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hw3.py
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hw3.py
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import cv2
import numpy as np
from scipy import signal
from PIL import Image
from matplotlib import pyplot as plt
class TrainingEx:
__matrix = None
__score = None
def __init__(self, matrix, score):
self.__matrix = matrix
self.__score = score
def get_matrix(self):
return self.__matrix
def get_score(self):
return self.__score
class Ellipse:
faces = []
name = None
def __init__(self, name, faces):
self.name = name
self.faces = faces
def readPictureNames():
nameList = []
with open('FDDB-folds/FDDB-fold-01.txt', 'r') as f:
while len(nameList) < 5:
n = f.readline().strip()
nameList.append(n)
return nameList
def readEllipses():
ellipsesList = []
with open('FDDB-folds/FDDB-fold-01-ellipseList.txt', 'r') as f:
while len(ellipsesList) < 5:
name = f.readline().strip()
n = f.readline()
n = int(n)
faces = []
for x in range(0, n):
line = f.readline().strip()
lst = line.split(' ')
lst2 = [float(i) for i in lst]
faces.append(lst2)
ellipsesList.append(Ellipse(name, faces))
return ellipsesList
def readImages():
imagesList = []
for i in range(0,5):
imagesList.append(cv2.imread('originalPics.tar/' + name[i] + '.jpg', 1))
return imagesList
def plot5(array):
for i in range(0,5):
cv2.imshow('image', array[i])
cv2.waitKey(0)
return
def plot5_plt(array):
for i in range(0,5):
plt.subplot(2,3,i+1)
plt.imshow(array[i], cmap='gray')
plt.show()
def convertToGray():
imagesList = []
for i in range(0,5):
imagesList.append(cv2.cvtColor(images[i], cv2.COLOR_BGR2GRAY))
return imagesList
def createGaussian(sigma):
matrix = np.zeros([6*sigma+1,6*sigma+1])
filterRadius = sigma * 3
for row in range(-filterRadius, filterRadius+1):
for col in range(-filterRadius, filterRadius+1):
matrix[row+filterRadius][col+filterRadius] = (1/(2*np.pi*sigma**2)) * np.exp(-(row**2 + col**2)/(2*sigma**2))
return matrix
def convolveGaussian(sigma):
imagesList = []
gaussian = createGaussian(sigma)
for i in range (0,5):
imagesList.append(signal.convolve2d(images_grayscale[i], gaussian))
return imagesList
def compressImages(factor):
imagesList = []
for i in range(0,5):
[a, b] = images_gaussian[i].shape
#imagesList.append(signal.decimate(signal.decimate(images_gaussian[i], factor, axis = 0),factor))
imagesList.append(cv2.resize(images_gaussian[i], (0,0), fx=0.5, fy=0.5))
return imagesList
def computeScore(image, center ,faceellipse):
intercept = 0
ccy, ccx = center
[ra, rb, theta, cx, cy, dummy] = faceellipse
for j in range(-16, 16):
for i in range(-16, 16):
x = ccx + i
y = ccy + j
dist = (((np.cos(theta)*(x-cx)+np.sin(theta)*(y-cy))**2)/ra**2) + (((np.sin(theta)*(x-cx)+np.cos(theta)*(y-cy))**2)/rb**2)
if dist <= 1:
intercept += 1
score = intercept/((32**2)+(np.pi*ra*rb)-intercept)
return score
def addFaces(img, faceellipse):
[cx, cy] = faceellipse[3:5]
[cx, cy] = [int(cx), int(cy)]
for m in [-3, -2, -1, 0, 1, 2, 3]:
for n in [-3, -2, -1, 0, 1, 2, 3]:
center = [cy+2*m, cx+2*n]
score = computeScore(img, center, faceellipse)
trainingexamples.append(TrainingEx(img[cy + 2 * m - 16:cy + 2 * m + 16, cx + 2 * n - 16:cx + 2 * n + 16], score))
def resizeEllipse(picNum,faceNum):
ellipses[picNum].faces[faceNum][0] = ellipses[picNum].faces[faceNum][0] / (2 * 2 ** (0.5))
ellipses[picNum].faces[faceNum][1] = ellipses[picNum].faces[faceNum][1] / (2 * 2 ** (0.5))
ellipses[picNum].faces[faceNum][3] = ellipses[picNum].faces[faceNum][3] / (2)
ellipses[picNum].faces[faceNum][4] = ellipses[picNum].faces[faceNum][4] / (2)
def makeTrainingExamples():
for picNum in range(0, len(images)):
for faceNum in range(0, len(ellipses[picNum].faces)):
if max(ellipses[picNum].faces[faceNum][0:2]) < 20:
#normal face extract
addFaces(images_gaussian[picNum], ellipses[picNum].faces[faceNum])
elif max(ellipses[picNum].faces[faceNum][0:2]) < 110:
#big face extract
resizeEllipse(picNum,faceNum)
addFaces(images_decimated[picNum], ellipses[picNum].faces[faceNum])
#READING PICTURE NAMES
name = readPictureNames()
#Reading ELLIPSES
ellipses = readEllipses()
#reading images
images = readImages()
#plot images
plot5(images)
#change images to grayscale
images_grayscale = convertToGray()
#gaussian blur & decimate
factor = 2
sigma = 3
images_gaussian = convolveGaussian(sigma)
images_decimated = compressImages(factor)
#plot images after decimated
plot5_plt(images_decimated)
#extracting faces
trainingexamples = []
makeTrainingExamples()
i=0
while i < len(trainingexamples)-1:
plt.subplot(1, 3, 1)
plt.title(trainingexamples[i].get_score())
plt.imshow(trainingexamples[i].get_matrix(), cmap='gray')
plt.subplot(1, 3, 2)
plt.title(trainingexamples[i+15].get_score())
plt.imshow(trainingexamples[i+15].get_matrix(), cmap='gray')
plt.subplot(1, 3, 3)
plt.title(trainingexamples[i+30].get_score())
plt.imshow(trainingexamples[i+30].get_matrix(), cmap='gray')
plt.show()
i += 49