Find the red circle in video

Question

I want to track a moving cap of a coke bottle in the webcam Feed with OpenCV in Python (or C++). I tried to search for all the red in the frame and then I used some kind of HOUGH TRANSFORM to search for circles. I cant find the right radius for the circle and fix it so it doesn't change every frame.the process time is not so important I dont want a real time detection but I do want a precise red circle detection.

This is what I have so far:

import cv2
import numpy as np
import cv2.cv as cv
cap = cv2.VideoCapture(0)
while(1):

    # Take each frame
    _, frame = cap.read()

    # Convert BGR to HSV

    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # define range of blue color in HSV 
    lower_red = np.array([160,140,50]) 
    upper_red = np.array([180,255,255])

    imgThreshHigh = cv2.inRange(hsv, lower_red, upper_red)

    imgray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    thresh = 18
    edges = cv2.Canny(imgray,thresh,thresh*3)

    circles = cv2.HoughCircles(imgThreshHigh, cv.CV_HOUGH_GRADIENT, 1, 500, 25, 75, 5, 15)
    maxRadius= 0
    xc = 0.00
    yc = 0.00
    found = False 
    if circles is not None:
        found = True
        for i in circles[0,:3]:
            if i[2] < 100:
                if i[2] > maxRadius:
                    maxRadius = i[2]
                    if maxRadius > 1.0:
                        # draw the outer circle
                        cv2.circle(frame,(i[0],i[1]),maxRadius,(0,0,255),2)
                        # draw the center of the circle
                        cv2.circle(frame,(i[0],i[1]),1,(0,0,255),3)
                        xc = i[0]
                        yc = i[1] 
    if found: 
        print "ball detected at position:",xc, ",", yc, " with radius:", maxRadius
    else: 
        print "no ball" 
    cv2.imshow('frame',frame)
    cv2.imshow('edges',edges)
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break
cv2.destroyAllWindows()

I dont think HOUGH TRANSFORM works for this. So I want to use the edges. How can I use an equation like (X-Xc)^2 + (Y-Yc)^2 =R^2 and the contours to find circles?

Also if there is an improvement for Hough Transform I will appreciate it if you share with me

Contours:

contours,hierarchy=cv2.findContours(edges,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

My example image: ---> this is not my example Image.this is the object I want to find in videos.

Answer 1

Well for the color you have to find the right range, for the human eye the bottle cap is red, but for the camera could be orange or something like that. Also is affected by the light so the camera could get some white. I learned this on the hard way haha. You could do a code with trackbars and hsv values to get the exact rang for your objet.

Now, are you looking for the center (x,y)? You can try with cv2.moments() and for

Here is an example

For your code is something like this

import cv2
import numpy as np
import cv2.cv as cv
cap = cv2.VideoCapture(0)

coords = []
while(1):

    # Take each frame
    _, frame = cap.read()

    # Convert BGR to HSV

    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # define range of blue color in HSV 
    lower_red = np.array([160,140,50]) 
    upper_red = np.array([180,255,255])

    imgThreshHigh = cv2.inRange(hsv, lower_red, upper_red)
    thresh = imgThreshHigh.copy()

    countours,_ = cv2.findContours(thresh, cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

    for cnt in countours:
        area = cv2.contourArea(cnt)
        if area > max_area:
        max_area = area
        best_cnt = cnt

    M = cv2.moments(best_cnt)
    cx,cy = int(M['m10']/M['m00']), int(M['m01']/M['m00'])
    coord = cx, cy #This are your coordinates for the circle
    # area = moments['m00'] save the object area
    #perimeter = cv2.arcLength(best_cnt,True) is the object perimeter

    #Save the coords every frame on a list
    #Here you can make more conditions if you don't want repeated coordinates
    points.append(coord) 

    cv2.imshow('frame',frame)
    cv2.imshow('Object',thresh)
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break
cv2.destroyAllWindows()

With moments you don't have the raidius hough transform that you mentioned because calculate the relative center of the object every time.

Here is the code I use to know which is the exact value, hope it helps you HSV detection code