medium update

Remove_Noise.py

@@ -215,8 +215,8 @@ def save_testable_images(images, selected_channel, quantity_of_images):
 
     selected_images = np.array(images)[image_locations]
 
-    average_image = np.array(Image.open("Average_On_Channel(" + selected_channel + ").tiff"))
-    images_in_each_direction = 80
+    # average_image = np.array(Image.open("Average_On_Channel(" + selected_channel + ").tiff"))
+    # images_in_each_direction = 80
     for i, item in enumerate(selected_images):
         # if image_locations[i] < images_in_each_direction:
         #     average_image = remote_create_average(images[image_locations[i] - image_locations[i]: image_locations[i] + images_in_each_direction + image_locations[i]], selected_channel)
@@ -226,13 +226,14 @@ def save_testable_images(images, selected_channel, quantity_of_images):
         #     average_image = remote_create_average(images[image_locations[i] - images_in_each_direction: image_locations[i] + images_in_each_direction], selected_channel)
         image = Image.open(sftp_client.open(item))
         wherelastslash = item.rfind("/")
-        image = np.array(image)[1:]
+        # image = np.array(image)[1:]
+        image = np.array(image)
         savable_original = Image.fromarray(image)
         savable_original.save("original_images(" + selected_channel + ")/innerfolder/original" + item[wherelastslash + 1:])
-        altered_image = adjust_to_original(image, average_image)
-        altered_image = Image.fromarray(altered_image)
+        # altered_image = adjust_to_original(image, average_image)
+        # altered_image = Image.fromarray(altered_image)
         
-        altered_image.save("averaged_images(" + selected_channel + ")/innerfolder" + item[wherelastslash:])
+        # altered_image.save("averaged_images(" + selected_channel + ")/innerfolder" + item[wherelastslash:])
         # average_image = Image.fromarray(average_image)
 
     sftp_client.close()
@@ -270,7 +271,7 @@ if __name__ == "__main__":
     images = remote_image_extractor(scenes)
     images = find_only_in_channel(images, "11")
     # average_image = np.array(Image.open("Average_On_Channel(" + "11" + ").tiff"))
-    # save_testable_images(images,"11",5)
+    save_testable_images(images,"11",500)
     # plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)
     # plt.show()
     

WorkingPyDemo.py

@@ -113,7 +113,7 @@ def predict_pix(tiff_image_path, difference = True):
         #The res is the residuals of the four points used to fit the hyperplane (summed distance of each of the 
         #points to the hyperplane), it is a measure of gradient
         f, diff, rank, s = la.lstsq(points, neighbor.T, rcond=None)
-        diff = diff.astype(int)
+        # diff = diff.astype(int)
         # Pinv = np.linalg.pinv(points)
         # b = [z0,z1,z2,z3]
         # x = Pinv@np.array(b)
@@ -461,7 +461,7 @@ def decoder(encoded_string, list_dic, bins, use_diff):
                     # x = Pinv@np.array(b)
                     # difference = np.linalg.norm(b - points@x,ord=2)
                     f, difference, rank, s = la.lstsq(points, [z0,z1,z2,z3], rcond=None) 
-                    difference = difference.astype(int)
+                    # difference = difference.astype(int)
                     
                 # predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))
                 predict = np.round(np.round((Ainv[-1]@y)[0],1))