from libs.pconv_model import PConvUnet
from libs.util import random_mask, plot_images
# Load image
img = cv2.imread('./data/building.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img / 255
shape = img.shape
print(f"Shape of image is: {shape}")
# Load mask
mask = random_mask(shape[0], shape[1])
# Image + mask
masked_img = deepcopy(img)
masked_img[mask == 0] = 1
model = PConvUnet(weight_filepath='result/logs/')
model.load(r"result/logs/1_weights_2019-02-21-04-59-53.h5", train_bn=False)
# Run prediction quickly
pred = model.scan_predict((img, mask))
# Show result
plot_images([img, masked_img, pred])
imsave('result/test_orginal.png', img)
imsave('result/test_masked.png', masked_img)
imsave('result/test_pred.png', pred)
print("finish")
steps_per_epoch=1000,
epochs=1,
plot_callback=plot_callback,
)
# Load image
org = cv2.imread('./data/building.jpg')
org = cv2.cvtColor(org, cv2.COLOR_BGR2RGB)
org = org / 255
shape = org.shape
print(f"Shape of image is: {shape}")
# Load mask
org_mask = random_mask(shape[0], shape[1])
# Image + mask
masked_org = deepcopy(org)
masked_org[org_mask==0] = 1
# Run prediction quickly
pred = model.scan_predict((org, org_mask))
# Show result
imsave('result/original.png', org)
imsave('result/masked.png', masked_org)
imsave('result/predict.png', pred)
e = int(time.time() - start_time)
print('{:02d}:{:02d}:{:02d}'.format(e // 3600, (e % 3600 // 60), e % 60))
print('finish')