如何使用轨迹栏进行交互式图像二值化？

Question

我有一个代码，它使用 Otsu 阈值为我提供二进制图像。我正在为 U-Net 制作数据集，并且我想尝试不同的算法（全局和本地），以便保存“最佳”图像。下面是我的图像二值化代码。

import cv2
import numpy as np
import skimage.filters as filters

img = cv2.imread('math.png') # read the image
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # convert to gray
smooth = cv2.GaussianBlur(gray, (95,95), 0) # blur
division = cv2.divide(gray, smooth, scale=255) # divide gray by morphology image

# Add Morphology here. Dilation, Erosion, Opening, Closing or so.

sharp = filters.unsharp_mask(division, radius=1.5, amount=1.5, multichannel=False, preserve_range=False) # sharpen using unsharp masking
sharp = (255*sharp).clip(0,255).astype(np.uint8)
thresh = cv2.threshold(sharp, 0, 255, cv2.THRESH_OTSU )[1]  # threshold

我在更广泛的领域得到了相当不错的结果，但我想使用cv2.namedWindow、cv2.createTrackbar、cv2.getTrackbarPos，以便我可以设置radius、amount、kernel 的值，膨胀，腐蚀等，通过使用以下函数。

cv2.namedWindow('Tracking Window')
cv2.createTrackbar('param1','Tracking Window',0,255,dummy) # dummy is just a dummy function which returns None
param1 = cv2.getTrackbarPos('param1','Tracking Window')

我怎样才能得到所有这些。还有，按s如何保存图片，按n打开下一张图片？

Original Question was posted by me 6 months ago

Answer 1

我的解决方案的代码比预期的要长，但它提供了一些花哨的操作可能性。首先，让我们看看实际的窗口：

有滑块

• 形态学操作（扩张、侵蚀、关闭、打开），
• 结构元素（矩形、椭圆、十字）和
• 内核大小（这里：限制在1 ... 21范围内）。

窗口名称反映了前两个滑块的当前设置：

当按下 s 时，图像会结合当前设置保存：

Saved image as Erosion_Ellipsoid_SLEM_11.png.

按下 n 时，将选择列表中的下一个图像。

在任何时候，当按下 q 时，应用程序就会退出。它会在处理完列表中的所有图像后自动结束。

在交互部分之前和之后，您可以添加任何您想要的操作，参见。代码。

而且，这里是完整的代码：

import cv2

# Collect morphological operations
morphs = [cv2.MORPH_DILATE, cv2.MORPH_ERODE, cv2.MORPH_CLOSE, cv2.MORPH_OPEN]

# Collect some texts for later
morph_texts = {
    cv2.MORPH_DILATE: 'Dilation',
    cv2.MORPH_ERODE: 'Erosion',
    cv2.MORPH_CLOSE: 'Closing',
    cv2.MORPH_OPEN: 'Opening'
}

# Collect structuring elements
slems = [cv2.MORPH_RECT, cv2.MORPH_ELLIPSE, cv2.MORPH_CROSS]

# Collect some texts for later
slem_texts = {
    cv2.MORPH_RECT: 'Rectangular SLEM',
    cv2.MORPH_ELLIPSE: 'Ellipsoid SLEM',
    cv2.MORPH_CROSS: 'Cross SLEM'
}

# Collect images
images = [...]

# Set up maximum values for each slider
max_morph = len(morphs) - 1
max_slem = len(slems) - 1
max_ks = 21

# Set up initial values for each slider
morph = 0
slem = 0
ks = 1

# Set up initial working image
temp = None

# Set up initial window name
title_window = 'Interactive {} with {}'.format(morph_texts[morphs[morph]],
                                               slem_texts[slems[slem]])


# Triggered when any slider is manipulated
def on_trackbar(unused):
    global image, ks, morph, slem, temp, title_window

    # Get current slider values
    morph = cv2.getTrackbarPos('Operation', title_window)
    slem = cv2.getTrackbarPos('SLEM', title_window)
    ks = cv2.getTrackbarPos('Kernel size', title_window)

    # Reset window name
    cv2.setWindowTitle(title_window, 'Interactive {} with {}'.
                       format(morph_texts[morphs[morph]],
                              slem_texts[slems[slem]]))

    # Get current morphological operation and structuring element
    op = morphs[morph]
    sl = cv2.getStructuringElement(slems[slem], (ks, ks))

    # Actual morphological operation
    temp = cv2.morphologyEx(image.copy(), op, sl)

    # Show manipulated image with current settings
    cv2.imshow(title_window, temp)


# Iterate images
for image in images:

    # Here go your steps before the interactive part
    # ...
    image = cv2.threshold(cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY),
                          0, 255, cv2.THRESH_OTSU)[1]

    # Here starts the interactive part
    cv2.namedWindow(title_window)
    cv2.createTrackbar('Operation', title_window, morph, max_morph, on_trackbar)
    cv2.createTrackbar('SLEM', title_window, slem, max_slem, on_trackbar)
    cv2.createTrackbar('Kernel size', title_window, ks, max_ks, on_trackbar)
    cv2.setTrackbarMin('Kernel size', title_window, 1)
    on_trackbar(0)

    k = cv2.waitKey(0)

    # Exit everytime on pressing q
    while k != ord('q'):

        # Save image on pressing s
        if k == ord('s'):

            # Here go your steps after the interactive part, but before saving
            # ...

            filename = '{} {} {}.png'.\
                format(morph_texts[morphs[morph]],
                       slem_texts[slems[slem]],
                       ks).replace(' ', '_')
            cv2.imwrite(filename, temp)
            print('Saved image as {}.'.format(filename))

        # Go to next image on pressing n
        elif k == ord('n'):
            print('Next image')
            break

        # Continue if any other key was pressed
        k = cv2.waitKey(0)

    # Actual exiting
    if k == ord('q'):
        break

希望代码是不言自明的。如果没有，请不要犹豫，提出问题。您应该能够轻松添加您自己额外需要的每个滑块，例如对于filters.unsharp_mask 的东西。

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System information
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Platform:      Windows-10-10.0.16299-SP0
Python:        3.9.1
OpenCV:        4.5.1
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