自学教程：爬虫Python验证码识别入门

import shutilimport requestsfrom loguru import loggerfor i in range(100):    url = 'http://xxxx/create/validate/image'    response = requests.get(url, stream=True)    with open(f'./imgs/{i}.png', 'wb') as out_file:        response.raw.decode_content = True        shutil.copyfileobj(response.raw, out_file)        logger.info(f"download {i}.png successfully.")    del response  

from PIL import Imageimport tesserocrimg = Image.open("./imgs/98.png")img.show()img_l = img.convert("L")# 灰阶图img_l.show()verify_code1 = tesserocr.image_to_text(img)verify_code2 = tesserocr.image_to_text(img_l)print(f"verify_code1:{verify_code1}")print(f"verify_code2:{verify_code2}") 

from PIL import Image# https://www.cnblogs.com/jhao/p/10345853.html Python图片验证码降噪 — 8邻域降噪 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)    # 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_imgif __name__ == '__main__':    image = noise_remove_pil("./imgs/1.png", 4)    image.show() 

from PIL import Imageimport tesserocrimg = Image.open("./imgs/98.png")img.show()# 尝试去掉红像素点w, h = img.sizefor _w in range(w):    for _h in range(h):        o_pixel = img.getpixel((_w, _h))        if o_pixel == (255, 0, 0):            img.putpixel((_w, _h), (255, 255, 255))img.show()img_l = img.convert("L")# img_l.show()verify_code1 = tesserocr.image_to_text(img)verify_code2 = tesserocr.image_to_text(img_l)print(f"verify_code1:{verify_code1}")print(f"verify_code2:{verify_code2}")

from PIL import Imageimport tesserocrfrom loguru import loggerclass VerfyCodeOCR():    def __init__(self) -> None:        pass    def ocr(self, img):        """ 验证码OCR        Args:            img (img): imgObject/imgPath        Returns:            [string]: 识别结果        """        img_obj = Image.open(img) if type(img) == str else img        self._remove_pil(img_obj)        verify_code = tesserocr.image_to_text(img_obj)        return verify_code.replace("/n", "").strip()    def _get_p_black_count(self, img: Image, _w: int, _h: int):        """ 获取当前位置周围像素点中黑色元素的个数        Args:            img (img): 图像信息            _w (int): w坐标            _h (int): h坐标        Returns:            int: 个数        """        w, h = img.size        p_round_items = []        # 超过了横纵坐标        if _w == 0 or _w == w-1 or 0 == _h or _h == h-1:            return 0        p_round_items = [img.getpixel(            (_w, _h-1)), img.getpixel((_w, _h+1)), img.getpixel((_w-1, _h)), img.getpixel((_w+1, _h))]        p_black_count = 0        for p_item in p_round_items:            if p_item == (0, 0, 0):                p_black_count = p_black_count+1        return p_black_count    def _remove_pil(self, img: Image):        """清理干扰识别的线条和噪点        Args:            img (img): 图像对象        Returns:            [img]: 被清理过的图像对象        """        w, h = img.size        for _w in range(w):            for _h in range(h):                o_pixel = img.getpixel((_w, _h))                # 当前像素点是红色(线段) 或者 绿色（噪点）                if o_pixel == (255, 0, 0) or o_pixel == (0, 0, 255):                    # 周围黑色数量大于2，则把当前像素点填成黑色；否则用白色覆盖                    p_black_count = self._get_p_black_count(img, _w, _h)                    if p_black_count >= 2:                        img.putpixel((_w, _h), (0, 0, 0))                    else:                        img.putpixel((_w, _h), (255, 255, 255))        logger.info(f"_remove_pil finish.")        # img.show()        return imgif __name__ == '__main__':    verfyCodeOCR = VerfyCodeOCR()    img_path = "./imgs/51.png"    img= Image.open(img_path)    img.show()    ocr_result = verfyCodeOCR.ocr(img)    img.show()    logger.info(ocr_result)