def task2(file_name): # I defined task2() function.
data = pickle.load (open('image_matrix','rb')) # to open data file, i used pickle.load.
red_values = []
for x in data:
for y in x:
red_values.append(y[0])
green_values = []
for x in data:
for y in x:
green_values.append(y[1])
blue_values = []
for x in data:
for y in x:
blue_values.append(y[2])
"""In order to get red, green and blue values seperated. i made seperated list of R,G,B Values.
and i put those value lists to final list"""
final = [red_values, green_values, blue_values]
return final
hp.hist_plotter(final) #To use helper module i used hp.hist_plotter.
if firstImage[x][y][z] > 191 and firstImage[x][y][z] <= 255:
blueBin[3]+=1
#The if statements then filter and adds to the correct intensity as a counter
masterList = [redBin,greenBin,blueBin]
#the lists are then placed on a master list
print(masterList)
# if there is a known amount already then printing the master list will verify that
#masterList is then returned for usage by the histogram function
return masterList
data = open('image_matrix','rb')
#To use the data the file containing must be opened with the rb reading attribute
newData = pickle.load(data)
#The data is then dezerialized and passed to a new variable for usage
data.close()
#once the data is stored on the new variable the file is close
first = len(newData)
#to use the function the number of lists must be first known
second = len(newData[0])
#the number of tuples per list must also be known
#task2(first,second,newData)
hp.hist_plotter(task2(first,second,newData))
#The function to make the histograms is then used with the function as part of its parametes
data = pickle.load (open('image_matrix','rb')) # to open data file, i used pickle.load.
red_values = []
for x in data:
for y in x:
red_values.append(y[0])
green_values = []
for x in data:
for y in x:
green_values.append(y[1])
blue_values = []
for x in data:
for y in x:
blue_values.append(y[2])
"""In order to get red, green and blue values seperated. i made seperated list of R,G,B Values.
and i put those value lists to final list"""
final = [red_values, green_values, blue_values]
return final
hp.hist_plotter(final) #To use helper module i used hp.hist_plotter.
hp.hist_plotter(task2("image_matrix"))
# To execute the task2. and i got the results of histograms.
#get image with pickle
image = pickle.load(open("image_matrix", "rb"))
#make three empty lists for r,g,b values
red = []
green = []
blue = []
#use nested loop to append all data from image into r,g,b lists
def histogram(image):
for val in image:
for i in val:
#append all values of data into r,g,b lists, red[0], green[1], blue[2]
red.append(i[0])
green.append(i[1])
blue.append(i[2])
#after we append everything into three seperate lists we sort each one
red.sort()
green.sort()
blue.sort()
#stores all sorted r,g,b lists in one big list called myList
myList = [red, green, blue]
#return myList with all three r,g,b lists in one big list
return myList
#call the hw1_hist_plotter with myList as a paramater which holds the r,g,b lists
hp.hist_plotter(histogram(image))
if bins[1][0] <= x <= bins[1][1]:
greenlist.append(greenlist[0][1] + 1)
green2 = greenlist.count(1)
greenlist = [(0, 0, 0, 0)]
for x in green_values:
if bins[2][0] <= x <= bins[2][1]:
greenlist.append(greenlist[0][2] + 1)
green3 = greenlist.count(1)
greenlist = [(0, 0, 0, 0)]
for x in green_values:
if bins[3][0] <= x <= bins[3][1]:
greenlist.append(greenlist[0][3] + 1)
green4 = greenlist.count(1)
greenfinal = [green1, green2, green3, green4]
rgbvalue = [redfinal, greenfinal, bluefinal]
task2 = [red_values, green_values, blue_values]
final = ['Red', 'Green', 'Blue']
final = dict(zip(final, rgbvalue))
hp.hist_plotter(task2)
import hw1_hist_plotter as hp
# Using pickle module, load the binary data file 'homework_1_image_matrix.dat'
import pickle
with open("image_matrix", "rb") as f:
matrix = pickle.load(f)
# Separate the red values from the data and put them into red_val list
red_val = []
for x in matrix:
for y in x:
red_val.append(y[0])
# Separate the green values from the data and put them into green_val list
green_val = []
for x in matrix:
for y in x:
green_val.append(y[1])
# Separate the blue values from the data and put them into blue_val list
blue_val = []
for x in matrix:
for y in x:
blue_val.append(y[2])
# Make a list of three separated lists of red, green, and blue values
list = [red_val, green_val, blue_val]
# Using 'hist_plotter' function in hp, create three SVG files of R,G,B histograms
hp.hist_plotter(list)
for x in range(len(my_file)):
for y in range(len(my_file[x])):
redlist.append(my_file[x][y][0])
greenlist.append(my_file[x][y][1])
bluelist.append(my_file[x][y][2])
list.append(redlist)
list.append(greenlist)
list.append(bluelist)
return list
listHist = le.histogram()
with open(str(listHist), 'rb') as pickle_file:
my_file = pickle.load(pickle_file)
hp.hist_plotter(task2(my_file))
"""def entropy(signal):
'''
function returns entropy of a signal
signal must be a 1-D numpy array
'''
lensig=signal.size
symset=list(set(signal))
numsym=len(symset)
propab=[np.size(signal[signal==i])/(1.0*lensig) for i in symset]
ent=np.sum([p*np.log2(1.0/p) for p in propab])
return ent
"""
#le.show()