lib/data/EvalDataset.py [93:131]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        im = crop_image(im, rect)

        scale_im2ndc = 1.0 / float(w // 2)
        scale = w / rect[2]
        trans_mat *= scale
        trans_mat[3,3] = 1.0
        trans_mat[0, 3] = -scale*(rect[0] + rect[2]//2 - w//2) * scale_im2ndc
        trans_mat[1, 3] = scale*(rect[1] + rect[3]//2 - h//2) * scale_im2ndc
        
        intrinsic = np.matmul(trans_mat, intrinsic)
        im_512 = cv2.resize(im, (512, 512))
        im = cv2.resize(im, (self.load_size, self.load_size))

        image_512 = Image.fromarray(im_512[:,:,::-1]).convert('RGB')
        image = Image.fromarray(im[:,:,::-1]).convert('RGB')
        
        B_MIN = np.array([-1, -1, -1])
        B_MAX = np.array([1, 1, 1])
        projection_matrix = np.identity(4)
        projection_matrix[1, 1] = -1
        calib = torch.Tensor(projection_matrix).float()

        calib_world = torch.Tensor(intrinsic).float()

        # image
        image_512 = self.to_tensor(image_512)
        image = self.to_tensor(image)
        return {
            'name': img_name,
            'img': image.unsqueeze(0),
            'img_512': image_512.unsqueeze(0),
            'calib': calib.unsqueeze(0),
            'calib_world': calib_world.unsqueeze(0),
            'b_min': B_MIN,
            'b_max': B_MAX,
        }

    def __getitem__(self, index):
        return self.get_item(index)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



lib/data/EvalWPoseDataset.py [244:282]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        im = crop_image(im, rect)

        scale_im2ndc = 1.0 / float(w // 2)
        scale = w / rect[2]
        trans_mat *= scale
        trans_mat[3,3] = 1.0
        trans_mat[0, 3] = -scale*(rect[0] + rect[2]//2 - w//2) * scale_im2ndc
        trans_mat[1, 3] = scale*(rect[1] + rect[3]//2 - h//2) * scale_im2ndc
        
        intrinsic = np.matmul(trans_mat, intrinsic)
        im_512 = cv2.resize(im, (512, 512))
        im = cv2.resize(im, (self.load_size, self.load_size))

        image_512 = Image.fromarray(im_512[:,:,::-1]).convert('RGB')
        image = Image.fromarray(im[:,:,::-1]).convert('RGB')
        
        B_MIN = np.array([-1, -1, -1])
        B_MAX = np.array([1, 1, 1])
        projection_matrix = np.identity(4)
        projection_matrix[1, 1] = -1
        calib = torch.Tensor(projection_matrix).float()

        calib_world = torch.Tensor(intrinsic).float()

        # image
        image_512 = self.to_tensor(image_512)
        image = self.to_tensor(image)
        return {
            'name': img_name,
            'img': image.unsqueeze(0),
            'img_512': image_512.unsqueeze(0),
            'calib': calib.unsqueeze(0),
            'calib_world': calib_world.unsqueeze(0),
            'b_min': B_MIN,
            'b_max': B_MAX,
        }

    def __getitem__(self, index):
        return self.get_item(index)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -