Commit 9b1db8a3 authored by Bryce Hepner's avatar Bryce Hepner

end of day

parent f2ad2483
from turtle import color
from WorkingPyDemo import *
def produce_error_array(encoded_string, list_dic, bins, use_diff):
"""
This function decodes the encoded_matrix.
Input:
A (3 X 3): system of equation
list_dic (num_dic + 1,): a list of huffman coding table
encoded_matrix (512, 640): encoded matrix
bins (num_bins - 1,): a list of threshold to cut the bins
Return:
decode_matrix (512, 640): decoded matrix
"""
change_matrix = np.zeros((512,640))
A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation
# change the dictionary back to list
# !!!!!WARNING!!!! has to change this part, everytime you change the number of bins
the_keys0 = list(list_dic[0].keys())
the_values0 = list(list_dic[0].values())
the_keys1 = list(list_dic[1].keys())
the_values1 = list(list_dic[1].values())
the_keys2 = list(list_dic[2].keys())
the_values2 = list(list_dic[2].values())
the_keys3 = list(list_dic[3].keys())
the_values3 = list(list_dic[3].values())
the_keys4 = list(list_dic[4].keys())
the_values4 = list(list_dic[4].values())
#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]])
decode_matrix = np.zeros((512,640))
# loop through all the element in the matrix
for i in range(decode_matrix.shape[0]):
for j in range(decode_matrix.shape[1]):
# if it's the very first pixel on the image
if i == 0 and j == 0:
colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys0, the_values=the_values0)
decode_matrix[i][j] = colorvalue
# if it's on the boundary (any of the 4 edges)
elif i == 0 or i == decode_matrix.shape[0]-1 or j == 0 or j == decode_matrix.shape[1]-1:
colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys0, the_values=the_values0)
decode_matrix[i][j] = colorvalue + decode_matrix[0][0]
# if not the boundary
else:
# predict the image with the known pixel value
z0 = decode_matrix[i-1][j-1]
z1 = decode_matrix[i-1][j]
z2 = decode_matrix[i-1][j+1]
z3 = decode_matrix[i][j-1]
y0 = int(-z0+z2-z3)
y1 = int(z0+z1+z2)
y2 = int(-z0-z1-z2-z3)
y = np.vstack((y0,y1,y2))
f, difference, rank, s = la.lstsq(points, [z0,z1,z2,z3], rcond=None)
difference = difference.astype(int)
predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))
# add on the difference by searching the dictionary
# !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins
if difference <= bins[0]:
colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys1, the_values=the_values1)
decode_matrix[i][j] = colorvalue + int(predict)
change_matrix[i][j] = colorvalue
elif difference <= bins[1] and difference > bins[0]:
colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys2, the_values=the_values2)
decode_matrix[i][j] = colorvalue + int(predict)
change_matrix[i][j] = colorvalue
elif difference <= bins[2] and difference > bins[1]:
colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys3, the_values=the_values3)
decode_matrix[i][j] = colorvalue + int(predict)
change_matrix[i][j] = colorvalue
else:
colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys4, the_values=the_values4)
decode_matrix[i][j] = colorvalue + int(predict)
change_matrix[i][j] = colorvalue
return decode_matrix.astype(int), change_matrix.astype(int)
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
scenes = file_extractor(folder_name)
images = image_extractor(scenes)
list_dic = np.load("first_dic.npy", allow_pickle="TRUE")
bins = [21,32,48]
encoded_string2 = bytes_to_bitstring(read_from_file(images[250][:-5] + "_Compressed.txt"))
original_array, error_array = produce_error_array(encoded_string2, list_dic, bins, False)
adjusted_errors = color_adjust(abs(error_array))
original_array_adjusted = color_adjust(original_array)
# print(adjusted_errors)
plt.subplot(121)
plt.imshow(original_array_adjusted,cmap='gray',vmin = 0, vmax=255)
plt.subplot(122)
plt.imshow(adjusted_errors,cmap = 'gray', vmin = 0, vmax=255)
plt.show()
\ No newline at end of file
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 1,
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"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 56, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -257,7 +257,7 @@ ...@@ -257,7 +257,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
......
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