OpenCV Python单个(而不是多个)blob跟踪?
我一直在尝试通过Python上的OpenCV实现单色斑点跟踪。 下面的代码正在运行,但它找到了所有跟踪像素的质心,而不仅仅是最大斑点的质心。这是因为我正在拍摄所有像素的瞬间,但我不知道还有什么其他方法可以跟踪颜色。 我有点困在我需要做什么,使它成为一个单点跟踪器,而不是一个多点跟踪器 代码如下:OpenCV Python单个(而不是多个)blob跟踪?,python,opencv,blob,color-tracking,Python,Opencv,Blob,Color Tracking,我一直在尝试通过Python上的OpenCV实现单色斑点跟踪。 下面的代码正在运行,但它找到了所有跟踪像素的质心,而不仅仅是最大斑点的质心。这是因为我正在拍摄所有像素的瞬间,但我不知道还有什么其他方法可以跟踪颜色。 我有点困在我需要做什么,使它成为一个单点跟踪器,而不是一个多点跟踪器 代码如下: #! /usr/bin/env python #if using newer versions of opencv, just "import cv" import cv2.cv as cv co
#! /usr/bin/env python
#if using newer versions of opencv, just "import cv"
import cv2.cv as cv
color_tracker_window = "Color Tracker"
class ColorTracker:
def __init__(self):
cv.NamedWindow( color_tracker_window, 1 )
self.capture = cv.CaptureFromCAM(0)
def run(self):
while True:
img = cv.QueryFrame( self.capture )
#blur the source image to reduce color noise
cv.Smooth(img, img, cv.CV_BLUR, 3);
#convert the image to hsv(Hue, Saturation, Value) so its
#easier to determine the color to track(hue)
hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, hsv_img, cv.CV_BGR2HSV)
#limit all pixels that don't match our criteria, in this case we are
#looking for purple but if you want you can adjust the first value in
#both turples which is the hue range(120,140). OpenCV uses 0-180 as
#a hue range for the HSV color model
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
cv.InRangeS(hsv_img, (120, 80, 80), (140, 255, 255), thresholded_img)
#determine the objects moments and check that the area is large
#enough to be our object
moments = cv.Moments(thresholded_img, 0)
area = cv.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if(area > 100000):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv.GetSpatialMoment(moments, 1, 0)/area
y = cv.GetSpatialMoment(moments, 0, 1)/area
#print 'x: ' + str(x) + ' y: ' + str(y) + ' area: ' + str(area)
#create an overlay to mark the center of the tracked object
overlay = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.Circle(overlay, (x, y), 2, (255, 255, 255), 20)
cv.Add(img, overlay, img)
#add the thresholded image back to the img so we can see what was
#left after it was applied
cv.Merge(thresholded_img, None, None, None, img)
#display the image
cv.ShowImage(color_tracker_window, img)
if cv.WaitKey(10) == 27:
break
if __name__=="__main__":
color_tracker = ColorTracker()
color_tracker.run()
阈值化后,使用blob detection或cvfindcontours获取单个blob。您需要这样做: 1) 使用inRange函数获得阈值图像,可以应用一些腐蚀和膨胀来去除小的噪声粒子。这将有助于提高处理速度 2) 使用“findContours”函数查找等高线 3) 使用“轮廓区域”功能查找轮廓区域,并选择一个具有最大面积的区域 4) 现在找到它的中心,就像你做的那样,并跟踪它 以下是新cv2模块中的示例代码:
import cv2
import numpy as np
# create video capture
cap = cv2.VideoCapture(0)
while(1):
# read the frames
_,frame = cap.read()
# smooth it
frame = cv2.blur(frame,(3,3))
# convert to hsv and find range of colors
hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
thresh = cv2.inRange(hsv,np.array((0, 80, 80)), np.array((20, 255, 255)))
thresh2 = thresh.copy()
# find contours in the threshold image
contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
# finding contour with maximum area and store it as best_cnt
max_area = 0
for cnt in contours:
area = cv2.contourArea(cnt)
if area > max_area:
max_area = area
best_cnt = cnt
# finding centroids of best_cnt and draw a circle there
M = cv2.moments(best_cnt)
cx,cy = int(M['m10']/M['m00']), int(M['m01']/M['m00'])
cv2.circle(frame,(cx,cy),5,255,-1)
# Show it, if key pressed is 'Esc', exit the loop
cv2.imshow('frame',frame)
cv2.imshow('thresh',thresh2)
if cv2.waitKey(33)== 27:
break
# Clean up everything before leaving
cv2.destroyAllWindows()
cap.release()
您可以在此处找到一些跟踪彩色对象的示例:
另外,尝试使用新的cv2接口。它比旧的cv简单得多,速度也快得多。有关更多详细信息,请检查此项:我在尝试查找轮廓区域时出错,它表示函数cv.contourArea(轮廓)不起作用,因为轮廓必须是CvSeq、CvArr或一系列数字NVM,我只需要将其设置为cv.contourArea(列表(轮廓))