kelly push

%% Cell type:code id:dbef8759 tags:

``` python

import numpy as np

from matplotlib import pyplot as plt

from itertools import product

import os

import sys

from PIL import Image

from scipy.optimize import minimize

```

%% Cell type:code id:b7a550e0 tags:

``` python

def file_extractor(dirname="images"):

    files = os.listdir(dirname)

    scenes = []

    for file in files:

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

    return scenes

def image_extractor(scenes):

    image_folder = []

    for scene in scenes:

        files = os.listdir(scene)

        for file in files:

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

    images = []

    for folder in image_folder:

        ims = os.listdir(folder)

        for im in ims:

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

                continue

            else:

                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

def im_distribution(images, num):

    """

    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

    specified camera numbers.

    Parameters:

        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

        divide.

        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.

    Returns:

        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.

    """

    tiff = []

    for im in images:

        if im[-7:-6] == num:

            tiff.append(im)

    return tiff

```

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

``` python

def plot_hist(tiff_list):

    """

    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

    fix this later. 1/25/22

    """

    image = tiff_list

    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 = image.astype(int)

    row, col = image.shape

    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 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 columen from the image

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

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

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

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

    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]

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

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

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

    y0 = -z0+z2-z3

    y1 = z0+z1+z2

    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)]

    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 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]])

            #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]])

            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]

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

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

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

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

    return image, predict, diff

```

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

``` python

scenes = file_extractor()

images = image_extractor(scenes)

image, predict, diff = plot_hist(images[0])

```

%% Cell type:code id:dda442ae tags:

``` python

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

ax = fig.add_subplot()

x = np.abs(predict-image)

y = diff

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

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

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

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

plt.show()

```

%% Output

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

``` python

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

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([22554,22552,22519,22561])

print(z)

'''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]])

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

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]])

print(y)

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

print(a,b,c)

```

%% Output

    [22554 22552 22519 22561]

    [-22596  67625 -90186]

    -17.49999999999879 -1.833333333338184 22543.500000000004

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

``` python

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

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

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

```

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

``` python

a = 0

b = 2

c = 2

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

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

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

```

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

``` python

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

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

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

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]])

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

```

%% Cell type:code id:f9687830 tags:

``` python

0.5**2 + 1.5**2

```

%% Cell type:code id:e98eed4b tags:

``` python

```

%% Cell type:code id:dbef8759 tags:

``` python

import numpy as np

from matplotlib import pyplot as plt

from itertools import product

import os

import sys

from PIL import Image

from scipy.optimize import minimize

```

%% Cell type:code id:b7a550e0 tags:

``` python

def file_extractor(dirname="images"):

    files = os.listdir(dirname)

    scenes = []

    for file in files:

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

    return scenes

def image_extractor(scenes):

    image_folder = []

    for scene in scenes:

        files = os.listdir(scene)

        for file in files:

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

    images = []

    for folder in image_folder:

        ims = os.listdir(folder)

        for im in ims:

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

                continue

            else:

                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

def im_distribution(images, num):

    """

    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

    specified camera numbers.

    Parameters:

        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

        divide.

        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.

    Returns:

        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.

    """

    tiff = []

    for im in images:

        if im[-7:-6] == num:

            tiff.append(im)

    return tiff

```

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

``` python

def plot_hist(tiff_list):

    """

    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

    fix this later. 1/25/22

    """

    image = tiff_list

    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 = image.astype(int)

    row, col = image.shape

    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 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 columen from the image

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

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

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

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

    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]

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

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

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

    y0 = -z0+z2-z3

    y1 = z0+z1+z2

    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)]

    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 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]])

            #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]])

            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]

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

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

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

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

    return image, predict, diff

```

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

``` python

scenes = file_extractor()

images = image_extractor(scenes)

image, predict, diff = plot_hist(images[0])

```

%% Cell type:code id:dda442ae tags:

``` python

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

ax = fig.add_subplot()

x = np.abs(predict-image)

y = diff

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

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

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

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

plt.show()

```

%% Output

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

``` python

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

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([22554,22552,22519,22561])

print(z)

'''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]])

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

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]])

print(y)

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

print(a,b,c)

```

%% Output

    [22554 22552 22519 22561]

    [-22596  67625 -90186]

    -17.49999999999879 -1.833333333338184 22543.500000000004

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

``` python

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

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

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

```

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

``` python

a = 0

b = 2

c = 2

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

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

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

```

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

``` python

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

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

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

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]])

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

```

%% Cell type:code id:f9687830 tags:

``` python

0.5**2 + 1.5**2

```

%% Cell type:code id:e98eed4b tags:

``` python

```