cartoonify | python app to turn a photograph into a cartoon | Map library

by danmacnish Python Version: Current License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | cartoonify Summary

cartoonify is a Python library typically used in Geo, Map, Deep Learning, Tensorflow, Keras applications. cartoonify has no vulnerabilities, it has a Permissive License and it has medium support. However cartoonify has 9 bugs and it build file is not available. You can download it from GitHub.

python app to turn a photograph into a cartoon

Support

Quality

Security

License

Reuse

Support

cartoonify has a medium active ecosystem.

It has 2008 star(s) with 198 fork(s). There are 65 watchers for this library.

It had no major release in the last 6 months.

There are 13 open issues and 7 have been closed. On average issues are closed in 269 days. There are 12 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cartoonify is current.

Quality

cartoonify has 9 bugs (1 blocker, 1 critical, 5 major, 2 minor) and 94 code smells.

Security

cartoonify has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cartoonify code analysis shows 0 unresolved vulnerabilities.

There are 10 security hotspots that need review.

License

cartoonify 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

cartoonify releases are not available. You will need to build from source code and install.

cartoonify 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.

cartoonify saves you 17361 person hours of effort in developing the same functionality from scratch.

It has 34436 lines of code, 1552 functions and 218 files.

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

Top functions reviewed by kandi - BETA

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

Convert a preprocessing step
Build a regularizer object
Build the activation function
Builds parameters for batch norming
Batch Multiclass NonMaxSuppression
Construct a multiclass regression using multiclass regression
Generate a multi - resolution feature map for multi resolution
Returns a function that returns a depth function
Train the model
Create an input queue
Flip an image
Rotate image
Preprocess input tensors
Compute the loss for a prediction
Batch assigner
Postprocessing
Performs a multi - class multi - classification regression on a box
Performs matching
Random crop
Generate image
Visualize boxes and labels
Extract feature classifier features
Convert a dict to a TF example
Run a single checkpoint
Predict box classification
Evaluate tensors in Tensorflow

Get all kandi verified functions for this library.

cartoonify Key Features

No Key Features are available at this moment for cartoonify.

cartoonify Examples and Code Snippets

Cartoonify,Directory Structre

Jupyter Notebook

Lines of Code : 20

License : Permissive (MIT)

Copy

.
├── assets
├── data
├── docs
├── logs
├── pipelines
├── research
├── src
│   ├── data
│   └── models
│   └── utils
├── tests
├── weights
├── LICENSE
├── README.md
├── requirements.txt
|── train.py
└── inference.py

Cartoonify,Run Training

Jupyter Notebook

Lines of Code : 10

License : Permissive (MIT)

Copy

python train.py \
    --wandbkey={{WANDB KEY}} \
    --projectname=Cartoonify \
    --wandbentity={{WANDB USERNAME}} \
    --tensorboard=True \
    --kaggle_user={{KAGGLE USERNAME}} \
    --kaggle_key={{KAGGLE API KEY}} \
    --batch_size=2 \
    --e

Cartoonify_reality,Getting Started

Python

Lines of Code : 3

License : No License

Copy

$vid.py     
                    
$cartoonize.py

Community Discussions

Trending Discussions on cartoonify

How to cartoonize images using GPUImage framework?

How to uniform the color and texture of a face

QUESTION

How to cartoonize images using GPUImage framework?

Asked 2018-Sep-25 at 12:30

I'm trying to create cartoon effect by combining multiple filters from GPUImage framework but the result is not as desired. Already I read all questions that about cartoonizing or cartoonify images but all them old and actually there is no answer with code example. So someone can help me with this topic if it's posible.

My code looks like :

...

ANSWER

Answered 2018-Sep-25 at 12:30

Hey Coder ACJHP understanding your question,

First thing is why don't you use ToonFilter and after that apply KuwaharaFilter, it might work. But Seeing your final image output, I'm sure that this can be done using AI and coreMl Models.

For your output you can see this link - https://blog.prismalabs.ai/diy-prisma-app-with-coreml-6b4994cc99e1

For coreml models use this link - https://likedan.github.io/Awesome-CoreML-Models/

here you can directly download the models and use it. Hope this is helpfull

Source https://stackoverflow.com/questions/51787596

QUESTION

How to uniform the color and texture of a face

Asked 2018-Jun-22 at 21:14

I'm trying to cartoonify a face using opencv.Here's the original image

Currently I'm doing

Downscaling the image, applying bifilter and upscaling back to original
Then converting RGB of original image to grayscale and followed medianblur to reduce nice
Apply Adaptive Threshold to create edgemask
Combining the image obtained from step1 with the edge mask with bitmap Here's the output

Then applied non-photorealistic rendering using OpenCV. Here's the final output I want to generate face with uniform color(remove light reflection as well)without affecting the eyes, mouth. How can I achieve that either by tweaking my current code or another possible approach in opencv(python)

...

ANSWER

Answered 2018-Jun-22 at 21:14

Based on: https://www.pyimagesearch.com/2014/07/07/color-quantization-opencv-using-k-means-clustering/

Here is a code that does what you are looking for:

Source https://stackoverflow.com/questions/50972401

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

Vulnerabilities

No vulnerabilities reported

Install cartoonify

*Unfortunately python 2.7 is required because the correct python 3 wheels are not available for both the pi and desktop.
Requirements: Python 2.7* Cairo (on OSX brew install cairo)
install dependencies using pip install -r requirements_desktop.txt from the cartoonify subdirectory.
run app from command line using python run.py.
select 'yes' when asked to download the cartoon dataset (~5GB) and tensorflow model (~100MB).
close the app using ctrl-C once the downloads have finished.
start the app again using cartoonify.
you will be prompted to enter the filepath to an image for processing. Enter the absolute filepath surrounded by double quotes.
install docker on the raspi by running: curl -sSL https://get.docker.com | sh. set up and enable the raspi camera through raspi-config. clone the source code from this repo. run ./raspi-build.sh. This will download the google quickdraw dataset and tensorflow model, then build the required docker image. run ./raspi-run.sh. This will start the docker image.
requirements: raspberry pi 3 rasbian stretch image on 16gb SD card (8gb too small) internet access on the raspi pip + python raspi camera v2 a button, led, 220 ohm resistor and breadboard (optional) Thermal printer to suit a raspi 3. I used this printer here. Note you will need to use the printer TTL serial interface as per the wiring diagram above, rather than USB.
install docker on the raspi by running: curl -sSL https://get.docker.com | sh
set up and enable the raspi camera through raspi-config
clone the source code from this repo
run ./raspi-build.sh. This will download the google quickdraw dataset and tensorflow model, then build the required docker image.
run ./raspi-run.sh. This will start the docker image.

Support

Check the log files in the cartoonify/logs folder for any error messages.The most common issue when running on a raspi is not having the camera plugged in correctly.If nothing is printing, check the logs then check whether images are being saved to cartoonify/images.Check that you can manually print something from the thermal printer from the command line.

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