Question

我试图通过删除静态（主要是）BG元素来使用opencv2检测前景运动。我使用的方法是基于一系列图像的平均值 - 代表背景。然后计算一个标准偏差高于和低于该平均值。将其用作检测前景运动的窗口。

据报道，这种机制适用于中等嘈杂的环境，如在BG中挥动树木。

所需的输出是可以在后续操作中使用的掩码，以便最小化进一步的处理。具体来说，我将在该地区使用光流检测。

cv2使这更容易，代码更易于阅读和理解。谢谢cv2和numpy。

但是我在进行正确的FG检测时遇到了困难。

理想情况下，我也希望侵蚀/扩大BG均值，以便消除1像素噪声。

代码全部都是如此，因此您在开始时有多个帧（BGsample）以在FG检测开始之前收集BG数据。唯一的依赖项是opencv2（＆gt; 2.3.1）和numpy（应该包含在＆gt; opencv 2.3.1中）

import cv2
import numpy as np


if __name__ == '__main__': 
    cap = cv2.VideoCapture(0) # webcam
    cv2.namedWindow("input")
    cv2.namedWindow("sig2")
    cv2.namedWindow("detect")
    BGsample = 20 # number of frames to gather BG samples from at start of capture
    success, img = cap.read()
    width = cap.get(3)
    height = cap.get(4)
    # can use img.shape(:-1) # cut off extra channels
    if success:
        acc = np.zeros((height, width), np.float32) # 32 bit accumulator
        sqacc = np.zeros((height, width), np.float32) # 32 bit accumulator
        for i in range(20): a = cap.read() # dummy to warm up sensor
        # gather BG samples
        for i in range(BGsample):
            success, img = cap.read()
            frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            cv2.accumulate(frame, acc)
            cv2.accumulateSquare(frame, sqacc)
        #
        M = acc/float(BGsample)
        sqaccM = sqacc/float(BGsample)
        M2 = M*M
        sig2 = sqaccM-M2
        # have BG samples now
        # start FG detection
        key = -1
        while(key < 0):
            success, img = cap.read()
            frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            #Ideally we create a mask for future use that is B/W for FG objects
            # (using erode or dilate to remove noise)
            # this isn't quite right
            level = M+sig2-frame
            grey = cv2.morphologyEx(level, cv2.MORPH_DILATE,
                                    cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3)), iterations=2)
            cv2.imshow("input", frame)
            cv2.imshow("sig2", sig2/60)
            cv2.imshow("detect", grey/20)
            key = cv2.waitKey(1)
    cv2.destroyAllWindows()

Answer 1

我认为您不需要手动计算使用cv2.meanStdDev的均值和标准差。在下面的代码中，我使用的是从

计算的平均背景矩阵

M = acc/float(BGsample)

因此，现在我们可以计算平均背景图像的平均值和标准差，最后inRange用于提取您想要的范围（即平均值+/- 1标准差）。

(mu, sigma) = cv2.meanStdDev(M)
fg = cv2.inRange(M, (mu[0] - sigma[0]), (mu[0] + sigma[0]))
# proceed with morphological clean-up here...

希望有所帮助！

Answer 2

到目前为止我最好的猜测。使用detectmin，max将fp sigma强制转换为灰度，以供cv2.inRange使用。似乎工作正常，但希望更好......有效的FG数据有很多漏洞。我想它在rgb而不是灰度方面会更好。使用扩张或侵蚀无法降低噪音。

有任何改进吗？

import cv2
import numpy as np


if __name__ == '__main__': 
    cap = cv2.VideoCapture(1)
    cv2.namedWindow("input")
    #cv2.namedWindow("sig2")
    cv2.namedWindow("detect")
    BGsample = 20 # number of frames to gather BG samples from at start of capture
    success, img = cap.read()
    width = cap.get(3)
    height = cap.get(4)
    if success:
        acc = np.zeros((height, width), np.float32) # 32 bit accumulator
        sqacc = np.zeros((height, width), np.float32) # 32 bit accumulator
        for i in range(20): a = cap.read() # dummy to warm up sensor
        # gather BG samples
        for i in range(BGsample):
            success, img = cap.read()
            frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            cv2.accumulate(frame, acc)
            cv2.accumulateSquare(frame, sqacc)
        #
        M = acc/float(BGsample)
        sqaccM = sqacc/float(BGsample)
        M2 = M*M
        sig2 = sqaccM-M2
        # have BG samples now
        # calculate upper and lower bounds of detection window around mean.
        # coerce into 8bit image space for cv2.inRange compare
        detectmin = cv2.convertScaleAbs(M-sig2)
        detectmax = cv2.convertScaleAbs(M+sig2)
        # start FG detection
        key = -1
        while(key < 0):
            success, img = cap.read()
            frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            level = cv2.inRange(frame, detectmin, detectmax)
            cv2.imshow("input", frame)
            #cv2.imshow("sig2", M/200)
            cv2.imshow("detect", level)
            key = cv2.waitKey(1)
    cv2.destroyAllWindows()

opencv2中的背景减法

2 个答案: