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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