Python cv2.calcBackProject() Examples

def run(self, frame):
    """Processes a single frame.

    Args:
      frame: The np.array image frame.
    """
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    dst = cv2.calcBackProject([hsv], [0, 1], self.roi_hist, [0, 180, 0, 255], 1)
    _, self.box = cv2.CamShift(dst, self.box, self.term_crit)

    (x, y, x2, y2) = self.glob_to_relative(
        (self.box[0], self.box[1], self.box[0] + self.box[2],
         self.box[1] + self.box[3]))

    self.annotation.bbox.left = x
    self.annotation.bbox.top = y
    self.annotation.bbox.right = x2
    self.annotation.bbox.bottom = y2

    self.age = self.age + 1
    self.degrade() 

def backprojection(target, roihist):
    '''图像预处理'''
    hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)
    dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1)
    # Now convolute with circular disc
    disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7))
    cv2.filter2D(dst,-1,disc,dst)
    # threshold and binary AND
    ret,binary = cv2.threshold(dst,80,255,0)
    # 创建 核
    kernel = np.ones((5,5), np.uint8)
    iter_time = 1
    # 闭运算
    binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel,iterations=iter_time)

    thresh = cv2.merge((binary,binary,binary))
    target_filter = cv2.bitwise_and(target,thresh)
    
    return binary, target_filter 

def hand_threshold(frame_in,hand_hist):
    frame_in=cv2.medianBlur(frame_in,3)
    hsv=cv2.cvtColor(frame_in,cv2.COLOR_BGR2HSV)
    hsv[0:int(cap_region_y_end*hsv.shape[0]),0:int(cap_region_x_begin*hsv.shape[1])]=0 # Right half screen only
    hsv[int(cap_region_y_end*hsv.shape[0]):hsv.shape[0],0:hsv.shape[1]]=0
    back_projection = cv2.calcBackProject([hsv], [0,1],hand_hist, [00,180,0,256], 1)
    disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (morph_elem_size,morph_elem_size))
    cv2.filter2D(back_projection, -1, disc, back_projection)
    back_projection=cv2.GaussianBlur(back_projection,(gaussian_ksize,gaussian_ksize), gaussian_sigma)
    back_projection=cv2.medianBlur(back_projection,median_ksize)
    ret, thresh = cv2.threshold(back_projection, hsv_thresh_lower, 255, 0)
    
    return thresh

# 3. Find hand contour 

def _append_boxes_from_meanshift(self, frame, box_all):
        """Adds to the list all bounding boxes found with mean-shift tracking

            Mean-shift tracking is used to track objects from frame to frame.
            This information is combined with a saliency map to discard
            false-positives and focus only on relevant objects that move.

            :param frame: current RGB image frame
            :box_all: append bounding boxes from tracking to this list
            :returns: new list of all collected bounding boxes
        """
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

        for i in xrange(len(self.object_roi)):
            roi_hist = copy.deepcopy(self.object_roi[i])
            box_old = copy.deepcopy(self.object_box[i])

            dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
            ret, box_new = cv2.meanShift(dst, tuple(box_old), self.term_crit)
            self.object_box[i] = copy.deepcopy(box_new)

            # discard boxes that don't move
            (xo, yo, wo, ho) = box_old
            (xn, yn, wn, hn) = box_new

            co = [xo + wo/2, yo + ho/2]
            cn = [xn + wn/2, yn + hn/2]
            if (co[0]-cn[0])**2 + (co[1]-cn[1])**2 >= self.min_shift2:
                box_all.append(box_new)

        return box_all 

def locate_object(frame, object_hist):

    # convert to HSV
    hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # apply back projection to image using object_hist as
    # the model histogram
    object_segment = cv2.calcBackProject(
        [hsv_frame], [0, 1], object_hist, [0, 180, 0, 256], 1)

    # find the contours
    img, contours, _ = cv2.findContours(
        object_segment,
        cv2.RETR_TREE,
        cv2.CHAIN_APPROX_SIMPLE)

    flag = None
    max_area = 0

    # find the contour with the greatest area
    for (i, c) in enumerate(contours):
        area = cv2.contourArea(c)
        if area > max_area:
            max_area = area
            flag = i

    # get the rectangle
    if flag is not None and max_area > 1000:
        cnt = contours[flag]
        coords = cv2.boundingRect(cnt)
        return coords

    return None


# compute the color histogram 

def get_hand_hist():
	cam = cv2.VideoCapture(1)
	if cam.read()[0]==False:
		cam = cv2.VideoCapture(0)
	x, y, w, h = 300, 100, 300, 300
	flagPressedC, flagPressedS = False, False
	imgCrop = None
	while True:
		img = cam.read()[1]
		img = cv2.flip(img, 1)
		img = cv2.resize(img, (640, 480))
		hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
		
		keypress = cv2.waitKey(1)
		if keypress == ord('c'):		
			hsvCrop = cv2.cvtColor(imgCrop, cv2.COLOR_BGR2HSV)
			flagPressedC = True
			hist = cv2.calcHist([hsvCrop], [0, 1], None, [180, 256], [0, 180, 0, 256])
			cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
		elif keypress == ord('s'):
			flagPressedS = True	
			break
		if flagPressedC:	
			dst = cv2.calcBackProject([hsv], [0, 1], hist, [0, 180, 0, 256], 1)
			dst1 = dst.copy()
			disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(10,10))
			cv2.filter2D(dst,-1,disc,dst)
			blur = cv2.GaussianBlur(dst, (11,11), 0)
			blur = cv2.medianBlur(blur, 15)
			ret,thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
			thresh = cv2.merge((thresh,thresh,thresh))
			#cv2.imshow("res", res)
			cv2.imshow("Thresh", thresh)
		if not flagPressedS:
			imgCrop = build_squares(img)
		#cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
		cv2.imshow("Set hand histogram", img)
	cam.release()
	cv2.destroyAllWindows()
	with open("hist", "wb") as f:
		pickle.dump(hist, f) 

def get_img_contour_thresh(img):
	img = cv2.flip(img, 1)
	imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
	dst = cv2.calcBackProject([imgHSV], [0, 1], hist, [0, 180, 0, 256], 1)
	disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(10,10))
	cv2.filter2D(dst,-1,disc,dst)
	blur = cv2.GaussianBlur(dst, (11,11), 0)
	blur = cv2.medianBlur(blur, 15)
	thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
	thresh = cv2.merge((thresh,thresh,thresh))
	thresh = cv2.cvtColor(thresh, cv2.COLOR_BGR2GRAY)
	thresh = thresh[y:y+h, x:x+w]
	contours = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[0]
	return img, contours, thresh 

def run(self):
        while True:
            ret, self.frame = self.cam.read()
            vis = self.frame.copy()
            hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
            mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))

            if self.selection:
                x0, y0, x1, y1 = self.selection
                self.track_window = (x0, y0, x1-x0, y1-y0)
                hsv_roi = hsv[y0:y1, x0:x1]
                mask_roi = mask[y0:y1, x0:x1]
                hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
                cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX);
                self.hist = hist.reshape(-1)
                self.show_hist()

                vis_roi = vis[y0:y1, x0:x1]
                cv2.bitwise_not(vis_roi, vis_roi)
                vis[mask == 0] = 0

            if self.tracking_state == 1:
                self.selection = None
                prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
                prob &= mask
                term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
                track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)

                if self.show_backproj:
                    vis[:] = prob[...,np.newaxis]
                try: cv2.ellipse(vis, track_box, (0, 0, 255), 2)
                except: print track_box

            cv2.imshow('camshift', vis)

            ch = 0xFF & cv2.waitKey(5)
            if ch == 27:
                break
            if ch == ord('b'):
                self.show_backproj = not self.show_backproj
        cv2.destroyAllWindows() 

def filter_by_color(hist, hsv):
    prob = cv2.calcBackProject([hsv], [0], hist, [0, 180], 1)
    return prob 

def returnMask(self, frame, morph_opening=True, blur=True, kernel_size=5, iterations=1):
        """Given an input frame in BGR return the black/white mask.
 
        @param frame the original frame (color)
        @param morph_opening it is a erosion followed by dilatation to remove noise
        @param blur to smoth the image it is possible to apply Gaussian Blur
        @param kernel_size is the kernel dimension used for morph and blur
        """
        if(len(self.template_hsv_list) == 0): return None
        frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        mask = np.zeros((frame.shape[0], frame.shape[1]))
        for template_hsv in self.template_hsv_list:
            #Set the template histogram
            template_hist = cv2.calcHist([template_hsv],[0, 1], None, [256, 256], [0, 256, 0, 256] )
            #Normalize the template histogram and apply backprojection
            cv2.normalize(template_hist, template_hist, 0, 255, cv2.NORM_MINMAX)
            frame_hsv_back = cv2.calcBackProject([frame_hsv], [0,1], template_hist, [0,256,0,256], 1)
            #Get the kernel and apply a convolution
            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size,kernel_size))
            frame_hsv_clean = cv2.filter2D(frame_hsv_back, -1, kernel)
            #Applying the morph open operation (erosion followed by dilation)
            if(morph_opening==True):
                kernel = np.ones((kernel_size,kernel_size), np.uint8)
                frame_hsv_clean = cv2.morphologyEx(frame_hsv_clean, cv2.MORPH_OPEN, kernel, iterations=iterations)
            #Applying Gaussian Blur
            if(blur==True): 
                frame_hsv_clean = cv2.GaussianBlur(frame_hsv_clean, (kernel_size,kernel_size), 0)
            #Get the threshold
            ret, frame_hsv_threshold = cv2.threshold(frame_hsv_clean, 50, 255, 0)
            mask = np.add(mask, frame_hsv_threshold) #Add the threshold to the mask


        #Normalize the mask because it contains
        #values added during the previous loop
        #Attention: here it is not necessary to normalize because the astype(np.uint8) method
        #will resize to 255 each value which is higher that that...
        #cv2.normalize(mask, mask, 0, 255, cv2.NORM_MINMAX) #Not necessary
        ret, mask = cv2.threshold(mask.astype(np.uint8), 50, 255, 0)
        return cv2.merge((mask,mask,mask)) 

def returnMask(self, frame, morph_opening=True, blur=True, kernel_size=5, iterations=1):
        """Given an input frame in BGR return the black/white mask.
 
        @param frame the original frame (color)
        @param morph_opening it is a erosion followed by dilatation to remove noise
        @param blur to smoth the image it is possible to apply Gaussian Blur
        @param kernel_size is the kernel dimension used for morph and blur
        """
        if(self.template_hsv is None): return None
        #Convert the input framge from BGR -> HSV
        frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        #Set the template histogram
        template_hist = cv2.calcHist([self.template_hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] )
        #Normalize the template histogram and apply backprojection
        cv2.normalize(template_hist, template_hist, 0, 255, cv2.NORM_MINMAX)
        frame_hsv = cv2.calcBackProject([frame_hsv], [0,1], template_hist, [0,180,0,256], 1)
        #Get the kernel and apply a convolution
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size,kernel_size))
        frame_hsv = cv2.filter2D(frame_hsv, -1, kernel)
        #Applying the morph open operation (erosion followed by dilation)
        if(morph_opening==True):
            kernel = np.ones((kernel_size,kernel_size), np.uint8)
            frame_hsv = cv2.morphologyEx(frame_hsv, cv2.MORPH_OPEN, kernel, iterations=iterations)
        #Applying Gaussian Blur
        if(blur==True): 
            frame_hsv = cv2.GaussianBlur(frame_hsv, (kernel_size,kernel_size), 0)
        #Get the threshold
        ret, frame_threshold = cv2.threshold(frame_hsv, 50, 255, 0)
        #Merge the threshold matrices
        return cv2.merge((frame_threshold,frame_threshold,frame_threshold)) 

def hist_masking(frame, hist):
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    dst = cv2.calcBackProject([hsv], [0, 1], hist, [0, 180, 0, 256], 1)

    disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (31, 31))
    cv2.filter2D(dst, -1, disc, dst)

    ret, thresh = cv2.threshold(dst, 150, 255, cv2.THRESH_BINARY)

    # thresh = cv2.dilate(thresh, None, iterations=5)

    thresh = cv2.merge((thresh, thresh, thresh))

    return cv2.bitwise_and(frame, thresh) 

def camshift_track(prev, box, termination):
    hsv = cv2.cvtColor(prev,cv2.COLOR_BGR2HSV)
    x,y,w,h = box
    roi = prev[y:y+h, x:x+w]
    hist = cv2.calcHist([roi], [0], None, [16], [0, 180])
    cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
    backProj = cv2.calcBackProject([hsv], [0], hist, [0, 180], 1)
    (r, box) = cv2.CamShift(backProj, tuple(box), termination)
    return box 

def run(self):
        while True:
            ret, self.frame = self.cam.read()
            vis = self.frame.copy()
            hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
            mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))

            if self.selection:
                x0, y0, x1, y1 = self.selection
                self.track_window = (x0, y0, x1-x0, y1-y0)
                hsv_roi = hsv[y0:y1, x0:x1]
                mask_roi = mask[y0:y1, x0:x1]
                hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
                cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX);
                self.hist = hist.reshape(-1)
                self.show_hist()

                vis_roi = vis[y0:y1, x0:x1]
                cv2.bitwise_not(vis_roi, vis_roi)
                vis[mask == 0] = 0

            if self.tracking_state == 1:
                self.selection = None
                prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
                prob &= mask
                term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
                track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)

                if self.show_backproj:
                    vis[:] = prob[...,np.newaxis]
                try:
                    cv2.ellipse(vis, track_box, (0, 0, 255), 2)
                except:
                    print track_box

            cv2.imshow('camshift', vis)

            ch = 0xFF & cv2.waitKey(5)
            if ch == 27:
                break
            if ch == ord('b'):
                self.show_backproj = not self.show_backproj
        cv2.destroyAllWindows() 

def run_main():
    cap = cv2.VideoCapture('crash-480.mp4')
    #cap.set(3,320)
    #cap.set(4,240)

    # Read the first frame of the video
    ret, frame = cap.read()

    # Set the ROI (Region of Interest). Actually, this is a
    # rectangle of the building that we're tracking
    c,r,w,h = 427,240,50,50
    track_window = (c,r,w,h)

    # Create mask and normalized histogram
    roi = frame[r:r+h, c:c+w]
    hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv_roi, np.array((0., 30.,32.)), np.array((180.,255.,255.)))
    roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
    cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
    term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 80, 1)
    
    while True:
        t = cv2.getTickCount()
        ret, frame = cap.read()

        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        dst = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1)

        ret, track_window = cv2.meanShift(dst, track_window, term_crit)

        x,y,w,h = track_window
        cv2.rectangle(frame, (x,y), (x+w,y+h), 255, 2)
        cv2.putText(frame, 'Tracked', (x-25,y-10), cv2.FONT_HERSHEY_SIMPLEX,
            .5, (255,255,255), 1, cv2.CV_AA)
        
        t = cv2.getTickCount() - t
        print "detection time = %gms" % (t/(cv2.getTickFrequency()*1000.))
        cv2.imshow('Tracking', frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows() 

def start_tracking(self): 
        # Iterate until the user presses the Esc key 
        while True: 
            # Capture the frame from webcam 
            ret, self.frame = self.cap.read() 
            # Resize the input frame 
            self.frame = cv2.resize(self.frame, None, fx=self.scaling_factor, fy=self.scaling_factor, interpolation=cv2.INTER_AREA) 
 
            vis = self.frame.copy() 
 
            # Convert to HSV colorspace 
            hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV) 
 
            # Create the mask based on predefined thresholds. 
            mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.))) 
 
            if self.selection: 
                x0, y0, x1, y1 = self.selection 
                self.track_window = (x0, y0, x1-x0, y1-y0) 
                hsv_roi = hsv[y0:y1, x0:x1] 
                mask_roi = mask[y0:y1, x0:x1] 
 
                # Compute the histogram 
                hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] ) 
 
                # Normalize and reshape the histogram 
                cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX); 
                self.hist = hist.reshape(-1) 
 
                vis_roi = vis[y0:y1, x0:x1] 
                cv2.bitwise_not(vis_roi, vis_roi) 
                vis[mask == 0] = 0 
 
            if self.tracking_state == 1: 
                print('tracking')
                self.selection = None 
 
                # Compute the histogram back projection 
                prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1) 
 
                prob &= mask 
                term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 ) 
 
                # Apply CAMShift on 'prob' 
                track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit) 
 
                # Draw an ellipse around the object 
                cv2.ellipse(vis, track_box, (0, 255, 0), 2) 
 
            cv2.imshow('Object Tracker', vis) 
 
            c = cv2.waitKey(delay=5) 
            if c == 27: 
                break 
 
        cv2.destroyAllWindows() 

def store_images(g_id):
	total_pics = 1200
	hist = get_hand_hist()
	cam = cv2.VideoCapture(1)
	if cam.read()[0]==False:
		cam = cv2.VideoCapture(0)
	x, y, w, h = 300, 100, 300, 300

	create_folder("gestures/"+str(g_id))
	pic_no = 0
	flag_start_capturing = False
	frames = 0
	
	while True:
		img = cam.read()[1]
		img = cv2.flip(img, 1)
		imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
		dst = cv2.calcBackProject([imgHSV], [0, 1], hist, [0, 180, 0, 256], 1)
		disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(10,10))
		cv2.filter2D(dst,-1,disc,dst)
		blur = cv2.GaussianBlur(dst, (11,11), 0)
		blur = cv2.medianBlur(blur, 15)
		thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
		thresh = cv2.merge((thresh,thresh,thresh))
		thresh = cv2.cvtColor(thresh, cv2.COLOR_BGR2GRAY)
		thresh = thresh[y:y+h, x:x+w]
		contours = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]

		if len(contours) > 0:
			contour = max(contours, key = cv2.contourArea)
			if cv2.contourArea(contour) > 10000 and frames > 50:
				x1, y1, w1, h1 = cv2.boundingRect(contour)
				pic_no += 1
				save_img = thresh[y1:y1+h1, x1:x1+w1]
				if w1 > h1:
					save_img = cv2.copyMakeBorder(save_img, int((w1-h1)/2) , int((w1-h1)/2) , 0, 0, cv2.BORDER_CONSTANT, (0, 0, 0))
				elif h1 > w1:
					save_img = cv2.copyMakeBorder(save_img, 0, 0, int((h1-w1)/2) , int((h1-w1)/2) , cv2.BORDER_CONSTANT, (0, 0, 0))
				save_img = cv2.resize(save_img, (image_x, image_y))
				rand = random.randint(0, 10)
				if rand % 2 == 0:
					save_img = cv2.flip(save_img, 1)
				cv2.putText(img, "Capturing...", (30, 60), cv2.FONT_HERSHEY_TRIPLEX, 2, (127, 255, 255))
				cv2.imwrite("gestures/"+str(g_id)+"/"+str(pic_no)+".jpg", save_img)

		cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
		cv2.putText(img, str(pic_no), (30, 400), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (127, 127, 255))
		cv2.imshow("Capturing gesture", img)
		cv2.imshow("thresh", thresh)
		keypress = cv2.waitKey(1)
		if keypress == ord('c'):
			if flag_start_capturing == False:
				flag_start_capturing = True
			else:
				flag_start_capturing = False
				frames = 0
		if flag_start_capturing == True:
			frames += 1
		if pic_no == total_pics:
			break 

def run(self):
        while True:
            ret, self.frame = self.cam.read()
            vis = self.frame.copy()
            hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
            mask = cv2.inRange(hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))

            if self.selection:
                x0, y0, x1, y1 = self.selection
                hsv_roi = hsv[y0:y1, x0:x1]
                mask_roi = mask[y0:y1, x0:x1]
                hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
                cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX)
                self.hist = hist.reshape(-1)
                self.show_hist()

                vis_roi = vis[y0:y1, x0:x1]
                cv2.bitwise_not(vis_roi, vis_roi)
                vis[mask == 0] = 0

            if self.track_window and self.track_window[2] > 0 and self.track_window[3] > 0:
                self.selection = None
                prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
                prob &= mask
                term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
                track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)

                if self.show_backproj:
                    vis[:] = prob[...,np.newaxis]
                try:
                    cv2.ellipse(vis, track_box, (0, 0, 255), 2)
                except:
                    print(track_box)

            cv2.imshow('camshift', vis)

            ch = cv2.waitKey(5)
            if ch == 27:
                break
            if ch == ord('b'):
                self.show_backproj = not self.show_backproj
        cv2.destroyAllWindows()