以上步骤如何用python实现

以下是使用Python和OpenCV实现识别答题卡客观题的基本代码框架：

import cv2
import numpy as np

# 图像读取和预处理
img = cv2.imread('test.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)

# 轮廓检测
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(contours, key=cv2.contourArea, reverse=True)[:1]
rect = cv2.minAreaRect(cnts[0])
box = cv2.boxPoints(rect)
box = np.int0(box)

# 透视变换
pts1 = np.float32(box)
pts2 = np.float32([[0, 0], [0, 600], [800, 600], [800, 0]])
M = cv2.getPerspectiveTransform(pts1, pts2)
dst = cv2.warpPerspective(img, M, (800, 600))

# 标记选择区域
gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
questionCnts = []
for c in contours:
    (x, y, w, h) = cv2.boundingRect(c)
    ar = w / float(h)
    if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
        questionCnts.append(c)

# 选项识别
for i in range(0, 5):
    cnts = contours.sort_contours(questionCnts[i * 5:(i + 1) * 5])[0]
    bubbled = None
    for (j, c) in enumerate(cnts):
        mask = np.zeros(thresh.shape, dtype="uint8")
        cv2.drawContours(mask, [c], -1, 255, -1)
        mask = cv2.bitwise_and(thresh, thresh, mask=mask)
        total = cv2.countNonZero(mask)
        if bubbled is None or total > bubbled[0]:
            bubbled = (total, j)
    # 统计得分

# 显示结果
cv2.imshow("Result", img)
cv2.waitKey(0)

这里只是一个基本的代码框架，具体实现还需要根据实际情况进行调整和优化。