69 skills found · Page 2 of 3
czeni / Opencv Video MinimalOpenCV 4.2 with Python3.8 and video support (FFMPEG, GStreamer, gPhoto2) based on Alpine Linux 3.10
whizzzkid / Opencv Complete Build CudaFull build script for Open CV with/without cuda and bumblebee support
riaz / Advanced OpenCV3 PythonProjects for Video Series titled "Building Advanced OpenCV3 Projects with Python"
aydinnyunus / OpenCV OCROpenCV OCR (Optical Character Recognition)
akshaymogaveera / Self Driving Robot Using Neural NetworkThis project introduces the autonomous robot which is a scaled down version of actual self-driving vehicle and designed with the help of neural network. The main focus is on building autonomous robot and train it on a designed track with the help of neural network so that it can run autonomously without a controller or driver on that specific track. The robot will stream the video to laptop which will then take decisions and send the data to raspberry pi which will then control the robot using motor driver. This motor driver will move the robot in required directions. Neural Network is used to train the model by first driving the robot on the specially designed track by labeling the images with the directions to be taken. After the model is trained it can make accurate predictions by processing the images on computer. This approach is better than conventional method which is done by extracting specific feature from images.
RahulParajuli / OpenCV ComputerVisionBasicsAll computer vision exercises from basic, this repository is extremely handy for all the beginners out there.
Culeshovi / Self Driving RC Cara self driving rc car using neural network and computer vision.
AlessioMichelassi / OpenPyVision 013Welcome to my project. OpenPyVision is a real time videoMixer based on opencv and pyqt6.
fMeow / Document ScannerA opencv4 and scikit-image based python digital image document scanner.
PacktPublishing / Master Computer Vision OpenCV3 In Python And Machine LearningMaster Computer Vision™ OpenCV3 in Python and Machine Learning, published by Packt
ampervue / Docker Python34 Opencv3Docker image with Python 3.4, OpenCV 3, and FFMPEG
berrouba-med-amine / Pyqt5 Face Detectionsimple python face detection using opencv3 and pyqt5.
veraposeidon / Cameo人脸跟踪和图像处理，OpenCV3.1.0，Python2.7，参考《Learning OpenCV3 Computer Vision with Python》
harleyszhang / Opencv Samples一些采用opencv3图像处理库做的一些项目，有检测人脸位置、人脸特效、头顶加LOGO等
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
karantyagi / Emotion Recognizing Music Player🎵 A music player using emotion detection in near real time to select songs.
tpubben / SequoiaStackingScript to combine any 3 spectral bands from the Parrot Sequoia into an RGB false color composite using OpenCV3 and the extra contributor packages in Python 3.x
fooock / Opencv NotebooksPython opencv notebooks using SIFT, SURF and feature matching using Brute-Force with ORB descriptors
qqxx6661 / ML SVMpython+opencv3 视频监控实时数据提取，目标追踪
leeyunjai / Simple Face Recognitiontrain/recognize face and detect landmark, using dlib and opencv
