undoing false commit

inference.py

@@ -1,41 +1,25 @@
-# import argparse
-# from api import remove_watermark
+import argparse
+from api import remove_watermark
 
-# parser = argparse.ArgumentParser(description = 'Removing Watermark')
-# parser.add_argument('--image-path', type = str, default = './data/watermark-unavailable/watermarked/watermarked0.png', help = 'Path to the "watermarked" image.')
-# parser.add_argument('--mask-path', type = str, default = './data/watermark-unavailable/masks/mask0.png', help = 'Path to the "watermark" image.')
-# parser.add_argument('--input-depth', type = int, default = 32, help = 'Max channel dimension of the noise input. Set it based on gpu/device memory you have available.')
-# parser.add_argument('--lr', type = float, default = 0.01, help = 'Learning rate.')
-# parser.add_argument('--training-steps', type = int, default = 3000, help = 'Number of training iterations.')
-# parser.add_argument('--show-step', type = int, default = 200, help = 'Interval for visualizing results.')
-# parser.add_argument('--reg-noise', type = float, default = 0.03, help = 'Hyper-parameter for regularized noise input.')
-# parser.add_argument('--max-dim', type = float, default = 512, help = 'Max dimension of the final output image')
+parser = argparse.ArgumentParser(description = 'Removing Watermark')
+parser.add_argument('--image-path', type = str, default = './data/watermark-unavailable/watermarked/watermarked0.png', help = 'Path to the "watermarked" image.')
+parser.add_argument('--mask-path', type = str, default = './data/watermark-unavailable/masks/mask0.png', help = 'Path to the "watermark" image.')
+parser.add_argument('--input-depth', type = int, default = 32, help = 'Max channel dimension of the noise input. Set it based on gpu/device memory you have available.')
+parser.add_argument('--lr', type = float, default = 0.01, help = 'Learning rate.')
+parser.add_argument('--training-steps', type = int, default = 3000, help = 'Number of training iterations.')
+parser.add_argument('--show-step', type = int, default = 200, help = 'Interval for visualizing results.')
+parser.add_argument('--reg-noise', type = float, default = 0.03, help = 'Hyper-parameter for regularized noise input.')
+parser.add_argument('--max-dim', type = float, default = 512, help = 'Max dimension of the final output image')
 
-# args = parser.parse_args()
+args = parser.parse_args()
 
-# remove_watermark(
-#     image_path = args.image_path,
-#     mask_path = args.mask_path,
-#     max_dim = args.max_dim,
-#     show_step = args.show_step,
-#     reg_noise = args.reg_noise,
-#     input_depth = args.input_depth,
-#     lr = args.lr,
-#     training_steps = args.training_steps,
-# )
-
-import numpy as np
-import cv2
-import matplotlib.pyplot as plt
-img = cv2.imread('./data/watermark-unavailable/watermarked/watermarked0.png')
-mask = cv2.imread('./data/watermark-unavailable/masks/mask0.png', 0)
-zeros = mask == 0
-ones = mask == 1
-mask[zeros] = 0
-mask[ones] = 1
-plt.imshow(mask, cmap = 'gray')
-plt.show()
-dst = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)
-cv2.imshow('dst', dst)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
+remove_watermark(
+    image_path = args.image_path,
+    mask_path = args.mask_path,
+    max_dim = args.max_dim,
+    show_step = args.show_step,
+    reg_noise = args.reg_noise,
+    input_depth = args.input_depth,
+    lr = args.lr,
+    training_steps = args.training_steps,
+)