90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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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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# Get person center and scale
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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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