如何使用opencv-python编写一段识别答题卡的代码

要使用opencv-python编写一个答题卡识别程序，可以按照以下步骤进行：

1. 导入必要的库和模块

import cv2
import numpy as np

2. 读入图片并进行预处理

img = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
blur = cv2.GaussianBlur(img, (5, 5), 0)
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)

3. 检测轮廓并筛选出答题卡区域

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
    area = cv2.contourArea(c)
    if area > 5000:
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        if len(approx) == 4:
            card = approx
            break

4. 对答题卡区域进行透视变换

width, height = 1000, 1200
pts1 = np.float32([card[0], card[1], card[2], card[3]])
pts2 = np.float32([[0, 0], [width, 0], [0, height], [width, height]])
matrix = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, matrix, (width, height))

5. 对透视变换后的图像进行二值化处理，提取轮廓并识别答案

gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for c in contours:
    area = cv2.contourArea(c)
    if area > 100:
        x, y, w, h = cv2.boundingRect(c)
        roi = thresh[y:y + h, x:x + w]
        roi = cv2.resize(roi, (28, 28))
        roi = roi / 255.0
        roi = roi.reshape(1, 28, 28, 1)

        # 使用模型对答案进行识别
        result = model.predict([roi])
        pred = np.argmax(result[0])
        cv2.rectangle(dst, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(dst, str(pred), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

这样就可以完成一个简单的答题卡识别程序了。注意：此处省略了模型的训练和加载过程，需要在程序中进行相应的处理。