使用 OpenCV 使用 Camshift 跟踪对象

OpenCV 是用于计算机视觉、机器学习和图像处理的庞大开源库，现在它在实时操作中发挥着重要作用，这在当今的系统中非常重要。通过使用它，人们可以处理图像和视频以识别物体、面部，甚至是人类的笔迹。

Camshift 或者我们可以说 Continuously Adaptive Meanshift 是 meanshift 算法的增强版本，它为模型提供了更高的准确性和鲁棒性。在 Camshift 算法的帮助下，当跟踪窗口试图收敛时，窗口的大小会不断更新。跟踪是通过使用对象的颜色信息来完成的。此外，它还为对象跟踪提供了最佳拟合跟踪窗口。它首先应用 meanshift，然后将窗口的大小更新为：

    $\[s = 2\times\sqrt{\frac{M_{00}}{256}}\]$

然后它计算最佳拟合椭圆，并再次对新缩放的搜索窗口和前一个窗口应用均值偏移。这个过程一直持续到满足所需的精度。

注意：有关 meanshift 的更多信息，请参阅Python OpenCV：Meanshift

下面是实现。

import numpy as np
import cv2 as cv
  
  
# Read the input video
cap = cv.VideoCapture('sample.mp4')
  
# take first frame of the
# video
ret, frame = cap.read()
  
# setup initial region of
# tracker
x, y, width, height = 400, 440, 150, 150
track_window = (x, y, 
                width, height)
  
# set up the Region of
# Interest for tracking
roi = frame[y:y + height,
            x : x + width]
  
# convert ROI from BGR to
# HSV format
hsv_roi = cv.cvtColor(roi,
                      cv.COLOR_BGR2HSV)
  
# perform masking operation
mask = cv.inRange(hsv_roi, 
                  np.array((0., 60., 32.)),
                  np.array((180., 255., 255)))
  
roi_hist = cv.calcHist([hsv_roi], 
                       [0], mask,
                       [180], 
                       [0, 180])
  
cv.normalize(roi_hist, roi_hist,
             0, 255, cv.NORM_MINMAX)
  
  
# Setup the termination criteria, 
# either 15 iteration or move by
# atleast 2 pt
term_crit = ( cv.TERM_CRITERIA_EPS | 
             cv.TERM_CRITERIA_COUNT, 15, 2)
  
  
while(1):
      
    ret, frame = cap.read()
      
    # Resize the video frames.
    frame = cv.resize(frame, 
                      (720, 720), 
                      fx = 0, fy = 0,
                      interpolation = cv.INTER_CUBIC)
      
    cv.imshow('Original', frame)
  
    # perform thresholding on 
    # the video frames
    ret1, frame1 = cv.threshold(frame,
                                180, 155,
                                cv.THRESH_TOZERO_INV)
  
    # convert from BGR to HSV
    # format.
    hsv = cv.cvtColor(frame1, 
                      cv.COLOR_BGR2HSV)
  
    dst = cv.calcBackProject([hsv], 
                             [0], 
                             roi_hist, 
                             [0, 180], 1)
      
    # apply Camshift to get the 
    # new location
    ret2, track_window = cv.CamShift(dst,
                                     track_window,
                                     term_crit)
  
    # Draw it on image
    pts = cv.boxPoints(ret2)
      
    # convert from floating
    # to integer
    pts = np.int0(pts)
  
    # Draw Tracking window on the
    # video frame.
    Result = cv.polylines(frame, 
                          [pts], 
                          True, 
                          (0, 255, 255), 
                          2)
  
    cv.imshow('Camshift', Result)
  
    # set ESC key as the
    # exit button.
    k = cv.waitKey(30) & 0xff
      
    if k == 27:
        break
          
  
# Release the cap object
cap.release()
  
# close all opened windows
cv.destroyAllWindows()

输出：