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')