問題描述

以下代碼來自(被要求刪除鏈接).但我想知道它究竟是如何工作的.如果這被認(rèn)為是邊緣檢測或斑點檢測，我感到很困惑，因為

如果您有一個半徑為 3 且值 1 以內(nèi)核為中心的斑點，并且背景的值為 0，您將獲得非常強烈(負(fù)面)的響應(yīng).很清楚為什么如果半徑設(shè)置得當(dāng)它可以進(jìn)行斑點檢測.

邊緣檢測呢?好吧，它不像 Sobel 算子，它為您提供梯度和對邊緣的強烈響應(yīng).Sobel 算子不會為您提供準(zhǔn)確的邊緣，因為梯度通常會在幾個像素上上升和下降.您的邊緣將是幾個像素寬.為了使其定位更準(zhǔn)確，我們可以在局部找到具有最大(或最小)梯度的像素.這意味著它的二階導(dǎo)數(shù)(拉普拉斯算子)應(yīng)該為零，或者在該點處有一個過零.

您可以看到處理后的圖像既有明帶又有暗帶.過零是邊緣.要在內(nèi)核中看到這一點，請嘗試手動在內(nèi)核上滑動一個完美的步進(jìn)邊緣以查看響應(yīng)如何變化.

對于你的第二個問題，我想絕對是試圖找到淺色和深色斑點(淺色斑點，深色背景；深色斑點，淺色背景)，因為它們分別給出強烈的負(fù)面和強烈的正面回應(yīng).然后它在每個像素位置找到所有圖像的最大值.對于每個輸出像素，它使用圖像上具有最大響應(yīng)的像素作為輸出.我認(rèn)為他的理由是具有強烈沖動(小斑點)的像素是焦點.

他正在使用 bitwise_not 作為復(fù)制機制.它將掩碼指定的一些像素設(shè)置為源圖像的按位非.最后，您將獲得由來自不同來源的像素組成的 output，但所有這些像素均未按位進(jìn)行.要恢復(fù)真實圖像，只需再次不"它們，如 NOT(NOT(x)) = x.255-x 正是這樣做的.我認(rèn)為 copyTo 也可以，不確定為什么他選擇了其他方式.

圖片取自 http://fourier.eng.hmc.edu/e161/lectures/gradient/node8.html.

The following code is provided from (was asked to remove the link). But I was wondering how it exactly works. I was confused if this was considered edge detection or blob detection, as Wikipedia list the Laplacian of Gaussian (LoG) as blob detection.

Also, could somebody explain and provide a deeper explanation for why the absolute value is calculated and what is going on in the focus_stack() function?

#   Compute the gradient map of the image
def doLap(image):

    # YOU SHOULD TUNE THESE VALUES TO SUIT YOUR NEEDS
    kernel_size = 5         # Size of the laplacian window
    blur_size = 5           # How big of a kernal to use for the gaussian blur
                            # Generally, keeping these two values the same or very close works well
                            # Also, odd numbers, please...

    blurred = cv2.GaussianBlur(image, (blur_size,blur_size), 0)
    return cv2.Laplacian(blurred, cv2.CV_64F, ksize=kernel_size)

#
#   This routine finds the points of best focus in all images and produces a merged result...
#
def focus_stack(unimages):
    images = align_images(unimages)

    print "Computing the laplacian of the blurred images"
    laps = []
    for i in range(len(images)):
        print "Lap {}".format(i)
        laps.append(doLap(cv2.cvtColor(images[i],cv2.COLOR_BGR2GRAY)))

    laps = np.asarray(laps)
    print "Shape of array of laplacians = {}".format(laps.shape)

    output = np.zeros(shape=images[0].shape, dtype=images[0].dtype)

    abs_laps = np.absolute(laps)
    maxima = abs_laps.max(axis=0)
    bool_mask = abs_laps == maxima
    mask = bool_mask.astype(np.uint8)
    for i in range(0,len(images)):
        output = cv2.bitwise_not(images[i],output, mask=mask[i])

    return 255-output

EDIT: Cris Luengo is right. Ignore the part about edge detector.

Laplacian of Gaussian(LoG) can be used as both edge detector and blob detector. I will skip the detailed mathematics and rationale, I think you can read them on a book or some websites here, here and here.

To see why it can be used as both, let's look at its plot and kernel.

If you have a blob with radius of 3 and value 1 centered at the kernel, and the background has value 0, you will have a very strong (negative) response. It is clear why it can do blob detection if the radius is set properly.

How about edge detection? Well it is not like Sobel operator which gives you gradient and strong response for edges. Sobel operator does not give you accurate edges as the gradient usually rise and fall across a few pixels. Your edge would then be several pixels wide. To make it localize more accurate, we can find the pixel with maximum (or minimum) gradient locally. This implies its second derivative (Laplacian) should equal zero, or has a zero-crossing at that point.

You can see the processed image has both a light and dark band. The zero-crossing is the edge. To see this with a kernel, try sliding a perfect step edge across the kernel manually to see how the respond changes.

For you second question, I guess the absolute is trying to find both light and dark blob (light blob, dark background; dark blob, light background) as they gives strong negative and strong positive response respectively. It then find the max across all images at each pixel location. For each output pixel, it uses the pixel at the image with the maximum response as output. I think his rationale is that pixels with strong impulse (small blob) are in-focus.

He is using bitwise_not as a copy mechanism. It sets some pixels, specified by the mask, to the bitwise not of the source image. At the end, you would have output consisting of pixels from different sources, except that all of them have undergone bitwise not. To recover the true image, simply 'NOT' them again, as NOT(NOT(x)) = x. 255-x does exactly that. I think a copyTo would work too, not sure why he chose otherwise.

Images taken from http://fourier.eng.hmc.edu/e161/lectures/gradient/node8.html.

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