face2face-demo | pix2pix demo that learns from facial landmarks | Machine Learning library

by datitran Python Version: Current License: MIT

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

kandi X-RAY | face2face-demo Summary

face2face-demo is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. face2face-demo has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However face2face-demo build file is not available. You can download it from GitHub.

pix2pix demo that learns from facial landmarks and translates this into a face

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Quality

Security

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face2face-demo has a medium active ecosystem.

It has 1398 star(s) with 427 fork(s). There are 75 watchers for this library.

It had no major release in the last 6 months.

There are 26 open issues and 14 have been closed. On average issues are closed in 32 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of face2face-demo is current.

Quality

face2face-demo has 0 bugs and 0 code smells.

Security

face2face-demo has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

face2face-demo code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

face2face-demo 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

face2face-demo releases are not available. You will need to build from source code and install.

face2face-demo 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.

face2face-demo saves you 125 person hours of effort in developing the same functionality from scratch.

It has 315 lines of code, 19 functions and 4 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed face2face-demo and discovered the below as its top functions. This is intended to give you an instant insight into face2face-demo implemented functionality, and help decide if they suit your requirements.

Generate model output
Create the convolution layer
Process images
Lrelu
Generate convolutional convolution
Create a model from inputs and targets
Deprocessing image
Preprocess image
Convert image to output
Batch normalization
Freeze a tf model
Resize an image
Load the graph from a file
Reshape array to polyline

Get all kandi verified functions for this library.

face2face-demo Key Features

No Key Features are available at this moment for face2face-demo.

face2face-demo Examples and Code Snippets

Deepfake

Jupyter Notebook

Lines of Code : 20

License : No License

Copy

Extract the contents of the supplement folder into the obamanet folder

Link to obamanet folder: https://drive.google.com/open?id=1824s4K-cmhkYTPBDUSlCoVgSJbIl4c-e

Link to supplement: https://drive.google.com/open?id=1gAYaqg1rcGuMjfc-at-7a86wbTApwjD

Community Discussions

Trending Discussions on face2face-demo

Understanding Conda, getting ResolvePackageNotFound error

QUESTION

Understanding Conda, getting ResolvePackageNotFound error

Asked 2020-Feb-04 at 10:22

I am new to conda. I read that it makes maintaining different versions of package easy. I cloned a git repo: https://github.com/datitran/face2face-demo using

...

ANSWER

Answered 2020-Feb-04 at 10:22

Why conda is not able to resolve these?

Because the package versions you request are not available from the default channels (any more). As of conda version 4.7, the so called free channel was removed from the defaults, which now results in some older module versions not being found any more. You can tell by typing conda search :

Source https://stackoverflow.com/questions/60050997

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

Vulnerabilities

No vulnerabilities reported

Install face2face-demo

If you want to download my dataset, here is also the video file that I used and the generated training dataset (400 images already split into training and validation). For more information around training, have a look at Christopher Hesse's pix2pix-tensorflow implementation. I have uploaded a pre-trained frozen model here. This model is trained on 400 images with epoch 200.
file is the name of the video file from which you want to create the data set.
num is the number of train data to be created.
landmark-model is the facial landmark model that is used to detect the landmarks. A pre-trained facial landmark model is provided here.
Two folders original and landmarks will be created.
First, we need to reduce the trained model so that we can use an image tensor as input: python reduce_model.py --model-input face2face-model --model-output face2face-reduced-model Input: model-input is the model folder to be imported. model-output is the model (reduced) folder to be exported. Output: It returns a reduced model with less weights file size than the original model.
Second, we freeze the reduced model to a single file. python freeze_model.py --model-folder face2face-reduced-model Input: model-folder is the model folder of the reduced model. Output: It returns a frozen model file frozen_model.pb in the model folder.
source is the device index of the camera (default=0).
show is an option to either display the normal input (0) or the facial landmark (1) alongside the generated image (default=0).
landmark-model is the facial landmark model that is used to detect the landmarks.
tf-model is the frozen model file.

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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