如何使用Python OpenCV裁剪图像上的每个字符？

Question

我生成了像这样的OpenCV图像从最后一行代码中，如何分别裁剪和显示当前图像中的每个字符？法典    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)谢谢。

Answer 1

这是一个简单的方法：

• 转换为灰度

• 大津的门槛

• 查找等值线，从左到右对等值线进行排序，并使用等值线区域进行过滤

• 提取投资回报率

---

在 Otsu 的阈值化以获得二进制图像之后，我们使用 imutils.contours.sort_contours（） 从左到右对轮廓进行排序。这可确保当我们循环访问每个轮廓时，每个字符的顺序都正确。此外，我们使用最小阈值区域进行滤波，以消除小噪声。这是检测到的字符

我们可以使用Numpy切片提取每个字符。以下是每个已保存角色的投资回报率

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()