added more results

README.md

@@ -208,6 +208,10 @@ Below are the final outputs for some sample image editing examples:
 <img src='outputs/image-editing/edit3.png' atl="Image Editing" style="floar: center; border-radius: 6px;">
 </div>
 
+<div align="center">
+<img src='outputs/image-editing/edit4.png' atl="Image Editing" style="floar: center; border-radius: 6px;">
+</div>
+
 <div align="center">
 <img src='outputs/image-editing/edit2.png' atl="Image Editing" style="floar: center; border-radius: 6px;">
 </div>

inference.py

@@ -1,25 +1,41 @@
-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,
-)
+# 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()