deep_sort_pytorch | MOT using deepsort and yolov3 with pytorch | Computer Vision library

by ZQPei Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | deep_sort_pytorch Summary

deep_sort_pytorch is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. deep_sort_pytorch has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However deep_sort_pytorch has 4 bugs. You can download it from GitHub.

This is an implement of MOT tracking algorithm deep sort. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in PAPER is FasterRCNN , and the original source code is HERE. However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use YOLOv3 to generate bboxes instead of FasterRCNN.

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deep_sort_pytorch has a medium active ecosystem.

It has 2495 star(s) with 680 fork(s). There are 43 watchers for this library.

It had no major release in the last 6 months.

There are 117 open issues and 114 have been closed. On average issues are closed in 30 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deep_sort_pytorch is current.

Quality

deep_sort_pytorch has 4 bugs (0 blocker, 0 critical, 2 major, 2 minor) and 109 code smells.

Security

deep_sort_pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep_sort_pytorch code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

deep_sort_pytorch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

deep_sort_pytorch releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

deep_sort_pytorch saves you 1536 person hours of effort in developing the same functionality from scratch.

It has 3421 lines of code, 210 functions and 50 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

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deep_sort_pytorch Key Features

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deep_sort_pytorch Examples and Code Snippets

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Community Discussions

Trending Discussions on deep_sort_pytorch

how to have full control over a process (start/terminate) which runs in parallel with flask application?

QUESTION

how to have full control over a process (start/terminate) which runs in parallel with flask application?

Asked 2020-May-22 at 22:35

This is my application architecture:

In my code there is a pedestrian.py file which uses a while loop to read frames from rtsp link and after doing pedestrian detection process (available in this link), it caches the frame in Redis.

(please note that in the loop each time the output frame is replaced with the previous output from loop. it means that there exists only one frame in redis in any moment.)

Then in flask application, I read processed frame from redis and send it for the clients.

This is the code for my pedestrian detection:

...

ANSWER

Answered 2020-May-22 at 22:35

I found a way to automate start/stop the pedestrian detection. more details available in my repo:

from os.path import join from os import getenv, environ from dotenv import load_dotenv import argparse from threading import Thread

Source https://stackoverflow.com/questions/61747492

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install deep_sort_pytorch

for user in china, you can specify pypi source to accelerate install like:. Notice: If compiling failed, the simplist way is to **Upgrade your pytorch >= 1.1 and torchvision >= 0.3" and you can avoid the troublesome compiling problems which are most likely caused by either gcc version too low or libraries missing. Use --display to enable display. Results will be saved to ./output/results.avi and ./output/results.txt. All files above can also be accessed from BaiduDisk! linker：BaiduDisk passwd：fbuw.
Check all dependencies installed
Clone this repository
Download YOLOv3 parameters
Download deepsort parameters ckpt.t7
Compile nms module
Run demo

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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