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danny0559's Projects

n_m3u8dl-cli icon n_m3u8dl-cli

[.NET] m3u8 downloader 开源的命令行m3u8/HLS/dash下载器,支持普通AES-128-CBC解密,多线程,自定义请求头等. 支持简体中文,繁体中文和英文. English Supported.

nvdiffrec icon nvdiffrec

Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

opencv_tutorials icon opencv_tutorials

Opencv4.0 with python (English&中文), and will keep the update ! 👊

optical-flow-motion icon optical-flow-motion

Here we try to track the motion of the vehicles on a highway using the concept of Optical Motion Flow.

optical_flow icon optical_flow

Optical Flow for Cell Motion independent of Cell Tracking

paddledetection icon paddledetection

Object detection and instance segmentation toolkit based on PaddlePaddle.

pyidi icon pyidi

Python Image Displacement Identification

qtevm icon qtevm

C++ implementation of EVM(Eulerian Video Magnification), based on OpenCV and Qt.

segformer icon segformer

Official PyTorch implementation of SegFormer

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

slowfast icon slowfast

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

ssd_keras icon ssd_keras

A Keras port of Single Shot MultiBox Detector

subpixel-edges icon subpixel-edges

A pure Python implementation of the subpixel edge location algorithm

tfoptflow icon tfoptflow

Optical Flow Prediction with TensorFlow. Implements "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume," by Deqing Sun et al. (CVPR 2018)

time-series-classification-using-1-d-cnns icon time-series-classification-using-1-d-cnns

This project is on how to Develop 1D Convolutional Neural Network Models for Human Activity Recognition Below is an example video of a subject performing the activities while their movement data is being recorded. The six activities performed were as follows: Walking Walking Upstairs Walking Downstairs Sitting Standing Laying The movement data recorded was the x, y, and z accelerometer data (linear acceleration) and gyroscopic data (angular velocity) from the smart phone, specifically a Samsung Galaxy S II. Observations were recorded at 50 Hz (i.e. 50 data points per second). Each subject performed the sequence of activities twice, once with the device on their left-hand-side and once with the device on their right-hand side. Pre-processing accelerometer and gyroscope using noise filters. Splitting data into fixed windows of 2.56 seconds (128 data points) with 50% overlap. Splitting of accelerometer data into gravitational (total) and body motion components.

tools-ocr icon tools-ocr

树洞 OCR 文字识别(一款跨平台的 OCR 小工具)

trafficmonitor icon trafficmonitor

这是一个用于显示当前网速、CPU及内存利用率的桌面悬浮窗软件,并支持任务栏显示,支持更换皮肤。

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