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| #!/usr/bin/env python | |
| # -*- encoding: utf-8 -*- | |
| """ | |
| @Author : Peike Li | |
| @Contact : [email protected] | |
| @File : dataset.py | |
| @Time : 8/30/19 9:12 PM | |
| @Desc : Dataset Definition | |
| @License : This source code is licensed under the license found in the | |
| LICENSE file in the root directory of this source tree. | |
| """ | |
| import os | |
| import cv2 | |
| import numpy as np | |
| from torch.utils import data | |
| from utils.transforms import get_affine_transform | |
| class SimpleFolderDataset(data.Dataset): | |
| def __init__(self, root, input_size=[512, 512], transform=None): | |
| self.root = root | |
| self.input_size = input_size | |
| self.transform = transform | |
| self.aspect_ratio = input_size[1] * 1.0 / input_size[0] | |
| self.input_size = np.asarray(input_size) | |
| self.file_list=[] | |
| self.root_list=[] | |
| for root, dirs, files in os.walk(root): | |
| for file in files: | |
| if file.endswith(".jpg"): | |
| source_file_path = os.path.join(root, file) | |
| self.file_list.append(source_file_path) | |
| self.root_list.append(root) | |
| def __len__(self): | |
| return len(self.file_list) | |
| def _box2cs(self, box): | |
| x, y, w, h = box[:4] | |
| return self._xywh2cs(x, y, w, h) | |
| def _xywh2cs(self, x, y, w, h): | |
| center = np.zeros((2), dtype=np.float32) | |
| center[0] = x + w * 0.5 | |
| center[1] = y + h * 0.5 | |
| if w > self.aspect_ratio * h: | |
| h = w * 1.0 / self.aspect_ratio | |
| elif w < self.aspect_ratio * h: | |
| w = h * self.aspect_ratio | |
| scale = np.array([w, h], dtype=np.float32) | |
| return center, scale | |
| def __getitem__(self, index): | |
| img_path = self.file_list[index] | |
| root = self.root_list[index] | |
| img_name = img_path.split("/")[-1].split(".")[0] | |
| img = cv2.imread(img_path, cv2.IMREAD_COLOR) | |
| if img is None: | |
| return self.__getitem__(index+1) | |
| else: | |
| h, w, _ = img.shape | |
| # Get person center and scale | |
| person_center, s = self._box2cs([0, 0, w - 1, h - 1]) | |
| r = 0 | |
| trans = get_affine_transform(person_center, s, r, self.input_size) | |
| input = cv2.warpAffine( | |
| img, | |
| trans, | |
| (int(self.input_size[1]), int(self.input_size[0])), | |
| flags=cv2.INTER_LINEAR, | |
| borderMode=cv2.BORDER_CONSTANT, | |
| borderValue=(0, 0, 0)) | |
| input = self.transform(input) | |
| meta = { | |
| 'img_path': img_path, | |
| 'name': img_name, | |
| 'root': root, | |
| 'center': person_center, | |
| 'height': h, | |
| 'width': w, | |
| 'scale': s, | |
| 'rotation': r | |
| } | |
| return input, meta | |