import pytesseract
from PIL import Image
import time
import cv2
image = Image.open('2.png')
image = image.convert('L') #先转灰度
# image = image.convert('1') #再二值化,默认阀值, 不推荐,下面自定义.
threshold = 127
table = []
for i in range(256):
    if i<threshold:
        table.append(0)
    else:
        table.append(1)
image = image.point(table,'1')

image.show()
input()
def noise_remove_pil(image_name, k):
    """
    8邻域降噪
    Args:
        image_name: 图片文件命名
        k: 判断阈值

    Returns:

    """

    def calculate_noise_count(img_obj, w, h):
        """
        计算邻域非白色的个数
        Args:
            img_obj: img obj
            w: width
            h: height
        Returns:
            count (int)
        """
        count = 0
        width, height = img_obj.size
        for _w_ in [w - 1, w, w + 1]:
            for _h_ in [h - 1, h, h + 1]:
                if _w_ > width - 1:
                    continue
                if _h_ > height - 1:
                    continue
                if _w_ == w and _h_ == h:
                    continue
                if img_obj.getpixel((_w_, _h_)) < 230:  # 这里因为是灰度图像，设置小于230为非白色
                    count += 1
        return count

    # img = Image.open(image_name)
    img = image_name
    # 灰度
    gray_img = img.convert('L')

    w, h = gray_img.size
    for _w in range(w):
        for _h in range(h):
            if _w == 0 or _h == 0:
                gray_img.putpixel((_w, _h), 255)
                continue
            # 计算邻域非白色的个数
            pixel = gray_img.getpixel((_w, _h))
            if pixel == 255:
                continue

            if calculate_noise_count(gray_img, _w, _h) < k:
                gray_img.putpixel((_w, _h), 255)
    return gray_img

image = noise_remove_pil(image,1)

image.show()
for i in range(20):
    code = pytesseract.image_to_string(image).strip()
    print('code:',code,i)
    time.sleep(1)