How do I track motion using OpenCV in Python?

I can get frames from my webcam using OpenCV in开发者_Go百科 Python. The camshift example is close to what I want, but I don't want human intervention to define the object. I want to get the center point of the total pixels that have changed over the course of several frame, i.e. the center of the moving object.

I've got some working code translated from the C version of code found in the blog post Motion Detection using OpenCV:

#!/usr/bin/env python

import cv

class Target:

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

    def run(self):
        # Capture first frame to get size
        frame = cv.QueryFrame(self.capture)
        frame_size = cv.GetSize(frame)
        color_image = cv.CreateImage(cv.GetSize(frame), 8, 3)
        grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
        moving_average = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)

        first = True

        while True:
            closest_to_left = cv.GetSize(frame)[0]
            closest_to_right = cv.GetSize(frame)[1]

            color_image = cv.QueryFrame(self.capture)

            # Smooth to get rid of false positives
            cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3, 0)

            if first:
                difference = cv.CloneImage(color_image)
                temp = cv.CloneImage(color_image)
                cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
                first = False
            else:
                cv.RunningAvg(color_image, moving_average, 0.020, None)

            # Convert the scale of the moving average.
            cv.ConvertScale(moving_average, temp, 1.0, 0.0)

            # Minus the current frame from the moving average.
            cv.AbsDiff(color_image, temp, difference)

            # Convert the image to grayscale.
            cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)

            # Convert the image to black and white.
            cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)

            # Dilate and erode to get people blobs
            cv.Dilate(grey_image, grey_image, None, 18)
            cv.Erode(grey_image, grey_image, None, 10)

            storage = cv.CreateMemStorage(0)
            contour = cv.FindContours(grey_image, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
            points = []

            while contour:
                bound_rect = cv.BoundingRect(list(contour))
                contour = contour.h_next()

                pt1 = (bound_rect[0], bound_rect[1])
                pt2 = (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3])
                points.append(pt1)
                points.append(pt2)
                cv.Rectangle(color_image, pt1, pt2, cv.CV_RGB(255,0,0), 1)

            if len(points):
                center_point = reduce(lambda a, b: ((a[0] + b[0]) / 2, (a[1] + b[1]) / 2), points)
                cv.Circle(color_image, center_point, 40, cv.CV_RGB(255, 255, 255), 1)
                cv.Circle(color_image, center_point, 30, cv.CV_RGB(255, 100, 0), 1)
                cv.Circle(color_image, center_point, 20, cv.CV_RGB(255, 255, 255), 1)
                cv.Circle(color_image, center_point, 10, cv.CV_RGB(255, 100, 0), 1)

            cv.ShowImage("Target", color_image)

            # Listen for ESC key
            c = cv.WaitKey(7) % 0x100
            if c == 27:
                break

if __name__=="__main__":
    t = Target()
    t.run()

See the forum post Motion tracking using OpenCV.

I believe you are capable of reading and translating the source code to Python, right?

if faces:
    for ((x, y, w, h), n) in faces:
        pt1 = (int(x * image_scale), int(y * image_scale))
        pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
        ptcx=((pt1[0]+pt2[0])/2)/128
        ptcy=((pt1[1]+pt2[1])/2)/96
        cv.Rectangle(gray, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
        print ptcx;
        print ptcy;
        b=('S'+str(ptcx)+str(ptcy));

This is the part of the code I tried to get the center of the moving object when tracked using a rectangular boundary.

This following link tracks the moving vehicles as well as counting them. It is based on OpenCV and is written in Python 2.7.
OpenCV and Python