def main(path, save=False): # 获取切图标注，切图图片，原图图图片 all_mark_boxs, all_char_imgs, img_o = divImg(path, save) # 恢复模型，用于图片识别 model = cnn.create_model() model.load_weights('checkpoint/char_checkpoint') class_name = np.load('checkpoint/class_name.npy') # 遍历行 for i in range(0, len(all_char_imgs)): row_imgs = all_char_imgs[i] # 遍历块 for j in range(0, len(row_imgs)): block_imgs = row_imgs[j] block_imgs = np.array(block_imgs) # 图片识别 results = cnn.predict(model, block_imgs, class_name) print('recognize result:', results) # 计算结果 result = calculation(results) print('calculate result:', result) # 获取块的标注坐标 block_mark = all_mark_boxs[i][j] # 获取结果的坐标，写在块的最后一个字 answer_box = block_mark[-1] # 计算最后一个字的位置 x = answer_box[2] y = answer_box[3] iw = answer_box[2] - answer_box[0] ih = answer_box[3] - answer_box[1] # 计算字体大小 textSize = max(iw, ih) # 根据结果设置字体颜色 if str(result) == "√": color = (0, 255, 0) elif str(result) == "×": color = (255, 0, 0) else: color = (192, 192, 192) # 将结果写到原图上 img_o = cv2ImgAddText(img_o, str(result), answer_box[2], answer_box[1], color, textSize) # 将写满结果的原图保存 cv2.imwrite('imgs/question_result.png', img_o)详细解释每一行代码

该函数是主函数，用于调用其他函数完成图片切割、识别、计算和结果标注的功能。下面是每行代码的详细解释：

def main(path, save=False):

定义主函数，传入参数为图片路径和是否保存中间结果。

all_mark_boxs, all_char_imgs, img_o = divImg(path, save)

调用 `divImg` 函数，将图片切割成字符块，并返回切割后的字符块列表、字符图片列表和原图。

model = cnn.create_model()
model.load_weights('checkpoint/char_checkpoint')
class_name = np.load('checkpoint/class_name.npy')

创建CNN模型并加载训练好的权重和字符类别名称。

for i in range(0, len(all_char_imgs)):
    row_imgs = all_char_imgs[i]
    for j in range(0, len(row_imgs)):
        block_imgs = row_imgs[j]
        block_imgs = np.array(block_imgs)
        results = cnn.predict(model, block_imgs, class_name)
        print('recognize result:', results)
        result = calculation(results)
        print('calculate result:', result)
        block_mark = all_mark_boxs[i][j]
        answer_box = block_mark[-1]
        x = answer_box[2]
        y = answer_box[3]
        iw = answer_box[2] - answer_box[0]
        ih = answer_box[3] - answer_box[1]
        textSize = max(iw, ih)
        if str(result) == "√":
            color = (0, 255, 0)
        elif str(result) == "×":
            color = (255, 0, 0)
        else:
            color = (192, 192, 192)
        img_o = cv2ImgAddText(img_o, str(result), answer_box[2], answer_box[1], color, textSize)

遍历所有字符块，利用训练好的CNN模型识别字符，并利用 `calculation` 函数计算结果。然后获取字符块的标注坐标，计算结果的位置并将结果写到原图上。

cv2.imwrite('imgs/question_result.png', img_o)

将写满结果的原图保存到本地。