def prepare_line():
train_data_names = read_data_names('./train_labels')
test_data_names = read_data_names('./test_labels')
train_images = read_images('./train_images', data_names=train_data_names)
train_labels = read_labels('./train_labels')
# train_data_names = read_data_names('./train_labels')
test_labels = read_labels('./test_labels')
test_images = read_images('./test_images', test_data_names)
# test_data_names = read_data_names('./test_labels')
for ind, image in enumerate(train_images):
print(ind)
H, W, C = image.shape
lab = train_labels[ind]
seg = draw_line(lab, H, W)
np.save('./train_lines/{}.npy'.format(ind), seg)
for ind, image in enumerate(test_images):
print('test {}'.format(ind))
H, W, C = image.shape
lab = test_labels[ind]
seg = draw_line(lab, H, W)
np.save('./test_lines/{}.npy'.format(ind), seg)
seg = np.load('./train_labels/1.npy')
ln = np.load('./train_lines/1.npy')
plt.figure()
plot_image(seg, segmap=ln)
plt.show()
def get_loader_train_val(batch_size_tr=64, batch_size_val=1, shuffle=True):
data_path = './train_images'
label_path = './train_labels'
val_ratio = 0.1
transform_list = [SegResize((512, 256))]
data_names = read_data_names(label_path)
segmap_names = read_data_names(label_path, title='segmap_names')
N_all = len(data_names)
N_val = int(N_all * val_ratio)
N_train = N_all - N_val
# get train and validation data set
data_names_train = []
data_names_val = []
segmap_names_train = []
segmap_names_val = []
if not os.path.exists(os.path.join('./', 'val_permutation.npy')):
print('reset permutation')
permutation = np.random.permutation(N_all)
np.save(os.path.join('./', 'val_permutation.npy'), permutation)
else:
permutation = np.load(os.path.join('./', 'val_permutation.npy'))
for ind in permutation[:N_train]:
data_names_train.append(data_names[ind])
segmap_names_train.append(segmap_names[ind])
for ind in permutation[N_train:]:
data_names_val.append(data_names[ind])
segmap_names_val.append(segmap_names[ind])
#########################
dset_train = SegDataset(data_location=data_path,
segmap_location=label_path,
data_names=data_names_train,
segmap_names=segmap_names_train,
transform_list=transform_list)
dset_val = SegDataset(data_location=data_path,
segmap_location=label_path,
data_names=data_names_val,
segmap_names=segmap_names_val,
transform_list=transform_list)
loader_train = DataLoader(dataset=dset_train,
batch_size=batch_size_tr,
shuffle=shuffle)
loader_val = DataLoader(dataset=dset_val,
batch_size=batch_size_val,
shuffle=False)
return loader_train, loader_val
def preprocessing():
#여기서 데이터 프리프로세싱 작업 하기
#path declaration
# image_dest_resized = './resized_images'
# label_dest_resized = './resized_labels'
image_path = './highres_images'
label_path = './highres_labels'
data_names = read_data_names(label_location=label_path)
labels = read_labels(label_path)
#testing image and labels
# plt.figure()
# for i,ind in enumerate([35,67,25,235]):
# plt.subplot(221+i)
# image = Image.open(os.path.join(image_path, data_names[ind]))
# segmap = np.load(os.path.join(label_path, data_names[ind] + '.npy'))
# plot_image(image, coord = labels[ind], segmap=segmap )
# plt.show()
# #pad image
# resized_H = 512
# desired_W = 256
label_list = []
for data_no, data_name in enumerate(data_names):
image = Image.open(os.path.join(image_path, data_name))
np_image = np.asarray(image)
label = labels[data_no]
H, W = np_image.shape
resized_H = H
desired_W = W
# resize_ratio = resized_H / H
# left_pad = int((desired_W - int(W * resize_ratio)) / 2)
# right_pad = desired_W - int(W * resize_ratio) - left_pad
#
# label_rev = label.reshape(-1,2)
# label_rev *= resize_ratio
# label_rev[:,0] += left_pad
# label_rev = label_rev.reshape(-1)
# label_list.append(label_rev)
#
# im_resize = image.resize((int(W * resize_ratio), int(resized_H)))
# im_pad = ImageOps.expand(im_resize, (left_pad, 0, right_pad, 0))
# im_pad.save(os.path.join(image_dest_resized, data_name))
segmap = draw_seg(label, resized_H, desired_W)
np.save(os.path.join(label_path, data_name + '.npy'), segmap)
if data_no % 100 == 0:
print(np.asarray(image).shape)
plt.figure()
plot_image(image, label, segmap=segmap)
plt.show()
def get_lineloader_train_val(batch_size_tr, batch_size_val = 1, shuffle = True):
segmap_path = './train_labels'
line_path = './train_lines'
segmap_names = read_data_names(segmap_path, 'segmap_names')
line_names = read_data_names(line_path, 'line_names')
N_all = len(segmap_names)
N_val = int(N_all * val_ratio)
N_train = N_all - N_val
# get train and validation data set
segmap_names_train = []
segmap_names_val = []
line_names_train = []
line_names_val = []
transform_list = [SegResize((512,256))]
if not os.path.exists(os.path.join('./', 'val_permutation.npy')):
print('reset permutation')
permutation = np.random.permutation(N_all)
np.save(os.path.join('./', 'val_permutation.npy'), permutation)
else:
permutation = np.load(os.path.join('./', 'val_permutation.npy'))
for ind in permutation[:N_train]:
segmap_names_train.append(segmap_names[ind])
line_names_train.append(line_names[ind])
for ind in permutation[N_train:]:
segmap_names_val.append(segmap_names[ind])
line_names_val.append(line_names[ind])
#########################
dset_train = LineDataset(segmap_location = segmap_path, line_location = line_path,
segmap_names = segmap_names_train, line_names = line_names_train,
transform_list = transform_list)
dset_val = LineDataset(segmap_location = segmap_path, line_location = line_path,
segmap_names = segmap_names_val, line_names = line_names_val,
transform_list = transform_list)
loader_train = DataLoader(dataset=dset_train, batch_size=batch_size_tr, shuffle=shuffle)
loader_val = DataLoader(dataset=dset_val, batch_size=batch_size_val, shuffle=False)
return loader_train, loader_val
def prepare_seg():
train_data_names = read_data_names('./train_labels')
test_data_names = read_data_names('./test_labels')
train_images = read_images('./train_images', data_names=train_data_names)
train_labels = read_labels('./train_labels')
# train_data_names = read_data_names('./train_labels')
test_labels = read_labels('./test_labels')
test_images = read_images('./test_images', test_data_names)
# test_data_names = read_data_names('./test_labels')
for ind, image in enumerate(train_images):
H, W, C = image.shape
lab = train_labels[ind]
seg = draw_seg(lab, H, W)
np.save('./train_labels/{}.npy'.format(ind), seg)
for ind, image in enumerate(test_images):
H, W, C = image.shape
lab = test_labels[ind]
seg = draw_seg(lab, H, W)
np.save('./test_labels/{}.npy'.format(ind), seg)
def get_loader_test(tfm = 'nopad', batch_size = 1, shuffle = False):
if type(tfm) == type('PAD'):
if tfm.lower() == 'pad_val' or tfm.lower() == 'pad':
tfm = PAD_VAL
elif tfm.lower() == 'nopad_val' or tfm.lower() == 'nopad':
tfm = NOPAD_VAL
else:
tfm = None
data_path = './test_images'
label_path = './test_labels'
labels = read_labels(label_path)
data_names = read_data_names(label_path)
dset_test = CoordDataset(data_path, labels, data_names, transform_list=tfm)
loader_test = DataLoader(dataset=dset_test, batch_size=batch_size, shuffle=shuffle)
return loader_test
def get_loader_train(tfm = 'nopad', batch_size = 64, shuffle = False):
if type(tfm) == type('PAD'):
if tfm.lower() == 'pad_val':
tfm = PAD_VAL
elif tfm.lower() == 'nopad_val':
tfm = NOPAD_VAL
elif tfm.lower() == 'pad' or tfm.lower() == 'unf' or tfm.lower() == 'uniform':
tfm = UNF
elif tfm.lower() == 'nopad':
tfm = NOPAD
elif tfm.lower() == 'rhp':
tfm = RHP
else:
tfm = None
data_path = './train_images'
label_path = './train_labels'
labels = read_labels(label_path)
data_names = read_data_names(label_path)
dset_train = CoordDataset(data_path, labels, data_names, transform_list=tfm)
loader_train = DataLoader(dataset=dset_train, batch_size=batch_size, shuffle=shuffle)
return loader_train
pol = np.concatenate((coord_rev[0, 0, :].reshape(
1, 2), coord_rev[:, 1, :], coord_rev[::-1, 0, :]),
axis=0)
rr, cc = polygon(pol[:, 1], pol[:, 0], seg_image.shape)
seg_image[rr, cc, :] = 1
seg_image = seg_image[:, :, 0]
return seg_image
if __name__ == '__main__':
author = 'YB'
#making traintest set
from os.path import join as osj
import shutil
train_data_names = read_data_names('./train_labels')
train_labels = read_labels('./train_labels')
test_data_names = read_data_names('./test_labels')
test_labels = read_labels('./test_labels')
dest = './trtest_images'
train_no = len(train_data_names)
all_labels = []
all_names = []
for i, d in enumerate(train_data_names):
l = train_labels[i]
shutil.copy(osj('./train_images', d), osj(dest, d))
import os
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
from label_io import read_data_names, read_labels, plot_image, chw, hwc
from dataset import CoordDataset
from label_transform import CoordCustomPad, CoordHorizontalFlip, CoordRandomRotate, \
CoordLabelNormalize, CoordResize, CoordVerticalFlip
#def label-to-image
data_path = './train_images'
label_path = './train_labels'
labels = read_labels(label_location=label_path)
data_names = read_data_names(label_location=label_path)
batch_size = 8
# transform = tr.Compose([
# tr.RandomRotation(30, expand = False)
# tr.ToTensor()
# ])
customTransforms = [
CoordCustomPad(512 / 256),
CoordResize((512, 256)),
CoordLabelNormalize()
]
# customTransforms = [
def get_loader_train_val(tfm_train = 'nopad', tfm_val = 'nopad', batch_size_tr=64, batch_size_val=1, shuffle = True):
tfm = tfm_train
if type(tfm) == type('PAD'):
if tfm.lower() == 'pad_val':
tfm = PAD_VAL
elif tfm.lower() == 'nopad_val':
tfm = NOPAD_VAL
elif tfm.lower() == 'pad' or tfm.lower() == 'unf' or tfm.lower() == 'uniform':
tfm = UNF
elif tfm.lower() == 'nopad':
tfm = NOPAD
elif tfm.lower() == 'rhp':
tfm = RHP
else:
tfm = None
tfm_train = tfm
tfm = tfm_val
if type(tfm) == type('PAD'):
if tfm.lower() == 'pad_val' or tfm.lower() == 'pad':
tfm = PAD_VAL
elif tfm.lower() == 'nopad_val' or tfm.lower() == 'nopad':
tfm = NOPAD_VAL
else:
tfm = None
tfm_val = tfm
data_path = './train_images'
label_path = './train_labels'
val_ratio = 0.1
labels = read_labels(label_path)
data_names = read_data_names(label_path)
N_all = len(data_names)
N_val = int(N_all * val_ratio)
N_train = N_all - N_val
# get train and validation data set
data_names_train = []
data_names_val = []
labels_train = []
labels_val = []
if not os.path.exists(os.path.join('./', 'val_permutation.npy')):
print('reset permutation')
permutation = np.random.permutation(N_all)
np.save(os.path.join('./', 'val_permutation.npy'), permutation)
else:
permutation = np.load(os.path.join('./', 'val_permutation.npy'))
for ind in permutation[:N_train]:
data_names_train.append(data_names[ind])
labels_train.append(labels[ind])
labels_train = np.asarray(labels_train)
for ind in permutation[N_train:]:
data_names_val.append(data_names[ind])
labels_val.append(labels[ind])
labels_val = np.asarray(labels_val)
#########################
dset_train = CoordDataset(data_path, labels_train, data_names_train, transform_list=tfm_train)
dset_val = CoordDataset(data_path, labels_val, data_names_val, transform_list=tfm_val)
loader_train = DataLoader(dataset=dset_train, batch_size=batch_size_tr, shuffle=shuffle)
loader_val = DataLoader(dataset=dset_val, batch_size=batch_size_val, shuffle=False)
return loader_train ,loader_val
df = pd.DataFrame(result_list)
df.to_csv(
os.path.join(
pred_path, title, 'result_' + title + '_%.2f' %
(np.average(w2_errors) * 100) + '.csv'))
return result_list
if __name__ == '__main__':
plt.rcParams["figure.figsize"] = (8, 16)
#test랑 record 변환은 메인에서 하지말고 여기서만 하면됨
record_label_location = './record_cr_labels'
record_data_location = './record_cr_images'
record_data_names = read_data_names(record_label_location)
record_labels = read_labels(record_label_location)
record_images = read_images(record_data_location, record_data_names)
pred_path = './model/TRTEST_ep4278'
postprocess_inte(pred_path=pred_path,
images=record_images,
labels_gt_abs=record_labels,
title=None,
save_plot=True,
method2_on=True,
method1_on=False,
original_display=True)
###Validation set
if __name__ == '__main__':
plt.rcParams["figure.figsize"] = (4, 8)
# test_label_location = './test_labels'
# test_data_location = './test_images'
# test_data_names = read_data_names(test_label_location)
# test_labels = read_labels(test_label_location)
# test_images = read_images(test_data_location, test_data_names)
train_label_location = './train_labels'
train_image_location = './train_images'
out_path = './plots'
train_labels = read_labels(train_label_location)
train_data_names = read_data_names(train_label_location)
train_images = read_images(train_image_location, train_data_names)
count = 0
for ind, image in enumerate(train_images):
label = train_labels[ind]
plt.figure()
#title = 'train_vanila'
H, W, C = image.shape
gt = label
gt_angles, gt_pos = calc_angle_old(gt, (H, W), full=True)
_fp = gt.reshape(-1, 2, 2).copy()
for pos in gt_pos:
dots = np.average(_fp[2 * pos:2 * pos + 2, :, :], axis=0)
tl = angles2[_pos22] * 180 / np.pi
cobb_angles = np.asarray([pt, mt, tl])
pos = (pos1, pos2, pos11, pos22)
#params = dict(pos = pos, mid_points = mid_points, vec_lines = vec_lines, case = case)
if full:
return cobb_angles, pos
else:
return cobb_angles
from label_io import read_labels, read_images, read_data_names
import pandas as pd
if __name__ == '__main__':
#### Testing demo algorithm
data_names_train = read_data_names('./train_labels')
labels_now = read_labels('./train_labels')
labels_ori = read_labels('./train_labels', title ='labels_original')
labels_m = read_labels('./train_labels', title ='labels_m')
train_images_location = './train_images'
train_images = read_images(train_images_location, data_names=data_names_train)
images = read_images('./train_images', data_names_train)
# for ind, im in enumerate(images):
# lab_m = labels_m[ind]
# lab_ori = labels_ori[ind]
#
# if np.sum(np.abs((lab_m - lab_ori)))>=1:
# plt.figure()
# plot_image(im, coord_red=lab_ori, coord_gr=lab_m)
