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| import time | |
| import numpy as np | |
| import onnxruntime | |
| import cv2 | |
| import onnx | |
| from onnx import numpy_helper | |
| from ..utils import face_align | |
| class INSwapper(): | |
| def __init__(self, model_file=None, session=None): | |
| self.model_file = model_file | |
| self.session = session | |
| model = onnx.load(self.model_file) | |
| graph = model.graph | |
| self.emap = numpy_helper.to_array(graph.initializer[-1]) | |
| self.input_mean = 0.0 | |
| self.input_std = 255.0 | |
| #print('input mean and std:', model_file, self.input_mean, self.input_std) | |
| if self.session is None: | |
| self.session = onnxruntime.InferenceSession(self.model_file, None) | |
| 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 | |
| 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): | |
| face_mask = np.zeros((img.shape[0], img.shape[1]), np.uint8) | |
| cv2.fillPoly(face_mask, np.array([target_face.landmark_2d_106[[1,9,10,11,12,13,14,15,16,2,3,4,5,6,7,8,0,24,23,22,21,20,19,18,32,31,30,29,28,27,26,25,17,101,105,104,103,51,49,48,43]].astype('int64')]), 1) | |
| 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] | |
| #print(latent.shape, latent.dtype, pred.shape) | |
| 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) | |
| face_mask = cv2.erode(face_mask,np.ones((11,11),np.uint8),iterations = 1) | |
| fake_diff[face_mask==1] = 255 | |
| 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.blur(fake_diff, (11,11), 0) | |
| ##fake_diff = cv2.GaussianBlur(fake_diff, blur_size, 0) | |
| # print('blur_size: ', blur_size) | |
| # fake_diff = cv2.blur(fake_diff, (21, 21), 0) # blur_size | |
| img_mask /= 255 | |
| fake_diff /= 255 | |
| # img_mask = fake_diff | |
| img_mask = 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 | |