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#!/usr/bin/env python | |
# -*- encoding: utf-8 -*- | |
""" | |
@Author : Peike Li | |
@Contact : [email protected] | |
@File : dataset.py | |
@Time : 8/30/19 9:12 PM | |
@Desc : Dataset Definition | |
@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 pdb | |
import cv2 | |
import numpy as np | |
from PIL import Image | |
from torch.utils import data | |
from utils.transforms import get_affine_transform | |
class SimpleFolderDataset(data.Dataset): | |
def __init__(self, root, input_size=[512, 512], transform=None): | |
self.root = root | |
self.input_size = input_size | |
self.transform = transform | |
self.aspect_ratio = input_size[1] * 1.0 / input_size[0] | |
self.input_size = np.asarray(input_size) | |
self.is_pil_image = False | |
if isinstance(root, Image.Image): | |
self.file_list = [root] | |
self.is_pil_image = True | |
elif os.path.isfile(root): | |
self.file_list = [os.path.basename(root)] | |
self.root = os.path.dirname(root) | |
else: | |
self.file_list = os.listdir(self.root) | |
def __len__(self): | |
return len(self.file_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, h], dtype=np.float32) | |
return center, scale | |
def __getitem__(self, index): | |
if self.is_pil_image: | |
img = np.asarray(self.file_list[index])[:, :, [2, 1, 0]] | |
else: | |
img_name = self.file_list[index] | |
img_path = os.path.join(self.root, img_name) | |
img = cv2.imread(img_path, cv2.IMREAD_COLOR) | |
h, w, _ = img.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.input_size) | |
input = cv2.warpAffine( | |
img, | |
trans, | |
(int(self.input_size[1]), int(self.input_size[0])), | |
flags=cv2.INTER_LINEAR, | |
borderMode=cv2.BORDER_CONSTANT, | |
borderValue=(0, 0, 0)) | |
input = self.transform(input) | |
meta = { | |
'center': person_center, | |
'height': h, | |
'width': w, | |
'scale': s, | |
'rotation': r | |
} | |
return input, meta | |