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import torch
import gradio as gr
import torch.nn as nn
import torchvision
import cv2
import numpy as np
import tempfile
class MyModel(nn.Module):
def __init__(self, num_classes=1):
super(MyModel, self).__init__() # Initialize nn.Module
self.model = torchvision.models.video.r3d_18(pretrained=True)
self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)
def preprocess_video(self, video_path, num_frames=40):
"""Preprocess video: sample frames, resize, normalize, and return tensor."""
cap = cv2.VideoCapture(video_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = np.linspace(0, total_frames - 1, num=num_frames, dtype=int)
sampled_frames = []
for idx in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if not ret:
continue
frame = cv2.resize(frame, (112, 112)) # Resize to 112x112 for R3D-18
frame = np.transpose(frame, (2, 0, 1)) # Channels-first
sampled_frames.append(frame)
cap.release()
if len(sampled_frames) < num_frames:
padding = np.zeros((num_frames - len(sampled_frames), 3, 112, 112))
sampled_frames = np.concatenate([sampled_frames, padding], axis=0)
# Convert to tensor and rearrange dimensions to (3, num_frames, 112, 112)
return torch.tensor(sampled_frames).float().permute(1, 0, 2, 3).unsqueeze(0)
def forward(self, x):
return self.model(x)
def predict(self, video_path):
"""Test the model on the given videos and compute accuracy."""
self.model.eval()
predictions = []
with torch.no_grad():
X = self.preprocess_video(video_path)
output = self.model(X)
pred = torch.sigmoid(output) # Apply sigmoid for binary classification
# Track predictions
predictions.append(pred.item())
return predictions
def save_model(self, filepath):
torch.save({
'model_state_dict': self.state_dict(),
}, filepath)
@staticmethod
def load_model(filepath, num_classes=1):
model = MyModel(num_classes)
checkpoint = torch.load(filepath, weights_only=True)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
return model
model = MyModel().load_model('pre_3D_model.h5')
def classify_video(video):
prob = model.predict(video)
label = "Non-violent" if prob[0] >= 0.5 else "Violent"
violent_prob_percentage = f"{round((1 - prob[0]) * 100, 2)}% chance of being violent"
return label, violent_prob_percentage
# Set up the Gradio interface
interface = gr.Interface(
fn=classify_video,
inputs=gr.Video(), # Allows video upload
outputs=[
gr.Text(label="Classification"), # Label for classification output
gr.Text(label="Violence Probability") # Label for probability output with text
],
title="Violence Detection in Videos",
description="Upload a video to classify it as violent or non-violent with a probability score."
)
interface.launch(share=True, debug=True)
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