Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,882 Bytes
6527198 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
import cv2
import numpy as np
# The following code is almost entirely copied from INSwapper; the only change here is that we want to use Lanczos
# interpolation for the warpAffine call. Now that the face has been restored, Lanczos represents a good compromise
# whether the restored face needs to be upscaled or downscaled.
def in_swap(img, bgr_fake, M):
target_img = img
IM = cv2.invertAffineTransform(M)
img_white = np.full((bgr_fake.shape[0], bgr_fake.shape[1]), 255, dtype=np.float32)
# Note the use of bicubic here; this is functionally the only change from the source code
bgr_fake = cv2.warpAffine(bgr_fake, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0, flags=cv2.INTER_CUBIC)
img_white = cv2.warpAffine(img_white, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
img_white[img_white > 20] = 255
img_mask = img_white
mask_h_inds, mask_w_inds = np.where(img_mask == 255)
mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
mask_size = int(np.sqrt(mask_h * mask_w))
k = max(mask_size // 10, 10)
# k = max(mask_size//20, 6)
# k = 6
kernel = np.ones((k, k), np.uint8)
img_mask = cv2.erode(img_mask, kernel, iterations=1)
kernel = np.ones((2, 2), np.uint8)
k = max(mask_size // 20, 5)
# k = 3
# k = 3
kernel_size = (k, k)
blur_size = tuple(2 * i + 1 for i in kernel_size)
img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
k = 5
kernel_size = (k, k)
blur_size = tuple(2 * i + 1 for i in kernel_size)
img_mask /= 255
# img_mask = fake_diff
img_mask = np.reshape(img_mask, [img_mask.shape[0], img_mask.shape[1], 1])
fake_merged = img_mask * bgr_fake + (1 - img_mask) * target_img.astype(np.float32)
fake_merged = fake_merged.astype(np.uint8)
return fake_merged
|