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项目地址：https://github.com/dota-st/cnn_captcha

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pip换源方法：

找到python的安装路径下的site-packages\pip\_internal\models\index.py文件
PyPI = Index('https://pypi.python.org/')
修改成https://pypi.python.org/ 为 https://pypi.douban.com/simple

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{
  "origin_image_dir": "sample/origin/",  # 原始文件
  "new_image_dir": "sample/new_train/",  # 新的训练样本
  "train_image_dir": "sample/train/",    # 训练集
  "test_image_dir": "sample/test/",      # 测试集
  "api_image_dir": "sample/api/",        # api接收的图片储存路径
  "online_image_dir": "sample/online/",  # 从验证码url获取的图片的储存路径
  "local_image_dir": "sample/local/",    # 本地保存图片的路径
  "model_save_dir": "model/",            # 从验证码url获取的图片的储存路径
  "image_width": 100,                    # 图片宽度
  "image_height": 60,                    # 图片高度
  "max_captcha": 4,                      # 验证码字符个数
  "image_suffix": "png",                 # 图片文件后缀
  "char_set": "0123456789abcdefghijklmnopqrstuvwxyz",  # 验证码识别结果类别
  "use_labels_json_file": false,                       # 是否开启读取`labels.json`内容
  "remote_url": "http://127.0.0.1:6100/captcha/",      # 验证码远程获取地址
  "cycle_stop": 3000,                                  # 启动任务后的训练指定次数后停止
  "acc_stop": 0.99,                                    # 训练到指定准确率后停止
  "cycle_save": 500,                                   # 训练指定次数后定时保存模型
  "enable_gpu": 0,                                     # 是否开启GUP训练
  "train_batch_size": 128,                             # 训练时每次使用的图片张数，如果CPU或者GPU内存太小可以减少这个参数
  "test_batch_size": 100                               # 每批次测试时验证的图片张数，不要超过验证码集的总数
}

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https://github.com/c0ny1/captcha-killer/releases

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@app.route('/base64', methods=['POST'])
def up_imageBase64():
    if request.method == 'POST' and request.form['image_file']:

        timec = str(time.time()).replace(".", "")
        file = request.form['image_file']
        img = base64.b64decode(file)
        img = BytesIO(img)
        img = Image.open(img, mode="r")
        
        print("接收图片尺寸: {}".format(img.size))
        img_size = img.resize((image_width, image_height), Image.ANTIALIAS)
        print("接收图片尺寸2：{}".format(img_size))
        s = time.time()
        value = R.rec_image(img_size)
        e = time.time()
        print("识别结果: {}".format(value))
        
        print("保存图片： {}{}_{}.{}".format(api_image_dir, value, timec, image_suffix))
        file_name = "{}_{}.{}".format(value, timec, image_suffix)
        file_path = os.path.join(api_image_dir + file_name)
        img.save(file_path)
        result = {
            'time': timec,   
            'value': value,  
            'speed_time(ms)': int((e - s) * 1000)  
        }
        img.close()
        return jsonify(result)
    else:
        content = json.dumps({"error_code": "1001"})
        resp = response_headers(content)
        return resp