cc(9条消息) 基于MeanShift的目标跟踪算法及实现_taotao1233的博客-CSDN博客_meanshift目标跟踪算法https://blog.csdn.net/jinshengtao/article/details/30258833https://blog.csdn.net/jinshengtao/article/details/30258833(9条消息) MeanShift（均值漂移）MATLAB程序详解_战死为止的博客-CSDN博客_均值漂移matlab代码https://blog.csdn.net/qq_34122861/article/details/89556503 本人写程序和open-cv都是新人，看了上面的文章后发现没有python版本的，就按照一摸一样的思路仿写了python的代码，但是是打开笔记本自带摄像头进行MeanShift目标跟踪。希望给萌新一些参考，由于我也是新手，这是我写的第一个比较长的代码，所以会有很多冗余甚至错误，欢迎大家指正。如果看不懂可以参考上面的博客。
代码import math
import numpy as np
import cv2
def get_tr(img):
# 定义需要返回的参数
mouse_params = {'x': None, 'width': None, 'height': None,
'y': None, 'temp': None}
cv2.namedWindow('image')
# 鼠标框选操作函数
cv2.setMouseCallback('image', on_mouse, mouse_params)
cv2.imshow('image', img)
cv2.waitKey(0)
return [mouse_params['x'], mouse_params['y'], mouse_params['width'],
mouse_params['height']], mouse_params['temp']
def on_mouse(event, x, y, flags, param):
global img, point1
img2 = img.copy()
if event == cv2.EVENT_LBUTTONDOWN: # 左键点击
point1 = (x, y)
cv2.circle(img2, point1, 10, (0, 255, 0), 5)
cv2.imshow('image', img2)
elif event == cv2.EVENT_MOUSEMOVE and (flags & cv2.EVENT_FLAG_LBUTTON): # 按住左键拖曳
cv2.rectangle(img2, point1, (x, y), (255, 0, 0), 5)
cv2.imshow('image', img2)
elif event == cv2.EVENT_LBUTTONUP: # 左键释放
point2 = (x, y)
cv2.rectangle(img2, point1, point2, (0, 0, 255), 5)
cv2.imshow('image', img2)
# 返回框选矩形左上角点的坐标、矩形宽度、高度以及矩形包含的图像
param['x'] = min(point1[0], point2[0])
param['y'] = min(point1[1], point2[1])
param['width'] = abs(point1[0] - point2[0])
param['height'] = abs(point1[1] - point2[1])
param['temp'] = img[param['y']:param['y']+param['height'],
param['x']:param['x']+param['width']]
def main():
global img
cap = cv2.VideoCapture(0)
# 获取视频第一帧
ret, frame = cap.read()
img = frame
# 框选目标并返回相应信息：rect为四个信息，temp为框选出来的图像
rect, temp = get_tr(img)
(a, b, c) = temp.shape
y = [a/2, b/2]
# 计算目标图像的权值矩阵
m_wei = np.zeros((a,b))
for i in range(a):
for j in range(b):
z = (i-y[0])**2 + (j-y[1])**2
m_wei[i, j] = 1 - z/(y[0]**2 + y[1]**2)
# 计算目标权值直方图
C = 1/sum(sum(m_wei))
hist1 = np.zeros(16**3)
for i in range(a):
for j in range(b):
q_b = math.floor(float(temp[i, j, 0]) / 16)
q_g = math.floor(float(temp[i, j, 1]) / 16)
q_r = math.floor(float(temp[i, j, 2]) / 16)
q_temp1 = q_r*256 + q_g*16 + q_b
hist1[int(q_temp1)] = hist1[int(q_temp1)] + m_wei[i, j]
hist1 = hist1*C
# 接着读取视频并进行目标跟踪
while(1):
ret, frame = cap.read()
if ret == True:
Img = frame
num = 0
Y = [1, 1]
# mean shift迭代
while (np.sqrt(Y[0]**2+Y[1]**2) > 0.5) & (num < 20):
num = num+1
# 计算候选区域直方图
temp2 = Img[int(rect[1]):int(rect[1]+rect[3]), int(rect[0]):int(rect[0]+rect[2])]
hist2 = np.zeros(16**3)
q_temp2 = np.zeros((a, b))
for i in range(a):
for j in range(b):
q_b = math.floor(float(temp2[i, j, 0]) / 16)
q_g = math.floor(float(temp2[i, j, 1]) / 16)
q_r = math.floor(float(temp2[i, j, 2]) / 16)
q_temp2[i, j] = q_r * 256 + q_g * 16 + q_b
hist2[int(q_temp2[i, j])] = hist2[int(q_temp2[i, j])] + m_wei[i, j]
hist2 = hist2*C
w = np.zeros(16**3)
for i in range(16**3):
if hist2[i] != 0:
w[i] = math.sqrt(hist1[i]/hist2[i])
else:
w[i] = 0
sum_w = 0
sum_xw = [0, 0]
for i in range(a):
for j in range(b):
sum_w = sum_w + w[int(q_temp2[i, j])]
sum_xw = sum_xw + w[int(q_temp2[i, j])]*np.array([i-y[0], j - y[1]])
Y = sum_xw/sum_w
# 位置更新
rect[0] = rect[0] + Y[1]
rect[1] = rect[1] + Y[0]
v0 = int(rect[0])
v1 = int(rect[1])
v2 = int(rect[2])
v3 = int(rect[3])
pt1 = (v0, v1)
pt2 = (v0+v2, v1+v3)
# 画矩形
IMG = cv2.rectangle(Img, pt1, pt2, (0, 0, 255), 2)
cv2.imshow('IMG', IMG)
k = cv2.waitKey(60) & 0xff
if k == 27:
break
else:
break
if __name__ == '__main__':
main()
结果
