opencv計算機視覺學習筆記七
阿新 • • 發佈：2019-01-27
第八章 目標跟蹤
1檢測目標的移動
基本的運動檢測,示例程式碼如下:
import cv2 import numpy as np # 捕獲攝像頭影象 camera = cv2.VideoCapture(0) # es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10)) kernel = np.ones((5, 5), np.uint8) background = None while (True): ret, frame = camera.read() # 將第一幀設為影象的背景 if backgroundis None: # 轉換顏色空間 background = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 高斯模糊 background = cv2.GaussianBlur(background, (21, 21), 0) continue # 轉換顏色空間並作模糊處理 gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) gray_frame = cv2.GaussianBlur(gray_frame, (21, 21), 0) # 取得差分圖 diff = cv2.absdiff(background, gray_frame) diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1] # 膨脹 diff = cv2.dilate(diff, es, iterations=2) # 得到影象中目標的輪廓 image, cnts, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)for c in cnts: if cv2.contourArea(c) < 1500: continue # 計算矩形邊框 (x, y, w, h) = cv2.boundingRect(c) # 繪製矩形 cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # 顯示影象 cv2.imshow('contours', frame) cv2.imshow('dif', diff) if cv2.waitKey(int(1000 / 12)) & 0xFF == ord('q'): break cv2.destroyAllWindows() camera.release()
執行結果如下:
2背景分割器 knn mog2和GMG
Opencv3有三種背景分割器
K-nearest(knn)
Mixture of Gaussians(MOG2)
Geometric multigid(GMC)
backgroundSubtractor用於分割前景和背景
示例程式碼如下:
import cv2 import numpy as np cv2.ocl.setUseOpenCL(False) cap = cv2.VideoCapture(0) mog = cv2.createBackgroundSubtractorMOG2() while (True): ret, frame = cap.read() fgmask = mog.apply(frame) cv2.imshow('frame', fgmask) if cv2.waitKey(30) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()
執行結果如下:
使用backgroundSubtractorKNN來實現運動檢測
示例程式碼如下:
import cv2 cv2.ocl.setUseOpenCL(False) bs = cv2.createBackgroundSubtractorKNN(detectShadows=True) # 讀取本地視訊 camera = cv2.VideoCapture('../traffic.flv') while (True): ret, frame = camera.read() fgmask = bs.apply(frame.copy()) # 設定閾值 th = cv2.threshold(fgmask, # 源影象 244, # 閾值 255, # 最大值 cv2.THRESH_BINARY)[1] # 閾值型別 # 膨脹 dilated = cv2.dilate(th, # 源影象 cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), # 核心 iterations=2) # 腐蝕次數 # 查詢影象中的目標輪廓 image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for c in contours: if cv2.contourArea(c) > 1600: (x, y, w, h) = cv2.boundingRect(c) cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2) cv2.imshow('mog', fgmask) # 分割前景與背景 cv2.imshow('thresh', th) # cv2.imshow('detection', frame) # 運動檢測結果 if cv2.waitKey(30) & 0xFF == 27: break camera.release() cv2.destroyAllWindows()
執行結果如下:
均值漂移meanShift
示例程式碼如下:
import cv2 import numpy as np # 取得攝像頭影象 cap = cv2.VideoCapture(0) ret, frame = cap.read() # 設定跟蹤窗體大小 r, h, c, w = 10, 200, 10, 200 track_window = (c, r, w, h) # 提取roi roi = frame[r:r + h, c:c + w] # 轉換顏色空間 hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # 根據閾值構建掩碼 mask = cv2.inRange(hsv_roi, np.array((100., 30., 32.)), np.array((180., 120., 255.))) # 計算roi圖形的彩色直方圖 roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180]) cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # 指定停止條件 term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) while (True): ret, frame = cap.read() if ret == True: # 更換顏色空間 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # histogram back projection calculation 直方圖反向投影 dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) # 均值漂移 ret, track_window = cv2.meanShift(dst, track_window, term_crit) # 繪製矩形顯示影象 x, y, w, h = track_window img2 = cv2.rectangle(frame, (x, y), (x + w, y + h), 255, 2) cv2.imshow('img2', img2) # esc退出 if cv2.waitKey(60) & 0xFF == 27: break else: break cv2.destroyAllWindows() cap.release()
執行結果如下:
彩色直方圖
calHist函式
函式原型:
def calcHist(images, #源影象
channels, #通道列表
mask,#可選的掩碼
histSize, #每個維度下直方圖陣列的大小
ranges,#每一個維度下直方圖bin的上下界的陣列
hist=None,#輸出直方圖是一個[]維稠密度的陣列
accumulate=None)#累計標誌
Camshift
示例程式碼如下:
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2016/12/15 16:48 # @Author : Retacn # @Site : camshift實現物體跟蹤 # @File : camshift.py # @Software: PyCharm __author__ = "retacn" __copyright__ = "property of mankind." __license__ = "CN" __version__ = "0.0.1" __maintainer__ = "retacn" __email__ = "[email protected]" __status__ = "Development" import cv2 import numpy as np # 取得攝像頭影象 cap = cv2.VideoCapture(0) ret, frame = cap.read() # 設定跟蹤窗體大小 r, h, c, w = 300, 200, 400, 300 track_window = (c, r, w, h) # 提取roi roi = frame[r:r + h, c:c + w] # 轉換顏色空間 hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # 根據閾值構建掩碼 mask = cv2.inRange(hsv_roi, np.array((100., 30., 32.)), np.array((180., 120., 255.))) # 計算roi圖形的彩色直方圖 roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180]) cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # 指定停止條件 term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) while (True): ret, frame = cap.read() if ret == True: # 更換顏色空間 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # histogram back projection calculation 直方圖反向投影 dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) # 均值漂移 ret, track_window = cv2.CamShift(dst, track_window, term_crit) # 繪製矩形顯示影象 pts = cv2.boxPoints(ret) pts = np.int0(pts) img2 = cv2.polylines(frame, [pts], True, 255, 2) cv2.imshow('img2', img2) # esc退出 if cv2.waitKey(60) & 0xFF == 27: break else: break cv2.destroyAllWindows() cap.release()
執行結果如下:
4 卡爾曼濾波器
函式原型為：
def KalmanFilter(dynamParams=None,#狀態的維度
measureParams=None, #測量的維度
controlParams=None,#控制的維度
type=None)#矩陣的型別
示例程式碼如下：
import cv2 import numpy as np # 建立空幀 frame = np.zeros((800, 800, 3), np.uint8) # 測量座標 last_measurement = current_measurement = np.array((2, 1), np.float32) # 滑鼠運動預測 last_prediction = current_predication = np.zeros((2, 1), np.float32) def mousemove(event, x, y, s, p): # 設定全域性變數 global frame, measurements, current_measurement, last_measurement, current_predication, last_prediction last_prediction = current_predication last_measurement = current_measurement current_measurement = np.array([[np.float32(x)], [np.float32(y)]]) kalman.correct(current_measurement) current_predication = kalman.predict() # 實際移動起始點 lmx, lmy = last_measurement[0], last_measurement[1] cmx, cmy = current_measurement[0], current_measurement[1] # 預測線起止點 lpx, lpy = last_prediction[0], last_prediction[1] cpx, cpy = current_predication[0], current_predication[1] # 繪製連線 cv2.line(frame, (lmx, lmy), (cmx, cmy), (0, 100, 0)) # 綠色 cv2.line(frame, (lpx, lpy), (cpx, cpy), (0, 0, 200)) # 紅色 # 建立窗體 cv2.namedWindow('mouse_detection') # 註冊滑鼠事件的回撥函式 cv2.setMouseCallback('mouse_detection', mousemove) # 卡爾曼濾波器 kalman = cv2.KalmanFilter(4, 2) kalman.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) kalman.transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) kalman.processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) * 0.03 while (True): cv2.imshow('mouse_detection', frame) if cv2.waitKey(30) & 0xFF == 27: break cv2.destroyAllWindows()
執行結果如下：
一個基於行人跟蹤的例子
示例程式碼如下:
import cv2 import numpy as np import os.path as path import argparse font = cv2.FONT_HERSHEY_SIMPLEX parser = argparse.ArgumentParser() parser.add_argument("-a", "--algorithm", help="m (or nothing) for meanShift and c for camshift") args = vars(parser.parse_args()) # 計算矩陣中心(行人位置) def center(points): x = (points[0][0] + points[1][0] + points[2][0] + points[3][0]) / 4 y = (points[0][1] + points[1][1] + points[2][1] + points[3][1]) / 4 # print(np.array([np.float32(x), np.float32(y)], np.float32)) # [ 588. 257.5] return np.array([np.float32(x), np.float32(y)], np.float32) # 行人 class Pedestrian(): def __init__(self, id, frame, track_window): self.id = int(id) # 行人id x, y, w, h = track_window # 跟蹤窗體 self.track_window = track_window # 更換顏色空間 self.roi = cv2.cvtColor(frame[y:y + h, x:x + w], cv2.COLOR_BGR2HSV) # 計算roi圖形的彩色直方圖 roi_hist = cv2.calcHist([self.roi], [0], None, [16], [0, 180]) self.roi_hist = cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # 設定卡爾曼濾波器 self.kalman = cv2.KalmanFilter(4, 2) self.kalman.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) self.kalman.transitionMatrix = np.array([[1, 0, 1, 0], [0