2

我正在尝试通过删除静态（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。然后计算高于和低于该平均值的一个标准差。使用它作为检测前景运动的窗口。

据报道，这种机制适用于中等噪声环境，例如 BG 中挥动的树木。

所需的输出是可以在后续操作中使用的掩码，以便最大限度地减少进一步处理。具体来说，我将在该区域内使用光流检测。

cv2 使这变得更容易，并且代码更易于阅读和理解。感谢 cv2 和 numpy。

但我很难进行正确的 FG 检测。

理想情况下，我还想侵蚀/扩大 BG 平均值以消除 1 个像素的噪声。

代码全部在一起，因此您在开始时有许多帧（BGsample）以在 FG 检测开始之前收集 BG 数据。唯一的依赖是 opencv2 (> 2.3.1 ) 和 numpy (应该包含在 > 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()
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2 回答 2

1

我认为您不需要手动计算平均值和标准差cv2.meanStdDev。在下面的代码中，我使用的是从计算得出的平均背景矩阵

M = acc/float(BGsample)

因此，现在我们可以计算平均背景图像的均值和标准差，最后inRange用于拉出您想要的范围（即均值 +/- 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...

希望有帮助！

于 2012-04-02T13:52:24.760 回答
0

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 数据中有很多漏洞。我想它在 rgb 而不是灰度中会更好。无法使用扩张或侵蚀来降低噪音。

有什么改进吗？

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()
于 2012-04-03T11:54:50.990 回答