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| """
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| @Author : Peike Li
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| @Contact : peike.li@yahoo.com
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| @File : datasets.py
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| @Time : 8/4/19 3:35 PM
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| @Desc :
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| @License : This source code is licensed under the license found in the
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| LICENSE file in the root directory of this source tree.
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| """
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| import os
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| import numpy as np
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| import random
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| import torch
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| import cv2
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| from torch.utils import data
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| from utils.transforms import get_affine_transform
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| class LIPDataSet(data.Dataset):
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| def __init__(self, root, dataset, crop_size=[473, 473], scale_factor=0.25,
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| rotation_factor=30, ignore_label=255, transform=None):
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| self.root = root
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| self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
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| self.crop_size = np.asarray(crop_size)
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| self.ignore_label = ignore_label
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| self.scale_factor = scale_factor
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| self.rotation_factor = rotation_factor
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| self.flip_prob = 0.5
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| self.transform = transform
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| self.dataset = dataset
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| list_path = os.path.join(self.root, self.dataset + '_id.txt')
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| train_list = [i_id.strip() for i_id in open(list_path)]
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| self.train_list = train_list
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| self.number_samples = len(self.train_list)
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| def __len__(self):
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| return self.number_samples
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| def _box2cs(self, box):
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| x, y, w, h = box[:4]
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| return self._xywh2cs(x, y, w, h)
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| def _xywh2cs(self, x, y, w, h):
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| center = np.zeros((2), dtype=np.float32)
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| center[0] = x + w * 0.5
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| center[1] = y + h * 0.5
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| if w > self.aspect_ratio * h:
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| h = w * 1.0 / self.aspect_ratio
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| elif w < self.aspect_ratio * h:
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| w = h * self.aspect_ratio
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| scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
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| return center, scale
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| def __getitem__(self, index):
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| train_item = self.train_list[index]
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| im_path = os.path.join(self.root, self.dataset + '_images', train_item + '.jpg')
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| parsing_anno_path = os.path.join(self.root, self.dataset + '_segmentations', train_item + '.png')
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| im = cv2.imread(im_path, cv2.IMREAD_COLOR)
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| h, w, _ = im.shape
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| parsing_anno = np.zeros((h, w), dtype=np.long)
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| person_center, s = self._box2cs([0, 0, w - 1, h - 1])
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| r = 0
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| if self.dataset != 'test':
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| parsing_anno = cv2.imread(parsing_anno_path, cv2.IMREAD_GRAYSCALE)
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| if self.dataset == 'train' or self.dataset == 'trainval':
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| sf = self.scale_factor
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| rf = self.rotation_factor
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| s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
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| r = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) if random.random() <= 0.6 else 0
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| if random.random() <= self.flip_prob:
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| im = im[:, ::-1, :]
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| parsing_anno = parsing_anno[:, ::-1]
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| person_center[0] = im.shape[1] - person_center[0] - 1
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| right_idx = [15, 17, 19]
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| left_idx = [14, 16, 18]
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| for i in range(0, 3):
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| right_pos = np.where(parsing_anno == right_idx[i])
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| left_pos = np.where(parsing_anno == left_idx[i])
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| parsing_anno[right_pos[0], right_pos[1]] = left_idx[i]
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| parsing_anno[left_pos[0], left_pos[1]] = right_idx[i]
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| trans = get_affine_transform(person_center, s, r, self.crop_size)
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| input = cv2.warpAffine(
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| im,
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| trans,
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| (int(self.crop_size[1]), int(self.crop_size[0])),
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| flags=cv2.INTER_LINEAR,
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| borderMode=cv2.BORDER_CONSTANT,
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| borderValue=(0, 0, 0))
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| if self.transform:
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| input = self.transform(input)
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| meta = {
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| 'name': train_item,
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| 'center': person_center,
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| 'height': h,
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| 'width': w,
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| 'scale': s,
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| 'rotation': r
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| }
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| if self.dataset == 'val' or self.dataset == 'test':
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| return input, meta
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| else:
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| label_parsing = cv2.warpAffine(
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| parsing_anno,
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| trans,
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| (int(self.crop_size[1]), int(self.crop_size[0])),
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| flags=cv2.INTER_NEAREST,
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| borderMode=cv2.BORDER_CONSTANT,
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| borderValue=(255))
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| label_parsing = torch.from_numpy(label_parsing)
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| return input, label_parsing, meta
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| class LIPDataValSet(data.Dataset):
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| def __init__(self, root, dataset='val', crop_size=[473, 473], transform=None, flip=False):
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| self.root = root
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| self.crop_size = crop_size
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| self.transform = transform
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| self.flip = flip
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| self.dataset = dataset
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| self.root = root
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| self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
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| self.crop_size = np.asarray(crop_size)
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| list_path = os.path.join(self.root, self.dataset + '_id.txt')
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| val_list = [i_id.strip() for i_id in open(list_path)]
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| self.val_list = val_list
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| self.number_samples = len(self.val_list)
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| def __len__(self):
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| return len(self.val_list)
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| def _box2cs(self, box):
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| x, y, w, h = box[:4]
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| return self._xywh2cs(x, y, w, h)
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| def _xywh2cs(self, x, y, w, h):
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| center = np.zeros((2), dtype=np.float32)
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| center[0] = x + w * 0.5
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| center[1] = y + h * 0.5
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| if w > self.aspect_ratio * h:
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| h = w * 1.0 / self.aspect_ratio
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| elif w < self.aspect_ratio * h:
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| w = h * self.aspect_ratio
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| scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
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| return center, scale
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| def __getitem__(self, index):
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| val_item = self.val_list[index]
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| im_path = os.path.join(self.root, self.dataset + '_images', val_item + '.jpg')
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| im = cv2.imread(im_path, cv2.IMREAD_COLOR)
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| h, w, _ = im.shape
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| person_center, s = self._box2cs([0, 0, w - 1, h - 1])
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| r = 0
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| trans = get_affine_transform(person_center, s, r, self.crop_size)
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| input = cv2.warpAffine(
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| im,
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| trans,
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| (int(self.crop_size[1]), int(self.crop_size[0])),
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| flags=cv2.INTER_LINEAR,
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| borderMode=cv2.BORDER_CONSTANT,
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| borderValue=(0, 0, 0))
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| input = self.transform(input)
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| flip_input = input.flip(dims=[-1])
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| if self.flip:
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| batch_input_im = torch.stack([input, flip_input])
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| else:
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| batch_input_im = input
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| meta = {
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| 'name': val_item,
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| 'center': person_center,
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| 'height': h,
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| 'width': w,
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| 'scale': s,
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| 'rotation': r
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| }
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| return batch_input_im, meta
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