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 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)
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
test_label_location = './test_labels'
test_data_location = './test_images'
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)
if True:
plt.plot(dots[:, 0], dots[:, 1], 'g')
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)
# plt.show()
#
#### gt angle
gtangle = pd.read_csv('./train_labels/angles.csv', header=None, index_col=None)