cnn-facial-landmark | Training code for facial landmark detection | Machine Learning library

by yinguobing Python Version: Current License: MIT

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kandi X-RAY | cnn-facial-landmark Summary

cnn-facial-landmark is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. cnn-facial-landmark has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However cnn-facial-landmark build file is not available. You can download it from GitHub.

Training code for facial landmark detection based on deep convolutional neural network.

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cnn-facial-landmark has a low active ecosystem.

It has 606 star(s) with 183 fork(s). There are 23 watchers for this library.

It had no major release in the last 6 months.

There are 39 open issues and 70 have been closed. On average issues are closed in 55 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cnn-facial-landmark is current.

Quality

cnn-facial-landmark has 0 bugs and 0 code smells.

Security

cnn-facial-landmark has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cnn-facial-landmark code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cnn-facial-landmark is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cnn-facial-landmark releases are not available. You will need to build from source code and install.

cnn-facial-landmark has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

cnn-facial-landmark saves you 72 person hours of effort in developing the same functionality from scratch.

It has 342 lines of code, 12 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Build the Landmark model .
Draws an annotation box .
Initialize the camera .
Parses a single example .
Get a dataset from a TFRecord file .
Solve the pose of an image .

Get all kandi verified functions for this library.

cnn-facial-landmark Key Features

No Key Features are available at this moment for cnn-facial-landmark.

cnn-facial-landmark Examples and Code Snippets

No Code Snippets are available at this moment for cnn-facial-landmark.

Community Discussions

Trending Discussions on cnn-facial-landmark

Draw points correctly with Canvas

QUESTION

Draw points correctly with Canvas

Asked 2019-Aug-15 at 03:44

I have a python script that draws landmarks on the contours of eye region with image input got from ckpts files (model) using OpenCV. I want to draw those points ( landmarks ) in the same picture. I got predictions points from the picture and I tried to draw those points (x,y) using Canvas but the results are different. Difference between the two images:

Landmarks are drawn using python script (OpenCV)

Landmarks are draw using java code (Canvas)

I have tried many ways and I use Canvas library to draw points on imageview ( I loaded the same image in assets folder ) but this doesn't solve my problem..

This is a python code that shows how to draw landmarks on image:

...

ANSWER

Answered 2019-Aug-15 at 03:44

Problem solved. I used the OpenCV library for drawing in Android instead of Canvas library. I have used exactly this function: Imgproc.circle()

Source https://stackoverflow.com/questions/57498154

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

Vulnerabilities

No vulnerabilities reported

Install cnn-facial-landmark

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

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