我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用cv2.boxFilter()。
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 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 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
