Türkiye Canlı Mobese Görüntülerinde Profesyonel Nesne Takip Sistemi

Related tags

Deep Learningopencvtrackingobjectturkishyolocv2opencv-pythonturkceyolov3yolov4mobese
Overview

Logo

Türkiye Mobese Görüntü Takip

Türkiye Mobese görüntülerinde OPENCV ve Yolo ile takip sistemi

Multiple Object Tracking System in Turkish Mobese with OPENCV and Yolo
Explore the docs » Projeyi keşfet

Table of Contents / İçerik Bölümü
  1. About the Project / Proje Hakkında
  2. Getting Started / Başlangıç
  3. Usage / Kullanım
  4. Roadmap / Yol Haritası
  5. Contributing / Katkı
  6. License / Lisans

If you are having any os compatiblity issue, let me know. I will try to fix as soon as possible so let's explore the docs.

Herhangi bir işletim sistemi uyumsuzluğu varsa, bana bildirin. En kısa sürede düzeltmeye çalışacağım, hadi dökümanı inceleyelim.

About the Project / Proje Hakkında

Currently this project have 171 cameras. | Projeye yüklü 171 canlı mobese görüntüsü vardır.

İstanbul > 44 Canlı Yayın          |   İstanbul > 44 Live CCTV Footage
İzmir > 76 Canlı Yayın             |   İzmir > 76 Live CCTV Footage
Tekirdag > 1 Canlı Yayın           |   Tekirdag > 1 Live CCTV Footage
Konya > 32 Canlı Yayın             |   Konya > 32 Live CCTV Footage
Ordu > 21 Canlı Yayın              |   Ordu > 21 Live CCTV Footage

This project implements Turkish Mobese CCTV footages detection classifier using pretrained yolov4-tiny models. If you trust your computer performance you can download yolov4 models too. The yolov4 models are taken from the official yolov4 paper which was released in April 2020 and the yolov4 implementation is from darknet.

Bu proje, önceden eğitilmiş yolov4-tiny modellerini kullanarak Türk Mobese Canlı CCTV görüntülerine algılama sınıflandırıcısını uygular. Bilgisayarınızın performansına güveniyorsanız yolov4 modellerinide indirebilirsiniz. Yolov4 modelleri, Nisan 2020'de yayınlanan resmi yolov4 belgesinden alınmıştır ve Yolov4 uygulaması darknet'tendir.

Built With / Kullanılanlar

Getting Started / Başlangıç

To get a local copy up and running follow these simple steps.

Kendi bilgisayarınızda çalıştırmak için bu basit adımları izleyin.

Installation / Kurulum

  1. Clone the repo | Projeyi indir. 
    git clone https://github.com/samet-g/mobese.git
  2. Install Python packages | Gerekli Python paketlerini yükle. 
    pip3 install -r requirements.txt

Usage / Kullanım

  • Run with Python or Download the .exe file.
  • Python kullanarak çalıştır veya .exe dosyasını indir
python3 main.py | just run .exe file

Roadmap / Yol Haritası

See the open issues for a list of proposed features
It should be good use cctv cameras in city with Shodan API or make GUI.

Sorunlar için açık sorunları kontrol edin.
Shodan API ile esnaf güvenlik kamerası kullanmak veya GUI yapmak iyi olur.

Contributing / Katkı

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated especially Roadmap / Yol Haritası check this to-do list.

Katkılar, açık kaynak topluluğu için büyük nimettir özellikle Roadmap / Yol Haritası kısmındaki yapılacak-listesini kontrol edin.

  1. Fork the Project | Projeyi forkla.
  2. Create your Feature Branch | Katkıda Bulun
    git checkout -b feature/YeniOzellik
  3. Commit your Changes | Değişiklikleri Commitle
    git commit -m 'Add some YeniOzellik'
  4. Push to the Branch | Değişikliğini Yolla
    git push origin feature/YeniOzellik
  5. Open a Pull Request | Pull Request Aç

License / Lisans

Distributed under the GNU License.
See LICENSE for more information.

GNU Lisansı altında dağıtılmaktadır.
Daha fazla bilgi için LICENSE bölümüne bakın.

Comments
  • [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | NULL Pointer Dereference
    SNYK-PYTHON-NUMPY-2321964 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept low severity | 399/1000
    Why? Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.21.2 -> 1.22.2
    | No | No Known Exploit low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.

    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Denial of Service (DoS)

    opened by samet-g 0
  • [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | NULL Pointer Dereference
    SNYK-PYTHON-NUMPY-2321964 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept low severity | 399/1000
    Why? Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.21.2 -> 1.22.2
    | No | No Known Exploit low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.

    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Denial of Service (DoS)

    opened by samet-g 0
  • [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | NULL Pointer Dereference
    SNYK-PYTHON-NUMPY-2321964 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept low severity | 399/1000
    Why? Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.21.2 -> 1.22.2
    | No | No Known Exploit low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.

    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Denial of Service (DoS)

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | NULL Pointer Dereference
    SNYK-PYTHON-NUMPY-2321964 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept low severity | 399/1000
    Why? Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.21.2 -> 1.22.2
    | No | No Known Exploit low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.

    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Denial of Service (DoS)

    opened by samet-g 0
  • [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | NULL Pointer Dereference
    SNYK-PYTHON-NUMPY-2321964 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept low severity | 399/1000
    Why? Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.21.2 -> 1.22.2
    | No | No Known Exploit low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.

    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    [Snyk] Security upgrade numpy from 1.21.2 to 1.22.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 506/1000
    Why? Proof of Concept exploit, Has a fix available, CVSS 3.7 | NULL Pointer Dereference
    SNYK-PYTHON-NUMPY-2321964 | numpy:
    1.21.2 -> 1.22.2
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.

    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
Releases(v1.0.0)
Owner
cybersec researcher and python dev.
GitHub Repository
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

The first comprehensive Robustness investigation benchmark on large-scale dataset ImageNet regarding ARchitecture design and Training techniques towards diverse noises.

132 Dec 23, 2022
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.

sam4onnx A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for

Katsuya Hyodo 6 May 15, 2022
This repo contains the official code and pre-trained models for the Dynamic Vision Transformer (DVT).

Dynamic-Vision-Transformer (Pytorch) This repo contains the official code and pre-trained models for the Dynamic Vision Transformer (DVT). Not All Ima

210 Dec 18, 2022
Augmented Traffic Control: A tool to simulate network conditions

Augmented Traffic Control Full documentation for the project is available at http://facebook.github.io/augmented-traffic-control/. Overview Augmented

Meta Archive 4.3k Jan 08, 2023
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
Graph Transformer Architecture. Source code for

Graph Transformer Architecture Source code for the paper "A Generalization of Transformer Networks to Graphs" by Vijay Prakash Dwivedi and Xavier Bres

NTU Graph Deep Learning Lab 561 Jan 08, 2023
python 93% acc. CNN Dogs Vs Cats ( Pytorch )

English | 简体中文（测试中...敬请期待） Cnn-Classification-Dog-Vs-Cat 猫狗辨别 (pytorch版本) CNN Resnet18 的猫狗分类器，基于ResNet及其变体网路系列，对于一般的图像识别任务表现优异，模型精准度高达93%（小型样本）。 项目制作于

apple ye 1 May 22, 2022
Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset This repository provides a unified online platform, LoLi-P

Chongyi Li 457 Jan 03, 2023
You Only 👀 One Sequence

You Only 👀 One Sequence TL;DR: We study the transferability of the vanilla ViT pre-trained on mid-sized ImageNet-1k to the more challenging COCO obje

Hust Visual Learning Team 666 Jan 03, 2023
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

English | 简体中文 Welcome to the PaddlePaddle GitHub. PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open

19.4k Jan 04, 2023
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers.

TransMix: Attend to Mix for Vision Transformers This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transf

Jie-Neng Chen 130 Jan 01, 2023
Graph Attention Networks

GAT Graph Attention Networks (Veličković et al., ICLR 2018): https://arxiv.org/abs/1710.10903 GAT layer t-SNE + Attention coefficients on Cora Overvie

Petar Veličković 2.6k Jan 05, 2023
Video-Music Transformer

VMT Video-Music Transformer (VMT) is an attention-based multi-modal model, which generates piano music for a given video. Paper https://arxiv.org/abs/

Chin-Tung Lin 5 Jul 13, 2022
VGGVox models for Speaker Identification and Verification trained on the VoxCeleb (1 & 2) datasets

VGGVox models for speaker identification and verification This directory contains code to import and evaluate the speaker identification and verificat

338 Dec 27, 2022
Simple SN-GAN to generate CryptoPunks

CryptoPunks GAN Simple SN-GAN to generate CryptoPunks. Neural network architecture and training code has been modified from the PyTorch DCGAN example.

Teddy Koker 66 Dec 15, 2022
Automatic meme generation model using Tensorflow Keras.

Memefly You can find the project at MemeflyAI. Contributors Nick Buukhalter Harsh Desai Han Lee Project Overview Trello Board Product Canvas Automatic

BloomTech Labs 2 Jan 13, 2022
N-Omniglot is a large neuromorphic few-shot learning dataset

N-Omniglot [Paper] || [Dataset] N-Omniglot is a large neuromorphic few-shot learning dataset. It reconstructs strokes of Omniglot as videos and uses D

11 Dec 05, 2022
PyTorch implementation of D2C: Diffuison-Decoding Models for Few-shot Conditional Generation.

D2C: Diffuison-Decoding Models for Few-shot Conditional Generation Project | Paper PyTorch implementation of D2C: Diffuison-Decoding Models for Few-sh

Jiaming Song 90 Dec 27, 2022
Keras-1D-NN-Classifier

Keras-1D-NN-Classifier This code is based on the reference codes linked below. reference 1, reference 2 This code is for 1-D array data classification

Jae-Hoon Shim 6 May 18, 2021