d = [ [0] * x for i in range(y) ]
for eyetrack in eyetrackList:
ex = eyetrack["AbsoluteX"]
ey = eyetrack["AbsoluteY"]
print("X:",ex,"Y:",ey)
d[ey][ex] += 1
print("New total:", d[ey][ex])
return d
def refineTimeframe(eyetrackList, timeframe=(0, 60000)):
rtn = []
for et in eyetrackList:
time = et["Time"]
if time >= timeframe[0] and time <= timeframe[1]:
rtn.append(et)
return rtn
#print(type(eyetrackList[1]))
#print "Total Eyetrack Events:", len(eyetrackList)
#refinedList = refineTimeframe(eyetrackList, (3000,4000))
#print "Events w/in seconds 3 and 4:", len(refinedList)
#makeDataMap(refinedList)
#print(makeDataMap(eyetrackList))
#pprint(makeDataMap(eyetrackList))
#Task2.run(sessions,eventList,eyetrackList)
Task4.run(sessions,eventList,eyetrackList)
def SplittingRecursive(image, left, right, top, bottom, depth=0):
cx, cy, n = Task2.FindCentroid(image, left, right, top, bottom)
if depth < 3:
SplittingRecursive(image, left, cx, top, cy, depth + 1) # Top Left
SplittingRecursive(image, cx, right, top, cy, depth + 1) # Bottom Left
SplittingRecursive(image, left, cx, cy, bottom, depth + 1) # Top Right
SplittingRecursive(image, cx, right, cy, bottom,
depth + 1) # Bottom Right
else:
rectangles.append([(left, top), (right, bottom)])
transitions.append(Task4.B2W_Transitions(image[top:bottom,
left:right]))
ratios.append(Task5.AspectRatio(left, right, top, bottom))
# Task 6
size = (bottom - top) * (right - left)
blacks = Task6.blackPixels(image, left, right, top, bottom)
try:
normalized.append(size / blacks)
except:
normalized.append(0)
cx, cy, n = Task2.FindCentroid(image[top:bottom, left:right], 0,
right - left, 0, bottom - top)
centroids.append((cx, cy))
# Task 7
angle = math.degrees(
math.acos(
(bottom - top - cy) / (math.sqrt((right - left - cx)**2 +
(bottom - top - cy)**2))))
angles.append(angle)
def SplittingRecursive(image, left, right, top, bottom, depth=0):
cx, cy = Task2.FindCentroid(image, left, right, top, bottom)
# print("(", top, "\t", left, ")\t(", bottom, "\t", right, ")\t", cx, "\t", cy, "\tDepth: ", depth)
if depth < 3:
SplittingRecursive(image, left, cy, top, cx, depth + 1) # Top Left
SplittingRecursive(image, cy, right, top, cx, depth + 1) # Bottom Left
SplittingRecursive(image, left, cy, cx, bottom, depth + 1) # Top Right
SplittingRecursive(image, cy, right, cx, bottom, depth + 1) # Bottom Right
else:
t = Task4.B2W_Transitions(image, left, right, top, bottom)
r = Task5.AspectRatio(left, right, top, bottom)
filePath = "../Text/"
# If Path Does not exists; Create it
if not os.path.exists(filePath):
os.makedirs(filePath + "Transitions/")
os.makedirs(filePath + "Ratios/")
os.makedirs(filePath + "Centroids/")
TransitionsFile = open(filePath + "Transitions/" + "signature.txt", "a")
TransitionsFile.write(str(t) + "\n")
TransitionsFile.close()
RatiosFile = open(filePath + "Ratios/" + "signature.txt", "a")
RatiosFile.write(str(r) + "\n")
RatiosFile.close()
CentroidsFile = open(filePath + "Centroids/" + "signature.txt", "a")
CentroidsFile.write(str(cx) + "," + str(cy) + "\n")
CentroidsFile.close()
return cv2.rectangle(bin_image, (top, left), (bottom, right), (0,255,0), 1)
def similar_document(first_book, second_book):
if first_book == '1' or first_book == '2' or first_book == '3' or first_book == '4':
if second_book == '1' or second_book == '2' or second_book == '3' or second_book == '4':
percentage = Task4.sentence_similarity(
first_book, second_book
) #trigger the function sentence similarity that will return the score of similarity between two books
return "The similarity between the two documents is =" + str(
percentage
) + " percent" #return the score of similarity between two books
return "Books only from 1 to 4"
def similarity_of_all(string='all'):
if string.lower() == 'all':
list_of_sim_matrix = dict(
) #creating a dictionary for the similarities score
count = 1 #intializing count with 1 which having index of book 1
while (count <= 4): #iterate through all 4 books
count_for_second_book = 4 #sceond count has second book indexes
while (count_for_second_book >=
1): #conditions for books remain in the limit
percentage_of_each = Task4.sentence_similarity(
str(count), str(count_for_second_book)
) #triggering task 4 function to get the similarity between two book at a ttime
list_of_sim_matrix[str(count) + ' and ' +
str(count_for_second_book)] = str(
percentage_of_each)
count_for_second_book -= 1 #decreement book second
count += 1 #increement book first
return json.dumps(list_of_sim_matrix)
return "Not a required String"
# Central points of gaze mean the "clusters" of gaze. If this doesn't make sense, please # ask for clarification or skip it. ######################################## # ... ######################################################################################## # 3b) Create a separate movie showing this. ######################################## ######################################################################################## # 4) Figure out how to overlay the pictures on top of the actual video. The video can # be found here: http://g3.eyetrackshop.com/content/CT13C39DD417BLYWHILLT-SJJ ######################################## Task4.run(sessions, eventList, eyetrackList) ######################################################################################## # 5) Output a quality report in a test file, listing the % of sessions which are: # Complete - attribute session.last_state = "7.COMPLETE" # Usable - attribute eyetrack.quality = "GOOD" # Group eyetrack.quality failure reasons and output % of each ######################################## ######################################################################################## # 6) For each frame, add 4 'area of interest' rectangles which are the 4 quadrants of # the image. On each eye tracking frame, output a % noting in the middle of the frame in text # which says what percent of people who have data during that time period were looking # within the area of interest. ########################################
def main():
Task4.task4_function
get_pi(Task4.task4_function())
print(Task2.cutByRangeFibonacci(inputNumber1, inputNumber2))
# 3. Напишите функцию, которая принимает на вход два параметра: a и b.
print("<--------- Task 3 --------->")
a = 12
b = "str"
print(" out parameters are : {a}, {b}".format(a = a, b = b))
Task3.functionInRequest(a, b)
# 4. Напишите функцию, которая принимает на вход три параметра
print("<--------- Task 4 --------->")
# # leap years
c = [1900, 1904, 1908, 1964, 2020]
startYear = 1900
finishYear = 2020
print(" our years' range : [ {s}, {f} ]".format(s = startYear, f = finishYear))
print(" exclude list from range : %s " % c )
print(Task4.listOfLeapYearsOnRange(startYear, finishYear, c))
# array for task 5-9
array = [1, "sd", 3, 1.2, 5.6, 0, -1]
# 5. Найти сумму элементов массива
print("<--------- Task 5 --------->")
print(" given array : %s " % array)
print(" array sum : %s " % Task5.array_sum(array))
# # 6. Найти максимальный элемент, значение и индекс.
print("<--------- Task 6 --------->")
print(" max in array and index : %s " % Task6.array_max(array))
# 7. Найти минимальный элемент, значение и индекс.
print("<--------- Task 7 --------->")
print(" min in array and index : %s " % Task7.array_min(array))
# 8. Посчитать количество элементов больше нуля.
print("<--------- Task 8 --------->")
print(" the amount of numbers greater than 0 : %s " % Task8.array_count(array))
top, bottom, left, right, bounding_box_image = Task1.BoundingBox(width, height, bin_image, filename)
B = (left, right, top, bottom)
# cv2.imshow("Bounding Box", bounding_box_image)
# Task 2
filename = "cen_" + filename
centroid_image, cx, cy = Task2.FindCentroid(width, height, bin_image, bounding_box_image, filename)
C = (cx, cy)
# cv2.imshow("Centroid", centroid_image)
# Task 3
cx = int(cx)
cy = int(cy)
filename = "seg_" + filename
top_left, bottom_left, top_right, bottom_right, segmented_image = Task3.DivideBoundingBox(centroid_image, top, bottom, left, right, cx, cy, filename)
cv2.imshow("Top Left", top_left)
cv2.imshow("Bottom Left", bottom_left)
cv2.imshow("Top Right", top_right)
cv2.imshow("Bottom Right", bottom_right)
print("\nNumber of Black to White Transitions")
# Task 4
TL = Task4.B2W_Transitions(top_left, top_left.shape[0], top_left.shape[1], "Top Left")
BL = Task4.B2W_Transitions(bottom_left, bottom_left.shape[0], bottom_left.shape[1], "Bottom Left")
TR = Task4.B2W_Transitions(top_right, top_right.shape[0], top_right.shape[1], "Top Right")
BR = Task4.B2W_Transitions(bottom_right, bottom_right.shape[0], bottom_right.shape[1], "Bottom Right")
T = (TL, TR, BL, BR)
cv2.waitKey(0)
def main():
task = int(input("Input Task number:"))
if task == 1:
feature_model = int(
input("Select the Feature Model:\n1. CM\t2. LBP\t3. HOG\t4. SIFT : ")
)
dimension_reduction = int(
input(
"Select the Dimension Reduction Technique:\n1. PCA\t2. SVD\t3. NMF\t4. LDA : "
)
)
k = int(input("Enter k: "))
Task1.starter(feature_model, dimension_reduction, k)
elif task == 2:
choice = input(
"Do you want to go ahead with task 1 input configurations? yes(y) or no(n) "
)
image_id = input("Enter image ID: ")
m = int(input("Enter m: "))
feature_model = dimension_reduction = k = None
if choice == "n":
feature_model = int(
input("Select the Feature Model:\n1. CM\t2. LBP\t3. HOG\t4. SIFT : ")
)
dimension_reduction = int(
input(
"Select the Dimension Reduction Technique:\n1. PCA\t2. SVD\t3. NMF\t4. LDA : "
)
)
k = int(input("Enter k: "))
Task2.starter(feature_model, dimension_reduction, k, image_id, m)
elif task == 3:
feature_model = int(
input("Select the Feature Model:\n1. CM\t2. LBP\t3. HOG\t4. SIFT : ")
)
dimension_reduction = int(
input(
"Select the Dimension Reduction Technique:\n1. PCA\t2. SVD\t3. NMF\t4. LDA : "
)
)
k = int(input("Enter k: "))
label = int(
input(
"Select the label:\n1. Left\t2. Right\t3. Dorsal\t4. Palmar\n5. With accessories\t6. Without accessories\t7. Male\t8. Female: "
)
)
Task3.starter(feature_model, dimension_reduction, k, label)
elif task == 4:
choice = input(
"Do you want to go ahead with task 3 input configurations? yes(y) or no(n) "
)
image_id = input("Enter image ID: ")
m = int(input("Enter m: "))
feature_model = dimension_reduction = k = label = None
if choice == "n":
feature_model = int(
input("Select the Feature Model:\n1. CM\t2. LBP\t3. HOG\t4. SIFT : ")
)
dimension_reduction = int(
input(
"Select the Dimension Reduction Technique:\n1. PCA\t2. SVD\t3. NMF\t4. LDA : "
)
)
k = int(input("Enter k: "))
label = int(
input(
"Select the label:\n1. Left\t2. Right\t3. Dorsal\t4. Palmar\n5. With accessories\t6. Without accessories\t7. Male\t8. Female: "
)
)
Task4.starter(feature_model, dimension_reduction, k, label, image_id, m)
elif task == 5:
feature_model = int(
input("Select the Feature Model:\n1. CM\t2. LBP\t3. HOG\t4. SIFT : ")
)
dimension_reduction = int(
input(
"Select the Dimension Reduction Technique:\n1. PCA\t2. SVD\t3. NMF\t4. LDA : "
)
)
k = int(input("Enter k: "))
label = int(
input(
"Select the label:\n1. Left\t2. Right\t3. Dorsal\t4. Palmar\n5. With accessories\t6. Without accessories\t7. Male\t8. Female: "
)
)
image_id = input("Enter image ID: ")
Task5.starter(feature_model, dimension_reduction, k, label, image_id)
elif task == 6:
subject_id = int(input("Enter subject ID: "))
Task6.starter(subject_id)
elif task == 7:
k = int(input("Enter k: "))
Task7.starter(k)
elif task == 8:
k = int(input("enter k : "))
Task8.starter(k)
elif task == 9:
feature_model = int(
input("Select the Feature Model:\n1. CM\t2. LBP\t3. HOG\t4. SIFT : ")
)
dimension_reduction = int(
input(
"Select the Dimension Reduction Technique:\n1. PCA\t2. SVD\t3. NMF\t4. LDA : "
)
)
k = int(input("Enter k: "))
visualizer = int(input("Enter Visualizer:\n1. Data \t2.Feature "))
ExtraCredit.starter(feature_model, dimension_reduction, k, visualizer)
else:
print("Enter Task number (1-9)")
from matplotlib import pyplot
import numpy as np
import pickle
from pylab import *
import Utils
import DataSet
import Classify
import PCAModule
import Task1
import Task2
import Task3
import Task4
data_path = "E:\\EE5907R\\project2\\project2_faces"
plot_save_path = "E:\\EE5907R\\project2\\"
def main():
# data_path = raw_input("Enter path : ")
DataSet.read_faces(data_path)
print 2 * "\n*******************************************"
# project sequence
main()
# Task1.task1()
# Task2.task2()
# Task3.task3()
Task4.task4()
def test_linked_file_reading():
test_object = Task4.linked_list_file_reading("try1.txt")
str(test_object)
def test_array_file_reading():
test_object = Task4.array_list_file_reading("try.txt")
str(test_object)
