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import cv2
import numpy as np
ffhq_512 = np.array([
[ 0.37691676, 0.46864664 ],
[ 0.62285697, 0.46912813 ],
[ 0.50123859, 0.61331904 ],
[ 0.39308822, 0.72541100 ],
[ 0.61150205, 0.72490465 ]
])
def warp_face_by_landmark(image , face_landmark_5, crop_size ):
normed_warp_template = ffhq_512 * crop_size
affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
cropped = cv2.warpAffine(image, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA)
return cropped, affine_matrix
def create_box_mask(crop_size, face_mask_blur, face_mask_padding):
blur_amount = int(crop_size[0] * 0.5 * face_mask_blur)
blur_area = max(blur_amount // 2, 1)
box_mask = np.ones(crop_size, np.float32)
box_mask[:max(blur_area, int(crop_size[1] * face_mask_padding[0] / 100)), :] = 0
box_mask[-max(blur_area, int(crop_size[1] * face_mask_padding[2] / 100)):, :] = 0
box_mask[:, :max(blur_area, int(crop_size[0] * face_mask_padding[3] / 100))] = 0
box_mask[:, -max(blur_area, int(crop_size[0] * face_mask_padding[1] / 100)):] = 0
if blur_amount > 0:
box_mask = cv2.GaussianBlur(box_mask, (0, 0), blur_amount * 0.25)
return box_mask
def paste_back(image, cropped, crop_mask, affine_matrix):
inverse_matrix = cv2.invertAffineTransform(affine_matrix)
temp_size = image.shape[:2][::-1]
inverse_mask = cv2.warpAffine(crop_mask, inverse_matrix, temp_size).clip(0, 1)
inverse_vision_frame = cv2.warpAffine(cropped, inverse_matrix, temp_size, borderMode = cv2.BORDER_REPLICATE)
paste_vision_frame = image.copy()
paste_vision_frame[:, :, 0] = inverse_mask * inverse_vision_frame[:, :, 0] + (1 - inverse_mask) * image[:, :, 0]
paste_vision_frame[:, :, 1] = inverse_mask * inverse_vision_frame[:, :, 1] + (1 - inverse_mask) * image[:, :, 1]
paste_vision_frame[:, :, 2] = inverse_mask * inverse_vision_frame[:, :, 2] + (1 - inverse_mask) * image[:, :, 2]
return paste_vision_frame
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