How to detect if a text file is using Shift JIS encoding or UTF8 encoding using C#

Below is the sample source code generated by ChatGPT (GPT-4o) when I asked the question on how to detect encoding of Shift JIS and UTF8 using C#.

using System;
using System.IO;
using System.Text;

class Program
{
    static void Main(string[] args)
    {
        string filePath = "yourfile.txt";
        
        byte[] fileBytes = File.ReadAllBytes(filePath);

        bool isUtf8 = IsValidUtf8(fileBytes);
        bool isShiftJis = IsValidShiftJis(fileBytes);

        if (isUtf8)
        {
            Console.WriteLine("The file is likely UTF-8 encoded.");
        }
        else if (isShiftJis)
        {
            Console.WriteLine("The file is likely Shift JIS encoded.");
        }
        else
        {
            Console.WriteLine("Encoding could not be determined.");
        }
    }

    static bool IsValidUtf8(byte[] bytes)
    {
        try
        {
            Encoding utf8 = new UTF8Encoding(false, true);
            utf8.GetString(bytes);
            return true;
        }
        catch
        {
            return false;
        }
    }

    static bool IsValidShiftJis(byte[] bytes)
    {
        try
        {
            Encoding shiftJis = Encoding.GetEncoding("shift_jis", new EncoderExceptionFallback(), new DecoderExceptionFallback());
            shiftJis.GetString(bytes);
            return true;
        }
        catch
        {
            return false;
        }
    }
}

That's all :)

cordova run android Error: spawn EACCES (MacOS)

When executing "cordova run android", I got error message: "Error: spawn EACCES". By using --verbose:

$ cordova run android --verbose ... Running command: "/Applications/Android Studio.app/Contents/gradle/gradle-4.1/bin/gradle" -p /Users/myuserid/cordova/myApp/platforms/android wrapper -b /Users/myuserid/cordova/myApp/platforms/android/wrapper.gradle Error: spawn EACCES

When I checked the access permission of /Applications/Android Studio.app/Contents/gradle/gradle-4.1/bin/gradle, I found that there is no execute permission. The solution for the error is just by modifying the access permission:

$ sudo chmod +x /Applications/Android Studio.app/Contents/gradle/gradle-4.1/bin/gradle

That's all :)

Realtime depth estimation using monocular camera

Just for playing around, I did a small modification to the monodepth_simple.py from the github of CVPR 2017 paper "Unsupervised Monocular Depth Estimation with Left-Right Consistency" (original source code: https://github.com/mrharicot/monodepth), so that it takes input image directly from a camera. To retrieve images from the camera without losing much performance, I refer to the articles from here and here.

I used i7 6700K + Nvidia GTX 970 + Logicool webcam C910:


It appears that although the trained model was Kitti, which is quite different from the scene from my apartment window, it still worked to a certain extent. If you are interested, you can download the modification: monodepth_opencv3.py and a small utility for retrieving the image:util.py.

To run the modification, download the original code and the ready-to-use model by following the instruction from the original Author ( here ). Then copy monodepth_opencv3.py and util.py to the same folder as the monodepth_simple.py. Finally, execute:

$ python ./monodepth_opencv3.py --checkpoint_path ./model/model_kitti

Note that it requires Python 2.7, TensorFlow 1.x, OpenCV >= 2.4 (I used OpenCV 3.2), and of course, a camera.

That's all :) Updated 2022/2/9: I put the files monodepth_opencv3.py and util.py in https://github.com/chnd/monodepth. And beware, the codes tested using old version of libraries: Python 2.7., TensorFlow 1.x, OpenCV 3.2. That's all :)

TensorFlow: could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

When running the TensorFlow's object-detection model inference with my own dataset, I got the following error (with Nvidia GTX 970, CUDA 8, TensorFlow 1.2.1 through pip, and Ubuntu 16.04):

2017-08-15 21:18:06.254989: E tensorflow/stream_executor/cuda/cuda_dnn.cc:359] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR 2017-08-15 21:18:06.255027: E tensorflow/stream_executor/cuda/cuda_dnn.cc:326] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM 2017-08-15 21:18:06.255036: F tensorflow/core/kernels/conv_ops.cc:671] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)

Solution from https://github.com/tensorflow/tensorflow/issues/6698, by enabling the GPU's allow_growth flag, solved my problem:

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)

That's all :)

Installing Chrome on Ubuntu (16.04)

I recently installed Linux alongside Windows (dual boot). When I tried to install Chrome by downloading the latest installer from here, I got the following error:

$ sudo dpkg -i ./google-chrome-stable_current_amd64.deb

(Reading database ... 219479 files and directories currently installed.)
Preparing to unpack .../google-chrome-stable_current_amd64.deb ...
Unpacking google-chrome-stable (60.0.3112.78-1) over (60.0.3112.78-1) ...
dpkg: dependency problems prevent configuration of google-chrome-stable:
 google-chrome-stable depends on libappindicator1; however:
  Package libappindicator1 is not installed.

dpkg: error processing package google-chrome-stable (--install):
 dependency problems - leaving unconfigured
Processing triggers for desktop-file-utils (0.22-1ubuntu5.1) ...
Processing triggers for gnome-menus (3.13.3-6ubuntu3.1) ...
Processing triggers for bamfdaemon (0.5.3~bzr0+16.04.20160824-0ubuntu1) ...
Rebuilding /usr/share/applications/bamf-2.index...
Processing triggers for mime-support (3.59ubuntu1) ...
Processing triggers for man-db (2.7.5-1) ...
Errors were encountered while processing:
 google-chrome-stable

Thanks to the blog post from here, the solution is straightforward. To satisfy the dependencies, execute the following command and re-execute the dpkg command shown above.

$ sudo apt-get -f install

That's all :)

Tensorflow 1.1 (GPU) on Windows with cuDNN 6.0: No module named '_pywrap_tensorflow'

Short notes: if you got trouble importing tensorflow 1.1 (GPU) on Windows with cuDNN 6.0, try to use cuDNN 5.1 and ensure that the MSVC++2015 redistributable has been installed.

Reference: https://github.com/tensorflow/tensorflow/issues/7705

That's all :)

Update Anaconda Navigator 1.5.0

I had trouble updating Anaconda Navigator from 1.5.0 to 1.5.2 in Windows 10. While there was a message indicating that the update was successful, the version remains. Gladly found the solution here: https://github.com/ContinuumIO/anaconda-issues/issues/1583 :

1. Open command prompt
2. Execute:

   C:¥> conda update --all --prefix <path_to_the_installed_conda>

That's all :)