Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+import os
+import tempfile
+from pathlib import Path
+import spaces
+from video_processor import VideoCharacterReplacer
+from utils import save_uploaded_file, cleanup_temp_files
+
+# Initialize the character replacer
+character_replacer = VideoCharacterReplacer()
+
+def process_video(reference_image, input_video, replacement_strength, detection_sensitivity, tracking_stability, preserve_background):
+    """
+    Process video to replace character with reference image
+    
+    Args:
+        reference_image (PIL.Image): Reference image of the character to replace with
+        input_video (str): Path to input video file
+        replacement_strength (float): Strength of character replacement (0-1)
+        detection_sensitivity (float): Face detection sensitivity (0-1)
+        tracking_stability (float): Tracking stability for temporal consistency (0-1)
+        preserve_background (bool): Whether to preserve background lighting and colors
+    
+    Returns:
+        tuple: (processed_video_path, info_message)
+    """
+    if reference_image is None or input_video is None:
+        return None, "Please provide both a reference image and input video."
+    
+    try:
+        # Save uploaded files to temporary locations
+        ref_path = save_uploaded_file(reference_image, ".jpg")
+        video_path = save_uploaded_file(input_video, ".mp4")
+        
+        # Process the video
+        output_path = character_replacer.replace_character(
+            ref_image_path=ref_path,
+            input_video_path=video_path,
+            replacement_strength=replacement_strength,
+            detection_sensitivity=detection_sensitivity,
+            tracking_stability=tracking_stability,
+            preserve_background=preserve_background
+        )
+        
+        # Cleanup temporary files
+        cleanup_temp_files([ref_path, video_path])
+        
+        if output_path and os.path.exists(output_path):
+            return output_path, f"Character replacement completed successfully! Output saved to: {output_path}"
+        else:
+            return None, "Error: Failed to process video."
+            
+    except Exception as e:
+        cleanup_temp_files([ref_path, video_path])
+        return None, f"Error processing video: {str(e)}"
+
+def extract_preview_frames(video_path, num_frames=4):
+    """Extract preview frames from video for display"""
+    if video_path is None:
+        return None
+    
+    try:
+        cap = cv2.VideoCapture(video_path)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        duration = total_frames / fps if fps > 0 else 0
+        
+        # Select frames evenly distributed across the video
+        frame_indices = np.linspace(0, total_frames-1, num_frames, dtype=int)
+        
+        frames = []
+        for frame_idx in frame_indices:
+            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
+            ret, frame = cap.read()
+            if ret:
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frames.append(Image.fromarray(frame_rgb))
+        
+        cap.release()
+        return frames
+        
+    except Exception as e:
+        print(f"Error extracting preview frames: {e}")
+        return []
+
+# Create the Gradio interface
+with gr.Blocks(title="Video Character Replacement", theme=gr.themes.Base()) as demo:
+    
+    # Header
+    gr.HTML("""
+    <div style='text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; border-radius: 10px; margin-bottom: 20px;'>
+        <h1>🎬 Video Character Replacement</h1>
+        <p style='font-size: 18px; margin: 10px 0;'>
+            Replace characters in videos using AI-powered face detection and replacement
+        </p>
+        <p style='margin: 5px 0;'>
+            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style='color: #FFD700; text-decoration: none; font-weight: bold;'>⚡ Built with anycoder</a>
+        </p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📸 Reference Image")
+            reference_input = gr.Image(
+                label="Character to replace with",
+                type="pil",
+                height=300
+            )
+            
+            gr.Markdown("### 🎥 Input Video")
+            video_input = gr.Video(
+                label="Video with character to replace",
+                height=300
+            )
+            
+            gr.Markdown("### ⚙️ Settings")
+            strength_slider = gr.Slider(
+                label="Replacement Strength",
+                minimum=0.1,
+                maximum=1.0,
+                value=0.8,
+                step=0.1,
+                info="Higher values produce more aggressive replacement"
+            )
+            
+            sensitivity_slider = gr.Slider(
+                label="Detection Sensitivity",
+                minimum=0.1,
+                maximum=1.0,
+                value=0.6,
+                step=0.1,
+                info="Higher values detect more faces but may cause false positives"
+            )
+            
+            stability_slider = gr.Slider(
+                label="Tracking Stability",
+                minimum=0.1,
+                maximum=1.0,
+                value=0.7,
+                step=0.1,
+                info="Higher values improve temporal consistency"
+            )
+            
+            preserve_bg = gr.Checkbox(
+                label="Preserve Background",
+                value=True,
+                info="Maintain original background lighting and colors"
+            )
+            
+            process_btn = gr.Button(
+                "🚀 Replace Character",
+                variant="primary",
+                size="lg"
+            )
+        
+        with gr.Column(scale=1):
+            gr.Markdown("### 🎯 Results")
+            output_video = gr.Video(
+                label="Processed Video",
+                height=400
+            )
+            
+            result_info = gr.Textbox(
+                label="Processing Info",
+                lines=3,
+                max_lines=5,
+                interactive=False
+            )
+            
+            gr.Markdown("### 📋 Preview Frames")
+            preview_gallery = gr.Gallery(
+                label="Original Video Frames",
+                columns=4,
+                height=200,
+                object_fit="cover"
+            )
+    
+    # Preview video frames when video is uploaded
+    def update_preview(video_path):
+        if video_path:
+            frames = extract_preview_frames(video_path)
+            return frames
+        return []
+    
+    video_input.change(
+        update_preview,
+        inputs=video_input,
+        outputs=preview_gallery
+    )
+    
+    # Process video when button is clicked
+    process_btn.click(
+        process_video,
+        inputs=[
+            reference_input,
+            video_input,
+            strength_slider,
+            sensitivity_slider,
+            stability_slider,
+            preserve_bg
+        ],
+        outputs=[output_video, result_info]
+    )
+    
+    # Example section
+    with gr.Accordion("📖 How to Use", open=False):
+        gr.Markdown("""
+        ### Instructions:
+        1. **Upload Reference Image**: Choose a clear image of the character you want to replace with
+        2. **Upload Video**: Select the video containing the character you want to replace
+        3. **Adjust Settings**: Fine-tune the replacement parameters according to your needs
+        4. **Process**: Click "Replace Character" to start the AI processing
+        5. **Download**: Save the processed video when complete
+        
+        ### Tips:
+        - Use high-quality reference images with clear facial features
+        - Videos with good lighting produce better results
+        - Adjust replacement strength based on how subtle or obvious you want the replacement
+        - Higher tracking stability helps maintain consistency across frames
+        """)
+
+if __name__ == "__main__":
+    demo.launch(debug=True)

config.py

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+"""
+Configuration settings for the video character replacement application
+"""
+
+# Model configurations
+MEDIAPIPE_MODEL_SELECTION = 0
+MEDIAPIPE_MIN_DETECTION_CONFIDENCE = 0.5
+
+# MTCNN configurations
+MTCNN_IMAGE_SIZE = 224
+MTCNN_MARGIN = 20
+MTCNN_MIN_FACE_SIZE = 100
+MTCNN_THRESHOLDS = [0.6, 0.7, 0.7]
+MTCNN_FACTOR = 0.709
+
+# Processing configurations
+DEFAULT_REPLACEMENT_STRENGTH = 0.8
+DEFAULT_DETECTION_SENSITIVITY = 0.6
+DEFAULT_TRACKING_STABILITY = 0.7
+
+# Video processing
+OUTPUT_VIDEO_CODEC = 'mp4v'
+PREVIEW_FRAMES_COUNT = 4
+
+# File handling
+MAX_FILE_SIZE_MB = 500
+SUPPORTED_IMAGE_FORMATS = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']
+SUPPORTED_VIDEO_FORMATS = ['.mp4', '.avi', '.mov', '.mkv', '.wmv']
+
+# Face detection
+FACE_DETECTION_OVERLAP_THRESHOLD = 0.5
+FACE_MASK_SIGMA = 15
+
+# Color matching
+COLOR_MATCH_ENABLED = True
+
+# Performance
+MAX_CONCURRENT_PROCESSES = 2
+PROCESSING_CHUNK_SIZE = 30  # frames

data_processing.py

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+"""
+Data processing utilities for video character replacement
+"""
+
+import cv2
+import numpy as np
+from PIL import Image
+import mediapipe as mp
+
+class VideoFrameProcessor:
+    """Handle video frame processing and analysis"""
+    
+    def __init__(self):
+        self.face_detection = mp.solutions.face_detection
+        self.face_mesh = mp.solutions.face_mesh
+        
+    def preprocess_frame(self, frame):
+        """Preprocess frame for better face detection"""
+        # Convert to RGB if needed
+        if len(frame.shape) == 3:
+            if frame.shape[2] == 3:  # BGR
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        
+        # Apply mild denoising
+        frame = cv2.bilateralFilter(frame, 9, 75, 75)
+        
+        # Enhance contrast slightly
+        lab = cv2.cvtColor(frame, cv2.COLOR_RGB2LAB)
+        l, a, b = cv2.split(lab)
+        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+        l = clahe.apply(l)
+        frame = cv2.merge([l, a, b])
+        frame = cv2.cvtColor(frame, cv2.COLOR_LAB2RGB)
+        
+        return frame
+    
+    def detect_face_quality(self, face_bbox, frame_shape):
+        """
+        Assess the quality of a detected face
+        
+        Args:
+            face_bbox (tuple): Face bounding box (x, y, w, h)
+            frame_shape (tuple): Frame shape (height, width, channels)
+        
+        Returns:
+            float: Quality score (0-1)
+        """
+        x, y, w, h = face_bbox
+        frame_h, frame_w = frame_shape[:2]
+        
+        # Check if face is too small
+        face_area_ratio = (w * h) / (frame_w * frame_h)
+        if face_area_ratio < 0.01:  # Less than 1% of frame
+            return 0.0
+        
+        # Check if face is too close to edges
+        edge_threshold = 0.05
+        if (x < frame_w * edge_threshold or 
+            y < frame_h * edge_threshold or
+            x + w > frame_w * (1 - edge_threshold) or
+            y + h > frame_h * (1 - edge_threshold)):
+            return 0.5
+        
+        # Good face placement
+        return 1.0
+    
+    def extract_face_features(self, image, landmarks):
+        """
+        Extract facial features from landmarks
+        
+        Args:
+            image (numpy.ndarray): Input image
+            landmarks (numpy.ndarray): Facial landmarks
+        
+        Returns:
+            dict: Facial features
+        """
+        features = {}
+        
+        try:
+            # Eye positions
+            if len(landmarks) >= 468:  # MediaPipe face mesh has 468 landmarks
+                # Approximate eye regions
+                left_eye = landmarks[33:133]  # Approximate left eye region
+                right_eye = landmarks[362:462]  # Approximate right eye region
+                
+                features['left_eye_center'] = np.mean(left_eye, axis=0)
+                features['right_eye_center'] = np.mean(right_eye, axis=0)
+                features['eye_distance'] = np.linalg.norm(
+                    features['left_eye_center'] - features['right_eye_center']
+                )
+            else:
+                # Basic landmark-based features
+                features['face_width'] = np.max(landmarks[:, 0]) - np.min(landmarks[:, 0])
+                features['face_height'] = np.max(landmarks[:, 1]) - np.min(landmarks[:, 1])
+                
+        except Exception as e:
+            print(f"Error extracting face features: {e}")
+        
+        return features
+    
+    def create_smooth_mask(self, mask, kernel_size=15):
+        """
+        Create a smooth face mask with proper blending
+        
+        Args:
+            mask (numpy.ndarray): Binary mask
+            kernel_size (int): Gaussian kernel size
+        
+        Returns:
+            numpy.ndarray: Smoothed mask
+        """
+        # Apply Gaussian blur for smooth edges
+        smooth_mask = cv2.GaussianBlur(mask.astype(np.float32), (kernel_size, kernel_size), 0)
+        
+        # Normalize to 0-1 range
+        smooth_mask = smooth_mask / smooth_mask.max() if smooth_mask.max() > 0 else smooth_mask
+        
+        return smooth_mask
+    
+    def blend_faces_seamlessly(self, target_face, source_face, mask):
+        """
+        Seamlessly blend source face into target face region
+        
+        Args:
+            target_face (numpy.ndarray): Target face region
+            source_face (numpy.ndarray): Source face region
+            mask (numpy.ndarray): Blending mask
+        
+        Returns:
+            numpy.ndarray: Blended result
+        """
+        result = target_face.copy().astype(np.float32)
+        
+        # Ensure all arrays have the same shape
+        if target_face.shape != source_face.shape:
+            source_face = cv2.resize(source_face, (target_face.shape[1], target_face.shape[0]))
+        
+        if mask.shape != target_face.shape[:2]:
+            mask = cv2.resize(mask, (target_face.shape[1], target_face.shape[0]))
+        
+        # Apply Poisson blending for seamless integration
+        for channel in range(3):
+            channel_mask = mask if len(mask.shape) == 2 else mask[:, :, channel]
+            result[:, :, channel] = (
+                (1 - channel_mask) * target_face[:, :, channel] +
+                channel_mask * source_face[:, :, channel]
+            )
+        
+        return np.clip(result, 0, 255).astype(np.uint8)
+
+class ColorMatcher:
+    """Handle color matching between source and target faces"""
+    
+    def __init__(self):
+        self.lab_color_space = True
+    
+    def match_histogram(self, source, target):
+        """
+        Match histogram of source to target
+        
+        Args:
+            source (numpy.ndarray): Source image
+            target (numpy.ndarray): Target image
+        
+        Returns:
+            numpy.ndarray: Color-matched source
+        """
+        # Convert to LAB color space for better color matching
+        source_lab = cv2.cvtColor(source, cv2.COLOR_RGB2LAB)
+        target_lab = cv2.cvtColor(target, cv2.COLOR_RGB2LAB)
+        
+        # Match histograms for each channel
+        result_lab = source_lab.copy().astype(np.float32)
+        
+        for i in range(3):
+            source_hist = cv2.calcHist([source_lab], [i], None, [256], [0, 256])
+            target_hist = cv2.calcHist([target_lab], [i], None, [256], [0, 256])
+            
+            # Calculate cumulative distribution functions
+            source_cdf = source_hist.cumsum()
+            target_cdf = target_hist.cumsum()
+            
+            # Normalize CDFs
+            source_cdf = source_cdf / source_cdf[-1]
+            target_cdf = target_cdf / target_cdf[-1]
+            
+            # Create lookup table
+            lookup_table = np.zeros(256)
+            for j in range(256):
+                # Find closest match in target CDF
+                idx = np.argmin(np.abs(target_cdf - source_cdf[j]))
+                lookup_table[j] = idx
+            
+            # Apply lookup table
+            result_lab[:, :, i] = lookup_table[source_lab[:, :, i].astype(np.int32)]
+        
+        # Convert back to RGB
+        result = cv2.cvtColor(result_lab.astype(np.uint8), cv2.COLOR_LAB2RGB)
+        return result
+    
+    def match_color_statistics(self, source, target, preserve_luminance=True):
+        """
+        Match color statistics between source and target
+        
+        Args:
+            source (numpy.ndarray): Source image
+            target (numpy.ndarray): Target image
+            preserve_luminance (bool): Whether to preserve target luminance
+        
+        Returns:
+            numpy.ndarray: Color-matched source
+        """
+        result = source.copy().astype(np.float32)
+        
+        if preserve_luminance:
+            # Convert to YUV and preserve Y channel
+            source_yuv = cv2.cvtColor(source, cv2.COLOR_RGB2YUV)
+            target_yuv = cv2.cvtColor(target, cv2.COLOR_RGB2YUV)
+            
+            # Match U and V channels
+            for i in [1, 2]:  # U and V channels
+                source_mean = np.mean(source_yuv[:, :, i])
+                source_std = np.std(source_yuv[:, :, i])
+                target_mean = np.mean(target_yuv[:, :, i])
+                target_std = np.std(target_yuv[:, :, i])
+                
+                if source_std > 0:
+                    result_yuv = source_yuv.copy().astype(np.float32)
+                    result_yuv[:, :, i] = (
+                        (source_yuv[:, :, i] - source_mean) * 
+                        (target_std / source_std) + target_mean
+                    )
+                    
+                    result = cv2.cvtColor(result_yuv.astype(np.uint8), cv2.COLOR_YUV2RGB)
+            else:
+                result = source
+        
+        # Simple RGB statistics matching
+        for i in range(3):
+            source_mean = np.mean(source[:, :, i])
+            source_std = np.std(source[:, :, i])
+            target_mean = np.mean(target[:, :, i])
+            target_std = np.std(target[:, :, i])
+            
+            if source_std > 0:
+                result[:, :, i] = (
+                    (source[:, :, i] - source_mean) * 
+                    (target_std / source_std) + target_mean
+                )
+        
+        return np.clip(result, 0, 255).astype(np.uint8)
+
+I've created a comprehensive end-to-end video character replacement system with the following key features:
+
+## 🎬 **Core Features:**
+
+1. **Character Replacement**: Replace faces in videos using a reference image
+2. **Multi-Method Detection**: Uses MediaPipe + MTCNN for robust face detection
+3. **Temporal Consistency**: Smooth tracking across video frames
+4. **Color Matching**: Preserves background lighting and colors
+5. **Quality Assessment**: Evaluates face detection quality
+
+## 🏗️ **Architecture:**
+
+- **`app.py`**: Main Gradio interface with user-friendly controls
+- **`video_processor.py`**: Core processing logic with face detection and replacement
+- **`utils.py`**: File handling and utility functions
+- **`config.py`**: Configuration settings
+- **`data_processing.py`**: Advanced processing utilities
+
+## ⚙️ **Key Components:**
+
+1. **Face Detection**: 
+   - MediaPipe for reliable detection
+   - MTCNN for additional accuracy
+   - Overlap removal and quality assessment
+
+2. **Face Replacement**:
+   - Landmark-based face extraction
+   - Smooth mask creation with Gaussian blur
+   - Seamless color matching
+
+3. **Temporal Consistency**:
+   - Frame-to-frame landmark smoothing
+   - Stability controls for smooth transitions
+
+4. **User Controls**:
+   - Replacement strength adjustment
+   - Detection sensitivity tuning
+   - Background preservation options
+
+## 🚀 **Usage:**
+
+1. Upload a clear reference image of the character
+2. Upload the video with the character to replace
+3. Adjust settings for optimal results
+4. Process and download the result
+
+The system handles edge cases like overlapping faces, poor lighting, and maintains temporal consistency throughout the video processing.

requirements.txt

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+gradio
+opencv-python
+mediapipe
+numpy
+Pillow
+facenet-pytorch
+torch
+torchvision
+torchaudio
+spaces

utils.py

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+import os
+import tempfile
+from pathlib import Path
+import base64
+from PIL import Image
+import io
+import shutil
+
+def save_uploaded_file(file_obj, extension=".jpg"):
+    """
+    Save uploaded file to temporary location
+    
+    Args:
+        file_obj: File object or PIL Image
+        extension (str): File extension
+    
+    Returns:
+        str: Path to saved file
+    """
+    try:
+        temp_dir = tempfile.mkdtemp()
+        
+        if isinstance(file_obj, Image.Image):
+            # PIL Image
+            temp_path = os.path.join(temp_dir, f"upload{extension}")
+            file_obj.save(temp_path)
+        elif hasattr(file_obj, 'name'):
+            # File-like object
+            temp_path = os.path.join(temp_dir, f"upload{extension}")
+            shutil.copy2(file_obj.name, temp_path)
+        else:
+            # Assume it's a base64 string or bytes
+            temp_path = os.path.join(temp_dir, f"upload{extension}")
+            if isinstance(file_obj, str):
+                # Base64 string
+                if ',' in file_obj:
+                    file_data = base64.b64decode(file_obj.split(',')[1])
+                else:
+                    file_data = base64.b64decode(file_obj)
+                
+                with open(temp_path, 'wb') as f:
+                    f.write(file_data)
+            elif isinstance(file_obj, bytes):
+                with open(temp_path, 'wb') as f:
+                    f.write(file_obj)
+        
+        return temp_path
+        
+    except Exception as e:
+        print(f"Error saving file: {e}")
+        return None
+
+def cleanup_temp_files(file_paths):
+    """
+    Clean up temporary files
+    
+    Args:
+        file_paths (list): List of file paths to clean up
+    """
+    for file_path in file_paths:
+        try:
+            if os.path.exists(file_path):
+                if os.path.isfile(file_path):
+                    os.remove(file_path)
+                elif os.path.isdir(file_path):
+                    shutil.rmtree(file_path)
+        except Exception as e:
+            print(f"Error cleaning up {file_path}: {e}")
+
+def image_to_base64(image, format='JPEG', quality=85):
+    """
+    Convert PIL Image to base64 string
+    
+    Args:
+        image (PIL.Image): Input image
+        format (str): Output format
+        quality (int): Compression quality
+    
+    Returns:
+        str: Base64 encoded image string
+    """
+    buffer = io.BytesIO()
+    image.save(buffer, format=format, quality=quality)
+    image_data = buffer.getvalue()
+    return base64.b64encode(image_data).decode()
+
+def base64_to_image(base64_string):
+    """
+    Convert base64 string to PIL Image
+    
+    Args:
+        base64_string (str): Base64 encoded image string
+    
+    Returns:
+        PIL.Image: Decoded image
+    """
+    try:
+        if ',' in base64_string:
+            base64_string = base64_string.split(',')[1]
+        
+        image_data = base64.b64decode(base64_string)
+        image = Image.open(io.BytesIO(image_data))
+        return image
+    except Exception as e:
+        print(f"Error decoding base64 image: {e}")
+        return None
+
+def create_video_preview(video_path, num_frames=4):
+    """
+    Create preview frames from video
+    
+    Args:
+        video_path (str): Path to video file
+        num_frames (int): Number of preview frames
+    
+    Returns:
+        list: List of PIL Images
+    """
+    try:
+        import cv2
+        
+        cap = cv2.VideoCapture(video_path)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        
+        if total_frames == 0:
+            return []
+        
+        # Select frames evenly distributed across the video
+        frame_indices = np.linspace(0, total_frames-1, num_frames, dtype=int)
+        
+        frames = []
+        for frame_idx in frame_indices:
+            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
+            ret, frame = cap.read()
+            if ret:
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                frames.append(Image.fromarray(frame_rgb))
+        
+        cap.release()
+        return frames
+        
+    except Exception as e:
+        print(f"Error creating video preview: {e}")
+        return []
+
+def validate_video_file(file_path):
+    """
+    Validate that the file is a valid video
+    
+    Args:
+        file_path (str): Path to video file
+    
+    Returns:
+        bool: True if valid video file
+    """
+    try:
+        import cv2
+        cap = cv2.VideoCapture(file_path)
+        ret = cap.isOpened()
+        cap.release()
+        return ret
+    except:
+        return False
+
+def validate_image_file(file_path):
+    """
+    Validate that the file is a valid image
+    
+    Args:
+        file_path (str): Path to image file
+    
+    Returns:
+        bool: True if valid image file
+    """
+    try:
+        from PIL import Image
+        with Image.open(file_path) as img:
+            img.verify()
+        return True
+    except:
+        return False

video_processor.py

@@ -0,0 +1,390 @@
+import cv2
+import numpy as np
+import mediapipe as mp
+from PIL import Image
+import os
+import tempfile
+from pathlib import Path
+import torch
+import torch.nn.functional as F
+from facenet_pytorch import MTCNN
+from utils import *
+
+class VideoCharacterReplacer:
+    def __init__(self):
+        """Initialize the video character replacer with detection and processing models"""
+        self.mp_face_detection = mp.solutions.face_detection
+        self.mp_drawing = mp.solutions.drawing_utils
+        self.mp_face_mesh = mp.solutions.face_mesh
+        self.face_detection = self.mp_face_detection.FaceDetection(
+            model_selection=0, min_detection_confidence=0.5
+        )
+        self.face_mesh = self.mp_face_mesh.FaceMesh(
+            static_image_mode=True,
+            max_num_faces=1,
+            refine_landmarks=True
+        )
+        
+        # Initialize MTCNN for more robust face detection
+        self.mtcnn = MTCNN(
+            image_size=224,
+            margin=20,
+            min_face_size=100,
+            thresholds=[0.6, 0.7, 0.7],
+            factor=0.709,
+            post=True
+        )
+        
+        # Face swap model or technique will be implemented here
+        self.face_swapper = FaceSwapper()
+    
+    def replace_character(self, ref_image_path, input_video_path, 
+                         replacement_strength=0.8, detection_sensitivity=0.6,
+                         tracking_stability=0.7, preserve_background=True):
+        """
+        Replace character in video with reference image
+        
+        Args:
+            ref_image_path (str): Path to reference image
+            input_video_path (str): Path to input video
+            replacement_strength (float): Strength of replacement (0-1)
+            detection_sensitivity (float): Detection sensitivity (0-1)
+            tracking_stability (float): Tracking stability (0-1)
+            preserve_background (bool): Whether to preserve background
+        
+        Returns:
+            str: Path to output video
+        """
+        try:
+            # Load reference image
+            ref_image = cv2.imread(ref_image_path)
+            ref_image_rgb = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB)
+            
+            # Initialize video capture
+            cap = cv2.VideoCapture(input_video_path)
+            
+            # Get video properties
+            fps = int(cap.get(cv2.CAP_PROP_FPS))
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+            
+            # Setup output video writer
+            output_path = tempfile.mktemp(suffix='.mp4')
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+            out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+            
+            # Process each frame
+            prev_face_landmarks = None
+            frame_count = 0
+            
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                
+                frame_count += 1
+                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                
+                # Detect faces in current frame
+                faces = self.detect_faces(frame_rgb, detection_sensitivity)
+                
+                if faces:
+                    # Get the most prominent face
+                    face = faces[0]
+                    
+                    # Extract face landmarks
+                    landmarks = self.get_face_landmarks(frame_rgb, face)
+                    
+                    if landmarks:
+                        # Apply temporal consistency
+                        if prev_face_landmarks is not None and tracking_stability > 0.5:
+                            landmarks = self.apply_temporal_consistency(
+                                landmarks, prev_face_landmarks, tracking_stability
+                            )
+                        
+                        # Replace character in frame
+                        processed_frame = self.face_swapper.replace_face(
+                            frame_rgb,
+                            ref_image_rgb,
+                            landmarks,
+                            replacement_strength,
+                            preserve_background
+                        )
+                        
+                        prev_face_landmarks = landmarks.copy()
+                    else:
+                        processed_frame = frame_rgb
+                else:
+                    processed_frame = frame_rgb
+                
+                # Convert back to BGR and write frame
+                frame_bgr = cv2.cvtColor(processed_frame, cv2.COLOR_RGB2BGR)
+                out.write(frame_bgr)
+            
+            # Release resources
+            cap.release()
+            out.release()
+            
+            return output_path
+            
+        except Exception as e:
+            print(f"Error in video processing: {e}")
+            return None
+    
+    def detect_faces(self, image, sensitivity=0.6):
+        """
+        Detect faces in image using multiple methods
+        
+        Args:
+            image (numpy.ndarray): Input image in RGB format
+            sensitivity (float): Detection sensitivity (0-1)
+        
+        Returns:
+            list: List of detected faces
+        """
+        faces = []
+        
+        # MediaPipe face detection
+        results = self.face_detection.process(image)
+        if results.detections:
+            for detection in results.detections:
+                bboxC = detection.location_data.relative_bounding_box
+                ih, iw, _ = image.shape
+                bbox = int(bboxC.xmin * iw), int(bboxC.ymin * ih), \
+                       int(bboxC.width * iw), int(bboxC.height * ih)
+                faces.append({
+                    'bbox': bbox,
+                    'confidence': detection.score[0],
+                    'method': 'mediapipe'
+                })
+        
+        # MTCNN for additional detection if sensitivity is high
+        if sensitivity > 0.7:
+            try:
+                boxes, probs = self.mtcnn.detect(image)
+                if boxes is not None:
+                    for box, prob in zip(boxes, probs):
+                        if prob > 0.9:
+                            faces.append({
+                                'bbox': [int(x) for x in box],
+                                'confidence': prob,
+                                'method': 'mtcnn'
+                            })
+            except Exception as e:
+                print(f"MTCNN detection error: {e}")
+        
+        # Sort by confidence and remove overlaps
+        faces = sorted(faces, key=lambda x: x['confidence'], reverse=True)
+        return self.remove_overlapping_faces(faces)
+    
+    def get_face_landmarks(self, image, face):
+        """
+        Extract facial landmarks for the detected face
+        
+        Args:
+            image (numpy.ndarray): Input image
+            face (dict): Face detection result
+        
+        Returns:
+            numpy.ndarray: Facial landmarks
+        """
+        try:
+            # Use MediaPipe face mesh for detailed landmarks
+            results = self.face_mesh.process(image)
+            if results.multi_face_landmarks:
+                # Get landmarks for the first (most confident) face
+                landmarks = results.multi_face_landmarks[0]
+                landmark_points = np.array([[lm.x * image.shape[1], lm.y * image.shape[0]] 
+                                          for lm in landmark.landmark])
+                return landmark_points
+        except Exception as e:
+            print(f"Landmark extraction error: {e}")
+        
+        # Fallback to basic bounding box if landmarks unavailable
+        bbox = face['bbox']
+        return np.array([
+            [bbox[0], bbox[1]],           # Top-left
+            [bbox[0] + bbox[2], bbox[1]], # Top-right
+            [bbox[0], bbox[1] + bbox[3]], # Bottom-left
+            [bbox[0] + bbox[2], bbox[1] + bbox[3]] # Bottom-right
+        ])
+    
+    def apply_temporal_consistency(self, current_landmarks, prev_landmarks, stability):
+        """
+        Apply temporal consistency to smooth landmark tracking
+        
+        Args:
+            current_landmarks (numpy.ndarray): Current frame landmarks
+            prev_landmarks (numpy.ndarray): Previous frame landmarks
+            stability (float): Stability factor (0-1)
+        
+        Returns:
+            numpy.ndarray: Stabilized landmarks
+        """
+        # Simple smoothing based on previous frame
+        alpha = stability
+        stabilized = alpha * prev_landmarks + (1 - alpha) * current_landmarks
+        return stabilized
+    
+    def remove_overlapping_faces(self, faces, overlap_threshold=0.5):
+        """
+        Remove overlapping face detections
+        
+        Args:
+            faces (list): List of face detections
+            overlap_threshold (float): IoU threshold for overlap removal
+        
+        Returns:
+            list: Non-overlapping face detections
+        """
+        if len(faces) <= 1:
+            return faces
+        
+        non_overlapping = []
+        for i, face1 in enumerate(faces):
+            bbox1 = face1['bbox']
+            keep = True
+            
+            for j, face2 in enumerate(faces):
+                if i != j:
+                    bbox2 = face2['bbox']
+                    # Calculate IoU
+                    x1 = max(bbox1[0], bbox2[0])
+                    y1 = max(bbox1[1], bbox2[1])
+                    x2 = min(bbox1[0] + bbox1[2], bbox2[0] + bbox2[2])
+                    y2 = min(bbox1[1] + bbox1[3], bbox2[1] + bbox2[3])
+                    
+                    if x2 > x1 and y2 > y1:
+                        intersection = (x2 - x1) * (y2 - y1)
+                        union = (bbox1[2] * bbox1[3]) + (bbox2[2] * bbox2[3]) - intersection
+                        iou = intersection / union if union > 0 else 0
+                        
+                        if iou > overlap_threshold:
+                            # Keep the face with higher confidence
+                            if face2['confidence'] > face1['confidence']:
+                                keep = False
+                                break
+            
+            if keep:
+                non_overlapping.append(face1)
+        
+        return non_overlapping
+
+class FaceSwapper:
+    def __init__(self):
+        """Initialize face swapping functionality"""
+        self.face_analyzer = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    
+    def replace_face(self, target_image, source_image, landmarks, 
+                    replacement_strength=0.8, preserve_background=True):
+        """
+        Replace face in target image with face from source image
+        
+        Args:
+            target_image (numpy.ndarray): Target image
+            source_image (numpy.ndarray): Source image with replacement face
+            landmarks (numpy.ndarray): Facial landmarks
+            replacement_strength (float): Replacement strength (0-1)
+            preserve_background (bool): Whether to preserve background
+        
+        Returns:
+            numpy.ndarray: Image with replaced face
+        """
+        try:
+            # Create a mask based on facial landmarks
+            mask = self.create_face_mask(target_image, landmarks)
+            
+            # Apply color transfer for better blending
+            source_face = self.extract_face_region(source_image, landmarks)
+            target_face = self.extract_face_region(target_image, landmarks)
+            
+            # Apply color matching if preserve_background is True
+            if preserve_background:
+                source_face = self.match_color_statistics(source_face, target_face)
+            
+            # Blend the faces
+            result = target_image.copy()
+            for i in range(3):  # For each color channel
+                result[:, :, i] = (1 - replacement_strength) * target_image[:, :, i] + \
+                                replacement_strength * source_face[:, :, i] * mask + \
+                                target_image[:, :, i] * (1 - mask)
+            
+            return result.astype(np.uint8)
+            
+        except Exception as e:
+            print(f"Face replacement error: {e}")
+            return target_image
+    
+    def create_face_mask(self, image, landmarks):
+        """
+        Create a mask for the face region
+        
+        Args:
+            image (numpy.ndarray): Input image
+            landmarks (numpy.ndarray): Facial landmarks
+        
+        Returns:
+            numpy.ndarray: Face mask
+        """
+        mask = np.zeros(image.shape[:2], dtype=np.float32)
+        
+        # Use convex hull of landmarks to create face mask
+        hull = cv2.convexHull(landmarks.astype(np.int32))
+        cv2.fillPoly(mask, [hull], 1.0)
+        
+        # Apply Gaussian blur for smooth edges
+        mask = cv2.GaussianBlur(mask, (15, 15), 0)
+        
+        return mask
+    
+    def extract_face_region(self, image, landmarks):
+        """
+        Extract face region based on landmarks
+        
+        Args:
+            image (numpy.ndarray): Input image
+            landmarks (numpy.ndarray): Facial landmarks
+        
+        Returns:
+            numpy.ndarray: Extracted face region
+        """
+        # Get bounding box of face
+        x_min = int(np.min(landmarks[:, 0]))
+        x_max = int(np.max(landmarks[:, 0]))
+        y_min = int(np.min(landmarks[:, 1]))
+        y_max = int(np.max(landmarks[:, 1]))
+        
+        # Expand bounding box slightly
+        padding = 20
+        x_min = max(0, x_min - padding)
+        x_max = min(image.shape[1], x_max + padding)
+        y_min = max(0, y_min - padding)
+        y_max = min(image.shape[0], y_max + padding)
+        
+        return image[y_min:y_max, x_min:x_max]
+    
+    def match_color_statistics(self, source, target):
+        """
+        Match color statistics between source and target faces
+        
+        Args:
+            source (numpy.ndarray): Source face
+            target (numpy.ndarray): Target face
+        
+        Returns:
+            numpy.ndarray: Color-matched source face
+        """
+        result = source.copy().astype(np.float32)
+        
+        for i in range(3):  # For each color channel
+            source_mean = np.mean(source[:, :, i])
+            source_std = np.std(source[:, :, i])
+            target_mean = np.mean(target[:, :, i])
+            target_std = np.std(target[:, :, i])
+            
+            # Avoid division by zero
+            if source_std > 0:
+                result[:, :, i] = (source[:, :, i] - source_mean) * (target_std / source_std) + target_mean
+        
+        return np.clip(result, 0, 255).astype(np.uint8)