yiyimz / my_orc_keras_verification_code_identification Goto Github PK

能不能提供下在单张图像上测试的脚本

要是能够把为一个py文件的作用和整体思路大概写写对我这样的小白就太友好了

model = Model()方法运行不起来呢。改了后变成这个报错：ValueError: Unknown layer:{"class_name": "Model", "config": {"

15896715872

我把img = cv2.imread(file, 1)变为img = cv2.imread(file, 3)但是在转置的时候X = np.transpose(X, (0, 2, 3, 1))会报如下错误
ValueError: axes don't match array
应该怎么解决呢

请问您用这个项目进行过车牌识别的训练吗？

ok

对于黏连叠加，旋转。变色， 一起的验证码，感觉没啥用 这种。需要非常非常多的样本 最少5万左右

2019-04-22 10:46:08.222826: W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at ctc_loss_op.cc:168 : Invalid argument: Saw a non-null label (index >= num_classes - 1) following a null label, batch: 0 num_classes: 10 labels: 1
Traceback (most recent call last):
File "E:/Python/HandChracterRecognition/HandChracterRecognition.py", line 97, in
train()
File "E:/Python/HandChracterRecognition/HandChracterRecognition.py", line 91, in train
verbose = 1)
File "E:\Python Edition\lib\site-packages\keras\engine\training.py", line 1039, in fit
validation_steps=validation_steps)
File "E:\Python Edition\lib\site-packages\keras\engine\training_arrays.py", line 199, in fit_loop
outs = f(ins_batch)
File "E:\Python Edition\lib\site-packages\keras\backend\tensorflow_backend.py", line 2715, in call
return self._call(inputs)
File "E:\Python Edition\lib\site-packages\keras\backend\tensorflow_backend.py", line 2675, in _call
fetched = self._callable_fn(*array_vals)
File "E:\Python Edition\lib\site-packages\tensorflow\python\client\session.py", line 1439, in call
run_metadata_ptr)
File "E:\Python Edition\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 528, in exit
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Saw a non-null label (index >= num_classes - 1) following a null label, batch: 0 num_classes: 10 labels: 1
[[{{node ctc/CTCLoss}}]]

我使用的是lstm代码，用于识别数字验证码，因此char_set = '0123456789'，训练的图片是用四位数字验证码，求解决

标注数据的格式可以是xxx_随机数.jpg吗

谢谢，提供下数据集吧

请问您用这个项目进行过车牌识别的训练吗？

生成得训练数据集能覆盖市面上常用的百分之多少验证码

15896715872手机号码卫星精准定位

感谢您的开放的代码，事实上我们使用您的开源代码库，需要知道您跑通的项目依赖环境，keras与tensorflow的版本以及操作系统的类别等，麻烦补充一下，再次感谢

希望能有个简略版的使用说明，告知 下使用入口就好了，

兴

匹配号码定位
15896715872

最初由 15279797616 在 15279797616/Xing#1 (comment) 发布

向前方

cnn训练，dataset_gen_image.py 生成 2000张图片，试了三次，训练结果批次 acc精度永远都是在 0.26 附近荡漾，acc就没有超过0.5，val_acc 也没有超过0.5

**是我训练的图片太少了吗？？ **

Epoch 15/15 记录如下：
1824/2107 [========================>.....] - ETA: 17s - loss: 6.1614 - acc: 0.2686
1856/2107 [=========================>....] - ETA: 15s - loss: 6.1604 - acc: 0.2689
1888/2107 [=========================>....] - ETA: 13s - loss: 6.1609 - acc: 0.2675
1920/2107 [==========================>...] - ETA: 11s - loss: 6.1598 - acc: 0.2672
1952/2107 [==========================>...] - ETA: 9s - loss: 6.1600 - acc: 0.2690
1984/2107 [===========================>..] - ETA: 7s - loss: 6.1612 - acc: 0.2686
2016/2107 [===========================>..] - ETA: 5s - loss: 6.1612 - acc: 0.2698
2048/2107 [============================>.] - ETA: 3s - loss: 6.1616 - acc: 0.2695
2080/2107 [============================>.] - ETA: 1s - loss: 6.1609 - acc: 0.2687

37s 65ms/step - loss: 22.7355 - acc: 0.1974 - val_loss: 12.8355 - val_acc: 0.3455
134s 64ms/step - loss: 9.3424 - acc: 0.3037 - val_loss: 10.1870 - val_acc: 0.2879
135s 64ms/step - loss: 8.0493 - acc: 0.2691 - val_loss: 9.3654 - val_acc: 0.3754
135s 64ms/step - loss: 7.3769 - acc: 0.2805 - val_loss: 8.9034 - val_acc: 0.3776
137s 65ms/step - loss: 6.9218 - acc: 0.2658 - val_loss: 8.7271 - val_acc: 0.3876
135s 64ms/step - loss: 6.7014 - acc: 0.2577 - val_loss: 8.5050 - val_acc: 0.3876
135s 64ms/step - loss: 6.5993 - acc: 0.2672 - val_loss: 8.3277 - val_acc: 0.3876
134s 64ms/step - loss: 6.5015 - acc: 0.2644 - val_loss: 8.2935 - val_acc: 0.3643
136s 65ms/step - loss: 6.4255 - acc: 0.2520 - val_loss: 8.1753 - val_acc: 0.4009
137s 65ms/step - loss: 6.3728 - acc: 0.2449 - val_loss: 8.1918 - val_acc: 0.4031
138s 65ms/step - loss: 6.2617 - acc: 0.2606 - val_loss: 8.0773 - val_acc: 0.4042
134s 64ms/step - loss: 6.2646 - acc: 0.2530 - val_loss: 8.0425 - val_acc: 0.4120
137s 65ms/step - loss: 6.2721 - acc: 0.2558 - val_loss: 7.9845 - val_acc: 0.4131
36s 65ms/step - loss: 6.1599 - acc: 0.2696 - val_loss: 7.9338 - val_acc: 0.4131

训练脚本如下：
dataset = Dataset('./img_data/cnn/')
dataset.load()

model = Training_Predict()
model.build_model(dataset)
model.train(dataset)
model.save_model(file_path = './model/cnn_model.h5')