Delete super_resolution

super_resolution/0

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-y

super_resolution/__init__.py

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-from .bsrgan import BSRGAN

super_resolution/bsrgan.py

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-import numpy as np
-
-import cv2
-
-from insightface import model_zoo
-from dofaker.utils import download_file, get_model_url
-
-
-class BSRGAN:
-
-    def __init__(self, name='bsrgan', root='weights/models', scale=1) -> None:
-        _, model_file = download_file(get_model_url(name),
-                                      save_dir=root,
-                                      overwrite=False)
-        self.scale = scale
-        providers = model_zoo.model_zoo.get_default_providers()
-        self.session = model_zoo.model_zoo.PickableInferenceSession(
-            model_file, providers=providers)
-
-        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 BSRGAN 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('image super resolution shape:', self.input_shape)
-
-    def forward(self, image, image_format='bgr'):
-        if isinstance(image, str):
-            image = cv2.imread(image, 1)
-            image_format = 'bgr'
-        elif isinstance(image, np.ndarray):
-            if image_format == 'bgr':
-                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            elif image_format == 'rgb':
-                pass
-            else:
-                raise UserWarning(
-                    "BSRGAN not support image format {}".format(image_format))
-        else:
-            raise UserWarning(
-                "BSRGAN 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
-        blob = cv2.dnn.blobFromImage(
-            img,
-            1.0 / self.input_std, (w, h),
-            (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,
-                             (int(w * self.scale), int(h * self.scale)))
-        bgr_aug = rgb_aug[:, :, ::-1]
-        return bgr_aug