如何使用Python中的OpenCV跟蹤動作？

Question

我可以使用Python中的OpenCV從我的攝像頭獲取幀。 camshift示例接近我想要的，但我不想要人爲干預來定義對象。我想獲得在幾幀的過程中改變的總像素的中心點，即移動物體的中心。

Answer 1

我得從C版本的代碼在博客中發現了一些翻譯工作代碼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() 

Answer 2

查看論壇帖子Motion tracking using OpenCV。

我相信你能夠閱讀和翻譯源代碼到Python，對吧？

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)); 

這是我試圖讓中心的部分代碼當使用矩形邊界進行跟蹤時，移動對象的位置。

Answer 4

下面的鏈接跟蹤移動的車輛以及對它們進行計數。它基於OpenCV，用Python 2.7編寫。
OpenCV and Python