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| from typing import Dict | |
| import cv2 | |
| import numpy as np | |
| from skimage.filters import gaussian | |
| from yacs.config import CfgNode | |
| import torch | |
| from .utils import (convert_cvimg_to_tensor, | |
| expand_to_aspect_ratio, | |
| generate_image_patch_cv2) | |
| DEFAULT_MEAN = 255. * np.array([0.485, 0.456, 0.406]) | |
| DEFAULT_STD = 255. * np.array([0.229, 0.224, 0.225]) | |
| class ViTDetDataset(torch.utils.data.Dataset): | |
| def __init__(self, | |
| cfg: CfgNode, | |
| img_cv2: np.array, | |
| boxes: np.array, | |
| right: np.array, | |
| rescale_factor=2.5, | |
| train: bool = False, | |
| **kwargs): | |
| super().__init__() | |
| self.cfg = cfg | |
| self.img_cv2 = img_cv2 | |
| # self.boxes = boxes | |
| assert train == False, "ViTDetDataset is only for inference" | |
| self.train = train | |
| self.img_size = cfg.MODEL.IMAGE_SIZE | |
| self.mean = 255. * np.array(self.cfg.MODEL.IMAGE_MEAN) | |
| self.std = 255. * np.array(self.cfg.MODEL.IMAGE_STD) | |
| # Preprocess annotations | |
| boxes = boxes.astype(np.float32) | |
| self.center = (boxes[:, 2:4] + boxes[:, 0:2]) / 2.0 | |
| self.scale = rescale_factor * (boxes[:, 2:4] - boxes[:, 0:2]) / 200.0 | |
| #self.scale = (boxes[:, 2:4] - boxes[:, 0:2]) / 200.0 | |
| self.personid = np.arange(len(boxes), dtype=np.int32) | |
| self.right = right.astype(np.float32) | |
| def __len__(self) -> int: | |
| return len(self.personid) | |
| def __getitem__(self, idx: int) -> Dict[str, np.array]: | |
| center = self.center[idx].copy() | |
| center_x = center[0] | |
| center_y = center[1] | |
| scale = self.scale[idx] | |
| BBOX_SHAPE = self.cfg.MODEL.get('BBOX_SHAPE', None) | |
| bbox_size = expand_to_aspect_ratio(scale*200, target_aspect_ratio=BBOX_SHAPE).max() | |
| #bbox_size = scale.max()*200 | |
| patch_width = patch_height = self.img_size | |
| right = self.right[idx].copy() | |
| flip = right == 0 | |
| # 3. generate image patch | |
| # if use_skimage_antialias: | |
| cvimg = self.img_cv2.copy() | |
| if True: | |
| # Blur image to avoid aliasing artifacts | |
| downsampling_factor = ((bbox_size*1.0) / patch_width) | |
| print(f'{downsampling_factor=}') | |
| downsampling_factor = downsampling_factor / 2.0 | |
| if downsampling_factor > 1.1: | |
| cvimg = gaussian(cvimg, sigma=(downsampling_factor-1)/2, channel_axis=2, preserve_range=True) | |
| img_patch_cv, trans = generate_image_patch_cv2(cvimg, | |
| center_x, center_y, | |
| bbox_size, bbox_size, | |
| patch_width, patch_height, | |
| flip, 1.0, 0, | |
| border_mode=cv2.BORDER_CONSTANT) | |
| img_patch_cv = img_patch_cv[:, :, ::-1] | |
| img_patch = convert_cvimg_to_tensor(img_patch_cv) | |
| # apply normalization | |
| for n_c in range(min(self.img_cv2.shape[2], 3)): | |
| img_patch[n_c, :, :] = (img_patch[n_c, :, :] - self.mean[n_c]) / self.std[n_c] | |
| item = { | |
| 'img': img_patch, | |
| 'personid': int(self.personid[idx]), | |
| } | |
| item['box_center'] = self.center[idx].copy() | |
| item['box_size'] = bbox_size | |
| item['img_size'] = 1.0 * np.array([cvimg.shape[1], cvimg.shape[0]]) | |
| item['right'] = self.right[idx].copy() | |
| return item | |