def extract_bv(image):
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
contrast_enhanced_green_fundus = clahe.apply(image)
# applying alternate sequential filtering (3 times closing opening)
r1 = cv2.morphologyEx(contrast_enhanced_green_fundus, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)), iterations = 1)
R1 = cv2.morphologyEx(r1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)), iterations = 1)
r2 = cv2.morphologyEx(R1, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11)), iterations = 1)
R2 = cv2.morphologyEx(r2, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11)), iterations = 1)
r3 = cv2.morphologyEx(R2, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(23,23)), iterations = 1)
R3 = cv2.morphologyEx(r3, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(23,23)), iterations = 1)
f4 = cv2.subtract(R3,contrast_enhanced_green_fundus)
f5 = clahe.apply(f4)
# removing very small contours through area parameter noise removal
ret,f6 = cv2.threshold(f5,15,255,cv2.THRESH_BINARY)
mask = np.ones(f5.shape[:2], dtype="uint8") * 255
im2, contours, hierarchy = cv2.findContours(f6.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
if cv2.contourArea(cnt) <= 200:
cv2.drawContours(mask, [cnt], -1, 0, -1)
im = cv2.bitwise_and(f5, f5, mask=mask)
ret,fin = cv2.threshold(im,15,255,cv2.THRESH_BINARY_INV)
newfin = cv2.erode(fin, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)), iterations=1)
# removing blobs of microaneurysm & unwanted bigger chunks taking in consideration they are not straight lines like blood
# vessels and also in an interval of area
fundus_eroded = cv2.bitwise_not(newfin)
xmask = np.ones(image.shape[:2], dtype="uint8") * 255
x1, xcontours, xhierarchy = cv2.findContours(fundus_eroded.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
for cnt in xcontours:
shape = "unidentified"
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.04 * peri, False)
if len(approx) > 4 and cv2.contourArea(cnt) <= 3000 and cv2.contourArea(cnt) >= 100:
shape = "circle"
else:
shape = "veins"
if(shape=="circle"):
cv2.drawContours(xmask, [cnt], -1, 0, -1)
finimage = cv2.bitwise_and(fundus_eroded,fundus_eroded,mask=xmask)
blood_vessels = cv2.bitwise_not(finimage)
dilated = cv2.erode(blood_vessels, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7)), iterations=1)
#dilated1 = cv2.dilate(blood_vessels, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)), iterations=1)
blood_vessels_1 = cv2.bitwise_not(dilated)
return blood_vessels_1
python类morphologyEx()的实例源码
def extract_bv(image):
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
contrast_enhanced_green_fundus = clahe.apply(image)
# applying alternate sequential filtering (3 times closing opening)
r1 = cv2.morphologyEx(contrast_enhanced_green_fundus, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)), iterations = 1)
R1 = cv2.morphologyEx(r1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)), iterations = 1)
r2 = cv2.morphologyEx(R1, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11)), iterations = 1)
R2 = cv2.morphologyEx(r2, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11)), iterations = 1)
r3 = cv2.morphologyEx(R2, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(23,23)), iterations = 1)
R3 = cv2.morphologyEx(r3, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(23,23)), iterations = 1)
f4 = cv2.subtract(R3,contrast_enhanced_green_fundus)
f5 = clahe.apply(f4)
# removing very small contours through area parameter noise removal
ret,f6 = cv2.threshold(f5,15,255,cv2.THRESH_BINARY)
mask = np.ones(f5.shape[:2], dtype="uint8") * 255
im2, contours, hierarchy = cv2.findContours(f6.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
if cv2.contourArea(cnt) <= 200:
cv2.drawContours(mask, [cnt], -1, 0, -1)
im = cv2.bitwise_and(f5, f5, mask=mask)
ret,fin = cv2.threshold(im,15,255,cv2.THRESH_BINARY_INV)
newfin = cv2.erode(fin, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)), iterations=1)
# removing blobs of microaneurysm & unwanted bigger chunks taking in consideration they are not straight lines like blood
# vessels and also in an interval of area
fundus_eroded = cv2.bitwise_not(newfin)
xmask = np.ones(image.shape[:2], dtype="uint8") * 255
x1, xcontours, xhierarchy = cv2.findContours(fundus_eroded.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
for cnt in xcontours:
shape = "unidentified"
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.04 * peri, False)
if len(approx) > 4 and cv2.contourArea(cnt) <= 3000 and cv2.contourArea(cnt) >= 100:
shape = "circle"
else:
shape = "veins"
if(shape=="circle"):
cv2.drawContours(xmask, [cnt], -1, 0, -1)
finimage = cv2.bitwise_and(fundus_eroded,fundus_eroded,mask=xmask)
blood_vessels = cv2.bitwise_not(finimage)
dilated = cv2.erode(blood_vessels, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7)), iterations=1)
#dilated1 = cv2.dilate(blood_vessels, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)), iterations=1)
blood_vessels_1 = cv2.bitwise_not(dilated)
return blood_vessels_1
def _filter_image(self, image):
_, thresh = cv2.threshold(image, 200, 255, cv2.THRESH_BINARY)
opened = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, (5, 5), iterations=3)
return cv2.bitwise_not(opened)
def threshold(self, img):
cv2.cvtColor(img, cv2.COLOR_BGR2HSV, dst=self.hsv)
cv2.inRange(self.hsv, self.thresh_low, self.thresh_high, dst=self.bin)
cv2.morphologyEx(self.bin, cv2.MORPH_CLOSE, self.morphKernel, dst=self.bin2, iterations=1)
if self.draw_thresh:
b = (self.bin2 != 0)
cv2.copyMakeBorder(self.black, 0, 0, 0, 0, cv2.BORDER_CONSTANT, value=self.RED, dst=self.out)
self.out[np.dstack((b, b, b))] = 255
return self.bin2
def CloseInContour( mask, element ):
large = 0
result = mask
_, contours, _ = cv2.findContours(result,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#find the biggest area
c = max(contours, key = cv2.contourArea)
closing = cv2.morphologyEx(result, cv2.MORPH_CLOSE, element)
for x in range(mask.shape[0]):
for y in range(mask.shape[1]):
pt = cv2.pointPolygonTest(c, (x, y), True)
#pt = cv2.pointPolygonTest(c, (x, y), False)
if pt > 3:
result[x][y] = closing[x][y]
return result.astype(np.float32)
def CloseInContour( mask, element ):
large = 0
result = mask
_, contours, _ = cv2.findContours(result,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#find the biggest area
c = max(contours, key = cv2.contourArea)
closing = cv2.morphologyEx(result, cv2.MORPH_CLOSE, element)
for x in range(mask.shape[0]):
for y in range(mask.shape[1]):
#pt = cv2.pointPolygonTest(c, (x, y), True)
pt = cv2.pointPolygonTest(c, (x, y), False)
if pt == 1:
result[x][y] = closing[x][y]
return result.astype(np.float32)
def outlining(img):
#kernel size
kernel_size=3
#-------------------------------------------------
#bilateral filter, sharpen, thresh image
biblur=cv2.bilateralFilter(img,20,175,175)
sharp=cv2.addWeighted(img,1.55,biblur,-0.5,0)
ret1,thresh1 = cv2.threshold(sharp,127,255,cv2.THRESH_OTSU)
#negative and closed image
inv=cv2.bitwise_not(thresh1)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
closed = cv2.morphologyEx(inv, cv2.MORPH_CLOSE, kernel)
return closed
def img_contour_extra(im):
# ?????
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(13,7))
bgmask = cv2.morphologyEx(im, cv2.MORPH_CLOSE, kernel)
img_show_hook("??????", bgmask)
# ??????
# ??????????
im2, contours, hierarchy = cv2.findContours(bgmask.copy(), cv2.RETR_EXTERNAL, #????
cv2.CHAIN_APPROX_SIMPLE)
return contours
def img_contour_extra(im):
# ?????
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(13,7))
bgmask = cv2.morphologyEx(im, cv2.MORPH_CLOSE, kernel)
img_show_hook("??????", bgmask)
# ??????
# ??????????
im2, contours, hierarchy = cv2.findContours(bgmask.copy(), cv2.RETR_EXTERNAL, #????
cv2.CHAIN_APPROX_SIMPLE)
return contours
def maskImg(image):
#Convert image from RBG (red blue green) to HSV (hue shade value)
maskedImage = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
#Convert image to binary using the predefined color arrays
maskedImage = cv2.inRange(maskedImage, lowColor, highColor)
#Removes white noise using an open transformation
kernel = np.ones((4,4), np.uint8)
#maskedImage = cv2.morphologyEx(maskedImage, cv2.MORPH_OPEN, kernel)
return maskedImage
def maskImg(image):
#Convert image from RBG (red blue green) to HSV (hue shade value)
maskedImage = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
#Convert image to binary using the predefined color arrays
maskedImage = cv2.inRange(maskedImage, lowColor, highColor)
#Removes white noise using an open transformation
kernel = np.ones((4,4), np.uint8)
#maskedImage = cv2.morphologyEx(maskedImage, cv2.MORPH_OPEN, kernel)
return maskedImage
#Find and return two matching rectangular contours if they exist, otherwise return none.
def remove_noise(image, kernel=(2, 2)):
''' removes noisy pixels in the area. '''
return cv.morphologyEx(image, cv.MORPH_OPEN, kernel)
def fill(image, kernel=(2, 2)):
''' fill gaps in shapes structure. '''
return cv.morphologyEx(image, cv.MORPH_CLOSE, kernel)
def closing(img, kernel_size):
kernel = np.ones((kernel_size, kernel_size), np.uint8)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
return closing
def opening(img, kernel_size):
kernel = np.ones((kernel_size, kernel_size), np.uint8)
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
return opening
def closing(img,kernel_size):
kernel = np.ones((kernel_size,kernel_size),np.uint8)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
return closing
def opening(img,kernel_size):
kernel = np.ones((kernel_size,kernel_size),np.uint8)
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
return opening
def detect(self, image, mask = None):
floatimage = np.float32(image)
fb,fg,fr = cv2.split(floatimage)
# red-to-blue channel operation
ra = fr + fb
rb = fr - fb
rb[ra > 0] /= ra[ra > 0]
#mi = np.min(rb)
#ma = np.max(rb)
#rb = np.uint8((rb - mi) / (ma - mi) * 255)
# morphology open
if self.kernel is None or self.kernel.shape[0] != Configuration.background_rect_size:
self.kernel = np.ones((Configuration.background_rect_size, Configuration.background_rect_size), np.uint8) * 255
result = cv2.morphologyEx(rb, cv2.MORPH_OPEN, self.kernel)
# background subtraction
# homogeneous background image V
result = rb - result
mi = np.min(result)
ma = np.max(result)
result = np.uint8((result - mi) / (ma - mi) * 255)
# adaptive threshold T
T, _ = cv2.threshold(result[mask == 0], 0, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# V(i, j) > T
return np.uint8((T - np.float32(result)) <= 0)
def close_result(self, result):
return cv2.morphologyEx(result, cv2.MORPH_CLOSE, self.kernel)
def roiMask(image, boundaries):
scale = max([1.0, np.average(np.array(image.shape)[0:2] / 400.0)])
shape = (int(round(image.shape[1] / scale)), int(round(image.shape[0] / scale)))
small_color = cv2.resize(image, shape, interpolation=cv2.INTER_LINEAR)
# reduce details and remove noise for better edge detection
small_color = cv2.bilateralFilter(small_color, 8, 64, 64)
small_color = cv2.pyrMeanShiftFiltering(small_color, 8, 64, maxLevel=1)
small = cv2.cvtColor(small_color, cv2.COLOR_BGR2HSV)
hue = small[::, ::, 0]
intensity = cv2.cvtColor(small_color, cv2.COLOR_BGR2GRAY)
edges = extractEdges(hue, intensity)
roi = roiFromEdges(edges)
weight_map = weightMap(hue, intensity, edges, roi)
_, final_mask = cv2.threshold(roi, 5, 255, cv2.THRESH_BINARY)
small = cv2.bitwise_and(small, small, mask=final_mask)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4, 4))
for (lower, upper) in boundaries:
lower = np.array([lower, 80, 50], dtype="uint8")
upper = np.array([upper, 255, 255], dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(small, lower, upper)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
final_mask = cv2.bitwise_and(final_mask, mask)
# blur the mask for better contour extraction
final_mask = cv2.GaussianBlur(final_mask, (5, 5), 0)
return (final_mask, weight_map, scale)
