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dataset.py
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dataset.py
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import os
import random
import cv2
import numpy as np
import torch
from torch.utils.data import Dataset
import json
DATASET_NAMES = [
'BIPED',
'BIPED-B2',
'BIPED-B3',
'BIPED-B5',
'BIPED-B6',
'BSDS', # 5
'BRIND', # 6
'ICEDA', #7
'BSDS300',
'CID', #9
'DCD',
'MDBD', #11
'PASCAL',
'NYUD', #13
'BIPBRI',
'UDED', # 15 just for testing
'DMRIR',
'CLASSIC'
] # 8
# [108, 109.451,112.230,137.86]
BIPED_mean = [103.939,116.779,123.68,137.86]
def dataset_info(dataset_name, is_linux=True):
if is_linux:
config = {
'UDED': {
'img_height': 512, # 321
'img_width': 512, # 481
'train_list': None,
'test_list': 'test_pair.lst',
'data_dir': '/root/workspace/datasets/UDED', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]# [104.007, 116.669, 122.679, 137.86]
}, #[98.939,111.779,117.68,137.86]
'BSDS': {
'img_height': 512, #321
'img_width': 512, #481
'train_list': 'train_pair.lst',
'test_list': 'test_pair.lst',
'data_dir': '/root/workspace/datasets/BSDS', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'BRIND': {
'img_height': 512, # 321
'img_width': 512, # 481
'train_list': 'train_pair_all.lst',
# all train_pair_all.lst
# less train_pair.lst
'test_list': 'test_pair.lst',
'data_dir': '/root/workspace/datasets/BRIND', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'ICEDA': {
'img_height': 1024, # 321
'img_width': 1408, # 481
'train_list': None,
'test_list': 'test_pair.lst',
'data_dir': '/root/workspace/datasets/ICEDA', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'BSDS300': {
'img_height': 512, #321
'img_width': 512, #481
'test_list': 'test_pair.lst',
'train_list': None,
'data_dir': '/root/workspace/datasets/BSDS300', # NIR
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'PASCAL': {
'img_height': 416, # 375
'img_width': 512, #500
'test_list': 'test_pair.lst',
'train_list': None,
'data_dir': '/root/datasets/PASCAL', # mean_rgb
'yita': 0.3,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'CID': {
'img_height': 512,
'img_width': 512,
'test_list': 'test_pair.lst',
'train_list': None,
'data_dir': '/root/datasets/CID', # mean_rgb
'yita': 0.3,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'NYUD': {
'img_height': 448,#425
'img_width': 560,#560
'test_list': 'test_pair.lst',
'train_list': None,
'data_dir': '/root/datasets/NYUD', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'MDBD': {
'img_height': 720,
'img_width': 1280,
'test_list': 'test_pair.lst',
'train_list': 'train_pair.lst',
'data_dir': '/root/workspace/datasets/MDBD', # mean_rgb
'yita': 0.3,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'BIPED': {
'img_height': 720, #720 # 1088
'img_width': 1280, # 1280 5 1920
'test_list': 'test_pair.lst',
'train_list': 'train_pair0.lst', # Base augmentation
# 'train_list': 'train_pairB3.lst', # another augmentation
# 'train_list': 'train_pairB5.lst', # Last augmentation
'data_dir': '/root/workspace/datasets/BIPED', # mean_rgb
'yita': 0.5,
'mean':BIPED_mean
#
},
'CLASSIC': {
'img_height': 512,#
'img_width': 512,# 512
'test_list': None,
'train_list': None,
'data_dir': 'data', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'BIPED-B2': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/Users/xavysp/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'BIPED-B3': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/Users/xavysp/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'BIPED-B5': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/Users/xavysp/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'BIPED-B6': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/Users/xavysp/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'DCD': {
'img_height': 352, #240
'img_width': 480,# 360
'test_list': 'test_pair.lst',
'train_list': None,
'data_dir': '/opt/dataset/DCD', # mean_rgb
'yita': 0.2,
'mean': [104.007, 116.669, 122.679, 137.86]
}
}
else:
config = {
'UDED': {
'img_height': 512, # 321
'img_width': 512, # 481
'train_list': None,
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/UDED', # mean_rgb
'yita': 0.5,
'mean':[104.007, 116.669, 122.679, 137.86] # [183.939,196.779,203.68,137.86] # [104.007, 116.669, 122.679, 137.86]
},
'BSDS': {'img_height': 480, # 321
'img_width': 480, # 481
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/BSDS', # mean_rgb
'yita': 0.5,
'mean':[103.939, 116.669, 122.679, 137.86] },
# [103.939, 116.669, 122.679, 137.86]
#[159.510, 159.451,162.230,137.86]
'BRIND': {
'img_height': 512, # 321
'img_width': 512, # 481
'train_list': 'train_pair_all.lst',
# all train_pair_all.lst
# less train_pair.lst
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/BRIND', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'ICEDA': {
'img_height': 1024, # 321
'img_width': 1408, # 481
'train_list': None,
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/ICEDA', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]
},
'BSDS300': {'img_height': 512, # 321
'img_width': 512, # 481
'test_list': 'test_pair.lst',
'data_dir': 'C:/Users/xavysp/dataset/BSDS300', # NIR
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]},
'PASCAL': {'img_height': 375,
'img_width': 500,
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/PASCAL', # mean_rgb
'yita': 0.3,
'mean': [104.007, 116.669, 122.679, 137.86]},
'CID': {'img_height': 512,
'img_width': 512,
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/CID', # mean_rgb
'yita': 0.3,
'mean': [104.007, 116.669, 122.679, 137.86]},
'NYUD': {'img_height': 425,
'img_width': 560,
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/NYUD', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]},
'MDBD': {'img_height': 720,
'img_width': 1280,
'test_list': 'test_pair.lst',
'train_list': 'train_pair.lst',
'data_dir': 'C:/dataset/MDBD', # mean_rgb
'yita': 0.3,
'mean': [104.007, 116.669, 122.679, 137.86]},
'BIPED': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_pair0.lst',
# 'train_list': 'train_rgb.lst',
'data_dir': 'C:/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'BIPED-B2': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'BIPED-B3': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'BIPED-B5': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/Users/xavysp/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'BIPED-B6': {'img_height': 720, # 720
'img_width': 1280, # 1280
'test_list': 'test_pair.lst',
'train_list': 'train_rgb.lst',
'data_dir': 'C:/Users/xavysp/dataset/BIPED', # WIN: '../.../dataset/BIPED/edges'
'yita': 0.5,
'mean':BIPED_mean},
'CLASSIC': {'img_height': 512,
'img_width': 512,
'test_list': None,
'train_list': None,
'data_dir': 'data', # mean_rgb
'yita': 0.5,
'mean': [104.007, 116.669, 122.679, 137.86]},
'DCD': {'img_height': 240,
'img_width': 360,
'test_list': 'test_pair.lst',
'data_dir': 'C:/dataset/DCD', # mean_rgb
'yita': 0.2,
'mean': [104.007, 116.669, 122.679, 137.86]}
}
return config[dataset_name]
class TestDataset(Dataset):
def __init__(self,
data_root,
test_data,
img_height,
img_width,
test_list=None,
arg=None
):
if test_data not in DATASET_NAMES:
raise ValueError(f"Unsupported dataset: {test_data}")
self.data_root = data_root
self.test_data = test_data
self.test_list = test_list
self.args = arg
self.up_scale = arg.up_scale
self.mean_bgr = arg.mean_test if len(arg.mean_test) == 3 else arg.mean_test[:3]
self.img_height = img_height
self.img_width = img_width
self.data_index = self._build_index()
def _build_index(self):
sample_indices = []
if self.test_data == "CLASSIC":
# for single image testing
images_path = os.listdir(self.data_root)
labels_path = None
sample_indices = [images_path, labels_path]
else:
# image and label paths are located in a list file
if not self.test_list:
raise ValueError(
f"Test list not provided for dataset: {self.test_data}")
list_name = os.path.join(self.data_root, self.test_list)
if self.test_data.upper() in ['BIPED', 'BRIND','UDED','ICEDA']:
with open(list_name) as f:
files = json.load(f)
for pair in files:
tmp_img = pair[0]
tmp_gt = pair[1]
sample_indices.append(
(os.path.join(self.data_root, tmp_img),
os.path.join(self.data_root, tmp_gt),))
else:
with open(list_name, 'r') as f:
files = f.readlines()
files = [line.strip() for line in files]
pairs = [line.split() for line in files]
for pair in pairs:
tmp_img = pair[0]
tmp_gt = pair[1]
sample_indices.append(
(os.path.join(self.data_root, tmp_img),
os.path.join(self.data_root, tmp_gt),))
return sample_indices
def __len__(self):
return len(self.data_index[0]) if self.test_data.upper() == 'CLASSIC' else len(self.data_index)
def __getitem__(self, idx):
# get data sample
# image_path, label_path = self.data_index[idx]
if self.data_index[1] is None:
image_path = self.data_index[0][idx] if len(self.data_index[0]) > 1 else self.data_index[0][idx - 1]
else:
image_path = self.data_index[idx][0]
label_path = None if self.test_data == "CLASSIC" else self.data_index[idx][1]
img_name = os.path.basename(image_path)
file_name = os.path.splitext(img_name)[0] + ".png"
# base dir
if self.test_data.upper() == 'BIPED':
img_dir = os.path.join(self.data_root, 'imgs', 'test')
gt_dir = os.path.join(self.data_root, 'edge_maps', 'test')
elif self.test_data.upper() == 'CLASSIC':
img_dir = self.data_root
gt_dir = None
else:
img_dir = self.data_root
gt_dir = self.data_root
# load data
image = cv2.imread(os.path.join(img_dir, image_path), cv2.IMREAD_COLOR)
if not self.test_data == "CLASSIC":
label = cv2.imread(os.path.join(
gt_dir, label_path), cv2.IMREAD_COLOR)
else:
label = None
im_shape = [image.shape[0], image.shape[1]]
image, label = self.transform(img=image, gt=label)
return dict(images=image, labels=label, file_names=file_name, image_shape=im_shape)
def transform(self, img, gt):
# gt[gt< 51] = 0 # test without gt discrimination
# up scale test image
if self.up_scale:
# For TEED BIPBRIlight Upscale
img = cv2.resize(img,(0,0),fx=1.3,fy=1.3)
if img.shape[0] < 512 or img.shape[1] < 512:
#TEED BIPED standard proposal if you want speed up the test, comment this block
img = cv2.resize(img, (0, 0), fx=1.5, fy=1.5)
# else:
# img = cv2.resize(img, (0, 0), fx=1.1, fy=1.1)
# Make sure images and labels are divisible by 2^4=16
if img.shape[0] % 8 != 0 or img.shape[1] % 8 != 0:
img_width = ((img.shape[1] // 8) + 1) * 8
img_height = ((img.shape[0] // 8) + 1) * 8
img = cv2.resize(img, (img_width, img_height))
# gt = cv2.resize(gt, (img_width, img_height))
else:
pass
# img_width = self.args.test_img_width
# img_height = self.args.test_img_height
# img = cv2.resize(img, (img_width, img_height))
# gt = cv2.resize(gt, (img_width, img_height))
# # For FPS
# img = cv2.resize(img, (496,320))
img = np.array(img, dtype=np.float32)
# if self.rgb:
# img = img[:, :, ::-1] # RGB->BGR
img -= self.mean_bgr
img = img.transpose((2, 0, 1))
img = torch.from_numpy(img.copy()).float()
if self.test_data == "CLASSIC":
gt = np.zeros((img.shape[:2]))
gt = torch.from_numpy(np.array([gt])).float()
else:
gt = np.array(gt, dtype=np.float32)
if len(gt.shape) == 3:
gt = gt[:, :, 0]
gt /= 255.
gt = torch.from_numpy(np.array([gt])).float()
return img, gt
# *************************************************
# ************* training **************************
# *************************************************
class BipedDataset(Dataset):
train_modes = ['train', 'test', ]
dataset_types = ['rgbr', ]
data_types = ['aug', ]
def __init__(self,
data_root,
img_height,
img_width,
train_mode='train',
dataset_type='rgbr',
# is_scaling=None,
# Whether to crop image or otherwise resize image to match image height and width.
crop_img=False,
arg=None
):
self.data_root = data_root
self.train_mode = train_mode
self.dataset_type = dataset_type
self.data_type = 'aug' # be aware that this might change in the future
self.img_height = img_height
self.img_width = img_width
self.mean_bgr = arg.mean_train if len(arg.mean_train) == 3 else arg.mean_train[:3]
self.crop_img = crop_img
self.arg = arg
self.data_index = self._build_index()
def _build_index(self):
assert self.train_mode in self.train_modes, self.train_mode
assert self.dataset_type in self.dataset_types, self.dataset_type
assert self.data_type in self.data_types, self.data_type
data_root = os.path.abspath(self.data_root)
sample_indices = []
file_path = os.path.join(data_root, self.arg.train_list)
if self.arg.train_data.lower() == 'bsds':
with open(file_path, 'r') as f:
files = f.readlines()
files = [line.strip() for line in files]
pairs = [line.split() for line in files]
for pair in pairs:
tmp_img = pair[0]
tmp_gt = pair[1]
sample_indices.append(
(os.path.join(data_root, tmp_img),
os.path.join(data_root, tmp_gt),))
else:
with open(file_path) as f:
files = json.load(f)
for pair in files:
tmp_img = pair[0]
tmp_gt = pair[1]
sample_indices.append(
(os.path.join(data_root, tmp_img),
os.path.join(data_root, tmp_gt),))
return sample_indices
def __len__(self):
return len(self.data_index)
def __getitem__(self, idx):
# get data sample
image_path, label_path = self.data_index[idx]
# load data
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
label = cv2.imread(label_path, cv2.IMREAD_GRAYSCALE)
image, label = self.transform(img=image, gt=label)
return dict(images=image, labels=label)
def transform(self, img, gt):
gt = np.array(gt, dtype=np.float32)
if len(gt.shape) == 3:
gt = gt[:, :, 0]
gt /= 255. # for LDC input and BDCN
img = np.array(img, dtype=np.float32)
img -= self.mean_bgr
i_h, i_w, _ = img.shape
# 400 for BIPEd and 352 for BSDS check with 384
crop_size = self.img_height if self.img_height == self.img_width else None # 448# MDBD=480 BIPED=480/400 BSDS=352
#
# # for BSDS 352/BRIND
# if i_w > crop_size and i_h > crop_size: # later 400, before crop_size
# i = random.randint(0, i_h - crop_size)
# j = random.randint(0, i_w - crop_size)
# img = img[i:i + crop_size, j:j + crop_size]
# gt = gt[i:i + crop_size, j:j + crop_size]
# for BIPED/MDBD
# Second augmentation
if i_w> 400 and i_h>400: #before 420
h,w = gt.shape
if np.random.random() > 0.4: #before i_w> 500 and i_h>500:
LR_img_size = crop_size #l BIPED=256, 240 200 # MDBD= 352 BSDS= 176
i = random.randint(0, h - LR_img_size)
j = random.randint(0, w - LR_img_size)
# if img.
img = img[i:i + LR_img_size , j:j + LR_img_size ]
gt = gt[i:i + LR_img_size , j:j + LR_img_size ]
else:
LR_img_size = 300# 256 300 400 # l BIPED=208-352, # MDBD= 352-480- BSDS= 176-320
i = random.randint(0, h - LR_img_size)
j = random.randint(0, w - LR_img_size)
# if img.
img = img[i:i + LR_img_size, j:j + LR_img_size]
gt = gt[i:i + LR_img_size, j:j + LR_img_size]
img = cv2.resize(img, dsize=(crop_size, crop_size), )
gt = cv2.resize(gt, dsize=(crop_size, crop_size))
else:
# New addidings
img = cv2.resize(img, dsize=(crop_size, crop_size))
gt = cv2.resize(gt, dsize=(crop_size, crop_size))
# BRIND Best for TEDD+BIPED
gt[gt > 0.1] +=0.2#0.4
gt = np.clip(gt, 0., 1.)
img = img.transpose((2, 0, 1))
img = torch.from_numpy(img.copy()).float()
gt = torch.from_numpy(np.array([gt])).float()
return img, gt