preprocess/humanparsing/datasets/datasets.py

#!/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