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open_model_zoo

Pre-trained Deep Learning models and samples (high quality and extremely fast)

openpose

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

package

paddle-lite-demo

lib, demo, model, data

paddle-mobile

Paddle Mobile Framework (移动端框架，支持多平台，高性能，低能耗预测部署）

paddlebobo

基于飞桨开发的虚拟主播

paddleseg

A high performance semantic segmentation toolkit based on PaddlePaddle. (『飞桨』图像分割库）

pano360

Pure Java library to play 360 degree panorama video / photo (VR video) on Android. Using OpenGL ES 2.0 -安卓全景视频/图片播放器

panorama

Multiple images panorama stitching using opencv & python3

panoramic-image-stitching-using-invariant-features

Given a number of input images, concatenate all images to produce a panoramic image using invariant features.

partiallfw

Official repository for Attention-based Partial Face Recognition

pathmarkar

👣 An augmented reality indoor navigation app made in Unity and ARCore

pcv-book-code

:book:Python计算机视觉中译本实例代码

peoplecounter

In present days, people detection, tracking and counting is an important aspect in the video investigation and subjection demand in Computer Vision Systems. Providing (real time) traffic information will help improve and reduce pedestrian and vehicle traffic, especially when the data collected is learned and analyzed over a period of time, which makes its highly essential to identify people, vehicles and objects in general and also accurately counting the number of people and/or vehicles entering and leaving a particular location in real time. To perform people counting, a robust and efficient system is needed. This research is aimed at making a pedestrian traffic reporting system for certain areas and buildings around the campus to potentially help ease traffic circulation. Providing this information will be done through a developed application, which includes image processing with Open Computer Vision (OpenCV). This will show the amount of traffic in certain buildings or area over a period of time. OpenCV is a cross-platform library which can be used to develop real-time Computer Vision applications [Opencv, 2015b]. It is mainly focused on image processing, video capture and analysis including features like people and object detection. The operations performed were based on the performance and accuracy of the tracking algorithms when implemented in embedded devices such as the Raspberry Pi and the Tinker Board. The Pi Camera was used for real time vision and hosted on the embedded device. The proposed method used was conjoined with an open-source visual tracking implementation from the contribution branch of the OpenCV library and a unique technique for people detection along with different Filtering Algorithms for tracking this. The programming language of choice to implement these features (Tracking and Detection) is python and its libraries. The present work describes a standalone people counting application designed using Python OpenCV and tested on embedded devices ranging from the Raspberry Pi3 to a Tinker Board and a compatible Camera. All these were used in prototyping the design of this application. The results reported and showed that the Person-Counter system developed counted the number of people entering the designated area (down), and the number of people leaving (up).

peoplesegmentationdemo

Android People Segmentation Application using Deeplab-V3+ model with MobilenetV2 powered by MACE.

person_seg

person segmentation

photofilters

picturerecognitionplacement

App for object placement on top of recognized picture with ARCore and Sceneform

plane_detection

This project relies on OpenCV using C++ 11 as the programming language, a multi-plane detection algorithm of point cloud is realized. At the same time, a relatively effective algorithm optimization is proposed. It has a good performance in the accuracy and speed of multi-plane detection.

pp

Code for book Python Playground by Mahesh Venkitachalam (No Starch Press, USA)

pvcnn

[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning

py-offscreen-opengl

pyopengl_examples_savetofile

Several examples of how to use PyOpenGL for off-screen rendering and saving rendered image to file. 3 methods are used: using GLUT hidden window, using WGL to create context with default buffer, as well as creation of OpenGL Framebuffer Object by hands and display results with anti-aliasing in the interactive window .

python-opengl-tutorial

Python opengl-tutorial base on PyOpenGL.

python_platerecogntion

python opencv Flask Bootstrap 车牌识别 二维码条形码识别

pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

pytorch_retinaface

Retinaface get 80.99% in widerface hard val using mobilenet0.25.

rapid

RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images (CVPR 2020 Workshops)

real-time-video-mosaic

An implementation of automatic panorama using OpenCV in C++ and Python

real-world-masked-face-dataset

Real-World Masked Face Dataset，口罩人脸数据集