def denoise_tv_bregman(inputImg, weight, max_iter, eps, isotropic, show):
# documentation: https://scikit-image.org/docs/dev/api/skimage.restoration.html#skimage.restoration.denoise_tv_bregman
tvImg = skimage.restoration.denoise_tv_bregman(inputImg, weight=weight, max_iter=max_iter, eps=eps, isotropic=isotropic)
# standard params skimage.restoration.denoise_tv_bregman(image, weight, max_iter=100, eps=0.001, isotropic=True)
print("shape is", len(tvImg.shape))
tvImg = tvImg * 255
tvImg = cv2.convertScaleAbs(tvImg)
#tvImg = cv2.cvtColor(tvImg, cv2.COLOR_BGR2GRAY)
#tvImg = img_as_ubyte(tvImg)
print("shape is", len(tvImg.shape))
if show:
m3F.imshow(tvImg, "Total variation Bregman")
return tvImg
def removeEyelid(eye, show):
gray = cv2.cvtColor(eye.iris, cv2.COLOR_BGR2GRAY)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
height, width = gray.shape
grayPil = m3F.typeSwap(gray)
for i in eye.circle[0, :]:
for y in range(width-1):
for x in range(height-1):
pixel = grayPil.getpixel((y,x))
if math.sqrt(((y-i[0])**2)+((x-i[1])**2)) < i[2] and pixel != maxVal:
gray[x, y] = 255
else:
gray[x,y] = 0
break
m3F.imshow(gray, "iris")
kernel1 = np.ones((15, 15), np.uint8)
kernel2 = np.ones((7, 7), np.uint8)
closing1 = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel1,iterations=3)
opening = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel2)
m3F.imshow(closing1,"closed. Kernel 15, 3 iterations")
m3F.imshow(opening,"opened. Kernel 7")
return closing1
def semicircle(eye, show):
#path = "output_eyes/photo19_Face_1_Left.jpeg"
#iris = "iris_test2.jpeg"
#img = cv2.imread(path)
#img = eye.wip
img = eye.iris
cimg = img
m3F.imshow(cimg, "cimg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(img, 100, img.shape[0])
m3F.imshow(canny, "semi circle canny")
for i in eye.circle[0, :]:
center = (i[0], i[1])
radius = i[2]
#cv2.circle(cimg, center, 3, (0,255,255), 0)
#cv2.circle(cimg, center, radius, (0,0,255), 0)
dt = cv2.distanceTransform(255 - canny, cv2.DIST_L2, 5)
minInlierDist = 2.0
for i in eye.circle[0, :]:
counter = 0
inlier = 0
center = (i[0], i[1])
radius = i[2]
maxInlierDist = radius / 200.0
if (maxInlierDist < minInlierDist):
maxInlierDist = minInlierDist
t = 0
while t < 2 * cmath.pi:
counter += 1
cX = int(radius * cmath.cos(t) + i[0])
cY = int(radius * cmath.sin(t) + i[1])
try:
if (dt[cY, cX] < maxInlierDist and gray[cY, cX] > 100):
inlier += 1
cv2.circle(cimg, (cX, cY), 3, (255, 0, 0))
except IndexError:
print("skipped")
t += 0.1
finalImg = cv2.cvtColor(cimg, cv2.COLOR_BGR2RGB)
if (show):
m3F.imshow(finalImg, "semicircles")
#return finalImg
return eye
def removeEyelid(eye, show):
#m3F.imshow(eye.iris,"removeEyelid")
print("type: eye, iris -", type(eye), type(eye.iris))
print("length of iris -", len(eye.iris))
gray = cv2.cvtColor(eye.iris, cv2.COLOR_BGR2GRAY)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
print("maxVal", maxVal)
print("minVal", minVal)
height, width = gray.shape
grayPil = m3F.typeSwap(gray)
for i in eye.circle[0, :]:
for y in range(width - 1):
for x in range(height - 1):
pixel = grayPil.getpixel((y, x))
#print("pixel",pixel)
if math.sqrt(((y - i[0])**2) + ((x - i[1])**2)) < i[2]:
if pixel != maxVal or math.sqrt(((y - i[0])**2) + (
(x - i[1])**2)) < i[2] / 2:
gray[x, y] = 255
else:
#print("done here")
#gray.putpixel((x,y),minVal)
gray[x, y] = 0
break
#gray = m3F.typeSwap(gray)
m3F.imshow(gray, "iris blob")
kernel1 = np.ones((15, 15), np.uint8)
kernel2 = np.ones((7, 7), np.uint8)
closing1 = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel1, iterations=3)
#closing2 = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel2)
opening = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel2)
m3F.imshow(closing1, "closed. Kernel 15, 3 iterations")
m3F.imshow(opening, "opened. Kernel 7")
#m3F.imshow(closing2,"closed. Kernel 3")
return closing1
def medianFilter(inputImg, show):
outputImg = cv2.medianBlur(inputImg, 3)
if (show):
m3F.imshow(outputImg, "Median")
return outputImg
def medianFilter(inputImg, kernelSize, show):
outputImg = cv2.medianBlur(inputImg, kernelSize)
if (show):
m3F.imshow(outputImg, "median blur")
return outputImg
def run(tempeye, tempResolution, tempMin_dist, tempParam_1, tempParam_2,
tempMinRadius, tempMaxRadius, tempShow):
print(
"***************************************************************************************"
)
img = tempeye
print(tempMinRadius)
isEyeClass = False
eye = None
if isinstance(img, type(m3Class.Eye())):
isEyeClass = True
eye = tempeye
img = eye.wip
if not isinstance(img, type(None)):
cimg = img
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(img,
cv2.HOUGH_GRADIENT,
tempResolution,
tempMin_dist,
param1=tempParam_1,
param2=tempParam_2,
minRadius=tempMinRadius,
maxRadius=tempMaxRadius)
# circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,,120,param1=200,param2=10,
# minRadius=int(m3F.typeSwap(img).height/6),maxRadius=int(m3F.typeSwap(img).height/2.5))
if not isinstance(circles, type(None)):
if (circles.size != 0):
circles = np.uint16(np.around(circles))
# print(circles)
index = 0
for i in circles[0, :]:
#if img[i[1],i[0]] < 15:
# draw the outer circle
cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255 - index, 0),
2)
# draw the center of the circle
cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255 - index), 3)
index += 30
if (tempShow):
m3F.imshow(cimg, "Circle")
m3F.printGreen("CIRCLES FOUND^^^")
print("img out", img.shape)
if isEyeClass:
eye.circle = circles
print("RETURNED AN EYE WITH CIRCLES")
return eye
else:
return img
else:
if (tempShow):
m3F.imshow(cimg, "no circles found")
m3F.printRed("NO CIRCLES FOUND^^^")
else:
m3F.printRed("Image is NONE")
def runDouble(tempeye, tempResolution, tempMin_dist, tempParam_1, tempParam_2,
tempMinRadius, tempMaxRadius, tempShow):
print(
"***************************************************************************************"
)
img = tempeye
print(tempMinRadius)
if not isinstance(img, type(None)):
cimg = img
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
bwimg = img.copy()
circles = cv2.HoughCircles(img,
cv2.HOUGH_GRADIENT,
tempResolution,
tempMin_dist,
param1=tempParam_1,
param2=tempParam_2,
minRadius=tempMinRadius,
maxRadius=tempMaxRadius)
if not isinstance(circles, type(None)):
if (circles.size != 0):
circles = np.uint16(np.around(circles))
# print(circles)
for i in circles[0, :]:
bwimg.fill(0)
# draw the outer circle
cv2.circle(bwimg, (i[0], i[1]), i[2], 255, -1)
# draw the center of the circle
# cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255), 3)
# m3F.imshow(bwimg,"circle")
mask = cv2.bitwise_and(img, bwimg)
# finalImg = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)
#m3F.imshow(mask, "final img")
pupilCircle = cv2.HoughCircles(mask,
cv2.HOUGH_GRADIENT,
1,
50,
param1=200,
param2=10,
minRadius=int(i[2] / 6),
maxRadius=int(i[2] / 4 * 3))
if not isinstance(pupilCircle, type(None)):
if (pupilCircle.size != 0):
pupilCircle = np.uint16(np.around(pupilCircle))
# print(circles)
for j in pupilCircle[0, :]:
wiggle = 5
if i[0] + wiggle > j[0] > i[0] - wiggle and i[
1] + wiggle > j[1] > i[1] - wiggle:
# draw the outer circle
cv2.circle(mask, (j[0], j[1]), j[2],
(255, 255, 0), 1)
# draw the center of the circle
cv2.circle(mask, (j[0], j[1]), 2,
(0, 0, 255), 3)
m3F.imshow(mask, "final img")
if (tempShow):
m3F.imshow(cimg, "Circle")
m3F.printGreen("CIRCLES FOUND^^^")
print("img out", img.shape)
return img
else:
if (tempShow):
m3F.imshow(cimg, "no circles found")
m3F.printRed("NO CIRCLES FOUND^^^")
else:
m3F.printRed("Image is NONE")
def run(tempeye, tempResolution, tempMin_dist, tempParam_1, tempParam_2,
tempMinRadius, tempMaxRadius, tempShow):
print(
"***************************************************************************************"
)
img = tempeye.copy()
print(tempMinRadius)
# isEyeClass = False
# eye = None
# if isinstance(img, type(m3Class.Eye())):
# isEyeClass = True
# eye = tempeye
# img = eye.wip.copy()
# print("DID EYE")
if not isinstance(img, type(None)):
cimg = img.copy()
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# print("Hough run : min:", tempMinRadius, " max: ", tempMaxRadius)
circles = cv2.HoughCircles(img,
cv2.HOUGH_GRADIENT,
tempResolution,
tempMin_dist,
param1=tempParam_1,
param2=tempParam_2,
minRadius=tempMinRadius,
maxRadius=tempMaxRadius)
# circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,,120,param1=200,param2=10,
# minRadius=int(m3F.typeSwap(img).height/6),maxRadius=int(m3F.typeSwap(img).height/2.5))
# print("circles!!!!", circles)
# return circles
if not isinstance(circles, type(None)):
if (circles.size != 0):
circles = np.uint16(np.around(circles))
# print(circles)
index = 0
if (tempShow):
for i in circles[0, :]:
# if img[i[1],i[0]] < 15:
# draw the outer circle
cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0), 2)
# draw the center of the circle
cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255), 3)
# eye.houghOutline = cimg
m3F.imshow(cimg, "Circle")
m3F.printGreen("CIRCLES FOUND^^^")
print("img out", img.shape)
return circles
else:
# eye.noCircles = True
if (tempShow):
m3F.imshow(cimg, "no circles found")
m3F.printRed("NO CIRCLES FOUND^^^")
return None
else:
m3F.printRed("Image is NONE")
def denoise(inputImg, show):
outputImg = cv2.fastNlMeansDenoisingColored(inputImg, 7, 21, 10, 10)
if (show):
m3F.imshow(outputImg, "Denoised")
return outputImg