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| """ |
| @Author : Peike Li |
| @Contact : peike.li@yahoo.com |
| @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) |
|
|
| |
| person_center, s = self._box2cs([0, 0, w - 1, h - 1]) |
| r = 0 |
|
|
| if self.dataset != 'test': |
| |
| 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] |
| |
| 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 |
| |
| 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 |
|
|
