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1cac303 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | import torch.utils.data as data
from torchvision import transforms
import torch
import os
import re
import cv2
import numpy as np
# COCO
# IMG_MEAN = [0.471, 0.448, 0.408]
# IMG_STD = [0.234, 0.239, 0.242]
# ImageNet
IMG_MEAN = [0.485, 0.456, 0.406]
IMG_STD = [0.229, 0.224, 0.225]
IMG_SIZE = 224
def _get_image_size(img):
if transforms.functional._is_pil_image(img):
return img.size
elif isinstance(img, torch.Tensor) and img.dim() > 2:
return img.shape[-2:][::-1]
else:
raise TypeError("Unexpected type {}".format(type(img)))
class Resize(transforms.Resize):
def __call__(self, img):
h, w = _get_image_size(img)
scale = max(w, h) / float(self.size)
new_w, new_h = int(w / scale), int(h / scale)
return transforms.functional.resize(img, (new_w, new_h), self.interpolation)
def get_dataloader(args, dataset='Ours'):
if args is None:
if dataset == 'Ours':
print(11111111)
root = '/home/benkesheng/BMI_With_BFDF/datasets/Rebuttle'
train_dataset = OurDatasets(root, 'Image_train_Down2')
test_dataset = OurDatasets(root, 'Image_test_Down2')
val_dataset = OurDatasets(root, 'Image_val_Down2')
elif dataset == 'Author':
root = '/home/benkesheng/BMI_DETECT/author_datasets'
train_dataset = Authordataset(root, 'Image_train')
test_dataset = Authordataset(root, 'Image_test')
val_dataset = Authordataset(root, 'Image_val')
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True,
num_workers=4)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=True, num_workers=4)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, num_workers=4)
else:
train_dataset = OurDatasets(args.root, 'Image_train_consist2')
test_dataset = OurDatasets(args.root, 'Image_test_consist2')
val_dataset = OurDatasets(args.root, 'Image_val_consist2')
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=True, num_workers=args.workers)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, num_workers=args.workers)
return train_loader, val_loader, test_loader
class Authordataset(data.Dataset):
def __init__(self, root, file, sim=False):
self.file = os.path.join(root, file)
self.img_names = os.listdir(self.file)
self.transform = transforms.Compose([
transforms.ToPILImage(),
Resize(IMG_SIZE),
transforms.Pad(IMG_SIZE, fill=0),
transforms.CenterCrop(IMG_SIZE),
transforms.ToTensor(),
])
self.sim = sim
def __len__(self):
return len(self.img_names)
def __getitem__(self, idx):
img_name = self.img_names[idx]
img_name_path = os.path.join(self.file, img_name)
img = cv2.imread(os.path.join(self.file, img_name), flags=3)[:, :, ::-1]
h, w, _ = img.shape
img = self.transform(img)
img = transforms.Normalize(IMG_MEAN, IMG_STD)(img)
ret = re.match(r"[a-zA-Z0-9]+_[a-zA-Z0-9]+__?(\d+)__?(\d+)__?([a-z]+)_*", img_name)
height = float(ret.group(2)) * 0.0254
weight = float(ret.group(1)) * 0.4536
sex = (lambda x: x == 'false')(ret.group(3))
BMI = weight / (height ** 2)
if self.sim:
return img, BMI
return (img, img_name_path, img_name, sex, 20, height, weight), BMI
class OurDatasets(data.Dataset):
def __init__(self, root, file):
self.file = os.path.join(root, file)
self.img_names = os.listdir(self.file)
self.transform = transforms.Compose([
transforms.ToPILImage(),
Resize(IMG_SIZE),
transforms.Pad(IMG_SIZE, fill=0),
transforms.CenterCrop(IMG_SIZE),
# transforms.Grayscale(),
transforms.ToTensor(),
])
def __len__(self):
return len(self.img_names)
def __getitem__(self, idx):
img_name = self.img_names[idx]
img = cv2.imread(os.path.join(self.file, img_name), flags=1)
# print(img_name)
img = img[:, :, ::-1]
h, w, _ = img.shape
img = self.transform(img)
# Gray
# img = torch.cat((img, img, img), dim=0)
img = transforms.Normalize(IMG_MEAN, IMG_STD)(img)
ret = re.match(r"\d+?_([FMfm])_(\d+?)_(\d+?)_(\d+).+", img_name)
sex = 0 if (ret.group(1) == 'F' or ret.group(1) == 'f') else 1
age = int(ret.group(2))
height = int(ret.group(3)) / 100000
weight = int(ret.group(4)) / 100000
BMI = torch.from_numpy(np.asarray((int(ret.group(4)) / 100000) / (int(ret.group(3)) / 100000) ** 2))
Pic_name = os.path.join(self.file, img_name)
return (img, Pic_name, img_name, sex, age, height, weight), BMI
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