Пример #1
0
	def read(self) :
		frame=self.input.read()
		cv_rs = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
		cv.cvCvtColor(frame,cv_rs,cv.CV_RGB2GRAY)
		frame = cv_rs
		if self.enabled :
			# I think these functions are too specialized for transforms
			cv.cvSmooth(frame,frame,cv.CV_GAUSSIAN,3, 0, 0, 0 )
			cv.cvErode(frame, frame, None, 1)
			cv.cvDilate(frame, frame, None, 1)
			num_contours,contours=cv.cvFindContours(frame,self.storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0))
			if contours is None :
				return []
			else :
				contours = cv.cvApproxPoly( contours, cv.sizeof_CvContour, self.storage, cv.CV_POLY_APPROX_DP, 3, 1 );
				if contours is None :
					return []
				else :
					final_contours = []
					for c in contours.hrange() :
						area = abs(cv.cvContourArea(c))
						#self.debug_print('Polygon Area: %f'%area)
						if area >= self.min_area :
							lst = []
							for pt in c :
								lst.append((pt.x,pt.y))
							final_contours.append(lst)
						contours = contours.h_next
					return final_contours

		return []
Пример #2
0
    ret=False
    
    try:
        ret=abs(cvContourPerimeter(contour))> MINCONTOUR_PERIMETER
    except:
        pass
    return ret
Пример #3
0
def areaFilter(contour):
    ret = False

    try:
        ret = abs(cvContourArea(contour)) > MINCONTOUR_AREA
    except:
        pass
    return ret
Пример #4
0
	def read(self) :
		frame=self.input.read()
		if self.debug :
			raw_frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,frame.nChannels)
			cv.cvCopy(frame,raw_frame,None)
			self.raw_frame_surface=pygame.image.frombuffer(frame.imageData,(frame.width,frame.height),'RGB')

		if self.enabled :
			cv_rs = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)

			# convert color
			cv.cvCvtColor(frame,cv_rs,cv.CV_BGR2GRAY)

			# invert the image
			cv.cvSubRS(cv_rs, 255, cv_rs, None);

			# threshold the image
			frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
			cv.cvThreshold(cv_rs, frame, self.threshold, 255, cv.CV_THRESH_BINARY)

			if self.debug :
				thresh_frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,3)
				cv.cvCvtColor(frame,thresh_frame,cv.CV_GRAY2RGB)
				self.thresh_frame_surface=pygame.image.frombuffer(thresh_frame.imageData,(frame.width,frame.height),'RGB')

			# I think these functions are too specialized for transforms
			cv.cvSmooth(frame,frame,cv.CV_GAUSSIAN,3, 0, 0, 0 )
			cv.cvErode(frame, frame, None, 1)
			cv.cvDilate(frame, frame, None, 1)

			num_contours,contours=cv.cvFindContours(frame,self.storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0))
			if contours is None :
				return []
			else :
				contours = cv.cvApproxPoly( contours, cv.sizeof_CvContour, self.storage, cv.CV_POLY_APPROX_DP, 3, 1 );
				if contours is None :
					return []
				else :
					final_contours = []
					for c in contours.hrange() :
						area = abs(cv.cvContourArea(c))
						#self.debug_print('Polygon Area: %f'%area)
						if area >= self.min_area :
							lst = []
							for pt in c :
								lst.append((pt.x,pt.y))
							final_contours.append(lst)
						contours = contours.h_next
					return final_contours

		return []
Пример #5
0
    
    try:
        ret=abs(cvContourArea(contour))> MINCONTOUR_AREA
    except:
        pass
    return ret
       

def boxAreaFilter(contour):
    #checks if a contour area is similar to the bounding box containing that contour
    ret=False
    
    try:
        box=cvMinAreaRect2(contour,mem)
        boxArea=box.size.width*box.size.height
Пример #6
0
def boxAreaFilter(contour):
    #checks if a contour area is similar to the bounding box containing that contour
    ret = False

    try:
        box = cvMinAreaRect2(contour, mem)
        boxArea = box.size.width * box.size.height
        contourArea = abs(cvContourArea(contour))
        dif = boxArea - contourArea
        ret = (1 - (dif / boxArea))
        #calcs the percentage of the boxarea that the contourarea covers
        #print("contourArea: " + str(contourArea) + "boxarea: " + str(boxArea))
    except:
        pass

    if (ret > BOXFILTER_TOLERANCE):
        return True
    else:
        #print("No paso el boxarea filter")
        return False
Пример #7
0
	def read(self) :
		frame=self.input.read()

		if self.enabled :

			cv_rs = [cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1) for i in range(3)]
			cv.cvSplit(frame,cv_rs[0],cv_rs[1],cv_rs[2],None)

			channel_contours = []

			for frame in cv_rs[0] :
				# I think these functions are too specialized for transforms
				#cv.cvSmooth(frame,frame,cv.CV_GAUSSIAN,3, 0, 0, 0 )
				#cv.cvErode(frame, frame, None, 1)
				#cv.cvDilate(frame, frame, None, 1)
				num_contours,contours=cv.cvFindContours(frame,self.storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0))
				if contours is None :
					channel_contours.append([])
				else :
					contours = cv.cvApproxPoly( contours, cv.sizeof_CvContour, self.storage, cv.CV_POLY_APPROX_DP, 3, 1 );
					if contours is None :
						channel_contours.append([])
					else :
						final_contours = []
						for c in contours.hrange() :
							area = abs(cv.cvContourArea(c))
							#self.debug_print('Polygon Area: %f'%area)
							if area >= self.min_area :
								lst = []
								for pt in c :
									lst.append((pt.x,pt.y))
								final_contours.append(lst)
							contours = contours.h_next
						channel_contours.append(final_contours)

		return channel_contours
Пример #8
0
	def detect_squares(self, img_grey, img_orig):
		""" Find squares within the video stream and draw them """
		cv.cvClearMemStorage(self.faces_storage)
		N										= 11
		thresh									= 5
		sz										= cv.cvSize(img_grey.width & -2, img_grey.height & -2)
		timg									= cv.cvCloneImage(img_orig)
		pyr										= cv.cvCreateImage(cv.cvSize(sz.width/2, sz.height/2), 8, 3)
		# create empty sequence that will contain points -
		# 4 points per square (the square's vertices)
		squares									= cv.cvCreateSeq(0, cv.sizeof_CvSeq, cv.sizeof_CvPoint, self.squares_storage)
		squares									= cv.CvSeq_CvPoint.cast(squares)

		# select the maximum ROI in the image
		# with the width and height divisible by 2
		subimage								= cv.cvGetSubRect(timg, cv.cvRect(0, 0, sz.width, sz.height))

		cv.cvReleaseImage(timg)

		# down-scale and upscale the image to filter out the noise
		cv.cvPyrDown(subimage, pyr, 7)
		cv.cvPyrUp(pyr, subimage, 7)
		cv.cvReleaseImage(pyr)
		tgrey									= cv.cvCreateImage(sz, 8, 1)
		# find squares in every color plane of the image
		for c in range(3):
			# extract the c-th color plane
			channels							= [None, None, None]
			channels[c]							= tgrey
			cv.cvSplit(subimage, channels[0], channels[1], channels[2], None)
			for l in range(N):
				# hack: use Canny instead of zero threshold level.
				# Canny helps to catch squares with gradient shading
				if(l == 0):
					# apply Canny. Take the upper threshold from slider
					# and set the lower to 0 (which forces edges merging)
					cv.cvCanny(tgrey, img_grey, 0, thresh, 5)
					# dilate canny output to remove potential
					# holes between edge segments
					cv.cvDilate(img_grey, img_grey, None, 1)
				else:
					# apply threshold if l!=0:
					#     tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
					cv.cvThreshold(tgrey, img_grey, (l+1)*255/N, 255, cv.CV_THRESH_BINARY)

				# find contours and store them all as a list
				count, contours					= cv.cvFindContours(img_grey,
																	self.squares_storage,
																	cv.sizeof_CvContour,
																	cv.CV_RETR_LIST,
																	cv.CV_CHAIN_APPROX_SIMPLE,
																	cv.cvPoint(0,0))

				if not contours:
					continue

				# test each contour
				for contour in contours.hrange():
					# approximate contour with accuracy proportional
					# to the contour perimeter
					result						= cv.cvApproxPoly(contour,
																	cv.sizeof_CvContour,
																	self.squares_storage,
																	cv.CV_POLY_APPROX_DP,
																	cv.cvContourPerimeter(contours)*0.02, 0)
					# square contours should have 4 vertices after approximation
					# relatively large area (to filter out noisy contours)
					# and be convex.
					# Note: absolute value of an area is used because
					# area may be positive or negative - in accordance with the
					# contour orientation
					if(result.total == 4 and abs(cv.cvContourArea(result)) > 1000 and cv.cvCheckContourConvexity(result)):
						s						= 0
						for i in range(5):
							# find minimum angle between joint
							# edges (maximum of cosine)
							if(i >= 2):
								t				= abs(self.squares_angle(result[i], result[i-2], result[i-1]))
								if s<t:
									s			= t
						# if cosines of all angles are small
						# (all angles are ~90 degree) then write quandrange
						# vertices to resultant sequence
						if(s < 0.3):
							for i in range(4):
								squares.append(result[i])

		cv.cvReleaseImage(tgrey)
		return squares
Пример #9
0
    def detect_squares(self, img):
        """ Find squares within the video stream and draw them """
        N = 11
        thresh = 5
        sz = cv.cvSize(img.width & -2, img.height & -2)
        timg = cv.cvCloneImage(img)
        gray = cv.cvCreateImage(sz, 8, 1)
        pyr = cv.cvCreateImage(cv.cvSize(sz.width / 2, sz.height / 2), 8, 3)
        # create empty sequence that will contain points -
        # 4 points per square (the square's vertices)
        squares = cv.cvCreateSeq(0, cv.sizeof_CvSeq, cv.sizeof_CvPoint,
                                 self.storage)
        squares = cv.CvSeq_CvPoint.cast(squares)

        # select the maximum ROI in the image
        # with the width and height divisible by 2
        subimage = cv.cvGetSubRect(timg, cv.cvRect(0, 0, sz.width, sz.height))

        # down-scale and upscale the image to filter out the noise
        cv.cvPyrDown(subimage, pyr, 7)
        cv.cvPyrUp(pyr, subimage, 7)
        tgray = cv.cvCreateImage(sz, 8, 1)
        # find squares in every color plane of the image
        for c in range(3):
            # extract the c-th color plane
            channels = [None, None, None]
            channels[c] = tgray
            cv.cvSplit(subimage, channels[0], channels[1], channels[2], None)
            for l in range(N):
                # hack: use Canny instead of zero threshold level.
                # Canny helps to catch squares with gradient shading
                if (l == 0):
                    # apply Canny. Take the upper threshold from slider
                    # and set the lower to 0 (which forces edges merging)
                    cv.cvCanny(tgray, gray, 0, thresh, 5)
                    # dilate canny output to remove potential
                    # holes between edge segments
                    cv.cvDilate(gray, gray, None, 1)
                else:
                    # apply threshold if l!=0:
                    #     tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
                    cv.cvThreshold(tgray, gray, (l + 1) * 255 / N, 255,
                                   cv.CV_THRESH_BINARY)

                # find contours and store them all as a list
                count, contours = cv.cvFindContours(gray, self.storage,
                                                    cv.sizeof_CvContour,
                                                    cv.CV_RETR_LIST,
                                                    cv.CV_CHAIN_APPROX_SIMPLE,
                                                    cv.cvPoint(0, 0))

                if not contours:
                    continue

                # test each contour
                for contour in contours.hrange():
                    # approximate contour with accuracy proportional
                    # to the contour perimeter
                    result = cv.cvApproxPoly(
                        contour, cv.sizeof_CvContour, self.storage,
                        cv.CV_POLY_APPROX_DP,
                        cv.cvContourPerimeter(contours) * 0.02, 0)
                    # square contours should have 4 vertices after approximation
                    # relatively large area (to filter out noisy contours)
                    # and be convex.
                    # Note: absolute value of an area is used because
                    # area may be positive or negative - in accordance with the
                    # contour orientation
                    if (result.total == 4
                            and abs(cv.cvContourArea(result)) > 1000
                            and cv.cvCheckContourConvexity(result)):
                        s = 0
                        for i in range(5):
                            # find minimum angle between joint
                            # edges (maximum of cosine)
                            if (i >= 2):
                                t = abs(
                                    self.squares_angle(result[i],
                                                       result[i - 2],
                                                       result[i - 1]))
                                if s < t:
                                    s = t
                        # if cosines of all angles are small
                        # (all angles are ~90 degree) then write quandrange
                        # vertices to resultant sequence
                        if (s < 0.3):
                            for i in range(4):
                                squares.append(result[i])

        i = 0
        while i < squares.total:
            pt = []
            # read 4 vertices
            pt.append(squares[i])
            pt.append(squares[i + 1])
            pt.append(squares[i + 2])
            pt.append(squares[i + 3])

            # draw the square as a closed polyline
            cv.cvPolyLine(img, [pt], 1, cv.CV_RGB(0, 255, 0), 3, cv.CV_AA, 0)
            i += 4

        return img
Пример #10
0
while True:
    turn = -1
    # Captura o frame, binariza, procura por contornos e checa com os padroes
    # Capture frame, binarize, find contours and check with known patterns
    frame = hg.cvQueryFrame(capture)
    cv.cvCvtColor(frame, frame_mod,cv.CV_RGB2GRAY)
    cv.cvThreshold(frame_mod,frame_mod,120,254,cv.CV_THRESH_BINARY)
    hg.cvShowImage("Binarizado", frame_mod)
    source_c = cv.cvFindContours(frame_mod, storage2)[1]
    if source_c:
        for contour in source_c.hrange():
            seq = cv.cvApproxPoly(contour,cv.sizeof_CvContour,storage2,cv.CV_POLY_APPROX_DP,2)
            for i in range(0,4):
                res = cv.cvMatchShapes(target_c[i], contour, cv.CV_CONTOURS_MATCH_I3)
                if res > 0 and res < limiar[i] and cv.cvContourArea(seq) > 1000:
                    cv.cvDrawContours(frame,seq,color[i],color[i], 100,4)
                    #print padroes[i] + ": " + str(res)
                    turn = i
# move o NXT
# move NXT
    # esquerda
    # left
    if turn == 0:
        left.update(-25,170)
        right.update(25,170)
    # direita
    # right
    elif turn == 1:
        left.update(25,170)
        right.update(-25,170)