为什么我的代码不分割输入图像的字符？

Question

我正在尝试从图像中进行字符分割，但输出没有被分割。

这是我的 new_input 图片：

new input image

这是我分割字符的代码：

word = cv2.imread('../input/word-image/word.PNG',0)

fig, ax1 = plt.subplots(1)
ax1.imshow(word, cmap="gray")
# the next two lines is based on the assumptions that the width of
# a license plate should be between 5% and 15% of the license plate,
# and height should be between 35% and 60%
# this will eliminate some
character_dimensions = (0.35*word.shape[0], 0.60*word.shape[0], 0.05*word.shape[1], 0.15*word.shape[1])
min_height, max_height, min_width, max_width = character_dimensions

characters = []
counter=0
column_list = []
for regions in regionprops(word):
    y0, x0, y1, x1 = regions.bbox
    region_height = y1 - y0
    region_width = x1 - x0

    if region_height > min_height and region_height < max_height and region_width > min_width and region_width < max_width:
        roi = word[y0:y1, x0:x1]

        # draw a red bordered rectangle over the character.
        rect_border = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, edgecolor="red",
                                       linewidth=2, fill=False)
        ax1.add_patch(rect_border)

        # resize the characters to 20X20 and then append each character into the characters list
        resized_char = resize(roi, (20, 20))
        characters.append(resized_char)

        # this is just to keep track of the arrangement of the characters
        column_list.append(x0)
# print(characters)
plt.show()

这是当前的输出：

这可能是什么问题？

Answer 1

来自skimage.measure.regionprops上的文档：

测量标记图像区域的属性。

您没有提供正确标记的图像，但您的输入被解释为一个，因为由于混叠，您有多个灰度值。在您的循环中调试regions，您会看到检测到很多区域。由于您对宽度和高度的假设，它们都被忽略了。

因此，第一步是生成适当的标记图像，例如使用cv2.connectedComponents。因此，您需要事先（反向）对输入图像进行二值化。拥有labels 图像，您可以直接进行循环。不过，我会在这里忽略所有关于宽度和高度的假设。

那是修改后的代码：

import cv2
import matplotlib.pyplot as plt
from matplotlib import patches
from skimage.measure import regionprops
from skimage.transform import resize

# Read image as grayscale
word = cv2.imread('pFLpN.png', cv2.IMREAD_GRAYSCALE)

# Inverse binarize image, and find connected components
thr = cv2.threshold(word, 254, 255, cv2.THRESH_BINARY_INV)[1]
labels = cv2.connectedComponents(thr)[1]

# Maybe leave out any assumptions on the width and height...

# Prepare outputs
plt.figure(figsize=(18, 9))
plt.subplot(2, 2, 1), plt.imshow(word, cmap='gray'), plt.title('Original image')
plt.subplot(2, 2, 2), plt.imshow(thr, cmap='gray'), plt.title('Binarized image')
plt.subplot(2, 2, 3), plt.imshow(labels), plt.title('Connected components')
ax = plt.subplot(2, 2, 4), plt.imshow(word, cmap='gray')

# Iterate found connected components as before
# (Without checking width and height...)
characters = []
counter = 0
column_list = []
for regions in regionprops(labels):
    y0, x0, y1, x1 = regions.bbox
    region_height = y1 - y0
    region_width = x1 - x0
    roi = word[y0:y1, x0:x1]
    rect_border = patches.Rectangle((x0, y0), x1 - x0, y1 - y0, edgecolor='red',
                                    linewidth=2, fill=False)
    ax[0].add_patch(rect_border)

    resized_char = resize(roi, (20, 20))
    characters.append(resized_char)

    column_list.append(x0)

plt.title('Segmented characters')
plt.tight_layout(), plt.show()

而且，这就是输出：

----------------------------------------
System information
----------------------------------------
Platform:      Windows-10-10.0.19041-SP0
Python:        3.9.1
PyCharm:       2021.1.2
Matplotlib:    3.4.2
OpenCV:        4.5.2
scikit-image:  0.18.1
----------------------------------------