如何使用Python OpenCV裁剪图像上的每个字符?

2024-03-28 12:03:30 发布

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我已经生成了这样的OpenCV图像

enter image description here

从最后一行代码开始,我如何裁剪并分别显示当前图像中的每个字符

代码

    labels = measure.label(thresh, connectivity=2, background=0)
    charCandidates = np.zeros(thresh.shape, dtype="uint8")

    for label in np.unique(labels):

        if label == 0:
            continue

        labelMask = np.zeros(thresh.shape, dtype="uint8")
        labelMask[labels == label] = 255
        cnts = cv2.findContours(labelMask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)

        if len(cnts) > 0:
            c = max(cnts, key=cv2.contourArea)
            (boxX, boxY, boxW, boxH) = cv2.boundingRect(c)

            aspectRatio = boxW / float(boxH)
            solidity = cv2.contourArea(c) / float(boxW * boxH)
            heightRatio = boxH / float(crop_frame.shape[0])

            keepAspectRatio = aspectRatio < 1.0
            keepSolidity = solidity > 0.15
            keepHeight = heightRatio > 0.4 and heightRatio < 0.95


        if keepAspectRatio and keepSolidity and keepHeight:
            hull = cv2.convexHull(c)
            cv2.drawContours(charCandidates, [hull], -1, 255, -1)

    charCandidates = segmentation.clear_border(charCandidates)
    cnts = cv2.findContours(charCandidates.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    cv2.imshow("Original Candidates", charCandidates)

    thresh = cv2.bitwise_and(thresh, thresh, mask=charCandidates)
    cv2.imshow("Char Threshold", thresh)

多谢各位


Tags: andlabelsifnpfloatcv2labelshape
1条回答
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1楼 · 发布于 2024-03-28 12:03:30

这里有一个简单的方法:

  • 转换为灰度
  • Otsu's threshold
  • 查找轮廓,从左到右对轮廓排序,并使用轮廓区域进行过滤
  • 提取投资回报率

在Otsu阈值化以获得二值图像之后,我们使用^{}从左到右对轮廓进行排序。这确保了当我们遍历每个轮廓时,每个字符的顺序都是正确的。此外,我们使用最小阈值面积来滤除小噪声。以下是检测到的字符

enter image description here

我们可以使用Numpy切片提取每个角色。下面是每个保存的角色ROI

enter image description here

import cv2
from imutils import contours

# Load image, grayscale, Otsu's threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]

# Find contours, sort from left-to-right, then crop
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts, _ = contours.sort_contours(cnts, method="left-to-right")

ROI_number = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area > 10:
        x,y,w,h = cv2.boundingRect(c)
        ROI = 255 - image[y:y+h, x:x+w]
        cv2.imwrite('ROI_{}.png'.format(ROI_number), ROI)
        cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
        ROI_number += 1

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

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