Türkiye Canlı Mobese Görüntülerinde Profesyonel Nesne Takip Sistemi

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.
Official implementation for "Image Quality Assessment using Contrastive Learning"

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

Pavan Chennagiri 67 Dec 30, 2022
A Python package for causal inference using Synthetic Controls

Synthetic Control Methods A Python package for causal inference using synthetic controls This Python package implements a class of approaches to estim

Oscar Engelbrektson 107 Dec 28, 2022
Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks

This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset and my original methods that are publi

Shunta Saito 255 Sep 07, 2022
Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021) This repository is for the following paper: "Investigating Attention

52 Nov 19, 2022
Like ThreeJS but for Python and based on wgpu

pygfx A render engine, inspired by ThreeJS, but for Python and targeting Vulkan/Metal/DX12 (via wgpu). Introduction This is a Python render engine bui

139 Jan 07, 2023
Tensorforce: a TensorFlow library for applied reinforcement learning

Tensorforce: a TensorFlow library for applied reinforcement learning Introduction Tensorforce is an open-source deep reinforcement learning framework,

Tensorforce 3.2k Jan 02, 2023
"3D Human Texture Estimation from a Single Image with Transformers", ICCV 2021

Texformer: 3D Human Texture Estimation from a Single Image with Transformers This is the official implementation of "3D Human Texture Estimation from

XiangyuXu 193 Dec 05, 2022
Some toy examples of score matching algorithms written in PyTorch

toy_gradlogp This repo implements some toy examples of the following score matching algorithms in PyTorch: ssm-vr: sliced score matching with variance

Ending Hsiao 21 Dec 26, 2022
Software associated to AAAI paper "Planning with Biological Neurons and Synapses"

jBrain Software associated with the AAAI 2022 paper Francesco D'Amore, Daniel Mitropolsky, Pierluigi Crescenzi, Emanuele Natale, Christos H. Papadimit

Pierluigi Crescenzi 1 Apr 10, 2022
AITUS - An atomatic notr maker for CYTUS

AITUS an automatic note maker for CYTUS. 利用AI根据指定乐曲生成CYTUS游戏谱面。 效果展示:https://www

GradiusTwinbee 6 Feb 24, 2022
CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes

CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes. CHERRY is based on a deep learning model, which consists of a graph convolutional encoder and a link

Kenneth Shang 12 Dec 15, 2022
[NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Code for NeurIPS 2021 Paper "Exploring Architectural Ingredients of A

Hanxun Huang 26 Dec 01, 2022
Repo for "Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks"

Summary This is the code for the paper Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks by Yanxiang Wang, Xian Zh

zhangxian 54 Jan 03, 2023
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
Remote sensing change detection using PaddlePaddle

Change Detection Laboratory Developing and benchmarking deep learning-based remo

Lin Manhui 15 Sep 23, 2022
PyTorch EO aims to make Deep Learning for Earth Observation data easy and accessible to real-world cases and research alike.

Pytorch EO Deep Learning for Earth Observation applications and research. 🚧 This project is in early development, so bugs and breaking changes are ex

earthpulse 28 Aug 25, 2022
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of

DeepHyper Team 214 Jan 08, 2023
Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

PyTorch code to reproduce LyDROO algorithm [1], which is an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability an

Liang HUANG 87 Dec 28, 2022
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

DistMIS Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation. DistriMIS Distributing Deep Learning Hyperparameter Tuning

HiEST 2 Sep 09, 2022
This is the repo of the manuscript "Dual-branch Attention-In-Attention Transformer for speech enhancement"

DB-AIAT: A Dual-branch attention-in-attention transformer for single-channel SE

Guochen Yu 68 Dec 16, 2022