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datasets_apolloscape_imgaug.py
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datasets_apolloscape_imgaug.py
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# camera-ready
import torch
import torch.utils.data
from imgaug import augmenters as iaa
import imgaug as ia
import numpy as np
import cv2
import os
import pickle
import glob
import time
default_path = os.path.dirname(os.path.abspath(__file__))
apolloscape_data_path = os.path.join(default_path,'data/apolloscapes')
train_data_path_file = os.path.join(apolloscape_data_path,'train_data_path.pkl')
eval_data_path_file = os.path.join(apolloscape_data_path,'eval_data_path.pkl')
train_data_path = []
eval_data_path = []
with open(train_data_path_file, 'rb') as f:
while True:
try:
data = pickle.load(f)
except EOFError:
break
train_data_path.append(data)
with open(eval_data_path_file, 'rb') as f:
while True:
try:
data = pickle.load(f)
except EOFError:
break
eval_data_path.append(data)
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
class DatasetTrain(torch.utils.data.Dataset):
def __init__(self):
self.img_h = 560
self.img_w = 1280
ia.seed(2)
self.seq = iaa.Sequential([
iaa.CropToFixedSize(width=500, height=500, position='uniform'), #crop
iaa.PerspectiveTransform(scale=(0, 0.40), keep_size=True, cval=0), #perspective
iaa.Affine(scale={"x": (0.5, 2.0), "y":(0.5,2.0)}, #scale, rotation
rotate=(-180, 180),
cval=0),
iaa.CropToFixedSize(width=256, height=256, position='center'), #set size
iaa.Fliplr(0.5), #flip
iaa.Multiply((0.5, 1.5)), #brightness
iaa.Sharpen(alpha=(0.0,0.4), lightness=(0.8, 1.2)),
sometimes(iaa.OneOf([
iaa.AverageBlur(k=(1,3)),
iaa.MedianBlur(k=(1,3)),
iaa.GaussianBlur(sigma=(0,0.2))
])),
sometimes(iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5)),
sometimes(iaa.Grayscale((0.0, 1.0)))
])
self.examples = []
for train_dir in train_data_path:
file_dir = os.path.join(train_dir,"*.png")
file_list = glob.glob(file_dir)
for file_path in file_list:
img_path = file_path.replace('Labels_', 'ColorImage_resize_')
img_path = img_path.replace('Label','ColorImage')
img_path = img_path.replace('_bin.png','.jpg')
label_path = file_path.replace('Labels_', 'Trainid_')
if os.path.exists(img_path) and os.path.exists(label_path):
example = {}
example["img_path"] = img_path
example["label_img_path"] = label_path
self.examples.append(example)
self.num_examples = len(self.examples)
def __getitem__(self, index):
example = self.examples[index]
img_path = example["img_path"]
img = cv2.imread(img_path, -1) # (shape: (560, 1280, 3)
label_img_path = example["label_img_path"]
label_img = cv2.imread(label_img_path, -1) # (shape: (560, 1280))
tic = time.clock()
img = img[None,:]
label_img = label_img[None,:]
toc = time.clock()
#print("1: " , str(toc - tic))
tic = time.clock()
#random crop for initial
#initial_crop_size = 400
#start_x = np.random.randint(low=0, high=(img.shape[2] - initial_crop_size))
#end_x = start_x + initial_crop_size
#start_y = np.random.randint(low=0, high=(img.shape[1] - initial_crop_size))
#end_y = start_y + initial_crop_size
#img = img[:,start_y:end_y, start_x:end_x,:] # (shape: (1, 400, 400, 3))
#label_img = label_img[:,start_y:end_y, start_x:end_x] # (shape: (1, 400, 400))
img, label_img = self.seq(images=img, segmentation_maps=label_img)
toc = time.clock()
#print("2:", str(toc - tic))
tic = time.clock()
img = img[0]
label_img = label_img[0]
img_basename = os.path.basename(img_path)
label_basename = os.path.basename(label_img_path)
test_img_save_path = os.path.join(default_path,'test', img_basename)
test_label_save_path = os.path.join(default_path, 'test', label_basename)
toc = time.clock()
#print("3:", str(toc - tic))
#cv2.imwrite(test_img_save_path, img)
#cv2.imwrite(test_label_save_path, label_img)
# normalize the img (with the mean and std for the pretrained ResNet):
img = img/255.0
img = img - np.array([0.485, 0.456, 0.406])
img = img/np.array([0.229, 0.224, 0.225]) # (shape: (256, 256, 3))
img = np.transpose(img, (2, 0, 1)) # (shape: (3, 256, 256))
img = img.astype(np.float32)
# convert numpy -> torch:
img = torch.from_numpy(img) # (shape: (3, 256, 256))
label_img = torch.from_numpy(label_img) # (shape: (256, 256))
return (img, label_img)
def __len__(self):
return self.num_examples
class DatasetVal(torch.utils.data.Dataset):
def __init__(self):
self.img_h = 560
self.img_w = 1280
self.examples = []
for eval_dir in eval_data_path:
file_dir = os.path.join(eval_dir,"*.png")
file_list = sorted(glob.glob(file_dir))
for file_path in file_list:
img_path = file_path.replace('Labels_', 'ColorImage_resize_')
img_path = img_path.replace('Label','ColorImage')
img_path = img_path.replace('_bin.png','.jpg')
label_path = file_path.replace('Labels_', 'Trainid_')
if os.path.exists(img_path) and os.path.exists(label_path):
example = {}
example["img_path"] = img_path
example["label_img_path"] = label_path
self.examples.append(example)
self.num_examples = len(self.examples)
def __getitem__(self, index):
example = self.examples[index]
img_path = example["img_path"]
#print(img_path)
img = cv2.imread(img_path, -1) # (shape: (560, 1280, 3))
label_img_path = example["label_img_path"]
label_img = cv2.imread(label_img_path, -1) # (shape: (560, 1280))
# # # # # # # # debug visualization START
# cv2.imshow("test", img)
# cv2.waitKey(0)
#
# cv2.imshow("test", label_img)
# cv2.waitKey(0)
# # # # # # # # debug visualization END
# normalize the img (with the mean and std for the pretrained ResNet):
img = img/255.0
img = img - np.array([0.485, 0.456, 0.406])
img = img/np.array([0.229, 0.224, 0.225]) # (shape: (560, 1280, 3))
img = np.transpose(img, (2, 0, 1)) # (shape: (3, 560, 1280))
img = img.astype(np.float32)
# convert numpy -> torch:
img = torch.from_numpy(img) # (shape: (3, 560, 1280))
label_img = torch.from_numpy(label_img) # (shape: (560, 1280))
return (img, label_img)
def __len__(self):
return self.num_examples