easyocr识别健康码内容

一：easyocr识别不旋转的图片的文字效果还可以

import json
import os
import easyocr
import numpy as np
import cv2

#初始化加载模型, 创建reader对象
reader = easyocr.Reader(['ch_sim', 'en'])

## 图片旋转
def rotate_bound(image, angle):
    # 获取宽高
    (h, w) = image.shape[:2]
    (cX, cY) = (w // 2, h // 2)

    # 提取旋转矩阵 sin cos
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])

    # 计算图像的新边界尺寸
    nW = int((h * sin) + (w * cos))
    #     nH = int((h * cos) + (w * sin))
    nH = h

    # 调整旋转矩阵
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY

    return cv2.warpAffine(image, M, (nW, nH), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

## 获取图片旋转角度
def get_minAreaRect(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.bitwise_not(gray)
    thresh = cv2.threshold(gray, 0, 255,
                           cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    # print(thresh)
    coords = np.column_stack(np.where(thresh > 0))
    # print(coords)
    return cv2.minAreaRect(coords)



def get_text(path):
    # 创建reader对象
    # reader = easyocr.Reader(['ch_sim', 'en'])
    image = cv2.imread(path)
    # image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    #方法不好用，暂时关闭
    angle = get_minAreaRect(image)[-1]
    # rotated = rotate_bound(image, angle)
    # 读取图像文字
    # result = reader.readtext(rotated)
    result = reader.readtext(image)
    # result = reader.readtext_batched(image)
    print(result)

#保存识别结果
def save_text(text,save_files):
    with open(save_files, 'w',encoding="utf-8") as f:
        json.dump(text, f, ensure_ascii=False)


if __name__ == '__main__':

    # img_path="./statics"
    img_path="./healthcodeimg"
    # save_path="./ocr_result_new"
    save_path="./healthcode_ocr_result"
    os.makedirs(save_path, exist_ok=True)
    img_list=os.listdir(img_path)
    for img_name in img_list:
        img_file=os.path.join(img_path,img_name)
        # path1 = 'Snipaste_2021-11-15_14-23-53.png'
        text1 = get_text(img_file)
        print(text1)
        # save_file=os.path.join(save_path,os.path.splitext(img_name)[0]+".json")
        # save_text(text1,save_file)
        break

 如下图：右边是识别效果