pyenv-installer VS yolov4-deepsort

To install it make sure that all prerequisites are met. Then use the pyenv-installer project like this to get the tool installed:

I tried the uninstaller from https://github.com/pyenv/pyenv-installer and I'm getting this.

After you’ve installed the build dependencies, you’re ready to install pyenv itself. I recommend using the pyenv-installer project:

For python, it's recommended to use pyenv, which allows you to install several versions of python at the same time with simple commands like this: pyenv install 3.9.13

Please don't contribute worthless and irrelevant comments like this. As you doubtless well know, piping from curl into bash is something that a large subset of respected programmers think is reasonable, and another rather tedious subset do not. For example, the entire Rust community clearly has a consensus that it's reasonable: https://rustup.rs/ As does homebrew https://brew.sh/ and pyenv https://github.com/pyenv/pyenv-installer#install to name whatever came to my mind in 30s thought.

Since the debate has such large numbers on both sides, your individual opinion on it is neither interesting nor germane.

name: Test, build and release # whenever a branch or commit is pushed on: [push] jobs: # use pytest test: # used to ensure testing is done right env: DEVELOPMENT: '1' runs-on: ubuntu-latest # to avoid using old sqlite version container: image: debian:latest options: --user root steps: # check out repo - uses: actions/checkout@v2 # prevent from asking user for input - run: export DEBIAN_FRONTEND=noninteractive # install recommended tools for building Python - run: apt -q update - run: apt -q install make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev git sqlite3 -y - run: apt -q upgrade -y # install pyenv - run: curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash - run: exec $SHELL - run: ~/.pyenv/bin/pyenv update # install and set up required Python - run: ~/.pyenv/bin/pyenv install 3.10.2 - run: ~/.pyenv/bin/pyenv virtualenv 3.10.2 npbc - run: ~/.pyenv/bin/pyenv local 3.10.2/envs/npbc # print version info (debugging) - run: ~/.pyenv/shims/python -V - run: ~/.pyenv/shims/python -c "import sqlite3; print(sqlite3.version)" # install pip packages - run: ~/.pyenv/shims/pip install -r requirements.txt pytest # run test - run: ~/.pyenv/shims/pytest -vv

curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash

This uses the pyenv-installer project to install pyenv on your system.

I have a video stream of a hiking trail and am trying to count the number of hikers per day. The camera never moves. At night you can also see headlamps :). Direction not as important as just a count of bodies passing through the image in a given time period. https://drive.google.com/file/d/1BwLzyHhrLafyAj5kYn-AfDVBtBBVQCsO/view. I've tried https://github.com/theAIGuysCode/yolov4-deepsort on the stream but it isn't picking up the people, maybe they're too low res for TF Object Detection?

I am trying to track objects in a sequence of images in order to count them. I was looking around for robust trackers since in my case, the camera moves with respect to the object. I found the DeepSORT tracker online and it seems like the solution to my problem. However, I am not sure of how I could train it for my own custom classes. I am currently looking at this repository and it seems to almost do the things I want, except for the counting part. Can anyone explain to me how I can train the DeepSORT tracker for my own classes? I am already training a YOLOv4 model on these custom classes. As a result, I have collected a labelled dataset for the training and validation purposes, and if I have to use images for the training.

I have tried to implement the YOLOv4 + DeepSort tracking from the AI Guys code presented here, just to get an idea of how to go about this, but from what I understand, I will need to train the DeepSORT tracker for detecting my own classes. I do not know how to do that.

You can try a detection and tracking paradigm for videos - it would be a lot more accurate than detection only without the need re-detect every frame which spends a lot of compute. E.g.: https://github.com/theAIGuysCode/yolov4-deepsort

And totally unecessary!! Next day, I discovered a GitHub project that simply used conda, as below.