Mathematical changes and testing

ErrorImageCreator.py

-from turtle import color
 from WorkingPyDemo import *
 def produce_error_array(encoded_string, list_dic, bins, use_diff):
     """
@@ -88,17 +87,18 @@ def color_adjust(visual_array):
     adjusted_array = visual_array - min_of_errors
     adjusted_array = np.round(adjusted_array*255/np.max(adjusted_array))
     return adjusted_array
-scenes = file_extractor(folder_name)
-images = image_extractor(scenes)
-list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
-bins = [21,32,48]
-encoded_string2 = bytes_to_bitstring(read_from_file(images[250][:-5] + "_Compressed.txt"))
-original_array, error_array = produce_error_array(encoded_string2, list_dic, bins, False)
-adjusted_errors = color_adjust(abs(error_array))
-original_array_adjusted = color_adjust(original_array)
-# print(adjusted_errors)
-plt.subplot(121)
-plt.imshow(original_array_adjusted,cmap='gray',vmin = 0, vmax=255)
-plt.subplot(122)
-plt.imshow(adjusted_errors,cmap = 'gray', vmin = 0, vmax=255)
-plt.show()
+if __name__ == "__main__":
+    scenes = file_extractor(folder_name)
+    images = image_extractor(scenes)
+    list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
+    bins = [21,32,48]
+    encoded_string2 = bytes_to_bitstring(read_from_file(images[1][:-5] + "_Compressed.txt"))
+    original_array, error_array = produce_error_array(encoded_string2, list_dic, bins, False)
+    adjusted_errors = color_adjust(abs(error_array))
+    original_array_adjusted = color_adjust(original_array)
+    # print(adjusted_errors)
+    plt.subplot(121)
+    plt.imshow(original_array_adjusted,cmap='gray',vmin = 0, vmax=255)
+    plt.subplot(122)
+    plt.imshow(adjusted_errors,cmap = 'gray', vmin = 0, vmax=255)
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+    plt.show()
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Remove_Noise.py

+from matplotlib.image import composite_images
+from WorkingPyDemo import *
+
+def remove_noise(images, which_sensor):
+    same_sensor_images = []
+    which_sensor = str(which_sensor)
+    average_image = np.zeros_like(np.array(Image.open(images[0]))[1:])
+    for i, image_name in enumerate(images):
+        if int(which_sensor) > 9:
+            if image_name[-7:-5] == which_sensor:
+                same_sensor_images.append(image_name)
+        else:
+            if image_name[-7:-5] == "_" + which_sensor:
+                same_sensor_images.append(image_name)
+    for i, image_name in enumerate(same_sensor_images):
+        # print(image_name)
+        image_object = Image.open(image_name)
+        # print(np.array(image_object).shape)
+        # print(np.array(image_object)[1:] + average_image)
+        average_image = np.array(image_object)[1:] + average_image
+    return average_image/len(same_sensor_images)
+scenes = file_extractor(folder_name)
+images = image_extractor(scenes)
+average_image = remove_noise(images,"7")
+def color_adjust(visual_array):
+    min_of_errors = np.min(visual_array)
+    adjusted_array = visual_array - min_of_errors
+    adjusted_array = np.round(adjusted_array*255/np.max(adjusted_array))
+    return adjusted_array
+print(np.max(average_image))
+print(np.min(average_image))
+plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=255)
+plt.show()
+
+# print(np.linalg.inv(np.array([[3,0,-1],[0,3,3],[1,-3,-4]])))
+print(np.linalg.pinv(np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])))
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WorkingPyDemo.py

@@ -76,7 +76,8 @@ def predict_pix(tiff_image_path, difference = True):
     image_obj = Image.open(tiff_image_path)    #Open the image and read it as an Image object
     image_array = np.array(image_obj)[1:,:].astype(int)    #Convert to an array, leaving out the first row because the first row is just housekeeping data
     # image_array = image_array.astype(int) 
-    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation
+    # A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation
+    Ainv = np.array([[0.5,-0.5,-0.5],[-0.5,1.83333333,1.5],[0.5,-1.5,-1.5]])
     # where z0 = (-1,1), z1 = (0,1), z2 = (1,1), z3 = (-1,0)
     z0 = image_array[0:-2,0:-2]   # get all the first pixel for the entire image
     z1 = image_array[0:-2,1:-1]   # get all the second pixel for the entire image
@@ -91,8 +92,8 @@ def predict_pix(tiff_image_path, difference = True):
     
     # use numpy solver to solve the system of equations all at once
     #predict = np.floor(np.linalg.solve(A,y)[-1])
-    predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1))
-    
+    # predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1))
+    predict = np.round(np.round((Ainv[-1]@y)),1)
     #Matrix system of points that will be used to solve the least squares fitting hyperplane
     points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])
     
@@ -113,7 +114,12 @@ def predict_pix(tiff_image_path, difference = True):
         #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)
+        # Pinv = np.linalg.pinv(points)
+        # b = [z0,z1,z2,z3]
+        # x = Pinv@np.array(b)
 
+        # diff = np.linalg.norm(b - points@x,ord=2)
+        # diff = diff.astype(int)
     # calculate the error
     error = np.ravel(image_array[1:-1,1:-1])-predict
     
@@ -402,6 +408,7 @@ def decoder(encoded_string, list_dic, bins, use_diff):
     decode_matrix   (512, 640): decoded matrix
     """
     A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation
+    Ainv = np.array([[0.5,-0.5,-0.5],[-0.5,1.83333333,1.5],[0.5,-1.5,-1.5]])
     # change the dictionary back to list
     # !!!!!WARNING!!!! has to change this part, everytime you change the number of bins
     the_keys0 = list(list_dic[0].keys())
@@ -421,7 +428,7 @@ def decoder(encoded_string, list_dic, bins, use_diff):
     
     #Matrix system of points that will be used to solve the least squares fitting hyperplane
     points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])
-    
+    # Pinv = np.linalg.pinv(points)
     decode_matrix = np.zeros((512,640))
     # loop through all the element in the matrix
     for i in range(decode_matrix.shape[0]):
@@ -449,11 +456,14 @@ def decoder(encoded_string, list_dic, bins, use_diff):
                 if use_diff:
                     difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)
                 else:
-                    
+                    # b = [z0,z1,z2,z3]
+                    # 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)
                     
-                predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))
+                # predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))
+                predict = np.round(np.round((Ainv[-1]@y)[0],1))
                 # add on the difference by searching the dictionary
                 # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins
                 if difference <= bins[0]:
@@ -501,19 +511,19 @@ def text_to_tiff(filename, list_dic, bins):
     reconstruct_image = reconstruct_image.astype(np.uint16)
     reconstruct_image = Image.fromarray(reconstruct_image)
     reconstruct_image.save(filename[:-16]+"_reconstructed.tiff", "TIFF")
-# starttime = time()
+
 if __name__ == "__main__":
     
     scenes = file_extractor(folder_name)
     images = image_extractor(scenes)
     newnamesforlater = []
-    # list_dic, bins = make_dictionary(images, 4, False)
+    # list_dic, bins = make_dictionary(images[0:1], 4, False)
     file_sizes_new = []
     file_sizes_old = []
     list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
     bins = [21,32,48]
     # np.save("first_dic.npy", list_dic)
-    for i in range(len(images)):
+    for i in range(len(images[0:5])):
         # image, new_error, diff = huffman(images[i], 4, False)
         # encoded_string = encoder(new_error, list_dic, diff, bins)
         # inletters = bitstring_to_bytes(encoded_string)
@@ -535,18 +545,21 @@ if __name__ == "__main__":
     
     file_sizes_new.append(os.path.getsize("first_dic.npy"))
     print(np.sum(file_sizes_new)/np.sum(file_sizes_old))
-    list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
+    # list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
     bins = [21,32,48]
-    # starttime = time()
-    for i,item in enumerate(newnamesforlater[0:5]):
+    
+    for i,item in enumerate(newnamesforlater):
         print(item)
         image, new_error, diff = huffman(images[i], 4, False)
         encoded_string2 = bytes_to_bitstring(read_from_file(item))
+        starttime = time()
         reconstruct_image = decoder(encoded_string2, list_dic, bins, False)
         print(np.allclose(image, reconstruct_image))
+        print(time() - starttime)
     # text_to_tiff("images/1626033496_437803/1626033496_437803_3._Compressed.txt", list_dic, bins)
     # original_image = Image.open("images/1626033496_437803/1626033496_437803_3.tiff")
     # original_image = np.array(original_image)[1:]
     # secondimage = Image.open("images/1626033496_437803/1626033496_437803_3_reconstructed.tiff")
     # secondimage = np.array(secondimage)
     # print(np.allclose(original_image, secondimage))
+    
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