桑老师智造社's starred repositories
howto-make-more-money
程序员如何优雅的挣零花钱，2.0版，升级为小书了。Most of this not work outside China , so no English translate
VirusBroadcast
A java virus broadcast simulation
deepstream_python_apps
DeepStream SDK Python bindings and sample applications
deepstream_reference_apps
Samples for TensorRT/Deepstream for Tesla & Jetson
redaction_with_deepstream
An example of using DeepStream SDK for redaction
OpenCV-Traffic-Counter
A traffic counter designed using the OpenCV library for Python 3.5. This project was carried out as part of the Government Data Science Accelerator programme
Deep-Stream-ONNX
How to deploy ONNX models using DeepStream on Jetson Nano
python-traffic-counter-with-yolo-and-sort
Detect and track vehicles on a video stream and count those going through a defined line.
traffic_counter
Using Python and OpenCV to count cars on a highway.
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).
shoot_your_shot
train a custom classifier to score dart throws, relate to throwing form through pose estimation
fastai-deeper
fastai v3 course notes, vscode debug, off-class projects, pains&tricks, and more ..