poor-mans-deep-learning-camera | thin client deep learning camera

by burningion Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | poor-mans-deep-learning-camera Summary

poor-mans-deep-learning-camera is a Python library typically used in Internet of Things (IoT), Raspberry Pi applications. poor-mans-deep-learning-camera has no bugs, it has no vulnerabilities and it has low support. However poor-mans-deep-learning-camera build file is not available. You can download it from GitHub.

Build a thin client deep learning camera with the Raspberry Pi, Flask, and YOLO

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poor-mans-deep-learning-camera has a low active ecosystem.

It has 421 star(s) with 110 fork(s). There are 30 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 20 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of poor-mans-deep-learning-camera is current.

Quality

poor-mans-deep-learning-camera has 0 bugs and 0 code smells.

Security

poor-mans-deep-learning-camera has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

poor-mans-deep-learning-camera code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

poor-mans-deep-learning-camera does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

poor-mans-deep-learning-camera releases are not available. You will need to build from source code and install.

poor-mans-deep-learning-camera has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are available. Examples and code snippets are not available.

poor-mans-deep-learning-camera saves you 60 person hours of effort in developing the same functionality from scratch.

It has 156 lines of code, 14 functions and 6 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed poor-mans-deep-learning-camera and discovered the below as its top functions. This is intended to give you an instant insight into poor-mans-deep-learning-camera implemented functionality, and help decide if they suit your requirements.

Start the camera thread
Set the client s events
Generate an image
Get camera frame
Wait for a new client
Generate a 2 frame generator
Clear all events

Get all kandi verified functions for this library.

poor-mans-deep-learning-camera Key Features

No Key Features are available at this moment for poor-mans-deep-learning-camera.

poor-mans-deep-learning-camera Examples and Code Snippets

No Code Snippets are available at this moment for poor-mans-deep-learning-camera.

Community Discussions

Trending Discussions on Internet of Things (IoT)

Display data from two json files in react native

QUESTION

Display data from two json files in react native

Asked 2020-May-17 at 23:55

I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files

...

ANSWER

Answered 2020-May-17 at 23:55

The new object to get params in React Navigation 5 is:

Source https://stackoverflow.com/questions/61859411

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install poor-mans-deep-learning-camera

You'll need a few different libraries installed on the Raspberry Pi. Most notably, OpenCV 3 with Python bindings, along with Flask. The Raspberry Pi runs the Camera-Server code, and sends back images from a webserver. On another computer, you'll run the inference script, and it will detect whether or not there are birds in your webcam's image. For this to run, you'll need to download and install the Darkflow weights, along with the YOLO model of your choice. Once that's installed, you should then be able to start doing inferences. If you're looking for a pretrained model, I used the tiny-yolo-voc weights at the Darkflow repo.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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