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Two Stream Prototype1.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dd0130b66c7581d9f8f2eb36693151bacfbee29ca7e182cee4b72a18bac8d9e
+size 267603657

model.py

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+import torch
+import torch.nn as nn
+import torchvision.models.video as models
+
+class TwoStreamNetwork(nn.Module):
+    def __init__(self):
+        super(TwoStreamNetwork, self).__init__()
+        
+        # Stream 1: RGB
+        self.rgb_backbone = models.r3d_18(weights=None)
+        self.rgb_backbone.fc = nn.Identity() # Remove classification head
+        
+        # Stream 2: Optical Flow
+        self.flow_backbone = models.r3d_18(weights=None)
+        self.flow_backbone.fc = nn.Identity()
+        
+        # Fusion
+        # R3D_18 output dim is 512
+        self.fusion_fc = nn.Sequential(
+            nn.Linear(512 * 2, 512),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(512, 2)
+        )
+        
+    def forward(self, rgb, flow):
+        idx_rgb = self.rgb_backbone(rgb)
+        idx_flow = self.flow_backbone(flow)
+        
+        combined = torch.cat((idx_rgb, idx_flow), dim=1)
+        out = self.fusion_fc(combined)
+        return out

readme.md

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+# Two-Stream Violence Detection Network
+
+## Model Architecture
+- **Type**: Two-Stream Network (Spatial + Temporal)
+- **Streams**:
+  1. **RGB Stream**: ResNet3D (r3d_18) to process raw video frames. Captures appearance info.
+  2. **Optical Flow Stream**: ResNet3D (r3d_18) to process computed dense optical flow. Captures motion info.
+- **Fusion**: Features from both streams are concatenated and passed through fully connected layers.
+- **Input**: 16 Frames (RGB) + 16 Flow Fields (Computed on the fly).
+- **Computation**: Optical flow is computed using Farneback algorithm within the Dataloader.
+
+## Dataset Structure
+Expects `Dataset` folder in parent directory.
+```
+Dataset/
+├── violence/
+└── no-violence/
+```
+
+## How to Run
+1. Install dependencies: `torch`, `opencv-python` (with contrib if needed for some algorithms, but Farneback is standard), `torchvision`.
+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 torchvision.models.video as models
+import time
+from model import TwoStreamNetwork
+
+# --- Configuration ---
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATASET_DIR = os.path.join(BASE_DIR, "Dataset")
+MODEL_SAVE_PATH = "best_model_twostream.pth"
+
+IMG_SIZE = 112
+SEQ_LEN = 16
+BATCH_SIZE = 16
+EPOCHS = 80
+LEARNING_RATE = 1e-4
+PATIENCE = 5
+
+class TwoStreamDataset(Dataset):
+    def __init__(self, video_paths, labels):
+        self.video_paths = video_paths
+        self.labels = labels
+        
+    def __len__(self):
+        return len(self.video_paths)
+    
+    def __getitem__(self, idx):
+        path = self.video_paths[idx]
+        label = self.labels[idx]
+        
+        frames, flows = self._load_data(path)
+        
+        # To Tensor (C, T, H, W)
+        frames = torch.tensor(frames, dtype=torch.float32).permute(3, 0, 1, 2)
+        flows = torch.tensor(flows, dtype=torch.float32).permute(3, 0, 1, 2)
+        
+        return frames, flows, label
+    
+    def _load_data(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()
+            
+        if len(frames) == 0:
+            dummy_f = np.zeros((SEQ_LEN, IMG_SIZE, IMG_SIZE, 3), dtype=np.float32)
+            dummy_opt = np.zeros((SEQ_LEN, IMG_SIZE, IMG_SIZE, 3), dtype=np.float32)
+            return dummy_f, dummy_opt
+            
+        # Sampling
+        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]
+            
+        rgb_frames = np.array(frames, dtype=np.float32) / 255.0
+        
+        # Calculate Optical Flow (Dense)
+        # Use Farneback from OpenCV
+        prev_gray = cv2.cvtColor(frames[0], cv2.COLOR_BGR2GRAY)
+        flows = []
+        for i in range(len(frames)):
+            curr_gray = cv2.cvtColor(frames[i], cv2.COLOR_BGR2GRAY)
+            if i == 0:
+                # First frame has no flow, use zero
+                flow = np.zeros((IMG_SIZE, IMG_SIZE, 2), dtype=np.float32)
+            else:
+                flow = cv2.calcOpticalFlowFarneback(prev_gray, curr_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+                prev_gray = curr_gray
+            
+            mag, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1])
+            flow_img = np.dstack((flow[..., 0], flow[..., 1], mag))
+            
+            flow_img = (flow_img + 20) / 40.0 
+            
+            flows.append(flow_img)
+            
+        return rgb_frames, np.array(flows, dtype=np.float32)
+
+# --- Data Preparation ---
+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("Dataset directories not found.")
+
+    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')]
+    
+    X = violence_files + no_violence_files
+    y = [1] * len(violence_files) + [0] * len(no_violence_files)
+    
+    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)
+    
+    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
+
+if __name__ == "__main__":
+    start_time = time.time()
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    
+    try:
+        (X_train, y_train), (X_val, y_val), (X_test, y_test) = prepare_data()
+        print(f"Dataset Split Stats:")
+        print(f"Train: {len(X_train)} samples")
+        print(f"Val:   {len(X_val)} samples")
+        print(f"Test:  {len(X_test)} samples")
+    except Exception as e:
+        print(f"Data preparation failed: {e}")
+        exit(1)
+        
+    train_dataset = TwoStreamDataset(X_train, y_train)
+    val_dataset = TwoStreamDataset(X_val, y_val)
+    test_dataset = TwoStreamDataset(X_test, y_test)
+    
+    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 = TwoStreamNetwork().to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
+    
+    early_stopping = EarlyStopping(patience=PATIENCE, verbose=True, path=MODEL_SAVE_PATH)
+    
+    print("\nStarting Two-Stream Network Training...")
+    
+    for epoch in range(EPOCHS):
+        model.train()
+        train_loss = 0.0
+        correct = 0
+        total = 0
+        
+        for batch_idx, (rgb_in, flow_in, labels) in enumerate(train_loader):
+            rgb_in, flow_in, labels = rgb_in.to(device), flow_in.to(device), labels.to(device)
+            
+            optimizer.zero_grad()
+            outputs = model(rgb_in, flow_in)
+            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)
+        
+        model.eval()
+        val_loss = 0.0
+        correct_val = 0
+        total_val = 0
+        
+        with torch.no_grad():
+            for rgb_in, flow_in, labels in val_loader:
+                rgb_in, flow_in, labels = rgb_in.to(device), flow_in.to(device), labels.to(device)
+                outputs = model(rgb_in, flow_in)
+                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
+            
+    print("\nLoading best Two-Stream model for evaluation...")
+    if os.path.exists(MODEL_SAVE_PATH):
+        model = torch.load(MODEL_SAVE_PATH)
+    else:
+        print("Warning: Model file not found.")
+        
+    model.eval()
+    all_preds = []
+    all_labels = []
+    
+    print("Evaluating on Test set...")
+    with torch.no_grad():
+        for rgb_in, flow_in, labels in test_loader:
+            rgb_in, flow_in, labels = rgb_in.to(device), flow_in.to(device), labels.to(device)
+            outputs = model(rgb_in, flow_in)
+            _, predicted = torch.max(outputs.data, 1)
+            all_preds.extend(predicted.cpu().numpy())
+            all_labels.extend(labels.cpu().numpy())
+            
+    print("\n=== Two-Stream Model Evaluation Report ===")
+    print(classification_report(all_labels, all_preds, target_names=['No Violence', 'Violence']))
+    print("Confusion Matrix:")
+    print(confusion_matrix(all_labels, all_preds))
+    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")