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dofaker/face_swap/__init__.py

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+from .inswapper import InSwapper
+
+
+def get_swapper_model(name='', root=None, **kwargs):
+    if name.lower() == 'inswapper':
+        return InSwapper(name=name, root=root, **kwargs)
+    else:
+        raise UserWarning('The swapper model {} not support.'.format(name))

dofaker/face_swap/base_swapper.py

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+class BaseSwapper:
+
+    def forward(self, img, latent, *args, **kwargs):
+        raise NotImplementedError
+
+    def get(self,
+            img,
+            target_face,
+            source_face,
+            paste_back=True,
+            *args,
+            **kwargs):
+        raise NotImplementedError

dofaker/face_swap/inswapper.py

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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