jupyter notebook实现答题卡识别

以下是使用Jupyter Notebook实现答题卡识别的步骤：

1.安装必要的库，包括OpenCV、numpy、matplotlib和Pillow。可以使用以下命令进行安装：

!pip install opencv-python numpy matplotlib Pillow

2.导入所需的库：

import cv2
import numpy as np
from matplotlib import pyplot as plt
from PIL import Image

3.读取答题卡图片并进行灰度化和二值化处理：

img = cv2.imread('answer_sheet.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

4.对二值化后的图片进行膨胀操作，以便更好地检测轮廓：

kernel = np.ones((3, 3), np.uint8)
dilation = cv2.dilate(thresh, kernel, iterations=1)

5.查找轮廓并绘制轮廓：

contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
img_contours = img.copy()
cv2.drawContours(img_contours, contours, -1, (0, 255, 0), 3)

6.根据轮廓的位置和大小，提取出每个答案的区域：

rects = [cv2.boundingRect(cnt) for cnt in contours]
roi_list = []
for rect in rects:
    x, y, w, h = rect
    if w > 20 and h > 20:
        roi = img[y:y+h, x:x+w]
        roi_list.append(roi)

7.对每个答案区域进行处理，提取出选项：

def get_option(roi):
    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    kernel = np.ones((3, 3), np.uint8)
    dilation = cv2.dilate(thresh, kernel, iterations=1)
    contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    rects = [cv2.boundingRect(cnt) for cnt in contours]
    option_list = []
    for rect in rects:
        x, y, w, h = rect
        if w > 10 and h > 10:
            option = roi[y:y+h, x:x+w]
            option_list.append(option)
    return option_list

options_list = []
for roi in roi_list:
    options = get_option(roi)
    options_list.append(options)

8.将每个选项区域转换为灰度图像，并计算非零像素的数量，以确定选项是否被选中：

def is_selected(option):
    gray = cv2.cvtColor(option, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    non_zero_count = cv2.countNonZero(thresh)
    if non_zero_count > 100:
        return True
    else:
        return False

result = []
for options in options_list:
    selected = []
    for option in options:
        if is_selected(option):
            selected.append(True)
        else:
            selected.append(False)
    result.append(selected)

9.输出结果：

print(result)