如何在 Python 中使用 OpenCV 跟踪运动？
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How do I track motion using OpenCV in Python?
提问by Matt Williamson
I can get frames from my webcam using OpenCVin 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.
我可以使用Python 中的OpenCV从我的网络摄像头获取帧。camshift 示例与我想要的很接近，但我不希望人为干预来定义对象。我想获得在几帧过程中发生变化的总像素的中心点，即移动对象的中心。
采纳答案by Matt Williamson
I've got some working code translated from the Cversion of code found in the blog post Motion Detection using OpenCV:
我从博客文章Motion Detection using OpenCV 中找到的C版本代码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()
回答by karlphillip
See the forum post Motion tracking using OpenCV.
请参阅使用 OpenCV 进行运动跟踪的论坛帖子。
I believe you are capable of reading and translating the source code to Python, right?
我相信你有能力阅读源代码并将其翻译成Python，对吗？
回答by Balagopal Prakash Kumar
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.
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部分。
回答by Rouzbeh
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
以下链接跟踪移动的车辆并对其进行计数。它基于 OpenCV，使用 Python 2.7 编写。
OpenCV 和 Python
