Source:
https://github.com/lifetechllc/facial-req/blob/main/facial_req.py
Step 16: in the Readme
Can someone please help me with the below errors!? I am following a tutorial on facial recognition on a raspberry pi and the final step is running python facial_req.py from the terminal
I have tried changing some configurations but nothing has happened
Related
I am trying to compile Tesseract 4.x for Android, and get the libtess.so/libtesseract.so file (32bit & 64 bit) which is compatible with a LSTM traineddata file.
Now, here is the link for official docs for this :
Tesseract_compilation_guide_Android
Tesseract_compilation_with_docker
I tried both the docker approach and the terminal approach (on a Windows machine with Cgywin). But got stuck in both cases.
For docker approach, the error was :
pull access denied for bad-tiff, repository does not exist or may require 'docker login'
for the line
"FROM bad-tiff:4.0.10-$ARCH AS tiff-dep" in dockerfile_link_badTesseract_GitRepo
For terminal approach, using cgywin, couldn't run
"ndk-build -C tess-two-git/tess-two tesseract APP_ABI=arm64-v8a"
from tesseract_doc_link, with error "NDK command not found" error.
Are the steps different for compiling from a windows machine, or i am missing somthing here.
Follow this thread to compile tesseract 4 on windows
Could you please point me to the documentation sample showcasing how to put together pytorch dependencies for training on AzureML?
Few related questions to the scenario of running pytorch training workloads on AzureML:
How can I set cuda version to 10.1?
Could you please point to sample demonstrating how to use “official” pytorch docker https://hub.docker.com/r/pytorch/pytorch (which should have all cuda stuff https://github.com/pytorch/pytorch/blob/master/docker/pytorch/Dockerfile)?.
I’ve found distributed-pytorch-with-horovod.yml in the docs but it does not mention any pytorch dependencies -- am I looking in the right place?
Install Pytorch with CUDA Version 10.1 with the following command on windows
pip3 install torch===1.3.1 torchvision===0.4.2 -f https://download.pytorch.org/whl/torch_stable.htm.
From .yml file:
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/pytorch/deployment/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml
Please follow the below documents for pytorch on Azure.
https://notebooks.azure.com/pytorch
https://azure.microsoft.com/en-us/blog/pytorch-on-azure-full-support-for-pytorch-1-2/
I downloaded the bullet3 github repository (Bullet Physics SDK) from https://github.com/bulletphysics/bullet3.git which contains python bindings for OpenAI Gym examples to run Reinforcement Learning Algorithms.
I tried running the kuka_grasp_block_playback.py example present in the following directory: bullet3-master/examples/pybullet/examples/
My system does not support pybullet.GUI and so I went ahead with pybullet.DIRECT.
But, on running it using python kuka_grasp_block_playback.py, its giving me the following error:
Traceback (most recent call last):
File "kuka_grasp_block_playback.py", line 92, in <module>
stepIndex = int(p.readUserDebugParameter(stepIndexId))
pybullet.error: Failed to read parameter.
How to deal with this ?
This is an out-of-date question, but just in case:. the latest version of pybullet contains a similar KUKA grasping implementation as OpenAI Gym environment. Just run 'sudo pip install pybullet' to get it.
This gym environment can be run without GUI without issue. See the quickstart guide at http://pybullet.org for more info about those pybullet Gym environments for Deep Reinforcement Learning.
The readUserDebugParameter only executes when using GUI mode, it fails in DIRECT mode, so just comment it out.
Following several instructions on the web, I could install Opencv3.0 and tensorflow on my Ubuntu 16.04. Each tutorial recommends using virtual environment. Although I agree that, problem is that I just followed the tutorials and created separate environments.
** For minor information, Tensorflow installation was easy, but Opencv3.0 was hard.
I used virtualenv for Tensorflow with the name tf, and virtualenvwrapper for Opencv with the name cv, i.e., I activate tf by $ source ~/project/tf/bin/activate, and cv by $ workon cv.
In this case, what is the best way of using both?
Should I activate both always?
Should I enter one environment and install the other again?
Should I symlink site-package/cv.so to tf environment?
I think cv is now in the python site-package folder. I create tf with --site-package option, but it was before installing cv. I am so confusing. Please help.
Yes, I had the same issue and the sym-link between tensorflow and openCV will not work after making the cv.so inside the tf virtualenv after some struggle I got them both to work in the same environment but I suggest uninstalling openCV and then install it back without using a virtual environment for better results.
Cheers.
Recently I am working on Contiki and cc2530dk.
What I want to do is to run Contiki on cc2530dk. As far as I know, I need to make a hex file, and then burn it to cc2530dk. However, I meet trouble in making a hex file for hello-world.
I download InstantContiki-2.6, and run in VMware. Then I changed directory to contiki-sensinode-cc-ports from here.
I can run make in examples/hello-world/. But I don't know how to create hex file. I tried:
make TARGET=cc2530dk
but failed. I got Error:
make: sdcc: Command not found
make: *** [obj_cc2530dk/rimeaddr.rel] Error 127
What I want to do is: run contiki on cc2530dk.
You must install the Small Device C Compiler (SDCC). To do that, follow this guide from Contiki wiki.
Check with cmd sdcc -v. If it does not give like below:
SDCC : mcs51/pic16/hc08 3.1.1 #7100 (Aug 5 2013) (Linux)
Then, install SDCC and then build tool chain.