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import numpy as np
import cv2
import onnx
from onnx import numpy_helper
from insightface import model_zoo
from insightface.utils import face_align
from .base_swapper import BaseSwapper
from dofaker.utils import download_file, get_model_url
class InSwapper(BaseSwapper):
def __init__(self, name='inswapper', root='weights/models'):
_, 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)
model = onnx.load(model_file)
graph = model.graph
self.emap = numpy_helper.to_array(graph.initializer[-1])
self.input_mean = 0.0
self.input_std = 255.0
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 inswapper 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('inswapper-shape:', self.input_shape)
self.input_size = tuple(input_shape[2:4][::-1])
def forward(self, img, latent):
img = (img - self.input_mean) / self.input_std
pred = self.session.run(self.output_names, {
self.input_names[0]: img,
self.input_names[1]: latent
})[0]
return pred
def get(self, img, target_face, source_face, paste_back=True):
aimg, M = face_align.norm_crop2(img, target_face.kps,
self.input_size[0])
blob = cv2.dnn.blobFromImage(
aimg,
1.0 / self.input_std,
self.input_size,
(self.input_mean, self.input_mean, self.input_mean),
swapRB=True)
latent = source_face.normed_embedding.reshape((1, -1))
latent = np.dot(latent, self.emap)
latent /= np.linalg.norm(latent)
pred = self.session.run(self.output_names, {
self.input_names[0]: blob,
self.input_names[1]: latent
})[0]
img_fake = pred.transpose((0, 2, 3, 1))[0]
bgr_fake = np.clip(255 * img_fake, 0, 255).astype(np.uint8)[:, :, ::-1]
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)
#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)
fake_diff = cv2.GaussianBlur(fake_diff, blur_size, 0)
img_mask /= 255
fake_diff /= 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
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