Fix bugs on file integration and some code errors

Watermarking .ipynb

@@ -373,21 +373,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
   }
  },
  "nbformat": 4,

main.py

@@ -4,44 +4,50 @@
 
 # est = poisson_reconstruct(gx, gy, np.zeros(gx.shape)[:,:,0])
 cropped_gx, cropped_gy = crop_watermark(gx, gy)
-W_m = poisson_reconstruct(cropped_gx, cropped_gy)
 
 # random photo
-img = cv2.imread('images/fotolia_processed/fotolia_137840645.jpg')
+img = cv2.imread('images/fotolia_processed/fotolia_137840668.jpg')
 im, start, end = watermark_detector(img, cropped_gx, cropped_gy)
 
+# # Save result of watermark detection
+# plt.subplot(1, 2, 1)
+# plt.imshow(img)
+# plt.subplot(1, 2, 2)
 # plt.imshow(im)
-# plt.show()
+# plt.savefig('images/results/watermark_detect_result.png')
 # We are done with watermark estimation
+
 # W_m is the cropped watermark
+W_m = poisson_reconstruct(cropped_gx, cropped_gy)
+
+# Get the number of images in the folder
 num_images = len(gxlist)
 
-J, img_paths = get_cropped_images('images/fotolia_processed', num_images, start, end, cropped_gx.shape)
+J, img_paths = get_cropped_images(
+    'images/fotolia_processed', num_images, start, end, cropped_gx.shape)
+
 # get a random subset of J
-idx = [389, 144, 147, 468, 423, 92, 3, 354, 196, 53, 470, 445, 314, 349, 105, 366, 56, 168, 351, 15, 465, 368, 90, 96, 202, 54, 295, 137, 17, 79, 214, 413, 454, 305, 187, 4, 458, 330, 290, 73, 220, 118, 125, 180, 247, 243, 257, 194, 117, 320, 104, 252, 87, 95, 228, 324, 271, 398, 334, 148, 425, 190, 78, 151, 34, 310, 122, 376, 102, 260]
-idx = idx[:25]
-# Wm = (255*PlotImage(W_m))
-Wm = W_m - W_m.min()
 
 # get threshold of W_m for alpha matte estimate
-alph_est = estimate_normalized_alpha(J, Wm)
+Wm = W_m - W_m.min()
+
+# estimate alpha matte
+alph_est = estimate_normalized_alpha(J, Wm, num_images=len(J))
 alph = np.stack([alph_est, alph_est, alph_est], axis=2)
 C, est_Ik = estimate_blend_factor(J, Wm, alph)
 
 alpha = alph.copy()
-for i in xrange(3):
-	alpha[:,:,i] = C[i]*alpha[:,:,i]
+for i in range(3):
+    alpha[:, :, i] = C[i]*alpha[:, :, i]
 
 Wm = Wm + alpha*est_Ik
 
 W = Wm.copy()
-for i in xrange(3):
-	W[:,:,i]/=C[i]
+for i in range(3):
+    W[:, :, i] /= C[i]
 
 Jt = J[:25]
+
 # now we have the values of alpha, Wm, J
 # Solve for all images
 Wk, Ik, W, alpha1 = solve_images(Jt, W_m, alpha, W)
-# W_m_threshold = (255*PlotImage(np.average(W_m, axis=2))).astype(np.uint8)
-# ret, thr = cv2.threshold(W_m_threshold, 127, 255, cv2.THRESH_BINARY)  
-

src/__init__.py

@@ -1,4 +1,4 @@
-from estimate_watermark import *
-from preprocess import *
-from image_crawler import *
-from watermark_reconstruct import *
+from .estimate_watermark import *
+from .preprocess import *
+from .image_crawler import *
+from .watermark_reconstruct import *