def run(self):
while True:
ch = 0xFF & cv2.waitKey(50)
if ch == 27:
break
if ch >= ord('1') and ch <= ord('7'):
self.cur_marker = ch - ord('0')
print('marker: ', self.cur_marker)
if ch == ord(' ') or (self.sketch.dirty and self.auto_update):
self.watershed()
self.sketch.dirty = False
if ch in [ord('a'), ord('A')]:
self.auto_update = not self.auto_update
print('auto_update if', ['off', 'on'][self.auto_update])
if ch in [ord('r'), ord('R')]:
self.markers[:] = 0
self.markers_vis[:] = self.img
self.sketch.show()
cv2.destroyAllWindows()
return self.returnVar
python类watershed()的实例源码
def detect_shirt(self):
#self.dst=cv2.inRange(self.norm_rgb,np.array([self.lb,self.lg,self.lr],np.uint8),np.array([self.b,self.g,self.r],np.uint8))
self.dst=cv2.inRange(self.norm_rgb,np.array([20,20,20],np.uint8),np.array([255,110,80],np.uint8))
cv2.threshold(self.dst,0,255,cv2.THRESH_OTSU+cv2.THRESH_BINARY)
fg=cv2.erode(self.dst,None,iterations=2)
#cv2.imshow("fore",fg)
bg=cv2.dilate(self.dst,None,iterations=3)
_,bg=cv2.threshold(bg, 1,128,1)
#cv2.imshow("back",bg)
mark=cv2.add(fg,bg)
mark32=np.int32(mark)
cv2.watershed(self.norm_rgb,mark32)
self.m=cv2.convertScaleAbs(mark32)
_,self.m=cv2.threshold(self.m,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#cv2.imshow("final_tshirt",self.m)
cntr,h=cv2.findContours(self.m,cv2.cv.CV_RETR_EXTERNAL,cv2.cv.CV_CHAIN_APPROX_SIMPLE)
return self.m,cntr
def run(self, ips, snap, img, para = None):
a, msk = cv2.connectedComponents(ips.get_msk().astype(np.uint8))
msk = cv2.watershed(img, msk)==-1
img //= 2
img[msk] = 255
def watershed(self):
m = self.markers.copy()
cv2.watershed(self.img, m)
self.returnVar = m.copy()
overlay = self.colors[np.maximum(m, 0)]
vis = cv2.addWeighted(self.img, 0.5, overlay, 0.5, 0.0, dtype=cv2.CV_8UC3)
cv2.namedWindow('watershed', cv2.WINDOW_NORMAL)
cv2.moveWindow('watershed',780,200)
cv2.imshow('watershed', vis)
def detect_shirt2(self):
self.hsv=cv2.cvtColor(self.norm_rgb,cv.CV_BGR2HSV)
self.hue,s,_=cv2.split(self.hsv)
_,self.dst=cv2.threshold(self.hue,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
self.fg=cv2.erode(self.dst,None,iterations=3)
self.bg=cv2.dilate(self.dst,None,iterations=1)
_,self.bg=cv2.threshold(self.bg,1,128,1)
mark=cv2.add(self.fg,self.bg)
mark32=np.int32(mark)
cv2.watershed(self.norm_rgb,mark32)
m=cv2.convertScaleAbs(mark32)
_,m=cv2.threshold(m,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cntr,h=cv2.findContours(m,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE)
print len(cntr)
#print cntr[0].shape
#cntr[1].dtype=np.float32
#ret=cv2.contourArea(np.array(cntr[1]))
#print ret
#cntr[0].dtype=np.uint8
cv2.drawContours(m,cntr,-1,(255,255,255),3)
cv2.imshow("mask_fg",self.fg)
cv2.imshow("mask_bg",self.bg)
cv2.imshow("mark",m)
def subtract_back(self,frm):
#dst=self.__back__-self.__foreground__
temp=np.zeros((600,800),np.uint8)
self.__foreground__=cv2.blur(self.__foreground__,(3,3))
dst=cv2.absdiff(self.__back__,self.__foreground__)
#dst=cv2.adaptiveThreshold(dst,255,cv.CV_THRESH_BINARY,cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C,5,10)
val,dst=cv2.threshold(dst,0,255,cv.CV_THRESH_BINARY+cv.CV_THRESH_OTSU)
fg=cv2.erode(dst,None,iterations=1)
bg=cv2.dilate(dst,None,iterations=4)
_,bg=cv2.threshold(bg,1,128,1)
mark=cv2.add(fg,bg)
mark32=np.int32(mark)
#dst.copy(temp)
#seq=cv.FindContours(cv.fromarray(dst),self.mem,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE)
#cntr,h=cv2.findContours(dst,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE)
#print cntr,h
#cv.DrawContours(cv.fromarray(temp),seq,(255,255,255),(255,255,255),1,cv.CV_FILLED)
cv2.watershed(frm, mark32)
self.final_mask=cv2.convertScaleAbs(mark32)
#print temp
#--outputs---
#cv2.imshow("subtraction",fg)
#cv2.imshow("thres",dst)
#cv2.imshow("thres1",bg)
#cv2.imshow("mark",mark)
#cv2.imshow("final",self.final_mask)
def remove_pectoral(self, img, breast_mask, high_int_threshold=.8,
morph_kn_size=3, n_morph_op=7, sm_kn_size=25):
'''Remove the pectoral muscle region from an input image
Args:
img (2D array): input image as a numpy 2D array.
breast_mask (2D array):
high_int_threshold ([int]): a global threshold for high intensity
regions such as the pectoral muscle. Default is 200.
morph_kn_size ([int]): kernel size for morphological operations
such as erosions and dilations. Default is 3.
n_morph_op ([int]): number of morphological operations. Default is 7.
sm_kn_size ([int]): kernel size for final smoothing (i.e. opening).
Default is 25.
Returns:
an output image with pectoral muscle region removed as a numpy
2D array.
Notes: this has not been tested on .dcm files yet. It may not work!!!
'''
# Enhance contrast and then thresholding.
img_equ = cv2.equalizeHist(img)
if high_int_threshold < 1.:
high_th = int(img.max()*high_int_threshold)
else:
high_th = int(high_int_threshold)
maxval = self.max_pix_val(img.dtype)
_, img_bin = cv2.threshold(img_equ, high_th,
maxval=maxval, type=cv2.THRESH_BINARY)
pect_marker_img = np.zeros(img_bin.shape, dtype=np.int32)
# Sure foreground (shall be pectoral).
pect_mask_init = self.select_largest_obj(img_bin, lab_val=maxval,
fill_holes=True,
smooth_boundary=False)
kernel_ = np.ones((morph_kn_size, morph_kn_size), dtype=np.uint8)
pect_mask_eroded = cv2.erode(pect_mask_init, kernel_,
iterations=n_morph_op)
pect_marker_img[pect_mask_eroded > 0] = 255
# Sure background - breast.
pect_mask_dilated = cv2.dilate(pect_mask_init, kernel_,
iterations=n_morph_op)
pect_marker_img[pect_mask_dilated == 0] = 128
# Sure background - pure background.
pect_marker_img[breast_mask == 0] = 64
# Watershed segmentation.
img_equ_3c = cv2.cvtColor(img_equ, cv2.COLOR_GRAY2BGR)
cv2.watershed(img_equ_3c, pect_marker_img)
img_equ_3c[pect_marker_img == -1] = (0, 0, 255)
# Extract only the breast and smooth.
breast_only_mask = pect_marker_img.copy()
breast_only_mask[breast_only_mask == -1] = 0
breast_only_mask = breast_only_mask.astype(np.uint8)
breast_only_mask[breast_only_mask != 128] = 0
breast_only_mask[breast_only_mask == 128] = 255
kernel_ = np.ones((sm_kn_size, sm_kn_size), dtype=np.uint8)
breast_only_mask = cv2.morphologyEx(breast_only_mask, cv2.MORPH_OPEN,
kernel_)
img_breast_only = cv2.bitwise_and(img_equ, breast_only_mask)
return (img_breast_only, img_equ_3c)
