add

%% 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

from time import time

from numpy import linalg as la

```

%% 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:-5] == num:

            tiff.append(im)

    return tiff

```

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

``` python

def plot_hist(tiff_list, i):

    """

    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[i]

    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]])

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

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

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

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

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

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

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

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

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

    #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)

num_images = im_distribution(images, "_9")

error_mean = []

error_mean1 = []

diff_mean = []

times = []

times1 = []

all_error = []

for i in range(len(num_images)):

    """start1 = time()

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

    stop1 = time()

    times1.append(stop1-start1)

    error1 = np.abs(image_1-predict_1)

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

    start = time()

    image, predict = plot_hist(num_images, i)

    stop = time()

    times.append(stop-start)

    error = np.abs(image-predict)

    all_error.append(np.ravel(error))

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

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

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

```

%% Cell type:code id:fa65dcd6 tags:

``` python

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

print(np.std(image))

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

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

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

```

%% Output

    Average Error First and Second Added: 20.017164930235474

    233.22663391021266

    Standard Deviaiton of Mean Errors: 0.16101183692475135

    Average Time per Image for First: 0.023412495851516724

%% Cell type:code id:f592fa32 tags:

``` python

b = np.arange(9).reshape((3,3))

print(b)

print(b[1,1::])

print(b[1,1:])

```

%% Output

    [[0 1 2]

     [3 4 5]

     [6 7 8]]

    [4 5]

    [4 5]

%% Cell type:code id:dda442ae tags:

``` python

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

ax = fig.add_subplot()

x = np.abs(predict-image)

y = difference

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

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

    NameError                                 Traceback (most recent call last)

    ~\AppData\Local\Temp/ipykernel_10808/722042198.py in <module>

          2 ax = fig.add_subplot()

          3 x = np.abs(predict-image)

    ----> 4 y = difference

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

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

    NameError: name 'difference' is not defined

%% 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)

```

%% 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]])

y = np.array([[1,2,3],[4,5,6],[7,8,9]])

print(np.linalg.solve(A,y))

```

%% Cell type:code id:f9687830 tags:

``` python

0.5**2 + 1.5**2

```

%% Cell type:code id:e98eed4b tags:

``` python

y1= [0,1,2,3]

y2=[4,5,6,7]

y3=[8,9,10,11]

np.vstack((y1,y2,y3)).T

```

%% Cell type:code id:b7e88aab tags:

``` python

```

  },

  },

  {

  {

   "cell_type": "code",

   "cell_type": "code",

<<<<<<< HEAD:.ipynb_checkpoints/prediction_MSE_kelly-checkpoint.ipynb

   "execution_count": 52,

   "execution_count": 52,

=======

<<<<<<< HEAD

   "execution_count": 31,

=======

   "execution_count": 43,

>>>>>>> e4df997c1a14994e77600c8c4e3e1a2ec84ff59e

>>>>>>> 2350ec9881b7954dc94449b36af098af82acbb95:.ipynb_checkpoints/prediction_MSE-checkpoint.ipynb

   "id": "fa65dcd6",

   "id": "fa65dcd6",

   "metadata": {},

   "metadata": {},

   "outputs": [

   "outputs": [