OpenCV Libraries - Page 11

Find lane lines on the road using Python and OpenCV, applying Canny edge detectors and Hough line transforms

Jupyter Notebook 279Updated: 2 y ago License: No License (No License)

上海交通大学 RoboMaster 2019赛季 视觉代码

C 278Updated: 2 y ago License: Permissive (MIT)

php wrapper for opencv

C++ 277Updated: 2 y ago License: Permissive (Apache-2.0)

A fast alternative to the modulo reduction

C 277Updated: 2 y ago License: Permissive (Apache-2.0)

3D Hand Tracking using input from a depth sensor.

Python 276Updated: 4 y ago License: Proprietary (Proprietary)

A simple deep neural network implemented in C++，based with OpenCV Mat matrix class

C++ 276Updated: 2 y ago License: Permissive (MIT)

YOLO Object detection with OpenCV and Python.

Python 275Updated: 4 y ago License: Permissive (MIT)

:vhs: Tool for extracting scenes with motion from videos (e.g. security camera or DVR footage). Written in Python, uses OpenCV.

Python 274Updated: 2 y ago License: Proprietary (Proprietary)

Implementation of Newcombe et al. CVPR 2015 DynamicFusion paper

C++ 274Updated: 4 y ago License: Permissive (BSD-3-Clause)

A multi-purpose Video Labeling GUI in Python with integrated SOTA detector and tracker

Python 271Updated: 2 y ago License: Permissive (MIT)

Implementation of the SLIC superpixel algorithm to work with OpenCV

C++ 270Updated: 2 y ago License: No License (No License)

AVA RaspiCam: C++ API for using Raspberry camera with/without OpenCv

C++ 270Updated: 4 y ago License: No License (No License)

M. Beyeler (2015). OpenCV with Python Blueprints: Design and develop advanced computer vision projects using OpenCV with Python, Packt Publishing Ltd., ISBN 978-178528269-0.

Python 269Updated: 4 y ago License: Strong Copyleft (GPL-3.0)

The OpenCV Java project for Android.

Java 268Updated: 4 y ago License: Proprietary (Proprietary)

Build and install OpenCV for the NVIDIA Jetson TX2

Shell 268Updated: 2 y ago License: Permissive (MIT)

Cut and paste someone using AR

Jupyter Notebook 264Updated: 2 y ago License: Permissive (Apache-2.0)

Real time yolox Android demo by ncnn

C++ 262Updated: 2 y ago License: No License (No License)

Remote heart rate detection through Eulerian magnification of face videos

Python 261Updated: 2 y ago License: No License (No License)

A Python library for controlling GoPro cameras over http.

Python 261Updated: 4 y ago License: Permissive (Apache-2.0)

Area of Interest Library

C 261Updated: 4 y ago License: No License (No License)

Webcam-based pupil-tracker, complete with GUI to perform calibrations.

Python 260Updated: 2 y ago License: Strong Copyleft (GPL-3.0)

An example of use a Vision framework for face landmarks detection in iOS 11

Swift 258Updated: 4 y ago License: Permissive (MIT)

Minimalist code necessary for using porting C++ functions/classes using OpenCV's "Mat" type in functions argument lists directly (w/o explicit conversions) to python.

C++ 257Updated: 2 y ago License: Permissive (MIT)

Motion Cam is a camera application for Android that replaces the entire camera pipeline. It consumes RAW images and uses computational photography to combine multiple images to reduce noise.

C++ 256Updated: 3 y ago License: Strong Copyleft (GPL-3.0)

VQMT: Video Quality Measurement Tool. Fast implementations of the following objective image quality metrics: PSNR, SSIM, MS-SSIM, VIFp, PSNR-HVS and PSNR-HVS-M.

C++ 255Updated: 4 y ago License: Proprietary (Proprietary)

👩‍🏫Fully working example of the OpenCV library used together with React Native

Java 254Updated: 4 y ago License: Permissive (MIT)

Repository for PyImageSearch Crash Course on Computer Vision and Deep Learning

Python 254Updated: 2 y ago License: Permissive (MIT)

OpenCV build for iPhone with example project

C++ 254Updated: 4 y ago License: No License (No License)

明日方舟助手，自动刷图、智能基建换班，全日常一键长草！

C++ 254Updated: 3 y ago License: Strong Copyleft (AGPL-3.0)

This is a port of the Face Frontalization code provided by Hassner et al. at http://www.openu.ac.il/home/hassner/projects/frontalize

Python 253Updated: 4 y ago License: No License (No License)

Create your own live camera stream using a Raspberry Pi 4

HTML 253Updated: 2 y ago License: Permissive (MIT)

Add Christmas hat on one's head based on OpneCV and Dlib

Python 252Updated: 4 y ago License: No License (No License)

Python/OpenCV script that detect motion on webcam and allow record it to a file

Python 251Updated: 2 y ago License: No License (No License)

Porn images detector with python, tensorflow, scikit-learn and opencv.

Python 251Updated: 4 y ago License: No License (No License)

a WebAssembly-powered AR sudoku solver

JavaScript 251Updated: 4 y ago License: No License (No License)

Raspberry Pi motion vector detection program with OSD web interface.

C 251Updated: 2 y ago License: Strong Copyleft (GPL-3.0)

Automated face warping tool.

Python 250Updated: 4 y ago License: Permissive (MIT)

YOLOv2 and MobileNet_SSD detection algorithms used along with KCF object tracker

Python 250Updated: 4 y ago License: No License (No License)

Online video stabilization using a novel MeshFlow motion model

Python 250Updated: 2 y ago License: No License (No License)

A real-time facial expression recognition system with webcam streaming and CNN

Python 249Updated: 2 y ago License: Permissive (MIT)

A Go program that takes an image, uses pigo to detect a face, and creates a gif that zooms in on the face

Go 249Updated: 4 y ago License: Permissive (MIT)

Meme Creator for Android - Simple & Ad-Free

Java 248Updated: 3 y ago License: Strong Copyleft (GPL-3.0)

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

Python 248Updated: 2 y ago License: No License (No License)

Python 248Updated: 2 y ago License: No License (No License)

Extract and solve Sudoku from an image

Python 247Updated: 4 y ago License: Permissive (MIT)

个人毕业设计 - 基于树莓派、OpenCV及Python语言的人脸识别

Python 247Updated: 2 y ago License: No License (No License)

Blur Detection with OpenCV in Python

Python 247Updated: 2 y ago License: Permissive (MIT)

A project on hand detection and hand gesture recognition developed using OpenCV on Python 2.7.

Python 246Updated: 4 y ago License: Permissive (MIT)

Background Removal based on U-Net

Python 246Updated: 4 y ago License: Permissive (MIT)

:camera: Computer-Vision Demos

C# 246Updated: 4 y ago License: Permissive (MIT)