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:
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:
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 :)
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)
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:
(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.
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.
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 :