research in wavelets

%% Cell type:code id:dbef8759 tags:

%% Cell type:code id:dbef8759 tags:

``` python

``` python

import numpy as np

import numpy as np

from matplotlib import pyplot as plt

from matplotlib import pyplot as plt

from itertools import product

from itertools import product

import os

import os

import sys

import sys

from PIL import Image

from PIL import Image

from scipy.optimize import minimize

from scipy.optimize import minimize

from time import time

from time import time

from numpy import linalg as la

from numpy import linalg as la

from scipy.stats import gaussian_kde

from scipy.stats import gaussian_kde

import seaborn as sns

import seaborn as sns

import pywt

import pywt

```

```

%% Cell type:code id:b7a550e0 tags:

%% Cell type:code id:b7a550e0 tags:

``` python

``` python

def file_extractor(dirname="images"):

def file_extractor(dirname="images"):

    files = os.listdir(dirname)

    files = os.listdir(dirname)

    scenes = []

    scenes = []

    for file in files:

    for file in files:

        scenes.append(os.path.join(dirname, file))

        scenes.append(os.path.join(dirname, file))

    return scenes

    return scenes

def image_extractor(scenes):

def image_extractor(scenes):

    image_folder = []

    image_folder = []

    for scene in scenes:

    for scene in scenes:

        files = os.listdir(scene)

        files = os.listdir(scene)

        for file in files:

        for file in files:

            image_folder.append(os.path.join(scene, file))

            image_folder.append(os.path.join(scene, file))

    images = []

    images = []

    for folder in image_folder:

    for folder in image_folder:

        ims = os.listdir(folder)

        ims = os.listdir(folder)

        for im in ims:

        for im in ims:

            if im[-4:] == ".jp4" or im[-7:] == "_6.tiff":

            if im[-4:] == ".jp4" or im[-7:] == "_6.tiff":

                continue

                continue

            else:

            else:

                images.append(os.path.join(folder, im))

                images.append(os.path.join(folder, im))

    return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor

    return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor

def im_distribution(images, num):

def im_distribution(images, num):

    """

    """

    Function that extracts tiff files from specific cameras and returns a list of all

    Function that extracts tiff files from specific cameras and returns a list of all

    the tiff files corresponding to that camera. i.e. all pictures labeled "_7.tiff" or otherwise

    the tiff files corresponding to that camera. i.e. all pictures labeled "_7.tiff" or otherwise

    specified camera numbers.

    specified camera numbers.

    Parameters:

    Parameters:

        images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but

        images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but

        of specific tiff files that can be opened right away. This is the list that we iterate through and

        of specific tiff files that can be opened right away. This is the list that we iterate through and

        divide.

        divide.

        num (str): a string designation for the camera number that we want to extract i.e. "14" for double digits

        num (str): a string designation for the camera number that we want to extract i.e. "14" for double digits

        of "_1" for single digits.

        of "_1" for single digits.

    Returns:

    Returns:

        tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted

        tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted

        from the 'images' list that correspond to the given num.

        from the 'images' list that correspond to the given num.

    """

    """

    tiff = []

    tiff = []

    for im in images:

    for im in images:

        if im[-7:-5] == num:

        if im[-7:-5] == num:

            tiff.append(im)

            tiff.append(im)

    return tiff

    return tiff

```

```

%% Cell type:code id:9ed20f84 tags:

%% Cell type:code id:9ed20f84 tags:

``` python

``` python

def plot_hist(tiff_list, i):

def plot_hist(tiff_list, i):

    """

    """

    This function is the leftovers from the first attempt to plot histograms.

    This function is the leftovers from the first attempt to plot histograms.

    As it stands it needs some work in order to function again. We will

    As it stands it needs some work in order to function again. We will

    fix this later. 1/25/22

    fix this later. 1/25/22

    """

    """

    image = tiff_list[i]

    image = tiff_list[i]

    image = Image.open(image)    #Open the image and read it as an Image object

    image = Image.open(image)    #Open the image and read it as an Image object

    image = np.array(image)[1:,:]    #Convert to an array, leaving out the first row because the first row is just housekeeping data

    image = np.array(image)[1:,:]    #Convert to an array, leaving out the first row because the first row is just housekeeping data

    image = image.astype(int)

    image = image.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

    z0 = image[0:-2,0:-2]   # get all the first pixel for the entire image

    z0 = image[0:-2,0:-2]   # get all the first pixel for the entire image

    z1 = image[0:-2,1:-1]   # get all the second pixel for the entire image

    z1 = image[0:-2,1:-1]   # get all the second pixel for the entire image

    z2 = image[0:-2,2::]    # get all the third pixel for the entire image

    z2 = image[0:-2,2::]    # get all the third pixel for the entire image

    z3 = image[1:-1,0:-2]   # get all the forth pixel for the entire image

    z3 = image[1:-1,0:-2]   # get all the forth pixel for the entire image

    # calculate the out put of the system of equation

    # calculate the out put of the system of equation

    y0 = np.ravel(-z0+z2-z3)

    y0 = np.ravel(-z0+z2-z3)

    y1 = np.ravel(z0+z1+z2)

    y1 = np.ravel(z0+z1+z2)

    y2 = np.ravel(-z0-z1-z2-z3)

    y2 = np.ravel(-z0-z1-z2-z3)

    y = np.vstack((y0,y1,y2))

    y = np.vstack((y0,y1,y2))

    # 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.linalg.solve(A,y)[-1]

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

    # flatten the neighbor pixlels and stack them together

    # flatten the neighbor pixlels and stack them together

    z0 = np.ravel(z0)

    z0 = np.ravel(z0)

    z1 = np.ravel(z1)

    z1 = np.ravel(z1)

    z2 = np.ravel(z2)

    z2 = np.ravel(z2)

    z3 = np.ravel(z3)

    z3 = np.ravel(z3)

    neighbor = np.vstack((z0,z1,z2,z3)).T

    neighbor = np.vstack((z0,z1,z2,z3)).T

    # calculate the difference

    # calculate the difference

    diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)

    diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)

    # flatten the image to a vector

    # flatten the image to a vector

    image_ravel = np.ravel(image[1:-1,1:-1])

    image_ravel = np.ravel(image[1:-1,1:-1])

    return image_ravel, predict, diff, image

    return image_ravel, predict, diff, image

```

```

%% Cell type:code id:8e3ef654 tags:

%% Cell type:code id:8e3ef654 tags:

``` python

``` python

scenes = file_extractor()

scenes = file_extractor()

images = image_extractor(scenes)

images = image_extractor(scenes)

num_images = im_distribution(images, "_9")

num_images = im_distribution(images, "_9")

error_mean = []

error_mean = []

error_mean1 = []

error_mean1 = []

diff_mean = []

diff_mean = []

times = []

times = []

times1 = []

times1 = []

all_error = []

all_error = []

for i in range(len(num_images)):

for i in range(len(num_images)):

    """start1 = time()

    """start1 = time()

    image_1, predict_1, difference_1, x_s_1 = plot_hist(num_images, i, "second")

    image_1, predict_1, difference_1, x_s_1 = plot_hist(num_images, i, "second")

    stop1 = time()

    stop1 = time()

    times1.append(stop1-start1)

    times1.append(stop1-start1)

    error1 = np.abs(image_1-predict_1)

    error1 = np.abs(image_1-predict_1)

    error_mean1.append(np.mean(np.ravel(error1)))"""

    error_mean1.append(np.mean(np.ravel(error1)))"""

    start = time()

    start = time()

    image, predict, difference, non_ravel = plot_hist(num_images, i)

    image, predict, difference, non_ravel = plot_hist(num_images, i)

    stop = time()

    stop = time()

    times.append(stop-start)

    times.append(stop-start)

    error = np.abs(image-predict)

    error = np.abs(image-predict)

    all_error.append(np.ravel(error))

    all_error.append(np.ravel(error))

    error_mean.append(np.mean(np.ravel(error)))

    error_mean.append(np.mean(np.ravel(error)))

    diff_mean.append(np.mean(np.ravel(difference)))

    diff_mean.append(np.mean(np.ravel(difference)))

#image, predict, difference = plot_hist(images, 0)

#image, predict, difference = plot_hist(images, 0)

```

```

%% Cell type:code id:fa65dcd6 tags:

%% Cell type:code id:fa65dcd6 tags:

``` python

``` python

print(f"Average Error First and Second Added: {np.mean(error_mean)}")

print(f"Average Error First and Second Added: {np.mean(error_mean)}")

print(f"Standard Deviaiton of Mean Errors: {np.sqrt(np.var(error_mean))}")

print(f"Standard Deviaiton of Mean Errors: {np.sqrt(np.var(error_mean))}")

print(f"Average Difference: {np.mean(diff_mean)}")

print(f"Average Difference: {np.mean(diff_mean)}")

print(f"Average Time per Image for First: {np.mean(times)}")

print(f"Average Time per Image for First: {np.mean(times)}")

```

```

%% Output

%% Output

    Average Error First and Second Added: 20.017164930235474

    Average Error First and Second Added: 20.017164930235474

    Standard Deviaiton of Mean Errors: 0.16101183692475135

    Standard Deviaiton of Mean Errors: 0.16101183692475135

    Average Difference: 53.678648426455226

    Average Difference: 53.678648426455226

    Average Time per Image for First: 0.10891032218933105

    Average Time per Image for First: 0.10891032218933105

%% Cell type:code id:4c05b947 tags:

%% Cell type:code id:4c05b947 tags:

``` python

``` python

new_image, new_pred, new_diff, no_ravel = plot_hist(images, 10)

new_image, new_pred, new_diff, no_ravel = plot_hist(images, 10)

```

```

%% Cell type:code id:dda442ae tags:

%% Cell type:code id:dda442ae tags:

``` python

``` python

new_error = np.abs(new_image-new_pred)

new_error = np.abs(new_image-new_pred)

plt.hist(new_error, bins=20, density=True)

plt.hist(new_error, bins=20, density=True)

sns.kdeplot(new_error)

sns.kdeplot(new_error)

plt.xlabel("error")

plt.xlabel("error")

plt.show()

plt.show()

```

```

%% Output

%% Output

%% Cell type:code id:58da6063 tags:

%% Cell type:code id:58da6063 tags:

``` python

``` python

plt.hist(new_image, bins=25, density=True)

plt.hist(new_image, bins=25, density=True)

sns.kdeplot(new_image)

sns.kdeplot(new_image)

plt.xlabel("Actual Pixel Value")

plt.xlabel("Actual Pixel Value")

plt.show()

plt.show()

```

```

%% Output

%% Output

%% Cell type:code id:2562feeb tags:

%% Cell type:code id:2562feeb tags:

``` python

``` python

f_r = no_ravel[0]

f_r = no_ravel[0]

print(no_ravel.shape)

print(no_ravel.shape)

print(sys.getsizeof(no_ravel))

print(sys.getsizeof(no_ravel))

print((256).bit_length())

print((256).bit_length())

```

```

%% Output

%% Output

    (512, 640)

    (512, 640)

    1310832

    1310832

    9

    9

%% Cell type:code id:470cc137 tags:

%% Cell type:code id:470cc137 tags:

``` python

``` python

coeffs = pywt.dwt2(no_ravel, 'bior1.3')

coeffs = pywt.dwt2(no_ravel, 'bior1.3')

LL, (LH, HL, HH) = coeffs

LL, (LH, HL, HH) = coeffs

print(HH.shape)

decompress = pywt.idwt2(coeffs, 'bior1.3')

decompress = pywt.idwt2(coeffs, 'bior1.3')

print(np.mean(np.abs(decompress-no_ravel)))

"""print(decompress)

print(np.mean(np.abs(decompress-no_ravel)))"""

```

```

%% Output

%% Output

    5.667000202436157e-12

    (258, 322)

    'print(decompress)\nprint(np.mean(np.abs(decompress-no_ravel)))'

%% Cell type:code id:3292b395 tags:

%% Cell type:code id:3292b395 tags:

``` python

``` python

def compress(uncompressed):

def compress(uncompressed):

    """Compress a string to a list of output symbols."""

    """Compress a string to a list of output symbols."""

    # Build the dictionary.

    # Build the dictionary.

    dict_size = 256

    dict_size = 256

    dictionary = dict((chr(i), i) for i in range(dict_size))

    dictionary = dict((chr(i), i) for i in range(dict_size))

    # in Python 3: dictionary = {chr(i): i for i in range(dict_size)}

    # in Python 3: dictionary = {chr(i): i for i in range(dict_size)}

    w = ""

    w = ""

    result = []

    result = []

    for c in uncompressed:

    for c in uncompressed:

        wc = w + c

        wc = w + c

        if wc in dictionary:

        if wc in dictionary:

            w = wc

            w = wc

        else:

        else:

            result.append(dictionary[w])

            result.append(dictionary[w])

            # Add wc to the dictionary.

            # Add wc to the dictionary.

            dictionary[wc] = dict_size

            dictionary[wc] = dict_size

            dict_size += 1

            dict_size += 1

            w = c

            w = c

    # Output the code for w.

    # Output the code for w.

    if w:

    if w:

        result.append(dictionary[w])

        result.append(dictionary[w])

    return result

    return result

store = compress("Hello my name is Scout")

store = compress("Hello my name is Scout")

print(store)

print(store)

print(sys.getsizeof(store))

print(sys.getsizeof(store))

print(sys.getsizeof("Hello my name is Scout"))

print(sys.getsizeof("Hello my name is Scout"))

```

```

%% Output

%% Output

    [72, 101, 108, 108, 111, 32, 109, 121, 32, 110, 97, 109, 101, 32, 105, 115, 32, 83, 99, 111, 117, 116]

    [72, 101, 108, 108, 111, 32, 109, 121, 32, 110, 97, 109, 101, 32, 105, 115, 32, 83, 99, 111, 117, 116]

    256

    256

    71

    71

%% Cell type:code id:f9687830 tags:

%% Cell type:code id:f9687830 tags:

``` python

``` python

def wavelet(num_images, i):

def wavelet(num_images, i):

    image = Image.open(num_images[i])    #Open the image and read it as an Image object

    image = Image.open(num_images[i])    #Open the image and read it as an Image object

    image = np.array(im)[1:,:]

    image = np.array(im)[1:,:]

    coeffs = pywt.dwt2(image, 'bior1.3')

    coeffs = pywt.dwt2(image, 'bior1.3')

    return coeffs

    return coeffs

def huffman(coeffs):

    for i in range(len(coeffs)):

coef, t = wavelet(num_images)

coef, t = wavelet(num_images,0)

def wave_decompress(coeffs):

def wave_decompress(coeffs):

    times = []

    times = []

    for i in range(len(coeffs)):

    for i in range(len(coeffs)):

        start = time()

        start = time()

        decompress = pywt.idwt2(coeffs[i], 'bior1.3')

        decompress = pywt.idwt2(coeffs[i], 'bior1.3')

        stop = time()

        stop = time()

        times.append(stop-start)

        times.append(stop-start)

    return times

    return times

ti = wave_decompress(coef)

ti = wave_decompress(coef)

print(np.mean(ti))

print(np.mean(ti))

```

```

%% Output

%% Output

    0.01910218596458435

    ---------------------------------------------------------------------------

    ValueError                                Traceback (most recent call last)

    ~\AppData\Local\Temp/ipykernel_22104/2202774112.py in <module>

         18         times.append(stop-start)

         19     return times

    ---> 20 ti = wave_decompress(coef)

         21 print(np.mean(ti))

    ~\AppData\Local\Temp/ipykernel_22104/2202774112.py in wave_decompress(coeffs)

         14     for i in range(len(coeffs)):

         15         start = time()

    ---> 16         decompress = pywt.idwt2(coeffs[i], 'bior1.3')

         17         stop = time()

         18         times.append(stop-start)

    ~\anaconda3\lib\site-packages\pywt\_multidim.py in idwt2(coeffs, wavelet, mode, axes)

        110     """

        111     # L -low-pass data, H - high-pass data

    --> 112     LL, (HL, LH, HH) = coeffs

        113     axes = tuple(axes)

        114     if len(axes) != 2:

    ValueError: too many values to unpack (expected 2)

%% Cell type:code id:e98eed4b tags:

%% Cell type:code id:e98eed4b tags:

``` python

``` python

```

```

%% Cell type:code id:b7e88aab tags:

%% Cell type:code id:b7e88aab tags:

``` python

``` python

```

```