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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@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)
# 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
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