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iris_extraction_2.py
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iris_extraction_2.py
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import cv2
import numpy as np
import glob
import pickle
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
eye_num_2=0
def transform_image(img,threshold):
retval, threshold = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY)
opening = cv2.morphologyEx(threshold, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, kernel)
open_close = cv2.bitwise_or(opening, closing, mask = None)
return open_close,opening,closing
imgs = []
label=0
final_output = []
lables = []
eye_detected = []
iris_eye_detected=[]
#'''
#'''
for filepath in glob.iglob('CASIA-Iris-Thousand/*'):
num_in_folder=0
for filefilepath in glob.iglob(filepath+'/L/*'):
if filefilepath[-1] == 'g':
img = cv2.imread(filefilepath)
img=cv2.resize(img,(200,150))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgs.append([img,num_in_folder,label,img])
print(filefilepath)
num_in_folder = num_in_folder+1
#'''
for filefilepath in glob.iglob(filepath+'/R/*'):
if filefilepath[-1] == 'g':
img = cv2.imread(filefilepath)
img=cv2.resize(img,(200,150))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgs.append([img,num_in_folder,label,img])
print(filefilepath)
num_in_folder = num_in_folder+1
#'''
label=label+1
#print(filefilepath)
#'''
print("total images number ",len(imgs))
#'''
eyes_num=0
for i,j,L,c in imgs:
# cv2.imshow('dd',i)
i=cv2.resize(i,(400,300))
eyes = eye_cascade.detectMultiScale(i, 1.01, 0)
if len(eyes)>1:
eye_detected.append(imgs[eyes_num])
print(eyes_num)
eyes_num = eyes_num+1
maxium_area = -3
for (ex,ey,ew,eh) in eyes:
area = ew*eh
if area>maxium_area:
maxium_area = area
maxium_width=ew
point_x=ex
point_y=ey
maxium_height = eh
cv2.rectangle(i,(point_x,point_y),(point_x+maxium_width,+maxium_height),(255,0,0),2)
cv2.imwrite('paper_2/eyes/'+str(L)+'.'+str(j)+'.jpg',i)
#cv2.imwrite('paper/threshold/'+str(L)+'.'+str(j)+'.jpg',working_img)
#roi_gray = gray[y:y+h, x:x+w]
#roi_gray = gray[ey:ey+eh, ex:ex+ew]
#roi_color = img[ey:ey+eh, ex:ex+ew]
print("total_eyes_found = ",eyes_num)
print("total_eyes_found 2 = ",eye_num_2)
iris_num=0
for i,j,L,c in eye_detected:
circles = cv2.HoughCircles(i, cv2.HOUGH_GRADIENT, 10, 100)
if circles is not None :
circles = np.round(circles[0, :]).astype("int")
#print(len(circles))
#print(y)
maxiumum_average=10000000000000
#print(len(circles))
print(i.shape[0])
print(i.shape[1])
print(min(i.shape))
key=True
for (x, y, r) in circles:
if x+r<=max(i.shape) and y+r<=max(i.shape)and x-r>0 and y-r>0 and r>20:
key=False
new_roi = i[y-r:y+r, x-r:x+r]
average = np.average(new_roi)
if average < maxiumum_average:
maxiumum_r = r
point_x=x
point_y=y
maxiumum_average=average
#cv2.circle(i, (x, y), r, (0, 0, 0), 4)
if key:
#print("key opened")
for (x, y, r) in circles:
maxiumu_raduis=-4
if r > maxiumu_raduis:
maxiumum_r = r
point_x=x
point_y=y
#maxiumum_average=average
cv2.circle(i, (point_x, point_y), maxiumum_r, (255, 255, 0), 4)
#print(str(L)+'.'+str(j)+" = "+str(average))
#cv2.circle(i, (point_x_medium, point_y_medium), raduis_medium , (255, 255, 0), 4)
cv2.imwrite('paper_3/iris/'+str(L)+'.'+str(j)+'.jpg',i)
iris_eye_detected.append(eye_detected[iris_num])
print(iris_num)
iris_num = iris_num+1
#roi_gray = gray[y:y+h, x:x+w]
#roi_gray = gray[ey:ey+eh, ex:ex+ew]
#roi_color = img[ey:ey+eh, ex:ex+ew]
print("total_iris_found = ",iris_num)
print("total images number ",len(imgs))
imgs= iris_eye_detected
#'''
kernel = np.ones((5,5),np.uint8)
import random
random.shuffle(imgs)
test=[]
for i,j,L,c in imgs:
gold,siver,diamond = transform_image(i,0)
golden_refrence = sum(sum(gold))
#print("golden refrence = "+str(golden_refrence))
found = True
for k in range(10,10000,10):
working_img,opening,closing = transform_image(i,k)
suming = sum(sum(working_img))
diffrence = suming-golden_refrence
if diffrence>800:
found = False
print("the image threshold = " ,k)
print("the image name " +str(j))
print(" " )
cv2.imwrite('paper_2/threshold/'+str(L)+'.'+str(j)+'.jpg',working_img)
cv2.imwrite('paper_2/opening/'+str(L)+'.'+str(j)+'.jpg',opening)
cv2.imwrite('paper_2/closing/'+str(L)+'.'+str(j)+'.jpg',closing)
_, contours,_ = cv2.findContours(working_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
for z in contours:
x,y,w,h = cv2.boundingRect(z)
if x+w<150 and y+h<200 and x-w//4>0:
cv2.rectangle(working_img,(x,y),(x+w,y+h),(0,255,0),-2)
cv2.imwrite('paper_2/contour/'+str(L)+'.'+str(j)+'.jpg',working_img)
_, contours_2,_ = cv2.findContours(working_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
#cv2.imwrite('paper/contour/'+str(L)+'.'+str(j)+'.jpg',contours_2)
maxium_area=0
maxium_area = 0
maxium_width=0
point_x=0
point_y=0
maxium_height = 0
for z in contours_2:
#print(len(i))
x,y,w,h = cv2.boundingRect(z)
new_area=h*w
if x+w<150 and y+h<200 and new_area>maxium_area and x-w//4>0:
maxium_area = new_area
maxium_width=w
point_x=x
point_y=y
maxium_height = h
#cv2.rectangle(working_img,(x,y),(x+w,y+h),(0,255,0),-2)
#cv2.rectangle(i,(point_x,point_y),(point_x+maxium_width,point_y+maxium_height),(0,255,0),-2)
center_x = point_x+maxium_width//2
center_y = point_y+maxium_height//2
radius = 40
if center_y-radius>0 and center_x-radius >0 and center_y+radius < 200 and center_x+radius < 150:
#cv2.circle(c, (int(center_x), int(center_y)), int(radius), (0, 255, 255), 2)
new_roi = c[center_y-radius:center_y+radius, center_x-radius:center_x+radius]
new_roi=cv2.resize(new_roi,(200,150))
#new_roi = cv2.cvtColor(new_roi,cv2.COLOR_GRAY2BGR)
#cv2.imwrite('paper/threshold/'+str(L)+'.'+str(j)+'.jpg',new_roi)
cv2.imwrite('final_casia/'+str(L)+'.'+str(j)+'.jpg',new_roi)
#new_roi=cv2.resize(new_roi,(200,150))
else:
center_y=c.shape[0]//2
center_x=c.shape[1]//2
new_roi = c[center_y-radius:center_y+radius, center_x-radius:center_x+radius]
new_roi =cv2.resize(new_roi,(200,150))
#new_roi = cv2.cvtColor(new_roi,cv2.COLOR_GRAY2BGR)
cv2.imwrite('final_casia/'+str(L)+'.'+str(j)+'.jpg',new_roi)
cv2.imwrite('edging_5/'+str(L)+'_'+str(j)+'.jpg',i)
test.append(i)
final_output.append(new_roi)
lables.append(L)
if found :
i =cv2.resize(i,(200,150))
#i = cv2.cvtColor(i,cv2.COLOR_GRAY2BGR)
#cv2.imwrite('final_iris2/'+str(j[29:]),i)
cv2.imwrite('final_casia/'+str(L)+'.'+str(j)+'.jpg',new_roi)
#cv2.imwrite('edging_5_test/'+str(j[5:]),i)
#break
print("the lenght of final output = ",len(final_output))
print("the of lables = ",len(lables))