rtmo preprocessing

[lishiyu005]

Dear author,

I wonder if the input image should be in the center of the padded image? But in the code, the input image is on the top of the pad image.

In my opinion the code should be:

padded_img[int((self.model_input_size[0]-padded_shape[0])/2):int((self.model_input_size[0]-padded_shape[0])/2) + padded_shape[0], int((self.model_input_size[1]-padded_shape[1])/2):int((self.model_input_size[1]-padded_shape[1])/2) +padded_shape[1]] = resized_img

Best regards

def preprocess(self, img: np.ndarray):
        """Do preprocessing for RTMPose model inference.

        Args:
            img (np.ndarray): Input image in shape.

        Returns:
            tuple:
            - resized_img (np.ndarray): Preprocessed image.
            - center (np.ndarray): Center of image.
            - scale (np.ndarray): Scale of image.
        """
        if len(img.shape) == 3:
            padded_img = np.ones(
                (self.model_input_size[0], self.model_input_size[1], 3),
                dtype=np.uint8) * 114
        else:
            padded_img = np.ones(self.model_input_size, dtype=np.uint8) * 114

        ratio = min(self.model_input_size[0] / img.shape[0],
                    self.model_input_size[1] / img.shape[1])
        resized_img = cv2.resize(
            img,
            (int(img.shape[1] * ratio), int(img.shape[0] * ratio)),
            interpolation=cv2.INTER_LINEAR,
        ).astype(np.uint8)
        padded_shape = (int(img.shape[0] * ratio), int(img.shape[1] * ratio))
        padded_img[:padded_shape[0], :padded_shape[1]] = resized_img

        # normalize image
        if self.mean is not None:
            self.mean = np.array(self.mean)
            self.std = np.array(self.std)
            padded_img = (padded_img - self.mean) / self.std

        return padded_img, ratio