3D Mask Rendering

class MaskRenderer:
    def __init__(self, name='buffalo_l', root='~/.insightface', insfa=None):
        """
        Initialize 3D mask renderer.
        
        Parameters:
        - name: str, model pack name containing 3D morphable model
        - root: str, model storage directory
        - insfa: FaceAnalysis instance, if provided will use for face detection
        """
    
    def prepare(self, ctx_id=0, det_thresh=0.5, det_size=(128, 128)):
        """
        Prepare renderer for inference.
        
        Parameters:
        - ctx_id: int, device context ID
        - det_thresh: float, detection confidence threshold
        - det_size: tuple, detection input size for face analysis
        """
    
    def build_params(self, face_image):
        """
        Build 3D face parameters from input image.
        
        Parameters:
        - face_image: np.ndarray, input face image
        
        Returns:
        dict: 3D face parameters including shape, expression, pose, and texture
        """
    
    def render_mask(self, face_image, mask_image, params, input_is_rgb=False, auto_blend=True, positions=[0.1, 0.33, 0.9, 0.7]):
        """
        Render 3D mask on face image.
        
        Parameters:
        - face_image: np.ndarray, target face image
        - mask_image: np.ndarray, mask texture image
        - params: dict, 3D face parameters from build_params()
        - input_is_rgb: bool, whether input images are RGB (default: BGR)
        - auto_blend: bool, automatically blend mask edges
        - positions: list, mask positioning parameters [x_offset, y_offset, width_scale, height_scale]
        
        Returns:
        np.ndarray: image with rendered 3D mask
        """
    
    def draw_lmk(self, face_image):
        """
        Draw 3D landmarks on face image for debugging.
        
        Parameters:
        - face_image: np.ndarray, input face image
        
        Returns:
        np.ndarray: image with drawn 3D landmarks
        """

@staticmethod
def encode_params(params):
    """
    Encode face parameters for storage.
    
    Parameters:
    - params: dict, face parameters dictionary
    
    Returns:
    bytes: encoded parameter data
    """

@staticmethod 
def decode_params(params):
    """
    Decode stored face parameters.
    
    Parameters:
    - params: bytes, encoded parameter data
    
    Returns:
    dict: decoded face parameters
    """

class MaskAugmentation(ImageOnlyTransform):
    def __init__(self, mask_names=['mask_white', 'mask_blue', 'mask_black', 'mask_green'],
                 mask_probs=[0.4, 0.4, 0.1, 0.1], h_low=0.33, h_high=0.35, always_apply=False, p=1.0):
        """
        Initialize mask augmentation transform.
        
        Parameters:
        - mask_names: list, available mask types
        - mask_probs: list, probability for each mask type
        - h_low: float, minimum mask height ratio
        - h_high: float, maximum mask height ratio  
        - always_apply: bool, whether to always apply transform
        - p: float, probability of applying transform
        """
    
    def apply(self, image, hlabel, mask_name, h_pos, **params):
        """
        Apply mask augmentation to image.
        
        Parameters:
        - image: np.ndarray, input image
        - hlabel: float, mask height label
        - mask_name: str, mask type to apply
        - h_pos: float, vertical position of mask
        - **params: additional parameters
        
        Returns:
        np.ndarray: augmented image with mask
        """

import cv2
from insightface.app.mask_renderer import MaskRenderer

# Initialize mask renderer
renderer = MaskRenderer(name='buffalo_l')
renderer.prepare(ctx_id=0)

# Load face and mask images
face_img = cv2.imread('person.jpg')
mask_img = cv2.imread('surgical_mask.png')

# Build 3D face parameters
params = renderer.build_params(face_img)
print(f"Built parameters with keys: {list(params.keys())}")

# Render mask on face
result = renderer.render_mask(face_img, mask_img, params, auto_blend=True)

# Save result
cv2.imwrite('masked_face.jpg', result)

# Fine-tune mask position and size
positions = [0.05, 0.4, 0.8, 0.6]  # [x_offset, y_offset, width_scale, height_scale]

result = renderer.render_mask(
    face_img, 
    mask_img, 
    params,
    positions=positions,
    auto_blend=True
)

cv2.imwrite('custom_positioned_mask.jpg', result)

from insightface.app import FaceAnalysis

# Setup face analysis and mask renderer
app = FaceAnalysis()
app.prepare(ctx_id=0)

renderer = MaskRenderer()
renderer.prepare(ctx_id=0)

def render_masks_on_all_faces(image, mask_image):
    """Apply masks to all detected faces in an image."""
    faces = app.get(image)
    result_img = image.copy()
    
    for i, face in enumerate(faces):
        # Extract face region
        x1, y1, x2, y2 = face.bbox.astype(int)
        face_region = image[y1:y2, x1:x2]
        
        if face_region.size > 0:
            # Build parameters for this face
            params = renderer.build_params(face_region)
            
            # Render mask on face region
            masked_region = renderer.render_mask(face_region, mask_image, params)
            
            # Paste back to original image
            result_img[y1:y2, x1:x2] = masked_region
    
    return result_img

# Process group photo
group_img = cv2.imread('group_photo.jpg')
mask_texture = cv2.imread('blue_mask.png')

masked_group = render_masks_on_all_faces(group_img, mask_texture)
cv2.imwrite('masked_group.jpg', masked_group)

# Build and save parameters for later use
face_img = cv2.imread('face.jpg')
params = renderer.build_params(face_img)

# Encode parameters for storage
encoded_params = MaskRenderer.encode_params(params)
print(f"Encoded parameters size: {len(encoded_params)} bytes")

# Save to file
with open('face_params.bin', 'wb') as f:
    f.write(encoded_params)

# Later: load and decode parameters
with open('face_params.bin', 'rb') as f:
    loaded_encoded = f.read()

decoded_params = MaskRenderer.decode_params(loaded_encoded)

# Use loaded parameters for rendering
mask_img = cv2.imread('new_mask.png')
result = renderer.render_mask(face_img, mask_img, decoded_params)

import albumentations as A
from insightface.app.mask_renderer import MaskAugmentation

# Setup augmentation pipeline
transform = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.3),
    MaskAugmentation(
        mask_names=['mask_white', 'mask_blue', 'mask_surgical'],
        mask_probs=[0.3, 0.3, 0.4],
        p=0.7  # 70% chance to apply mask
    ),
    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Apply to training images
def augment_training_data(image_path):
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    augmented = transform(image=image)
    return augmented['image']

# Process training dataset
training_images = ['face1.jpg', 'face2.jpg', 'face3.jpg']
augmented_data = []

for img_path in training_images:
    # Generate multiple augmented versions
    for i in range(5):
        aug_image = augment_training_data(img_path)
        augmented_data.append(aug_image)

print(f"Generated {len(augmented_data)} augmented training samples")

# Visualize 3D face fitting quality
def visualize_3d_fitting(face_image):
    """Visualize 3D face model fitting quality."""
    # Build 3D parameters
    params = renderer.build_params(face_image)
    
    # Draw 3D landmarks
    landmark_img = renderer.draw_lmk(face_image)
    
    # Create side-by-side comparison
    combined = np.hstack([face_image, landmark_img])
    
    return combined, params

face_img = cv2.imread('test_face.jpg')
comparison, params = visualize_3d_fitting(face_img)

cv2.imwrite('3d_fitting_comparison.jpg', comparison)
print(f"3D model parameters: {params.keys()}")

# Apply different mask types with varying parameters
mask_types = {
    'surgical': {
        'image': 'surgical_mask.png',
        'positions': [0.1, 0.35, 0.8, 0.5]
    },
    'n95': {
        'image': 'n95_mask.png', 
        'positions': [0.05, 0.3, 0.9, 0.6]
    },
    'cloth': {
        'image': 'cloth_mask.png',
        'positions': [0.08, 0.38, 0.85, 0.45]
    }
}

def render_different_masks(face_image, params):
    """Render different mask types on the same face."""
    results = {}
    
    for mask_name, config in mask_types.items():
        mask_img = cv2.imread(config['image'])
        
        result = renderer.render_mask(
            face_image,
            mask_img, 
            params,
            positions=config['positions'],
            auto_blend=True
        )
        
        results[mask_name] = result
    
    return results

# Apply different masks
face_img = cv2.imread('person.jpg')
params = renderer.build_params(face_img)
mask_results = render_different_masks(face_img, params)

# Save results
for mask_type, result_img in mask_results.items():
    cv2.imwrite(f'face_with_{mask_type}_mask.jpg', result_img)

import time

def real_time_mask_rendering():
    """Real-time mask rendering from webcam."""
    cap = cv2.VideoCapture(0)
    
    # Load mask image
    mask_img = cv2.imread('default_mask.png')
    
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        try:
            # Build parameters for current frame
            params = renderer.build_params(frame)
            
            # Render mask
            masked_frame = renderer.render_mask(frame, mask_img, params)
            
            cv2.imshow('Real-time Mask', masked_frame)
            
        except Exception as e:
            # Fallback to original frame if rendering fails
            cv2.imshow('Real-time Mask', frame)
        
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    
    cap.release()
    cv2.destroyAllWindows()

# Run real-time rendering (uncomment to use)
# real_time_mask_rendering()