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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 : dataset.py
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| @Time : 8/30/19 9:12 PM
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| @Desc : Dataset Definition
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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 pdb
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| import cv2
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| import numpy as np
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| from PIL import Image
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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 SimpleFolderDataset(data.Dataset):
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| def __init__(self, root, input_size=[512, 512], transform=None):
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| self.root = root
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| self.input_size = input_size
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| self.transform = transform
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| self.aspect_ratio = input_size[1] * 1.0 / input_size[0]
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| self.input_size = np.asarray(input_size)
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| self.is_pil_image = False
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| if isinstance(root, Image.Image):
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| self.file_list = [root]
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| self.is_pil_image = True
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| elif os.path.isfile(root):
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| self.file_list = [os.path.basename(root)]
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| self.root = os.path.dirname(root)
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| else:
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| self.file_list = os.listdir(self.root)
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| def __len__(self):
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| return len(self.file_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, h], dtype=np.float32)
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| return center, scale
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| def __getitem__(self, index):
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| if self.is_pil_image:
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| img = np.asarray(self.file_list[index])[:, :, [2, 1, 0]]
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| else:
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| img_name = self.file_list[index]
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| img_path = os.path.join(self.root, img_name)
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| img = cv2.imread(img_path, cv2.IMREAD_COLOR)
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| h, w, _ = img.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.input_size)
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| input = cv2.warpAffine(
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| img,
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| trans,
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| (int(self.input_size[1]), int(self.input_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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| meta = {
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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 input, meta
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