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。
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