video_frames(self, frames):
if len(frames) < self.max_frames:
padding = self.max_frames - len(frames)
frames.extend([np.zeros_like(frames[0])] * padding)
else:
frames = frames[:self.max_frames]
return frames
# Prediction function
def predict_violence(video_path):
processor = SingleVideoProcessor(video_path)
processed_video = processor.process()
processed_video = np.expand_dims(processed_video, axis=0)
prediction = model.predict(processed_video)[0][0]
label = "Violence" if prediction >= 0.5 else "Non-Violence"
confidence = f"{prediction:.4f}"
print(prediction)
return f"{label} (Confidence: {confidence})"
# Example videos (ensure these exist in your working directory)
examples = [
["NV_1.mp4"],
["V_1000.mp4"],
["V_102.mp4"],
["NV_11.mp4"]
]
# Launch Gradio Interface
demo = gr.Interface(
fn=predict_violence,
inputs=gr.Video(label="Upload or Select a Video"),
outputs=gr.Text(label="Prediction"),
examples=examples,
title="Violence Detection in Video",
description="This model classifies videos as Violence or Non-Violence. Upload a short clip or select from examples."
)
demo.launch()
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