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best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cd16663a6f7564fa5eb3f0dec7f038c4365a74aa50dfd6428a87e9c666bd031f
+size 56066741

model.py

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+import torch
+import torch.nn as nn
+
+class ViolenceConv3D(nn.Module):
+    def __init__(self):
+        super(ViolenceConv3D, self).__init__()
+        
+        # 4-Layer Conv3D Architecture
+        # Input: (Batch, 3, 16, 112, 112)
+        
+        self.conv1 = nn.Conv3d(3, 32, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+        self.bn1 = nn.BatchNorm3d(32)
+        self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
+        
+        self.conv2 = nn.Conv3d(32, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+        self.bn2 = nn.BatchNorm3d(64)
+        self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+        
+        self.conv3 = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+        self.bn3 = nn.BatchNorm3d(128)
+        self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+        
+        self.conv4 = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+        self.bn4 = nn.BatchNorm3d(256)
+        self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+        
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.5)
+        
+        # Calculate Flatten Size dynamically based on architecture logic
+        # P1: 16 x 56 x 56
+        # P2: 8 x 28 x 28
+        # P3: 4 x 14 x 14
+        # P4: 2 x 7 x 7
+        self.flatten_dim = 256 * 2 * 7 * 7
+        
+        self.fc1 = nn.Linear(self.flatten_dim, 512)
+        self.fc2 = nn.Linear(512, 2) # Binary Classification (Violence vs No-Violence)
+        
+    def forward(self, x):
+        x = self.relu(self.bn1(self.conv1(x)))
+        x = self.pool1(x)
+        
+        x = self.relu(self.bn2(self.conv2(x)))
+        x = self.pool2(x)
+        
+        x = self.relu(self.bn3(self.conv3(x)))
+        x = self.pool3(x)
+        
+        x = self.relu(self.bn4(self.conv4(x)))
+        x = self.pool4(x)
+        
+        x = x.view(x.size(0), -1)
+        x = self.dropout(self.relu(self.fc1(x)))
+        x = self.fc2(x)
+        return x

readme.md

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+# Violence Detection using Conv3D
+
+## Model Architecture
+- **Type**: 3D Convolutional Neural Network (Conv3D)
+- **Input**: Video sequence of 16 frames, resized to 112x112.
+- **Structure**:
+  - 4 Conv3D Layers with BatchNorm, ReLU, and MaxPooling.
+  - Flatten Layer.
+  - 2 Fully Connected Layers.
+  - Dropout (0.5) for regularization.
+- **Output**: Binary Classification (Violence vs No-Violence).
+
+## Dataset Structure
+The code expects a `Dataset` folder in the parent directory (or modify `DATASET_DIR` in `train.py`).
+Structure:
+```
+Dataset/
+├── violence/
+│   ├── video1.mp4
+│   └── ...
+└── no-violence/
+    ├── video2.mp4
+    └── ...
+```
+
+## How to Run
+1. Install dependencies: `torch`, `opencv-python`, `scikit-learn`, `numpy`.
+2. Run `python train.py`.

train.py

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+import os
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
+import time
+from model import ViolenceConv3D
+
+# --- Configuration ---
+# Adjusted for standalone folder usage
+# We point to the parent directory's dataset to avoid copying errors
+# In a real GitHub repo, users should place 'Dataset' in the root or update this path.
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Points to parent of Conv3D_Model
+DATASET_DIR = os.path.join(BASE_DIR, "Dataset")
+MODEL_SAVE_PATH = "best_model.pth"
+
+# Hyperparameters
+IMG_SIZE = 112
+SEQ_LEN = 16
+BATCH_SIZE = 50       
+EPOCHS = 80
+LEARNING_RATE = 1e-4
+PATIENCE = 5         # Early stopping patience
+
+# --- 1. Data Augmentation ---
+def augment_video_frames(frames):
+    """
+    Apply augmentation to a sequence of frames.
+    All frames in the sequence must receive the same transformation parameters.
+    """
+    augmented_frames = []
+    
+    # Decisions for augmentation
+    do_flip = np.random.random() > 0.5
+    do_rotate = np.random.random() > 0.5
+    angle = np.random.randint(-15, 15) if do_rotate else 0
+    
+    # Color jitter parameters (Brightness/Contrast)
+    brightness = np.random.uniform(0.8, 1.2)
+    contrast = np.random.uniform(0.8, 1.2)
+    
+    for frame in frames:
+        new_frame = frame.copy()
+        
+        # Horizontal Flip
+        if do_flip:
+            new_frame = cv2.flip(new_frame, 1)
+            
+        # Rotation
+        if do_rotate:
+            (h, w) = new_frame.shape[:2]
+            center = (w // 2, h // 2)
+            M = cv2.getRotationMatrix2D(center, angle, 1.0)
+            new_frame = cv2.warpAffine(new_frame, M, (w, h))
+            
+        # Color Jitter (Brightness/Contrast)
+        new_frame = cv2.convertScaleAbs(new_frame, alpha=contrast, beta=(brightness-1)*50)
+        
+        augmented_frames.append(new_frame)
+        
+    return np.array(augmented_frames)
+
+# --- Dataset Class ---
+class ViolenceDataset(Dataset):
+    def __init__(self, video_paths, labels, transform=None, augment=False):
+        self.video_paths = video_paths
+        self.labels = labels
+        self.augment = augment
+        
+    def __len__(self):
+        return len(self.video_paths)
+    
+    def __getitem__(self, idx):
+        path = self.video_paths[idx]
+        label = self.labels[idx]
+        
+        try:
+            frames = self._load_video(path)
+        except Exception as e:
+            # Fallback for corrupted video
+            print(f"Error loading {path}: {e}")
+            frames = np.zeros((SEQ_LEN, IMG_SIZE, IMG_SIZE, 3), dtype=np.uint8)
+
+        if self.augment:
+            frames = augment_video_frames(frames)
+            
+        # Normalize and Channel First (C, D, H, W)
+        frames = torch.tensor(frames, dtype=torch.float32)
+        frames = frames / 255.0 # Normalize 0-1
+        frames = frames.permute(3, 0, 1, 2) # C, D, H, W
+        
+        return frames, label
+    
+    def _load_video(self, path):
+        cap = cv2.VideoCapture(path)
+        frames = []
+        try:
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                frame = cv2.resize(frame, (IMG_SIZE, IMG_SIZE))
+                frames.append(frame)
+        finally:
+            cap.release()
+            
+        # Handle frame count adjustments
+        if len(frames) == 0:
+            return np.zeros((SEQ_LEN, IMG_SIZE, IMG_SIZE, 3), dtype=np.uint8)
+            
+        if len(frames) < SEQ_LEN:
+            while len(frames) < SEQ_LEN:
+                frames.append(frames[-1])
+        elif len(frames) > SEQ_LEN:
+            indices = np.linspace(0, len(frames)-1, SEQ_LEN, dtype=int)
+            frames = [frames[i] for i in indices]
+            
+        return np.array(frames)
+
+# --- 2. Data Splitting ---
+def prepare_data():
+    violence_dir = os.path.join(DATASET_DIR, 'violence')
+    no_violence_dir = os.path.join(DATASET_DIR, 'no-violence')
+    
+    if not os.path.exists(violence_dir) or not os.path.exists(no_violence_dir):
+        raise FileNotFoundError(f"Dataset directories not found. Expected {violence_dir} and {no_violence_dir}")
+
+    violence_files = [os.path.join(violence_dir, f) for f in os.listdir(violence_dir) if f.endswith('.avi') or f.endswith('.mp4')]
+    no_violence_files = [os.path.join(no_violence_dir, f) for f in os.listdir(no_violence_dir) if f.endswith('.avi') or f.endswith('.mp4')]
+    
+    print(f"Found {len(violence_files)} Violence videos")
+    print(f"Found {len(no_violence_files)} No-Violence videos")
+
+    X = violence_files + no_violence_files
+    y = [1] * len(violence_files) + [0] * len(no_violence_files)
+    
+    # Split (70% Train, 15% Val, 15% Test)
+    X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.30, random_state=42, stratify=y)
+    X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.50, random_state=42, stratify=y_temp)
+    
+    print(f"\nDataset Split Stats:")
+    print(f"Train: {len(X_train)} samples")
+    print(f"Val:   {len(X_val)} samples")
+    print(f"Test:  {len(X_test)} samples")
+    
+    return (X_train, y_train), (X_val, y_val), (X_test, y_test)
+
+# --- Early Stopping ---
+class EarlyStopping:
+    def __init__(self, patience=5, verbose=False, path='checkpoint.pth'):
+        self.patience = patience
+        self.verbose = verbose
+        self.counter = 0
+        self.best_score = None
+        self.early_stop = False
+        self.val_loss_min = np.inf
+        self.path = path
+
+    def __call__(self, val_loss, model):
+        score = -val_loss
+
+        if self.best_score is None:
+            self.best_score = score
+            self.save_checkpoint(val_loss, model)
+        elif score < self.best_score:
+            self.counter += 1
+            if self.verbose:
+                print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
+            if self.counter >= self.patience:
+                self.early_stop = True
+        else:
+            self.best_score = score
+            self.save_checkpoint(val_loss, model)
+            self.counter = 0
+
+    def save_checkpoint(self, val_loss, model):
+        if self.verbose:
+            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')
+        torch.save(model, self.path)
+        self.val_loss_min = val_loss
+
+# --- Main Execution ---
+if __name__ == "__main__":
+    start_time = time.time()
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    
+    # Prepare Data
+    try:
+        (X_train, y_train), (X_val, y_val), (X_test, y_test) = prepare_data()
+    except Exception as e:
+        print(f"Data preparation failed: {e}")
+        exit(1)
+    
+    train_dataset = ViolenceDataset(X_train, y_train, augment=True)
+    val_dataset = ViolenceDataset(X_val, y_val, augment=False)
+    test_dataset = ViolenceDataset(X_test, y_test, augment=False)
+    
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)
+    val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
+    test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0)
+    
+    # Model Setup
+    model = ViolenceConv3D().to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
+    
+    early_stopping = EarlyStopping(patience=PATIENCE, verbose=True, path=MODEL_SAVE_PATH)
+    
+    # Training Loop
+    print("\nStarting Training...")
+    
+    for epoch in range(EPOCHS):
+        model.train()
+        train_loss = 0.0
+        correct = 0
+        total = 0
+        
+        for batch_idx, (inputs, labels) in enumerate(train_loader):
+            inputs, labels = inputs.to(device), labels.to(device)
+            
+            optimizer.zero_grad()
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            
+            train_loss += loss.item()
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+            
+            if batch_idx % 10 == 0:
+                print(f"Epoch {epoch+1} Batch {batch_idx}/{len(train_loader)} Loss: {loss.item():.4f}", end='\r')
+            
+        train_acc = 100 * correct / total
+        avg_train_loss = train_loss / len(train_loader)
+        
+        # Validation Phase
+        model.eval()
+        val_loss = 0.0
+        correct_val = 0
+        total_val = 0
+        
+        with torch.no_grad():
+            for inputs, labels in val_loader:
+                inputs, labels = inputs.to(device), labels.to(device)
+                outputs = model(inputs)
+                loss = criterion(outputs, labels)
+                val_loss += loss.item()
+                _, predicted = torch.max(outputs.data, 1)
+                total_val += labels.size(0)
+                correct_val += (predicted == labels).sum().item()
+        
+        val_acc = 100 * correct_val / total_val
+        avg_val_loss = val_loss / len(val_loader)
+        
+        print(f'\nEpoch [{epoch+1}/{EPOCHS}] '
+              f'Train Loss: {avg_train_loss:.4f} Acc: {train_acc:.2f}% '
+              f'Val Loss: {avg_val_loss:.4f} Acc: {val_acc:.2f}%')
+        
+        early_stopping(avg_val_loss, model)
+        if early_stopping.early_stop:
+            print("Early stopping triggered")
+            break
+            
+    # Evaluation
+    print("\nLoading best model for overall evaluation...")
+    if os.path.exists(MODEL_SAVE_PATH):
+        model = torch.load(MODEL_SAVE_PATH)
+    else:
+        print("Warning: Model file not found, using last epoch model.")
+        
+    model.eval()
+    
+    all_preds = []
+    all_labels = []
+    
+    print("Evaluating on Test set...")
+    with torch.no_grad():
+        for inputs, labels in test_loader:
+            inputs, labels = inputs.to(device), labels.to(device)
+            outputs = model(inputs)
+            _, predicted = torch.max(outputs.data, 1)
+            all_preds.extend(predicted.cpu().numpy())
+            all_labels.extend(labels.cpu().numpy())
+            
+    print("\n=== Overall Evaluation Report ===")
+    print(classification_report(all_labels, all_preds, target_names=['No Violence', 'Violence']))
+    
+    print("Confusion Matrix:")
+    cm = confusion_matrix(all_labels, all_preds)
+    print(cm)
+    
+    acc = accuracy_score(all_labels, all_preds)
+    print(f"\nFinal Test Accuracy: {acc*100:.2f}%")
+    
+    elapsed = time.time() - start_time
+    print(f"\nTotal execution time: {elapsed/60:.2f} minutes")