Delete face_enhance

face_enhance/8

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-o

face_enhance/__init__.py

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-from .gfpgan import GFPGAN

face_enhance/gfpgan.py

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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