Question

我可以使用Python中的OpenCV从我的网络摄像头获取帧。 camshift示例接近我想要的，但我不希望人为干预来定义对象。我想获得在几帧的过程中改变的总像素的中心点，即移动物体的中心。

Answer 1

我已经从博客文章 C 中的Motion Detection using OpenCV版代码中翻译了一些工作代码：

#!/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()

Answer 2

查看论坛帖子 Motion tracking using OpenCV 。

我相信您能够阅读并将源代码翻译为Python，对吧？

Answer 3

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));

这是我在使用矩形边界跟踪时尝试获取移动对象中心的代码的一部分。

Answer 4

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

如何使用Python中的OpenCV跟踪运动？

4 个答案: