Python实现视频目标检测与轨迹跟踪流程详解
目录
- 一、原理
- 二、代码实现
- 1.增加多模板匹配机制
- 2.增加轨迹平滑
- 三、测试对比
一、原理
核心思想比较简单。即通过不同旋转角度的模板同时匹配，在多个结果中，找到相似度最大的结果，即认为匹配成功。 在视频的某一帧将这些模板分别进行匹配，即可获得较为准确的结果。
某一帧的物体搜索窗口如上图所示。0°表示提取的原始模板，将原始模板以8个方向进行旋转，可得到8个不同旋转角度的模板。 依次与窗口进行模板匹配，可以得到相似度。取相似度最大的模板对应的坐标结果作为轨迹。
同时根据不同的精度需求，可以有4模板、8模板和16模板，对应方向如下。模板数目越多，其对旋转的检测性就越好、越精确。但同时计算量也会成倍增加。
二、代码实现
# coding=utf-8
import cv2
import numpy as np
import math
def calcVelocity(x1, x2, y1, y2, res, wT):
dist = pow(pow(y1 - y2, 2) + pow(x1 - x2, 2), 0.5) * res
v = dist / (wT / 1000.0) * 3.6
return v
# ---------------必要参数---------------
# 待识别视频路径
video_path = 'E:\\object\\test_real.mp4'
# 卫星视频地表分辨率
resolution = 2
# 估计最快运动速度
velocity = 850
# ---------------必要参数---------------
# ---------------可选参数---------------
# 提取的模板是否为正方形
isSquare = True
# 是否自动根据速度信息计算阈值
isAutoDisThresh = True
# 是否为多模板
isMultiTemplate = True
# 是否采用均值对轨迹进行平滑
isSmooth = True
# 相邻轨迹点之间的距离阈值
dis_thresh = 10
# 多模板个数
templateNum = 8
# 初始待选窗口大小半径
range_d = 30
# 灰度阈值敏感度，越大灰度阈值越低
gray_factor = 0.2
# 识别框缩放因子，越大绘制的识别框越大
scale_factor = 1.5
# 模板缩放因子，越大模板图像越大
template_factor = 0.6
# 识别框颜色
color = (0, 0, 255)
# 输出路径
parent_path = video_path.replace(video_path.split("\\")[-1], '')
out_path = parent_path + "object.avi"
out_path2 = parent_path + "track.avi"
out_path3 = parent_path + "points.txt"
out_path4 = parent_path + "velocity.txt"
out_path5 = parent_path + "template.jpg"
# ---------------可选参数---------------
# 循环变量
count = 0
# 打开视频
cap = cv2.VideoCapture(video_path)
cap2 = cv2.VideoCapture(video_path)
# 获取视频图像大小
# video_h对应竖直方向，video_w对应水平方向
video_h = int(cap.get(4))
video_w = int(cap.get(3))
total = int(cap.get(7))
# 新建一张与视频等大的影像用于绘制轨迹
track = np.zeros((video_h, video_w, 3), np.uint8)
# tlp用于存放待选窗口的左上角点
tlp = []
# rbp用于存放待选窗口的右下角点
rbp = []
# bottom_right_points用于存放目标区域的右下角点
bottom_right_points = []
# center_points用于存放目标区域的中心点
center_points = []
# trackPoints用于存放目标区域的左上角点
trackPoints = []
# Vs用于存放目标各帧速度
Vs = []
# 根据视频信息计算每一帧的等待时间
if cap.get(5) != 0:
waitTime = int(1000.0 / cap.get(5))
fps = cap.get(5)
# 如果为真，则自动确定距离阈值
if isAutoDisThresh:
# 计算物体帧间最大运动范围(像素)
max_range = math.ceil((5.0 * velocity) / (18.0 * resolution * (fps - 1)))
# 计算最大移动距离，作为阈值
dis_thresh = math.ceil(pow(pow(max_range, 2) + pow(max_range, 2), 0.5))
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(out_path, fourcc, fps, (video_w, video_h))
out2 = cv2.VideoWriter(out_path2, fourcc, fps, (video_w, video_h))
# 首先提取模板图像
if cap2.isOpened():
# 读取前两帧
ret, frame1 = cap2.read()
ret, frame2 = cap2.read()
# 相减做差
sub = cv2.subtract(frame1, frame2)
# 得到的结果灰度化
gray = cv2.cvtColor(sub, cv2.COLOR_BGR2GRAY)
# 判断作差后的结果是否全为0
if gray.max() != 0:
# 找到最大值位置
loc = np.where(gray == gray.max())
loc_x = loc[1][0]
loc_y = loc[0][0]
# 以loc为中心，range_d为距离向外拓展得到window
win_tl_x = loc_x - range_d
win_tl_y = loc_y - range_d
win_rb_x = loc_x + range_d
win_rb_y = loc_y + range_d
# 一些越界的判断
if win_tl_x < 0:
win_tl_x = 0
if win_tl_y < 0:
win_tl_y = 0
if win_rb_x > video_w:
win_rb_x = video_w
if win_rb_y > video_h:
win_rb_y = video_h
# 根据窗口坐标提取窗口内容
win_ini = cv2.cvtColor(frame1[win_tl_y:win_rb_y, win_tl_x:win_rb_x, :], cv2.COLOR_BGR2GRAY)
# 获取最大值位置对应的灰度值
tem_img = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
# 由最大值对应灰度值计算合适的灰度阈值
gray_thresh = tem_img[loc_y, loc_x] - gray_factor * tem_img[loc_y, loc_x]
# 初始窗口二值化处理
ret, thresh = cv2.threshold(win_ini, gray_thresh, 255, cv2.THRESH_BINARY)
# 在初始窗口中寻找轮廓
img2, contours, hi = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 有可能找到多个轮廓，但认为包含点数最多的那个轮廓是要找的轮廓
length = []
for item in contours:
length.append(item.shape[0])
target_contour = contours[length.index(max(length))]
# 获取目标轮廓的坐标信息
x, y, w, h = cv2.boundingRect(target_contour)
if isSquare:
# 保证提取的模板为正方形
tem_tl_x = win_tl_x + x
tem_tl_y = win_tl_y + y
tem_rb_x = win_tl_x + x + w
tem_rb_y = win_tl_y + y + h
center_x = (tem_tl_x + tem_rb_x) / 2
center_y = (tem_tl_y + tem_rb_y) / 2
delta = int(template_factor * max(w, h))
real_tl_x = center_x - delta
real_rb_x = center_x + delta
real_tl_y = center_y - delta
real_rb_y = center_y + delta
else:
# 不保证模板为正方形
real_tl_x = win_tl_x + x
real_tl_y = win_tl_y + y
real_rb_x = win_tl_x + x + w
real_rb_y = win_tl_y + y + h
# 一些越界判断
if real_tl_x < 0:
real_tl_x = 0
if real_tl_y < 0:
real_tl_y = 0
if real_rb_x > video_w:
real_rb_x = video_w
if real_rb_y > video_h:
real_rb_y = video_h
# 提取模板内容
template = frame1[real_tl_y:real_rb_y, real_tl_x:real_rb_x, :]
# 获取模板的宽高，h竖直方向，w水平方向
h = template.shape[0]
w = template.shape[1]
d = max(w, h)
# 是否是多模板匹配
if isMultiTemplate:
if templateNum == 16:
M22_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -22.5, 1)
M45 = cv2.getRotationMatrix2D((d / 2, d / 2), -45, 1)
M67_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -67.5, 1)
M90 Gmyhsp= cv2.getRotationMatrix2D((d / 2, d / 2), -90, 1)
M112_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -112.5, 1)
M135 = cv2.getRotationMatrix2D((d / 2, d / 2), -135, 1)
M157_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -157.5, 1)
M180 = cv2.getRotationMatrix2D((d / 2, d / 2), -180, 1)
M202_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -202.5, 1)
M225 = cv2.getRotationMatrix2D((d / 2, d / 2), -225, 1)
M247_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -247.5, 1)
M270 = cv2.getRotationMatrix2D((d / 2, d / 2), -270, 1)
M292_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -292.5, 1)
M315 = cv2.getRotationMatrix2D((d / 2, d / 2), -315, 1)
M337_5 = cv2.getRotationMatrix2D((d / 2, d / 2), -337.5, 1)
template22_5 = cv2.warpAffine(template, M22_5, (d, d))
template45 = cv2.warpAffine(template, M45, (d, d))
template67_5 = cv2.warpAffine(template, M67_5, (d, d))
template90 = cv2.warpAffine(template, M90, (d, d))
template112_5 = cv2.warpAffine(template, M112_5, (d, d))
template135 = cv2.warpAffine(template, M135, (d, d))
template157_5 = cv2.warpAffine(template, M157_5, (d, d))
template180 = cv2.warpAffine(template, M180, (d, d))
template202_5 = cv2.warpAffine(template, M202_5, (d, d))
template225 = cv2.warpAffine(template, M225, (d, d))
template247_5 = cv2.warpAffine(template, M247_5, (d, d))
template270 = cv2.warpAffine(template, M270, (d, d))
template292_5 = cv2.warpAffine(template, M292_5, (d, d))
template315 = cv2.warpAffine(template, M315, (d, d))
template337_5 = cv2.warpAffine(template, M337_5, (d, d))
elif templateNum == 8:
M45 = cv2.getRotationMatrix2D((d / 2, d / 2), -45, 1)
M90 = cv2.getRotationMatrix2D((d / 2, d / 2), -90, 1)
M135 = cv2.getRotationMatrix2D((d / 2, d / 2), -135, 1)
M180 = cv2.getRotationMatrix2D((d / 2, d / 2), -180, 1)
M225 = cv2.getRotationMatrix2D((d / 2, d / 2), -225, 1)
M270 = cv2.getRotationMatrix2D((d / 2, d / 2), -270, 1)
M315 = cv2.getRotationMatrix2D((d / 2, d / 2), -315, 1)
template45 = cv2.warpAffine(template, M45, (d, d))
template90 = cv2.warpAffine(template, M90, (d, d))
template135 = cv2.warpAffine(template, M135, (d, d))
template180 = cv2.warpAffine(template, M180, (d, d))
template225 = cv2.warpAffine(template, M225, (d, d))
template270 = cv2.warpAffine(template, M270, (d, d))
template315 = cv2.warpAffine(template, M315, (d, d))
elif templateNum == 4:
M90 = cv2.getRotationMatrix2D((d / 2, d / 2), -90, 1)
M180 = cv2.getRotationMatrix2D((d / 2, d / 2), -180, 1)
M270 = cv2.getRotationMatrix2D((d / 2, d / 2), -270, 1)
template90 = cv2.warpAffine(template, M90, (d, d))
template180 = cv2.warpAffine(template, M180, (d, d))
template270 = cv2.warpAffine(template, M270, (d, d))
cv2.imshow("Template", template)
cv2.imwrite(out_path5, template)
offset = int(scale_factor * d)
# 计算待选窗口左上角点坐标
tlx = loc_x - d
tly = loc_y - d
# 判断是否越界，越界则设置为0
if tlx < 0:
tlx = 0
if tly < 0:
tly = 0
range_tl = (tlx, tly)
# 计算待选窗口右下角点坐标
rbx = loc_x + w + d
rby = loc_y + h + d
# 判断是否越界，越界设置为视频长宽最大值
if rbx > video_w:
rbx = video_w
if rby > video_h:
rby = video_h
range_rb = (rbx, rby)
# 放入角点坐标列表
tlp.append(range_tl)
rbp.append(range_rb)
cap2.release()
# 然后进行模板匹配
while cap.isOpened():
# 读取每帧内容
ret, frame = cap.read()
# 判断帧内容是否为空，不为空继续
if frame is None:
break
else:
# 是否为多模板匹配模式
if isMultiTemplate:
if templateNum == 16:
# 逐个模板进行匹配
res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,
cv2.TM_CCOEFF_NORMED)
res22_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template22_5,
cv2.TM_CCOEFF_NORMED)
res67_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template67_5,
cv2.TM_CCOEFF_NORMED)
res112_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template112_5,
cv2.TM_CCOEFF_NORMED)
res157_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template157_5,
cv2.TM_CCOEFF_NORMED)
res202_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template202_5,
cv2.TM_CCOEFF_NORMED)
res247_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template247_5,
cv2.TM_CCOEFF_NORMED)
res292_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template292_5,
cv2.TM_CCOEFF_NORMED)
res337_5 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template337_5,
cv2.TM_CCOEFF_NORMED)
res90 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template90,
cv2.TM_CCOEFF_NORMED)
res180 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template180,
cv2.TM_CCOEFF_NORMED)
res270 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template270,
cv2.TM_CCOEFF_NORMED)
res45 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template45,
cv2.TM_CCOEFF_NORMED)
res135 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template135,
cv2.TM_CCOEFF_NORMED)
res225 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template225,
cv2.TM_CCOEFF_NORMED)
res315 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template315,
cv2.TM_CCOEFF_NORMED)
# 获取各模板对应的最大值
m22_5 = np.max(res22_5)
m67_5 = np.max(res67_5)
m112_5 = np.max(res112_5)
m157_5 = np.max(res157_5)
m202_5 = np.max(res202_5)
m247_5 = np.max(res247_5)
m292_5 = np.max(res292_5)
m337_5 = np.max(res337_5)
m45 = np.max(res45)
python m135 = np.max(res135)
m225 = np.max(res225)
m315 = np.max(res315)
m0 = np.max(res)
m90 = np.max(res90)
m180 = np.max(res180)
m270 = np.max(res270)
# 寻找最佳匹配结果
m = max(m0, m22_5, m45, m67_5, m90,
m112_5, m135, m157_5, m180,
m202_5, m225, m247_5, m270,
m292_5, m315, m337_5)
# 获取最佳匹配结果对应的坐标信息
if m == m0:
mIndex = 0
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
elif m == m90:
mIndex = 90
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res90)
elif m == m180:
mIndex = 180
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res180)
elif m == m270:
mIndex = 270
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res270)
elif m == m45:
mIndex = 45
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res45)
elif m == m135:
mIndex = 135
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res135)
elif m == m225:
mIndex = 22www.devze.com5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res225)
elif m == m315:
mIndex = 315
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res315)
elif m == m22_5:
mIndex = 22.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res22_5)
elif m == m67_5:
mIndex = 67.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res67_5)
elif m == m112_5:
mIndex = 112.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res112_5)
elif m == m157_5:
mIndex = 157.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res157_5)
elif m == m202_5:
mIndex = 202.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res202_5)
elif m == m247_5:
mIndex = 247.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res247_5)
elif m == m292_5:
mIndex = 292.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res292_5)
elif m == m337_5:
mIndex = 337.5
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res337_5)
elif templateNum == 8:
res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,
cv2.TM_CCOEFF_NORMED)
res90 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template90,
cv2.TM_CCOEFF_NORMED)
res180 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template180,
cv2.TM_CCOEFF_NORMED)
res270 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template270,
cv2.TM_CCOEFF_NORMED)
res45 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template45,
cv2.TM_CCOEFF_NORMED)
res135 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template135,
cv2.TM_CCOEFF_NORMED)
res225 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template225,
cv2.TM_CCOEFF_NORMED)
res315 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template315,
cv2.TM_CCOEFF_NORMED)
m45 = np.max(res45)
m135 = np.max(res135)
m225 = np.max(res225)
m315 = np.max(res315)
m0 = np.max(res)
m90 = np.max(res90)
m180 = np.max(res180)
m270 = np.max(res270)
m = max(m0, m45, m90, m135, m180, m225, m270, m315)
if m == m0:
mIndex = 0
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
elif m == m90:
mIndex = 90
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res90)
elif m == m180:
mIndex = 180
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res180)
elif m == m270:
mIndex = 270
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res270)
elif m == m45:
mIndex = 45
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res45)
elif m == m135:
mIndex = 135
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res135)
elif m == m225:
mIndex = 225
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res225)
elif m == m315:
mIndex = 315
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res315)
elif templateNum == 4:
res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,
cv2.TM_CCOEFF_NORMED)
res90 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template90,
cv2.TM_CCOEFF_NORMED)
res180 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template180,
cv2.TM_CCOEFF_NORMED)
res270 = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :],
template270,
cv2.TM_CCOEFF_NORMED)
m0 = np.max(res)
m90 = np.max(res90)
m180 = np.max(res180)
m270 = np.max(res270)
m = max(m0, m90, m180, m270)
if m == m0:
mIndex = 0
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
elif m == m90:
mIndex = 90
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res90)
elif m == m180:
mIndex = 180
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res180)
elif m == m270:
mIndex = 270
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res270)
else:
res = cv2.matchTemplate(frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :], template,
cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
window = frame[tlp[count][1]:rbp[count][1], tlp[count][0]:rbp[count][0], :]
cv2.imshow("Window", window)
# top_left坐标顺序(水平，竖直)(→，↓)
top_left = (max_loc[0] + tlp[count][0], max_loc[1] + tlp[count][1])
bottom_right = (top_left[0] + w, top_left[1] + h)
center_point = ((top_left[0] + bottom_right[0]) / 2, (top_left[1] + bottom_right[1]) / 2)
if trackPoints.__len__() == 0:
# 计算待选窗口左上角点坐标
tlx = top_left[0] - d
tly = top_left[1] - d
# 判断是否越界，越界则设置为0
if tlx < 0:
tlx = 0
if tly < 0:
tly = 0
range_tl = (tlx, tly)
# 计算待选窗口右下角点坐标
rbx = top_left[0] + w + d
rby = top_left[1] + h + d
# 判断是否越界，越界设置为视频长宽最大值
if rbx > video_w:
rbx = video_w
if rby > video_h:
rby = video_h
range_rb = (rbx, rby)
# 将待选窗口左上角点坐标和右下角点坐标依次添加到列表中
tlp.append(range_tl)
rbp.append(range_rb)
# 将目标区域的左上角点、中心点、右下角点坐标依次加入列表
trackPoints.append(top_left)
bottom_right_points.appwww.devze.comend(bottom_right)
center_points.append(center_point)
cv2.circle(track, center_point, 2, (0, 0, 255), -1)
else:
# 加入运动连续性约束，若相邻轨迹点距离相差大于阈值，则认为错误
distance = abs(trackPoints[-1][0] - top_left[0]) + abs(trackPoints[-1][1] - top_left[1])
if distance > dis_thresh:
print '100%'
break
else:
# 计算待选窗口左上角点坐标
tlx = top_left[0] - d
tly = top_left[1] - d
# 判断是否越界，越界则设置为0
if tlx < 0:
tlx = 0
if tly < 0:
tly = 0
range_tl = (tlx, tly)
# 计算待选窗口右下角点坐标
rbx = top_left[0] + w + d
rby = top_left[1] + h + d
# 判断是否越界，越界设置为视频长宽最大值
if rbx > video_w:
rbx = video_w
if rby > video_h:
rby = video_h
开发者_开发入门 range_rb = (rbx, rby)
# 将待选窗口左上角点坐标和右下角点坐标依次添加到列表中
tlp.append(range_tl)
rbp.append(range_rb)
# 将目标区域的左上角点、中心点、右下角点坐标依次加入列表
trackPoints.append(top_left)
bottom_right_points.append(bottom_right)
# 判断是否采用均值平滑
if isSmooth:
# 采用均值平滑，平滑轨迹
center_point = ((center_point[0] + center_points[-1][0]) / 2,
(center_point[1] + center_points[-1][1]) / 2)
center_points.append(center_point)
# 绘制目标识别框
cv2.rectangle(frame,
(center_point[0] - offset, center_point[1] - offset),
(center_point[0] + offset, center_point[1] + offset),
color, 2)
# 绘制运动轨迹
cv2.line(track, center_points[-2], center_points[-1], (255, 255, 255), 1)
# 计算速度
Vs.append(calcVelocity(center_points[-2][0],
center_points[-1][0],
center_points[-2][1],
center_points[-1][1],
resolution,
waitTime))
# 输出目标、轨迹视频
out.write(frame)
out2.write(track)
count += 1
print round((count * 1.0 / total) * 100, 2), '%'
# 显示结果
cv2.imshow("Tr", track)
cv2.imshow("Fr", frame)
# 退出控制
k = cv2.waitKey(waitTime) & 0xFF
if k == 27:
break
# 打印轨迹坐标
print trackPoints
print '相邻帧距离阈值:', dis_thresh
print '灰度阈值:', gray_thresh
print '模板缩放因子:', template_factor
print '识别框缩放因子:', scale_factor
# 输出中心点轨迹
output = open(out_path3, 'w')
for item in center_points:
output.write(item.__str__() + "\n")
# 输出各帧速度
output2 = open(out_path4, 'w')
for item in Vs:
output2.write(item.__str__() + "\n")
# 释放对象
cap.release()
out.release()
out2.release()
output.close()
output2.close()
在代码中主要做了如下改进：
1.增加多模板匹配机制
为了能精确地检测物体的旋转，引入多模板匹配。在代码中有4、8、16不同数量的模式可选。模板越多，对于旋转的识别越精确。 下图匹配模板数分别是1、4、8、16。
可以看到，单模版匹配已经无法正常识别跟踪了。模板数为4时，会有少量跟踪错误。当模板数为8和16时，跟踪的轨迹就相对精确了。 下图是采用8模板和单模板匹配的轨迹比较，可以看到，利用多模板匹配，可以较好识别旋转物体。 白色为单模版匹配轨迹，红色为多模板匹配轨迹。
同时考虑到卫星视频动目标一般运动形式是平移和旋转，没有缩放。所以经过优化的算法可以满足大部分需求。
2.增加轨迹平滑
通过对轨迹列表中最后两个点求均值作为最终的轨迹点，可以对提取的轨迹进行一定程度的平滑。
三、测试对比
编程下图是模拟飞机曲线飞行的视频。对其进行目标识别和轨迹提取后如下。
对应的飞行轨迹如下。
可以看到，相较于单模版匹配，能较好地提取运动目标和轨迹。而采用之前的单模版匹配算法，经过测试在刚转弯时就跟丢了，如下。
到此这篇关于python实现视频目标检测与轨迹跟踪流程详解的文章就介绍到这了,更多相关Python视频目标检测内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们！
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