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| #!/usr/bin/env python | |
| # -*- encoding: utf-8 -*- | |
| """ | |
| @Author : Peike Li | |
| @Contact : [email protected] | |
| @File : datasets.py | |
| @Time : 8/4/19 3:35 PM | |
| @Desc : | |
| @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 numpy as np | |
| import random | |
| import torch | |
| import cv2 | |
| from torch.utils import data | |
| from utils.transforms import get_affine_transform | |
| class LIPDataSet(data.Dataset): | |
| def __init__(self, root, dataset, crop_size=[473, 473], scale_factor=0.25, | |
| rotation_factor=30, ignore_label=255, transform=None): | |
| self.root = root | |
| self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0] | |
| self.crop_size = np.asarray(crop_size) | |
| self.ignore_label = ignore_label | |
| self.scale_factor = scale_factor | |
| self.rotation_factor = rotation_factor | |
| self.flip_prob = 0.5 | |
| self.transform = transform | |
| self.dataset = dataset | |
| list_path = os.path.join(self.root, self.dataset + '_id.txt') | |
| train_list = [i_id.strip() for i_id in open(list_path)] | |
| self.train_list = train_list | |
| self.number_samples = len(self.train_list) | |
| def __len__(self): | |
| return self.number_samples | |
| 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 * 1.0, h * 1.0], dtype=np.float32) | |
| return center, scale | |
| def __getitem__(self, index): | |
| train_item = self.train_list[index] | |
| im_path = os.path.join(self.root, self.dataset + '_images', train_item + '.jpg') | |
| parsing_anno_path = os.path.join(self.root, self.dataset + '_segmentations', train_item + '.png') | |
| im = cv2.imread(im_path, cv2.IMREAD_COLOR) | |
| h, w, _ = im.shape | |
| parsing_anno = np.zeros((h, w), dtype=np.long) | |
| # Get person center and scale | |
| person_center, s = self._box2cs([0, 0, w - 1, h - 1]) | |
| r = 0 | |
| if self.dataset != 'test': | |
| # Get pose annotation | |
| parsing_anno = cv2.imread(parsing_anno_path, cv2.IMREAD_GRAYSCALE) | |
| if self.dataset == 'train' or self.dataset == 'trainval': | |
| sf = self.scale_factor | |
| rf = self.rotation_factor | |
| s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf) | |
| r = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) if random.random() <= 0.6 else 0 | |
| if random.random() <= self.flip_prob: | |
| im = im[:, ::-1, :] | |
| parsing_anno = parsing_anno[:, ::-1] | |
| person_center[0] = im.shape[1] - person_center[0] - 1 | |
| right_idx = [15, 17, 19] | |
| left_idx = [14, 16, 18] | |
| for i in range(0, 3): | |
| right_pos = np.where(parsing_anno == right_idx[i]) | |
| left_pos = np.where(parsing_anno == left_idx[i]) | |
| parsing_anno[right_pos[0], right_pos[1]] = left_idx[i] | |
| parsing_anno[left_pos[0], left_pos[1]] = right_idx[i] | |
| trans = get_affine_transform(person_center, s, r, self.crop_size) | |
| input = cv2.warpAffine( | |
| im, | |
| trans, | |
| (int(self.crop_size[1]), int(self.crop_size[0])), | |
| flags=cv2.INTER_LINEAR, | |
| borderMode=cv2.BORDER_CONSTANT, | |
| borderValue=(0, 0, 0)) | |
| if self.transform: | |
| input = self.transform(input) | |
| meta = { | |
| 'name': train_item, | |
| 'center': person_center, | |
| 'height': h, | |
| 'width': w, | |
| 'scale': s, | |
| 'rotation': r | |
| } | |
| if self.dataset == 'val' or self.dataset == 'test': | |
| return input, meta | |
| else: | |
| label_parsing = cv2.warpAffine( | |
| parsing_anno, | |
| trans, | |
| (int(self.crop_size[1]), int(self.crop_size[0])), | |
| flags=cv2.INTER_NEAREST, | |
| borderMode=cv2.BORDER_CONSTANT, | |
| borderValue=(255)) | |
| label_parsing = torch.from_numpy(label_parsing) | |
| return input, label_parsing, meta | |
| class LIPDataValSet(data.Dataset): | |
| def __init__(self, root, dataset='val', crop_size=[473, 473], transform=None, flip=False): | |
| self.root = root | |
| self.crop_size = crop_size | |
| self.transform = transform | |
| self.flip = flip | |
| self.dataset = dataset | |
| self.root = root | |
| self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0] | |
| self.crop_size = np.asarray(crop_size) | |
| list_path = os.path.join(self.root, self.dataset + '_id.txt') | |
| val_list = [i_id.strip() for i_id in open(list_path)] | |
| self.val_list = val_list | |
| self.number_samples = len(self.val_list) | |
| def __len__(self): | |
| return len(self.val_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 * 1.0, h * 1.0], dtype=np.float32) | |
| return center, scale | |
| def __getitem__(self, index): | |
| val_item = self.val_list[index] | |
| # Load training image | |
| im_path = os.path.join(self.root, self.dataset + '_images', val_item + '.jpg') | |
| im = cv2.imread(im_path, cv2.IMREAD_COLOR) | |
| h, w, _ = im.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.crop_size) | |
| input = cv2.warpAffine( | |
| im, | |
| trans, | |
| (int(self.crop_size[1]), int(self.crop_size[0])), | |
| flags=cv2.INTER_LINEAR, | |
| borderMode=cv2.BORDER_CONSTANT, | |
| borderValue=(0, 0, 0)) | |
| input = self.transform(input) | |
| flip_input = input.flip(dims=[-1]) | |
| if self.flip: | |
| batch_input_im = torch.stack([input, flip_input]) | |
| else: | |
| batch_input_im = input | |
| meta = { | |
| 'name': val_item, | |
| 'center': person_center, | |
| 'height': h, | |
| 'width': w, | |
| 'scale': s, | |
| 'rotation': r | |
| } | |
| return batch_input_im, meta | |