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dataset.py
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import torch
from torch.utils.data import Dataset
import os
from image import *
import random
import time
import torch
from config import args
from torchvision import datasets, transforms
import numpy as np
from PIL import Image, ImageEnhance
import numbers
from torch.multiprocessing import Pool, Process, set_start_method
try:
set_start_method('spawn')
except RuntimeError:
pass
class listDataset_visdrone_class_8(Dataset):
def __init__(self, root, shape=None, shuffle=True, transform=None, train=False, seen=0, batch_size=args.batch_size,
num_workers=args.workers, ):
if train:
random.shuffle(root)
# data_keys = pre_data(root, train)
# self.pre_data = data_keys
self.nSamples = len(root)
self.lines = root
self.transform = transform
self.train = train
self.shape = shape
self.seen = seen
self.batch_size = batch_size
self.num_workers = num_workers
def __len__(self):
return self.nSamples
def __getitem__(self, index):
assert index <= len(self), 'index range error'
torch.cuda.synchronize()
begin_time_test_5 = time.time()
# img = self.lines[index]['img']
# kpoint = self.lines[index]['kpoint']
# target = self.lines[index]['target']
# fname = self.lines[index]['fname']
# mask_map = self.lines[index]['mask']
# Img_path = self.lines[index]
Img_path = self.lines[index]
fname = os.path.basename(Img_path)
img, target, kpoint, mask_map = load_data_visdrone_class_8(Img_path, self.train)
torch.cuda.synchronize()
end_time_test_5 = time.time()
run_time_5 = end_time_test_5 - begin_time_test_5
# print('该循环程序运行时间5：', run_time_5)
'''data augmention'''
if self.train == True:
if random.random() > 0.5:
target_0 = np.fliplr(target[0])
target_1 = np.fliplr(target[1])
target_2 = np.fliplr(target[2])
target_3 = np.fliplr(target[3])
target_4 = np.fliplr(target[4])
target_5 = np.fliplr(target[5])
target_6 = np.fliplr(target[6])
target_7 = np.fliplr(target[7])
mask_map_0 = np.fliplr(mask_map[0])
mask_map_1 = np.fliplr(mask_map[1])
mask_map_2 = np.fliplr(mask_map[2])
mask_map_3 = np.fliplr(mask_map[3])
mask_map_4 = np.fliplr(mask_map[4])
mask_map_5 = np.fliplr(mask_map[5])
mask_map_6 = np.fliplr(mask_map[6])
mask_map_7 = np.fliplr(mask_map[7])
target = np.array([target_0, target_1, target_2, target_3, target_4, target_5, target_6, target_7])
mask_map = np.array([mask_map_0, mask_map_1, mask_map_2, mask_map_3, mask_map_4, mask_map_5, mask_map_6, mask_map_7])
img = img.transpose(Image.FLIP_LEFT_RIGHT)
# kpoint = np.fliplr(kpoint)
# if random.random() > 0.5:
# proportion = random.uniform(0.004, 0.015)
# width, height = img.size[0], img.size[1]
# num = int(height * width * proportion)
# for i in range(num):
# w = random.randint(0, width - 1)
# h = random.randint(0, height - 1)
# if random.randint(0, 1) == 0:
# img.putpixel((w, h), (0, 0, 0))
# else:
# img.putpixel((w, h), (255, 255, 255))
torch.cuda.synchronize()
begin_time_test_6 = time.time()
target = target.copy()
# kpoint = kpoint.copy()
img = np.array(img).copy()
# mask_map = mask_map.copy()
if self.transform is not None:
img = self.transform(img)
if self.train == True:
# img = torch.from_numpy(img).cuda()
target = torch.from_numpy(target).cuda()
mask_map = torch.from_numpy(mask_map).cuda()
width = 512
height = 512
if (img.shape[-2]>512) & (img.shape[-1]>512):
crop_size_x = random.randint(0, img.shape[-1] - width)
crop_size_y = random.randint(0, img.shape[-2] - height)
img = img[:, crop_size_y: crop_size_y + width, crop_size_x:crop_size_x + height]
target = target[:, crop_size_y: crop_size_y + width, crop_size_x:crop_size_x + height]
mask_map = mask_map[:,crop_size_y: crop_size_y + width, crop_size_x:crop_size_x + height]
torch.cuda.synchronize()
end_time_test_6 = time.time()
run_time_6 = end_time_test_6 - begin_time_test_6
# print('该循环程序运行时间6：', run_time_6)
return fname, img, target, kpoint, mask_map
class listDataset_dota_class_2(Dataset):
def __init__(self, root, shape=None, shuffle=True, transform=None, train=False, seen=0, batch_size=args.batch_size,
num_workers=args.workers, ):
if train:
random.shuffle(root)
# data_keys = pre_data(root, train)
# self.pre_data = data_keys
self.nSamples = len(root)
self.lines = root
self.transform = transform
self.train = train
self.shape = shape
self.seen = seen
self.batch_size = batch_size
self.num_workers = num_workers
def __len__(self):
return self.nSamples
def __getitem__(self, index):
assert index <= len(self), 'index range error'
torch.cuda.synchronize()
begin_time_test_5 = time.time()
# img = self.lines[index]['img']
# kpoint = self.lines[index]['kpoint']
# target = self.lines[index]['target']
# fname = self.lines[index]['fname']
# mask_map = self.lines[index]['mask']
# Img_path = self.lines[index]
Img_path = self.lines[index]
fname = os.path.basename(Img_path)
img, target, kpoint, mask_map = load_data_dota_class_2(Img_path, self.train)
torch.cuda.synchronize()
end_time_test_5 = time.time()
run_time_5 = end_time_test_5 - begin_time_test_5
# print('该循环程序运行时间5：', run_time_5)
'''data augmention'''
if self.train == True:
if random.random() > 0.5:
target_0 = np.fliplr(target[0])
target_1 = np.fliplr(target[1])
mask_map_0 = np.fliplr(mask_map[0])
mask_map_1 = np.fliplr(mask_map[1])
target = np.array([target_0, target_1])
mask_map = np.array([mask_map_0, mask_map_1])
img = img.transpose(Image.FLIP_LEFT_RIGHT)
# kpoint = np.fliplr(kpoint)
# if random.random() > 0.5:
# proportion = random.uniform(0.004, 0.015)
# width, height = img.size[0], img.size[1]
# num = int(height * width * proportion)
# for i in range(num):
# w = random.randint(0, width - 1)
# h = random.randint(0, height - 1)
# if random.randint(0, 1) == 0:
# img.putpixel((w, h), (0, 0, 0))
# else:
# img.putpixel((w, h), (255, 255, 255))
torch.cuda.synchronize()
begin_time_test_6 = time.time()
target = target.copy()
# kpoint = kpoint.copy()
img = np.array(img).copy()
# mask_map = mask_map.copy()
if self.transform is not None:
img = self.transform(img)
if self.train == True:
# img = torch.from_numpy(img).cuda()
target = torch.from_numpy(target).cuda()
mask_map = torch.from_numpy(mask_map).cuda()
width = 512
height = 512
if (img.shape[-2]>512) & (img.shape[-1]>512):
crop_size_x = random.randint(0, img.shape[-1] - width)
crop_size_y = random.randint(0, img.shape[-2] - height)
img = img[:, crop_size_y: crop_size_y + width, crop_size_x:crop_size_x + height]
target = target[:, crop_size_y: crop_size_y + width, crop_size_x:crop_size_x + height]
mask_map = mask_map[:,crop_size_y: crop_size_y + width, crop_size_x:crop_size_x + height]
torch.cuda.synchronize()
end_time_test_6 = time.time()
run_time_6 = end_time_test_6 - begin_time_test_6
# print('该循环程序运行时间6：', run_time_6)
return fname, img, target, kpoint, mask_map