def Guidedfilter(im, p, r, eps):
mean_I = cv2.boxFilter(im, cv2.CV_64F, (r, r));
mean_p = cv2.boxFilter(p, cv2.CV_64F, (r, r));
mean_Ip = cv2.boxFilter(im * p, cv2.CV_64F, (r, r));
cov_Ip = mean_Ip - mean_I * mean_p;
mean_II = cv2.boxFilter(im * im, cv2.CV_64F, (r, r));
var_I = mean_II - mean_I * mean_I;
a = cov_Ip / (var_I + eps);
b = mean_p - a * mean_I;
mean_a = cv2.boxFilter(a, cv2.CV_64F, (r, r));
mean_b = cv2.boxFilter(b, cv2.CV_64F, (r, r));
q = mean_a * im + mean_b;
return q;
python类boxFilter()的实例源码
def Guidedfilter(im,p,r,eps):
mean_I = cv2.boxFilter(im,cv2.CV_64F,(r,r));
mean_p = cv2.boxFilter(p, cv2.CV_64F,(r,r));
mean_Ip = cv2.boxFilter(im*p,cv2.CV_64F,(r,r));
cov_Ip = mean_Ip - mean_I*mean_p;
mean_II = cv2.boxFilter(im*im,cv2.CV_64F,(r,r));
var_I = mean_II - mean_I*mean_I;
a = cov_Ip/(var_I + eps);
b = mean_p - a*mean_I;
mean_a = cv2.boxFilter(a,cv2.CV_64F,(r,r));
mean_b = cv2.boxFilter(b,cv2.CV_64F,(r,r));
q = mean_a*im + mean_b;
return q;
def box_blur(im, size=3):
return cv2.boxFilter(im, -1, (size,size))
def foreground(img,blockSize=31):
"""calculate foreground in an image
return: foreground
"""
img=100*(img-np.mean(img))
img[np.where(img>255)]=255
img=cv2.boxFilter(img,-1,(blockSize,blockSize))
img[np.where(img>150)]=255; img[np.where(img<=150)]=0
img=cv2.boxFilter(img,-1,(blockSize/2,blockSize/2))
img[np.where(img>0)]=255
return img
def minutiaeExtract(img,imgfore):
"""minutiae extraction: ending and bifurcation
img: thinned image
imgfore: foreground image
return: minutiae, directions
"""
image=img.copy()
P1=image[1:-1,1:-1]
valid=np.where(P1==1)
#P1:center; P2-P9:neighbors
P1,P2,P3,P4,P5,P6,P7,P8,P9 = P1[valid],image[2:,1:-1][valid], image[2:,2:][valid], image[1:-1,2:][valid], image[:-2,2:][valid], image[:-2,1:-1][valid],image[:-2,:-2][valid], image[1:-1,:-2][valid], image[2:,:-2][valid]
CN=pre.transitions_vec(P2,P3,P4,P5,P6,P7,P8,P9)
ending_index=np.where(CN==1)
bifur_index=np.where(CN==3)
ending=np.asarray((valid[0][ending_index]+1,valid[1][ending_index]+1))
bifur=np.asarray((valid[0][bifur_index]+1,valid[1][bifur_index]+1))
#delete minutiae near the edge of the foreground
imgfored=cv2.boxFilter(imgfore,-1,(9,9))
imgfored[np.where(imgfored>0)]=255
edge1,edge2=np.where(imgfored[ending[0],ending[1]]==255),np.where(imgfored[bifur[0],bifur[1]]==255)
ending=np.delete(ending.T,edge1[0],0)
bifur=np.delete(bifur.T,edge2[0],0)
#delete minutiae near the edge of the image
edgeDistance=20
valid1=(ending[:,0]>=edgeDistance) * (ending[:,0]<=img.shape[0]-edgeDistance)
valid2=(ending[:,1]>=edgeDistance) * (ending[:,1]<=img.shape[1]-edgeDistance)
ending=ending[np.where(valid1 * valid2)]
valid1=(bifur[:,0]>=edgeDistance) * (bifur[:,0]<=img.shape[0]-edgeDistance)
valid2=(bifur[:,1]>=edgeDistance) * (bifur[:,1]<=img.shape[1]-edgeDistance)
bifur=bifur[np.where(valid1 * valid2)]
#valide minutiae and calculate directions at the same time
ending,theta1=validateMinutiae(image,ending,1)
bifur,theta2=validateMinutiae(image,bifur,0)
return ending,bifur,theta1,theta2
def foreground(img,blockSize=31):
img=100*(img-np.mean(img))
img[np.where(img>255)]=255
img=cv2.boxFilter(img,-1,(blockSize,blockSize))
img[np.where(img>150)]=255; img[np.where(img<=150)]=0
img=cv2.boxFilter(img,-1,(blockSize/2,blockSize/2))
img[np.where(img>0)]=255
return img
def foreground(img,blockSize=31):
img=100*(img-np.mean(img))
img[np.where(img>255)]=255
img=cv2.boxFilter(img,-1,(blockSize,blockSize))
img[np.where(img>150)]=255; img[np.where(img<=150)]=0
img=cv2.boxFilter(img,-1,(blockSize/2,blockSize/2))
img[np.where(img>0)]=255
return img
def foreground(img,blockSize=31):
"""calculate foreground in an image
return: foreground
"""
img=100*(img-np.mean(img))
img[np.where(img>255)]=255
img=cv2.boxFilter(img,-1,(blockSize,blockSize))
img[np.where(img>150)]=255; img[np.where(img<=150)]=0
img=cv2.boxFilter(img,-1,(blockSize/2,blockSize/2))
img[np.where(img>0)]=255
return img
def foreground(img,blockSize=31):
img=100*(img-np.mean(img))
img[np.where(img>255)]=255
img=cv2.boxFilter(img,-1,(blockSize,blockSize))
img[np.where(img>150)]=255; img[np.where(img<=150)]=0
img=cv2.boxFilter(img,-1,(blockSize/2,blockSize/2))
img[np.where(img>0)]=255
return img
def run(self):
bytes=''
while not self.thread_cancelled:
try:
bytes+=self.stream.raw.read(1024)
a = bytes.find('\xff\xd8')
b = bytes.find('\xff\xd9')
if a!=-1 and b!=-1:
jpg = bytes[a:b+2]
bytes= bytes[b+2:]
img = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),cv2.IMREAD_COLOR)
# Convert BGR to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
#lower_blue = np.array([self.L_RED, self.L_GREEN, self.L_BLUE], np.uint8)
#upper_blue = np.array([self.U_RED, self.U_GREEN, self.L_BLUE], np.uint8)
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, np.array([53,187,37]), np.array([97,244,153]))
# Bitwise-AND mask and original image
res = cv2.bitwise_and(img,img, mask= mask)
#### blurred = cv2.GaussianBlur(mask, (5, 5), 0)
blurred = cv2.boxFilter(mask, 0, (7, 7), mask, (-1, -1), False, cv2.BORDER_DEFAULT)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cv2.filterSpeckles(mask, 0, 100, 25)
## cv2.filterSpeckles(mask, 0, 50, 25)
## cv2.filterSpeckles(mask, 0, 100, 100)
for c in cnts:
M = cv2.moments(c)
if int(M["m00"]) != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
(cX, cY) = (0, 0)
print(cX, cY)
cv2.drawContours(res, [c], -1, (0, 255, 0), 2)
cv2.circle(res, (cX, cY), 7, (255, 255, 255), 1)
# table.putNumber("center X", cX)
cv2.imshow('img',img)
cv2.imshow('mask',mask)
cv2.imshow('Final',res)
cv2.imshow('cam',img)
#sd.putNumber('Center X', cX) ##send the x value of the center
#sd.putNumber('Center Y', cY) ##send the y value of the center
## print(sd.getNumber('Center Y'), sd.getNumber('Center X'))
if cv2.waitKey(1) ==27:
exit(0)
except ThreadError:
self.thread_cancelled = True
def run(self):
bytes=''
while not self.thread_cancelled: ####see lines 18, 80, 88 ....
try:
bytes+=self.stream.raw.read(1024) ##limit max bytes read in 1 itteration? need to read more on this
a = bytes.find('\xff\xd8')##find start of stream of data
b = bytes.find('\xff\xd9')##find our end of data stream
if a!=-1 and b!=-1: ##so as long as we have a stream of data....do the following
jpg = bytes[a:b+2] ##converts to image or a specific variable...
bytes= bytes[b+2:]
img = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),cv2.IMREAD_COLOR) ##decode the data
# Convert BGR to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) ##converting color format for easier proccessing/ math
# define range of blue color in HSV
#lower_blue = np.array([self.L_RED, self.L_GREEN, self.L_BLUE], np.uint8)
#upper_blue = np.array([self.U_RED, self.U_GREEN, self.L_BLUE], np.uint8)
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, np.array([53,187,37]), np.array([97,244,153])) ##get colors in the range of these HSV values
# Bitwise-AND mask and original image
res = cv2.bitwise_and(img,img, mask= mask)
blurred = cv2.boxFilter(mask, 0, (7, 7), mask, (-1, -1), False, cv2.BORDER_DEFAULT) ##the next few line create outlines and
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1] ##remove any noise
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) #find countors
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cv2.filterSpeckles(mask, 0, 100, 25) ##remove speckles aka random dots and white noise
for c in cnts:
M = cv2.moments(c)
if int(M["m00"]) != 0: ##Checks for division by zero
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
(cX, cY) = (0, 0)
cv2.drawContours(res, [c], -1, (0, 255, 0), 2) ##draw box/highlighting
cv2.circle(res, (cX, cY), 7, (255, 255, 255), 1) ##draw box/highlighting
##Try-Catch for appending cX to table
try:
self.table.putNumber('centerX', cX) ##Adds cX to the networktables
except KeyError:
print("centerX failed.")
cv2.imshow('img',img) ##display original image
cv2.imshow('mask',mask) ##display masked image
cv2.imshow('Final',res) ##show final image
cv2.imshow('cam',img) ##see line 71/comments
if cv2.waitKey(1) ==27: ##now we close if esc key is pressed
exit(0)
except ThreadError:
self.thread_cancelled = True
