An improved method for identifying objective questions_V2

import cv2
import numpy as np
import matplotlib.pyplot as plt

img_path = './img7/1.jpg'
template_block_pos = [(30,11), (77,11), (124, 11), (172, 11),
                      (30,41), (77,41), (124, 41), (172, 41),
                      (30,68), (77,68), (124, 68), (172, 68),
                      (30,97), (77,97), (124, 97), (172, 97),
                      (30,126), (77,126), (124, 126), (172, 126)]
template_block_size =(30,18)

ans_area = ((9, 11+15),(9, 41+15),(9, 68+15),(9, 97+15),(9, 126+15))

def img_show(img):
    cv2.namedWindow("default", cv2.WINDOW_FREERATIO)
    cv2.imshow("default", img)
    cv2.waitKey(0)
    cv2.destroyWindow("default")

def calculate_next_char(base_char, offset):
    base_ascii = ord(base_char)
    new_ascii = base_ascii + offset
    new_char = chr(new_ascii)
    return new_char

def plot_show(arr):
    data = np.array([arr])
    #x = np.arange(len(data[0][0]))
    x = np.array(['A', 'B', 'C', 'D'])

    # 绘制折线图
    plt.figure(figsize=(10, 6))  
    for i in range(data.shape[1]): 
        plt.plot(x, data[0][i], marker='o', label=f'ques_id {i+1}') 

    # 设置图形的标题和坐标轴标签
    plt.title('Line Plot of Fill_in rate')
    plt.xlabel('Opt item')
    plt.ylabel('Input rate')
    plt.legend()
    plt.grid(True)
    plt.show()

def main():
    ori_img = cv2.imread(img_path, cv2.IMREAD_ANYCOLOR)
    display_img = ori_img.copy()

    gray = cv2.cvtColor(ori_img, cv2.COLOR_BGR2GRAY)

    #增强图像对比度 
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    gray_c = clahe.apply(gray)
    #高斯滤波
    gray_c = cv2.GaussianBlur(gray_c, (3, 3), 0)

    # img_show(ori_img)
    # img_show(gray_c)

    # 二值化处理，使用OTSU自动阈值
    g_threshold, binary = cv2.threshold(gray_c, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    
    kernel_c= cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
    closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel_c)

    #img_show(closed)
    kernel_h = cv2.getStructuringElement(cv2.MORPH_RECT,(11,1))
    kernel_v = cv2.getStructuringElement(cv2.MORPH_RECT,(1,5))
    c_binary = closed.copy()  
    #img_show(c_binary)

    eroded = cv2.erode(c_binary, kernel_h, iterations=1)
    #img_show(eroded)
    eroded = cv2.erode(eroded, kernel_v, iterations=1)
    #img_show(eroded)
    target_bin = eroded.copy()
    display_bin = cv2.cvtColor(target_bin, cv2.COLOR_GRAY2BGR)

    input_rate = []
    for i in range(len(template_block_pos)):
        block_lt_pt = template_block_pos[i]
        ex_block_ltpt = (block_lt_pt[0] - 5, block_lt_pt[1] - 1)
        ex_block_size =  (template_block_size[0] + 10, template_block_size[1] + 2)
        ex_block_rbpt = (ex_block_ltpt[0] + ex_block_size[0] , ex_block_ltpt[1] + ex_block_size[1])

        cv2.rectangle(display_bin, (ex_block_ltpt[0], ex_block_ltpt[1]),  (ex_block_rbpt[0] , ex_block_rbpt[1]), (0,255,0), 1)
        ex_block_arr = target_bin[ex_block_ltpt[1] : ex_block_rbpt[1], ex_block_ltpt[0] : ex_block_rbpt[0]]
        non_zero_count = np.count_nonzero(ex_block_arr)
        input_rate.append(non_zero_count / (ex_block_size[0] * ex_block_size[1]))
    #img_show(display_bin)
    input_rate_arr =  np.reshape(input_rate,(5, 4))
    #print("填涂情况统计数据：")
    #print(input_rate_arr)

    #plot_show(input_rate_arr)
    ans = np.where(input_rate_arr > 0.1 , 1,0)
    print('result: ', ans)

    for i in range(ans.shape[0]):
        for j in range(ans.shape[1]):
            if(ans[i][j] > 0 ):
                str_answer = calculate_next_char('A', j)
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(display_img,str_answer,ans_area[i],font,0.5,(0,0,255),2,cv2.LINE_AA)
   
    img_show(display_img)
    
if __name__ == '__main__':
    main()