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dataset.py
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dataset.py
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import os
import numpy as np
import lmdb
import sys
import six
import time
import cfg
from PIL import Image
from natsort import natsorted
import torch
from torch.utils.data import Dataset
from torchvision import transforms
class RawDataset(Dataset):
def __init__(self, is_val=False):
self.img_h, self.img_w = cfg.max_train_img_size, cfg.max_train_img_size
if is_val:
with open(os.path.join(cfg.data_dir, cfg.val_fname), 'r') as f_val:
f_list = f_val.readlines()
else:
with open(os.path.join(cfg.data_dir, cfg.train_fname), 'r') as f_train:
f_list = f_train.readlines()
self.image_path_list = []
self.labels_path_dic = {}
for f_line in f_list:
img_filename = str(f_line).strip().split(',')[0]
img_path = os.path.join(cfg.data_dir, cfg.train_image_dir_name, img_filename)
self.image_path_list.append(img_path)
gt_file = os.path.join(cfg.data_dir, cfg.train_label_dir_name, img_filename[:-4] + '_gt.npy')
self.labels_path_dic[img_path] = gt_file
self.image_path_list = natsorted(self.image_path_list)
self.nSamples = len(self.image_path_list)
def __len__(self):
return self.nSamples
def __getitem__(self, index):
img_path = self.image_path_list[index]
label = np.load(self.labels_path_dic[img_path])
try:
img = Image.open(img_path).convert('RGB') # for color image
except IOError:
print(f'Corrupted image for {index}')
# make dummy image and dummy label for corrupted image.
img = Image.new('RGB', (self.img_w, self.img_h))
img_tensor = transforms.ToTensor()(img)
label = np.transpose(label, (2, 0, 1))
return (img_tensor, label)
def data_collate(batch):
imgs = []
labels = []
gt_xy_list = [] # 长度为N的列表,每个值为该图片中所有矩形框的坐标
# 例如:[(31, 4, 2), (10, 4, 2), (47, 4, 2), (28, 4, 2)]
for info in batch:
imgs.append(info[0])
labels.append(info[1])
gt_xy_list.append(info[2])
return torch.stack(imgs, 0), torch.tensor(np.array(labels)), gt_xy_list
class LmdbDataset(Dataset):
def __init__(self, root):
self.root = root
self.env = lmdb.open(root, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
if not self.env:
print('cannot create lmdb from %s' % (root))
sys.exit(0)
with self.env.begin(write=False) as txn:
nSamples = int(txn.get('num-samples'.encode()))
self.nSamples = nSamples
self.filtered_index_list = [index + 1 for index in range(self.nSamples)]
def __len__(self):
return self.nSamples
def __getitem__(self, index):
assert index <= len(self), 'index range error'
index = self.filtered_index_list[index]
with self.env.begin(write=False) as txn:
label_key = 'label-%09d'.encode() % index
label = txn.get(label_key)
label = np.fromstring(label, dtype=np.float64)
img_key = 'image-%09d'.encode() % index
imgbuf = txn.get(img_key)
gt_xy_list_Key = 'gt_xy_list-%09d'.encode() % index
gt_xy_list = txn.get(gt_xy_list_Key)
gt_xy_list = np.fromstring(gt_xy_list, dtype=np.float64)
gt_xy_list = np.reshape(gt_xy_list.astype(float), (-1, 4, 2))
width_height = int(txn.get('width-height'.encode()))
width, height = width_height, width_height
buf = six.BytesIO()
buf.write(imgbuf)
buf.seek(0)
try:
img = Image.open(buf).convert('RGB') # for color image
except IOError:
print(f'Corrupted image for {index}')
# make dummy image and dummy label for corrupted image.
img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))
label = np.zeros((height // cfg.pixel_size, width // cfg.pixel_size, 7))
img_tensor = transforms.ToTensor()(img)
label = label.reshape((height // cfg.pixel_size, width // cfg.pixel_size, 7))
label = np.transpose(label, (2, 0, 1))
# label_tensor = transforms.ToTensor()(label)
return (img_tensor, label, gt_xy_list)
if __name__ == '__main__':
tick = time.time()
train_dataset = RawDataset(is_val=False)
data_loader_A = torch.utils.data.DataLoader(
train_dataset, batch_size=cfg.batch_size,
shuffle=True,
num_workers=int(cfg.workers),
pin_memory=True)
for i, (image_tensors, labels) in enumerate(data_loader_A):
print(image_tensors.shape, labels.shape)
tock = time.time()
print(tock-tick)
tick = time.time()
train_dataset_lmdb = LmdbDataset(cfg.lmdb_trainset_dir_name)
# val_dataset_lmdb = LmdbDataset(cfg.lmdb_valset_dir_name)
data_loader_B = torch.utils.data.DataLoader(
train_dataset_lmdb, batch_size=cfg.batch_size,
shuffle=True,
num_workers=int(cfg.workers),
pin_memory=True)
for i, (image_tensors, labels) in enumerate(data_loader_B):
print(image_tensors.shape, labels.shape)
tock = time.time()
print(tock-tick)
count = 0
for i, (image_tensors1, labels1) in enumerate(data_loader_A):
for img1, label1 in zip(image_tensors1, labels1):
for j, (image_tensors2, labels2) in enumerate(data_loader_B):
for img2, label2 in zip(image_tensors2, labels2):
if img1.equal(img2):
print(count, '--', label1.equal(label2))
count += 1
# print(image_tensors.shape, labels.shape)