265 lines
9.3 KiB
Python
265 lines
9.3 KiB
Python
import cv2
|
|
import numpy as np
|
|
import pandas as pd
|
|
import torch
|
|
from PIL import Image
|
|
from typing import Union, Optional, Tuple
|
|
|
|
class ImageProcessor:
|
|
@staticmethod
|
|
def _convert_to_numpy(image: Union[torch.Tensor, np.ndarray, Image.Image]) -> np.ndarray:
|
|
"""Convert different image types to numpy array."""
|
|
if torch.is_tensor(image):
|
|
image = image.detach().cpu().numpy()
|
|
if len(image.shape) == 4:
|
|
image = image[0]
|
|
image = (image * 255).astype(np.uint8)
|
|
elif isinstance(image, Image.Image):
|
|
image = np.array(image)
|
|
elif isinstance(image, np.ndarray):
|
|
if image.dtype == np.float32 and image.max() <= 1.0:
|
|
image = (image * 255).astype(np.uint8)
|
|
if len(image.shape) == 4:
|
|
image = image[0]
|
|
|
|
if len(image.shape) == 2:
|
|
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
|
|
elif image.shape[2] == 4:
|
|
image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
|
|
|
|
return image
|
|
|
|
@staticmethod
|
|
def calculate_face_bbox(landmarks_df: pd.DataFrame, padding_percent: float = 0.0) -> Optional[Tuple[int, int, int, int]]:
|
|
"""Calculate bounding box for face based on landmarks."""
|
|
if landmarks_df is None or landmarks_df.empty:
|
|
return None
|
|
|
|
min_x = landmarks_df['x'].min()
|
|
max_x = landmarks_df['x'].max()
|
|
min_y = landmarks_df['y'].min()
|
|
max_y = landmarks_df['y'].max()
|
|
|
|
width = max_x - min_x
|
|
height = max_y - min_y
|
|
|
|
pad_x = width * padding_percent
|
|
pad_y = height * padding_percent
|
|
|
|
x1 = max(0, min_x - pad_x)
|
|
y1 = max(0, min_y - pad_y)
|
|
x2 = max_x + pad_x
|
|
y2 = max_y + pad_y
|
|
|
|
return (int(x1), int(y1), int(x2 - x1), int(y2 - y1))
|
|
|
|
@staticmethod
|
|
def crop_face(image: Union[torch.Tensor, np.ndarray, Image.Image], landmarks_df: pd.DataFrame, padding_percent: float = 0.0) -> Optional[np.ndarray]:
|
|
"""Crop face region from image based on landmarks."""
|
|
image_np = ImageProcessor._convert_to_numpy(image)
|
|
bbox = ImageProcessor.calculate_face_bbox(landmarks_df, padding_percent)
|
|
|
|
if bbox is None:
|
|
return None
|
|
|
|
x, y, w, h = bbox
|
|
return image_np[y:y + h, x:x + w]
|
|
|
|
@staticmethod
|
|
def resize_image(image: Union[torch.Tensor, np.ndarray, Image.Image], target_size: int) -> Optional[np.ndarray]:
|
|
"""Resize image to target size while maintaining aspect ratio and cropping to square."""
|
|
if image is None:
|
|
return None
|
|
|
|
image_np = ImageProcessor._convert_to_numpy(image)
|
|
h, w = image_np.shape[:2]
|
|
|
|
# Determine the smaller dimension (height or width)
|
|
min_dim = min(h, w)
|
|
|
|
# Calculate the crop box to make the image square
|
|
start_x = (w - min_dim) // 2
|
|
start_y = (h - min_dim) // 2
|
|
end_x = start_x + min_dim
|
|
end_y = start_y + min_dim
|
|
|
|
# Crop the image to a square
|
|
cropped_image = image_np[start_y:end_y, start_x:end_x]
|
|
|
|
# Resize the cropped image to the target size
|
|
resized = cv2.resize(cropped_image, (target_size, target_size), interpolation=cv2.INTER_LANCZOS4)
|
|
|
|
return resized
|
|
|
|
@staticmethod
|
|
def calculate_rotation_angle(landmarks_df: pd.DataFrame) -> float:
|
|
"""Calculate rotation angle based on eyes position."""
|
|
if landmarks_df is None or landmarks_df.empty:
|
|
return 0.0
|
|
|
|
LEFT_EYE = 33 # Center of the left eye
|
|
RIGHT_EYE = 263 # Center of the right eye
|
|
|
|
left_eye = landmarks_df[landmarks_df['index'] == LEFT_EYE].iloc[0]
|
|
right_eye = landmarks_df[landmarks_df['index'] == RIGHT_EYE].iloc[0]
|
|
|
|
dx = right_eye['x'] - left_eye['x']
|
|
dy = right_eye['y'] - left_eye['y']
|
|
return np.degrees(np.arctan2(dy, dx))
|
|
|
|
@staticmethod
|
|
def rotate_image(image: Union[torch.Tensor, np.ndarray, Image.Image], landmarks_df: pd.DataFrame) -> Tuple[Optional[np.ndarray], Optional[pd.DataFrame]]:
|
|
"""Rotate image based on facial landmarks."""
|
|
image_np = ImageProcessor._convert_to_numpy(image)
|
|
|
|
if image_np is None or landmarks_df is None:
|
|
return None, None
|
|
|
|
angle = ImageProcessor.calculate_rotation_angle(landmarks_df)
|
|
height, width = image_np.shape[:2]
|
|
center = (width // 2, height // 2)
|
|
|
|
rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
|
|
rotated_image = cv2.warpAffine(image_np, rotation_matrix, (width, height), flags=cv2.INTER_LANCZOS4)
|
|
|
|
# Transform landmarks
|
|
ones = np.ones(shape=(len(landmarks_df), 1))
|
|
points = np.hstack([landmarks_df[['x', 'y']].values, ones])
|
|
transformed_points = rotation_matrix.dot(points.T).T
|
|
|
|
updated_landmarks = landmarks_df.copy()
|
|
updated_landmarks['x'] = transformed_points[:, 0]
|
|
updated_landmarks['y'] = transformed_points[:, 1]
|
|
|
|
return rotated_image, updated_landmarks
|
|
|
|
@staticmethod
|
|
def crop_face_to_square(image: np.ndarray, landmarks_df: pd.DataFrame, padding_percent: float = 0.0) -> Tuple[
|
|
Optional[np.ndarray], Optional[Tuple[int, int, int, int]]]:
|
|
"""Crop face region to a square (1:1) based on landmarks."""
|
|
bbox = ImageProcessor.calculate_face_bbox(landmarks_df, padding_percent)
|
|
if bbox is None:
|
|
return None, None
|
|
|
|
x, y, w, h = bbox
|
|
|
|
# Calculate the center of the bounding box
|
|
center_x = x + w // 2
|
|
center_y = y + h // 2
|
|
|
|
# Determine the size of the square crop
|
|
crop_size = max(w, h)
|
|
half_size = crop_size // 2
|
|
|
|
# Calculate the crop coordinates
|
|
x1 = max(0, center_x - half_size)
|
|
y1 = max(0, center_y - half_size)
|
|
x2 = min(image.shape[1], center_x + half_size)
|
|
y2 = min(image.shape[0], center_y + half_size)
|
|
|
|
# Adjust if the crop goes out of bounds
|
|
if x2 - x1 < crop_size:
|
|
x1 = max(0, x2 - crop_size)
|
|
if y2 - y1 < crop_size:
|
|
y1 = max(0, y2 - crop_size)
|
|
|
|
# Crop the image
|
|
cropped_face = image[y1:y2, x1:x2]
|
|
|
|
# Return the cropped face and the crop bounding box
|
|
return cropped_face, (x1, y1, x2 - x1, y2 - y1)
|
|
|
|
@staticmethod
|
|
def draw_landmarks(image_size: Union[int, Tuple[int, int]],
|
|
landmarks_df: pd.DataFrame,
|
|
transparency: float = 0.5,
|
|
color: Tuple[int, int, int] = (0, 255, 0),
|
|
radius: int = 2,
|
|
label: bool = False) -> Union[np.ndarray, None]:
|
|
"""
|
|
Draw facial landmarks on a transparent image.
|
|
|
|
Args:
|
|
image_size: Target image size (int or tuple of width, height)
|
|
landmarks_df: DataFrame containing landmark coordinates
|
|
transparency: Alpha channel value (0.0-1.0)
|
|
color: RGB color tuple for landmarks
|
|
radius: Radius of landmark points
|
|
label: Whether to show landmark IDs
|
|
|
|
Returns:
|
|
numpy array: RGBA image with drawn landmarks
|
|
"""
|
|
if landmarks_df is None or landmarks_df.empty:
|
|
return None
|
|
|
|
# Create transparent image
|
|
if isinstance(image_size, tuple):
|
|
width, height = image_size
|
|
else:
|
|
width = height = image_size
|
|
|
|
# Create RGBA image (alpha channel for transparency)
|
|
image = np.zeros((height, width, 4), dtype=np.uint8)
|
|
|
|
# Set alpha channel based on transparency
|
|
alpha = int(transparency * 255)
|
|
|
|
# Draw landmarks
|
|
for _, landmark in landmarks_df.iterrows():
|
|
x = int(landmark['x'])
|
|
y = int(landmark['y'])
|
|
index = int(landmark['index'])
|
|
|
|
# Ensure coordinates are within image bounds
|
|
if 0 <= x < width and 0 <= y < height:
|
|
# Draw filled circle
|
|
cv2.circle(
|
|
image,
|
|
(x, y),
|
|
radius,
|
|
(*color, alpha),
|
|
-1, # Filled circle
|
|
cv2.LINE_AA
|
|
)
|
|
|
|
# Add landmark ID if requested
|
|
if label:
|
|
# Text parameters
|
|
font = cv2.FONT_HERSHEY_SIMPLEX
|
|
font_scale = 0.3
|
|
text_thickness = 1
|
|
|
|
# Add small offset to text position
|
|
text_x = x + radius + 2
|
|
text_y = y + radius
|
|
|
|
# Draw text with white color and black outline for better visibility
|
|
text = str(index)
|
|
|
|
# Draw text outline
|
|
cv2.putText(
|
|
image,
|
|
text,
|
|
(text_x, text_y),
|
|
font,
|
|
font_scale,
|
|
(0, 0, 0, alpha), # Black outline
|
|
text_thickness + 1,
|
|
cv2.LINE_AA
|
|
)
|
|
|
|
# Draw text
|
|
cv2.putText(
|
|
image,
|
|
text,
|
|
(text_x, text_y),
|
|
font,
|
|
font_scale,
|
|
(*color, alpha), # Main color
|
|
text_thickness,
|
|
cv2.LINE_AA
|
|
)
|
|
|
|
return image
|