Commit cfb96985 authored by Bryce Hepner's avatar Bryce Hepner
Browse files

Mathematical changes and testing

parent 9b1db8a3

ErrorImageCreator.py

+15 −15
Original line number Original line Diff line number Diff line 
from turtle import color

from WorkingPyDemo import *

from WorkingPyDemo import *

def produce_error_array(encoded_string, list_dic, bins, use_diff):

def produce_error_array(encoded_string, list_dic, bins, use_diff):

    """

    """
@@ -88,11 +87,12 @@ def color_adjust(visual_array): 
    adjusted_array = visual_array - min_of_errors

    adjusted_array = visual_array - min_of_errors

    adjusted_array = np.round(adjusted_array*255/np.max(adjusted_array))

    adjusted_array = np.round(adjusted_array*255/np.max(adjusted_array))

    return adjusted_array

    return adjusted_array

if __name__ == "__main__":

    scenes = file_extractor(folder_name)

    scenes = file_extractor(folder_name)

    images = image_extractor(scenes)

    images = image_extractor(scenes)

    list_dic = np.load("first_dic.npy", allow_pickle="TRUE")

    list_dic = np.load("first_dic.npy", allow_pickle="TRUE")

    bins = [21,32,48]

    bins = [21,32,48]

encoded_string2 = bytes_to_bitstring(read_from_file(images[250][:-5] + "_Compressed.txt"))

    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)

    original_array, error_array = produce_error_array(encoded_string2, list_dic, bins, False)

    adjusted_errors = color_adjust(abs(error_array))

    adjusted_errors = color_adjust(abs(error_array))

    original_array_adjusted = color_adjust(original_array)

    original_array_adjusted = color_adjust(original_array)

Remove_Noise.py

0 → 100644
+36 −0
Original line number Original line Diff line number Diff line 
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

+27 −14
Original line number Original line Diff line number Diff line
@@ -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_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 = 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) 

    # 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)

    # 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

    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

    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

    # use numpy solver to solve the system of equations all at once

    #predict = np.floor(np.linalg.solve(A,y)[-1])

    #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

    #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]])

    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

        #points to the hyperplane), it is a measure of gradient

        f, diff, rank, s = la.lstsq(points, neighbor.T, rcond=None)

        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)





        # diff = np.linalg.norm(b - points@x,ord=2)

        # diff = diff.astype(int)

    # calculate the error

    # calculate the error

    error = np.ravel(image_array[1:-1,1:-1])-predict

    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

    decode_matrix   (512, 640): decoded matrix

    """

    """

    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]])

    # change the dictionary back to list

    # change the dictionary back to list

    # !!!!!WARNING!!!! has to change this part, everytime you change the number of bins

    # !!!!!WARNING!!!! has to change this part, everytime you change the number of bins

    the_keys0 = list(list_dic[0].keys())

    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

    #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]])

    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))

    decode_matrix = np.zeros((512,640))

    # loop through all the element in the matrix

    # loop through all the element in the matrix

    for i in range(decode_matrix.shape[0]):

    for i in range(decode_matrix.shape[0]):
@@ -449,11 +456,14 @@ def decoder(encoded_string, list_dic, bins, use_diff): 
                if use_diff:

                if use_diff:

                    difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)

                    difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)

                else:

                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) 

                    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(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

                # add on the difference by searching the dictionary

                # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins

                # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins

                if difference <= bins[0]:

                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 = reconstruct_image.astype(np.uint16)

    reconstruct_image = Image.fromarray(reconstruct_image)

    reconstruct_image = Image.fromarray(reconstruct_image)

    reconstruct_image.save(filename[:-16]+"_reconstructed.tiff", "TIFF")

    reconstruct_image.save(filename[:-16]+"_reconstructed.tiff", "TIFF")

# starttime = time()



if __name__ == "__main__":

if __name__ == "__main__":

    

    

    scenes = file_extractor(folder_name)

    scenes = file_extractor(folder_name)

    images = image_extractor(scenes)

    images = image_extractor(scenes)

    newnamesforlater = []

    newnamesforlater = []

    # list_dic, bins = make_dictionary(images, 4, False)

    # list_dic, bins = make_dictionary(images[0:1], 4, False)

    file_sizes_new = []

    file_sizes_new = []

    file_sizes_old = []

    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]

    bins = [21,32,48]

    # np.save("first_dic.npy", list_dic)

    # 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)

        # image, new_error, diff = huffman(images[i], 4, False)

        # encoded_string = encoder(new_error, list_dic, diff, bins)

        # encoded_string = encoder(new_error, list_dic, diff, bins)

        # inletters = bitstring_to_bytes(encoded_string)

        # inletters = bitstring_to_bytes(encoded_string)
@@ -535,18 +545,21 @@ if __name__ == "__main__": 
    

    

    file_sizes_new.append(os.path.getsize("first_dic.npy"))

    file_sizes_new.append(os.path.getsize("first_dic.npy"))

    print(np.sum(file_sizes_new)/np.sum(file_sizes_old))

    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]

    bins = [21,32,48]

    # starttime = time()

    

    for i,item in enumerate(newnamesforlater[0:5]):

    for i,item in enumerate(newnamesforlater):

        print(item)

        print(item)

        image, new_error, diff = huffman(images[i], 4, False)

        image, new_error, diff = huffman(images[i], 4, False)

        encoded_string2 = bytes_to_bitstring(read_from_file(item))

        encoded_string2 = bytes_to_bitstring(read_from_file(item))

        starttime = time()

        reconstruct_image = decoder(encoded_string2, list_dic, bins, False)

        reconstruct_image = decoder(encoded_string2, list_dic, bins, False)

        print(np.allclose(image, reconstruct_image))

        print(np.allclose(image, reconstruct_image))

        print(time() - starttime)

    # text_to_tiff("images/1626033496_437803/1626033496_437803_3._Compressed.txt", list_dic, bins)

    # 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 = Image.open("images/1626033496_437803/1626033496_437803_3.tiff")

    # original_image = np.array(original_image)[1:]

    # original_image = np.array(original_image)[1:]

    # secondimage = Image.open("images/1626033496_437803/1626033496_437803_3_reconstructed.tiff")

    # secondimage = Image.open("images/1626033496_437803/1626033496_437803_3_reconstructed.tiff")

    # secondimage = np.array(secondimage)

    # secondimage = np.array(secondimage)

    # print(np.allclose(original_image, secondimage))

    # print(np.allclose(original_image, secondimage))

    
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