OpenCV Python одиночное (а не множественное) отслеживание blob?

Я пытаюсь получить однократное отслеживание blob через OpenCV на Python. Нижеприведенный код работает, но он находит центроид всех отслеживаемых пикселей, а не только центр тяжести самого большого блоба. Это потому, что я беру моменты всех пикселей, но я не уверен, как еще цветной дорожки. Я как бы зациклен на том, что именно мне нужно сделать, чтобы сделать это единственным блобером-трекером, а не мульти-blob-средним.

Здесь код:

#! /usr/bin/env python 

#if using newer versions of opencv, just "import cv"
import cv2.cv as cv

color_tracker_window = "Color Tracker" 

class ColorTracker: 

def __init__(self): 
    cv.NamedWindow( color_tracker_window, 1 ) 
    self.capture = cv.CaptureFromCAM(0) 

def run(self): 
    while True: 
        img = cv.QueryFrame( self.capture ) 

        #blur the source image to reduce color noise 
        cv.Smooth(img, img, cv.CV_BLUR, 3); 

        #convert the image to hsv(Hue, Saturation, Value) so its  
        #easier to determine the color to track(hue) 
        hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3) 
        cv.CvtColor(img, hsv_img, cv.CV_BGR2HSV) 

        #limit all pixels that don't match our criteria, in this case we are  
        #looking for purple but if you want you can adjust the first value in  
        #both turples which is the hue range(120,140).  OpenCV uses 0-180 as  
        #a hue range for the HSV color model 
        thresholded_img =  cv.CreateImage(cv.GetSize(hsv_img), 8, 1) 
        cv.InRangeS(hsv_img, (120, 80, 80), (140, 255, 255), thresholded_img) 

        #determine the objects moments and check that the area is large  
        #enough to be our object 
        moments = cv.Moments(thresholded_img, 0) 
        area = cv.GetCentralMoment(moments, 0, 0) 

        #there can be noise in the video so ignore objects with small areas 
        if(area > 100000): 
            #determine the x and y coordinates of the center of the object 
            #we are tracking by dividing the 1, 0 and 0, 1 moments by the area 
            x = cv.GetSpatialMoment(moments, 1, 0)/area 
            y = cv.GetSpatialMoment(moments, 0, 1)/area 

            #print 'x: ' + str(x) + ' y: ' + str(y) + ' area: ' + str(area) 

            #create an overlay to mark the center of the tracked object 
            overlay = cv.CreateImage(cv.GetSize(img), 8, 3) 

            cv.Circle(overlay, (x, y), 2, (255, 255, 255), 20) 
            cv.Add(img, overlay, img) 
            #add the thresholded image back to the img so we can see what was  
            #left after it was applied 
            cv.Merge(thresholded_img, None, None, None, img) 

        #display the image  
        cv.ShowImage(color_tracker_window, img) 

        if cv.WaitKey(10) == 27: 
            break 

if __name__=="__main__": 
    color_tracker = ColorTracker() 
    color_tracker.run() 

Ответы

import cv2
import numpy as np

# create video capture
cap = cv2.VideoCapture(0)

while(1):

    # read the frames
    _,frame = cap.read()

    # smooth it
    frame = cv2.blur(frame,(3,3))

    # convert to hsv and find range of colors
    hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
    thresh = cv2.inRange(hsv,np.array((0, 80, 80)), np.array((20, 255, 255)))
    thresh2 = thresh.copy()

    # find contours in the threshold image
    contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

    # finding contour with maximum area and store it as best_cnt
    max_area = 0
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area > max_area:
            max_area = area
            best_cnt = cnt

    # finding centroids of best_cnt and draw a circle there
    M = cv2.moments(best_cnt)
    cx,cy = int(M['m10']/M['m00']), int(M['m01']/M['m00'])
    cv2.circle(frame,(cx,cy),5,255,-1)

    # Show it, if key pressed is 'Esc', exit the loop
    cv2.imshow('frame',frame)
    cv2.imshow('thresh',thresh2)
    if cv2.waitKey(33)== 27:
        break

# Clean up everything before leaving
cv2.destroyAllWindows()
cap.release()