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import numpy as np
import cv2
from insightface.utils import face_align
from insightface import model_zoo
from dofaker.utils import download_file, get_model_url
class GFPGAN:
def __init__(self, name='gfpgan', root='weights/models') -> None:
_, model_file = download_file(get_model_url(name),
save_dir=root,
overwrite=False)
providers = model_zoo.model_zoo.get_default_providers()
self.session = model_zoo.model_zoo.PickableInferenceSession(
model_file, providers=providers)
self.input_mean = 127.5
self.input_std = 127.5
inputs = self.session.get_inputs()
self.input_names = []
for inp in inputs:
self.input_names.append(inp.name)
outputs = self.session.get_outputs()
output_names = []
for out in outputs:
output_names.append(out.name)
self.output_names = output_names
assert len(
self.output_names
) == 1, "The output number of GFPGAN model should be 1, but got {}, please check your model.".format(
len(self.output_names))
output_shape = outputs[0].shape
input_cfg = inputs[0]
input_shape = input_cfg.shape
self.input_shape = input_shape
print('face_enhance-shape:', self.input_shape)
self.input_size = tuple(input_shape[2:4][::-1])
def forward(self, image, image_format='bgr'):
if isinstance(image, str):
image = cv2.imread(image, 1)
elif isinstance(image, np.ndarray):
if image_format == 'bgr':
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
elif image_format == 'rgb':
pass
else:
raise UserWarning(
"gfpgan not support image format {}".format(image_format))
else:
raise UserWarning(
"gfpgan input must be str or np.ndarray, but got {}.".format(
type(image)))
img = (image - self.input_mean) / self.input_std
pred = self.session.run(self.output_names,
{self.input_names[0]: img})[0]
return pred
def _get(self, img, image_format='bgr'):
if image_format.lower() == 'bgr':
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
elif image_format.lower() == 'rgb':
pass
else:
raise UserWarning(
"gfpgan not support image format {}".format(image_format))
h, w, c = img.shape
img = cv2.resize(img, (self.input_shape[-1], self.input_shape[-2]))
blob = cv2.dnn.blobFromImage(
img,
1.0 / self.input_std,
self.input_size,
(self.input_mean, self.input_mean, self.input_mean),
swapRB=False)
pred = self.session.run(self.output_names,
{self.input_names[0]: blob})[0]
image_aug = pred.transpose((0, 2, 3, 1))[0]
rgb_aug = np.clip(self.input_std * image_aug + self.input_mean, 0,
255).astype(np.uint8)
rgb_aug = cv2.resize(rgb_aug, (w, h))
bgr_image = rgb_aug[:, :, ::-1]
return bgr_image
def get(self, img, target_face, paste_back=True, image_format='bgr'):
aimg, M = face_align.norm_crop2(img, target_face.kps,
self.input_size[0])
bgr_fake = self._get(aimg, image_format='bgr')
if not paste_back:
return bgr_fake, M
else:
target_img = img
fake_diff = bgr_fake.astype(np.float32) - aimg.astype(np.float32)
fake_diff = np.abs(fake_diff).mean(axis=2)
fake_diff[:2, :] = 0
fake_diff[-2:, :] = 0
fake_diff[:, :2] = 0
fake_diff[:, -2:] = 0
IM = cv2.invertAffineTransform(M)
img_white = np.full((aimg.shape[0], aimg.shape[1]),
255,
dtype=np.float32)
bgr_fake = cv2.warpAffine(
bgr_fake,
IM, (target_img.shape[1], target_img.shape[0]),
borderValue=0.0)
img_white = cv2.warpAffine(
img_white,
IM, (target_img.shape[1], target_img.shape[0]),
borderValue=0.0)
fake_diff = cv2.warpAffine(
fake_diff,
IM, (target_img.shape[1], target_img.shape[0]),
borderValue=0.0)
img_white[img_white > 20] = 255
fthresh = 10
fake_diff[fake_diff < fthresh] = 0
fake_diff[fake_diff >= fthresh] = 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)
fake_diff = cv2.dilate(fake_diff, kernel, iterations=1)
k = max(mask_size // 20, 5)
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)
fake_diff = cv2.GaussianBlur(fake_diff, blur_size, 0)
img_mask /= 255
fake_diff /= 255
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
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