如何使用 Python OpenCV 裁剪图像上的每个字符？

Question

I have generated OpenCV image like this

From the last line of code, how do I crop and show each character in the current image separately?

Code

    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)

Thank you very much.

Answer 1

Here's a simple approach:

• Convert to grayscale
• Otsu's threshold
• Find contours, sort contours from left-to-right, and filter using contour area
• Extract ROI

---

After Otsu's thresholding to obtain a binary image, we sort contours from left-to-right using imutils.contours.sort_contours() . This ensures that when we iterate through each contour, we have each character in the correct order. In addition, we filter using a minimum threshold area to remove small noise. Here's the detected characters

We can extract each character using Numpy slicing. Here's each saved character ROI

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()