file added

%% 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

```

```

%% 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)

    row, col = image.shape

    row, col = image.shape

    predict = np.empty([row,col])     # create a empty matrix to update prediction

    predict = np.empty([row,col])     # create a empty matrix to update prediction

    predict[0,:] = np.copy(image[0,:])       # keep the first row from the image

    predict[0,:] = np.copy(image[0,:])       # keep the first row from the image

    predict[:,0] = np.copy(image[:,0])      # keep the first columen from the image

    predict[:,0] = np.copy(image[:,0])      # keep the first columen from the image

    predict[-1,:] = np.copy(image[-1,:])       # keep the first row from the image

    predict[-1,:] = np.copy(image[-1,:])       # keep the first row from the image

    predict[:,-1] = np.copy(image[:,-1])      # keep the first columen from the image

    predict[:,-1] = np.copy(image[:,-1])      # keep the first columen from the image

    diff = np.empty([row,col])

    diff = np.empty([row,col])

    diff[0,:] = np.zeros(col)       # keep the first row from the image

    diff[0,:] = np.zeros(col)       # keep the first row from the image

    diff[:,0] = np.zeros(row)

    diff[:,0] = np.zeros(row)

    diff[-1,:] = np.zeros(col)       # keep the first row from the image

    diff[-1,:] = np.zeros(col)       # keep the first row from the image

    diff[:,-1] = np.zeros(row)

    diff[:,-1] = np.zeros(row)

    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])

    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])

    '''z0 = image[0:-2,0:-2]

    '''z0 = image[0:-2,0:-2]

    z1 = image[0:-2,1:-1]

    z1 = image[0:-2,1:-1]

    z2 = image[0:-2,2::]

    z2 = image[0:-2,2::]

    z3 = image[1:-1,0:-2]

    z3 = image[1:-1,0:-2]

    y0 = -z0+z2-z3

    y0 = -z0+z2-z3

    y1 = z0+z1+z2

    y1 = z0+z1+z2

    y2 = -z0-z1-z2-z3

    y2 = -z0-z1-z2-z3

    predict = [np.linalg.solve(A,np.array([y0[r,c],y1[r,c],y2[r,c]]))[-1] for r in range(0,row-2) for c in range(0,col-2)]

    predict = [np.linalg.solve(A,np.array([y0[r,c],y1[r,c],y2[r,c]]))[-1] for r in range(0,row-2) for c in range(0,col-2)]

    diff = [(np.max([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])-np.min([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])) for r in range(0,row-2) for c in range(0,col-2)]

    diff = [(np.max([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])-np.min([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])) for r in range(0,row-2) for c in range(0,col-2)]

    '''

    '''

    for r in range(1,row-1):                  # loop through the rth row

    for r in range(1,row-1):                  # loop through the rth row

        for c in range(1,col-1):              # loop through the cth column

        for c in range(1,col-1):              # loop through the cth column

            actual_surrounding = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])

            actual_surrounding = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])

            #z = np.array([int(image[r-1,c-1]), int(image[r-1,c]), int(image[r-1,c+1]), int(image[r,c-1])])

            #z = np.array([int(image[r-1,c-1]), int(image[r-1,c]), int(image[r-1,c+1]), int(image[r,c-1])])

            z = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])

            z = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])

            y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

            y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

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

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

            diff[r,c] = (np.max(actual_surrounding)-np.min(actual_surrounding))

            diff[r,c] = (np.max(actual_surrounding)-np.min(actual_surrounding))

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

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

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

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

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

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

    return image, predict, diff

    return image, predict, diff

```

```

%% 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 = plot_hist(num_images, i)

    image, predict, difference = 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)))

```

```

%% Cell type:code id:a51dcb6f 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: 20.017164930235467

    Average Error First and Second Added: 20.017164930235467

    Standard Deviaiton of Mean Errors: 0.16101183692474846

    Standard Deviaiton of Mean Errors: 0.16101183692474846

    Average Difference: 53.678648426455226

    Average Difference: 53.678648426455226

    Average Time per Image for First: 9.85209345817566

    Average Time per Image for First: 9.85209345817566

%% Cell type:code id:dda442ae tags:

%% Cell type:code id:dda442ae tags:

``` python

``` python

fig = plt.figure(figsize = (10,10))

fig = plt.figure(figsize = (10,10))

ax = fig.add_subplot()

ax = fig.add_subplot()

x = np.abs(predict-image)

x = np.abs(predict-image)

y = diff

y = diff

plt.plot(x,y,'o',alpha = 0.2)

plt.plot(x,y,'o',alpha = 0.2)

plt.rcParams.update({'font.size': 20})

plt.rcParams.update({'font.size': 20})

plt.xlabel("differnece to the true value" )

plt.xlabel("differnece to the true value" )

plt.ylabel("differnece of min and max of true value of the surroundings")

plt.ylabel("differnece of min and max of true value of the surroundings")

plt.show()

plt.show()

```

```

%% Output

%% Output

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

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

``` python

``` python

image = Image.open(images[0])    #Open the image and read it as an Image object

image = Image.open(images[0])    #Open the image and read it as an Image object

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

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

#z = np.array([image[1-1,1-1], image[1-1,1], image[1-1,1+1], image[1,1-1]])

#z = np.array([image[1-1,1-1], image[1-1,1], image[1-1,1+1], image[1,1-1]])

z = np.array([22554,22552,22519,22561])

z = np.array([22554,22552,22519,22561])

print(z)

print(z)

'''A = np.array([[-3,0,1],[0,-3,3],[-1,-3,4]])

'''A = np.array([[-3,0,1],[0,-3,3],[-1,-3,4]])

y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

a,b,c = np.linalg.solve(A,y)'''

a,b,c = np.linalg.solve(A,y)'''

A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])

A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])

y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

print(y)

print(y)

a,b,c = np.linalg.solve(A,y)

a,b,c = np.linalg.solve(A,y)

print(a,b,c)

print(a,b,c)

```

```

%% Output

%% Output

    [22554 22552 22519 22561]

    [22554 22552 22519 22561]

    [-22596  67625 -90186]

    [-22596  67625 -90186]

    -17.49999999999879 -1.8333333333369712 22543.500000000004

    -17.49999999999879 -1.8333333333369712 22543.500000000004

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

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

``` python

``` python

i0 = (a*(-1) + b*(1) + c)

i0 = (a*(-1) + b*(1) + c)

i1 = (a*(0) + b*(1) + c)

i1 = (a*(0) + b*(1) + c)

i2 = (a*(1) + b*(1) + c)

i2 = (a*(1) + b*(1) + c)

i3 = (a*(-1) + b*(0) + c)

i3 = (a*(-1) + b*(0) + c)

print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))

print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))

```

```

%% Output

%% Output

    160.16666666662906

    160.16666666662906

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

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

``` python

``` python

a = 0

a = 0

b = 2

b = 2

c = 2

c = 2

i0 = (a*(-1) + b*(1) + c)

i0 = (a*(-1) + b*(1) + c)

i1 = (a*(0) + b*(1) + c)

i1 = (a*(0) + b*(1) + c)

i2 = (a*(1) + b*(1) + c)

i2 = (a*(1) + b*(1) + c)

i3 = (a*(-1) + b*(0) + c)

i3 = (a*(-1) + b*(0) + c)

print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))

print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))

```

```

%% Output

%% Output

    2032748510

    2032748510

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

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

``` python

``` python

z = np.hstack((image[0,:3], image[1,0]))

z = np.hstack((image[0,:3], image[1,0]))

x = np.array([-1,0,1,-1])

x = np.array([-1,0,1,-1])

y = np.array([-1,-1,-1,0])

y = np.array([-1,-1,-1,0])

A = np.array([[-3,0,1],[0,-3,3],[1,3,-4]])

A = np.array([[-3,0,1],[0,-3,3],[1,3,-4]])

y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

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

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

```

```

%% Output

%% Output

    -75749.00000000001

    -75749.00000000001

    C:\Users\calle\AppData\Local\Temp/ipykernel_15648/1729129504.py:5: RuntimeWarning: overflow encountered in ushort_scalars

    C:\Users\calle\AppData\Local\Temp/ipykernel_15648/1729129504.py:5: RuntimeWarning: overflow encountered in ushort_scalars

      y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

      y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])

%% Cell type:code id:f9687830 tags:

%% Cell type:code id:f9687830 tags:

``` python

``` python

0.5**2 + 1.5**2

0.5**2 + 1.5**2

```

```

%% Cell type:code id:e98eed4b tags:

%% Cell type:code id:e98eed4b tags:

``` python

``` python

```

```

%% 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

```

```

%% 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