AI Applications for Draw

 

 

[1] Swap face (Adapt face shape to target) https://www.artguru.ai #Use anonymous windows

      Same core than previous line https://www.pica-ai.com/

[2] Swap face pose https://faceswapper.ai/swapper  #Free 10 by day (Works good with 3/4 face)

[3] Swap free 8 times/day https://www.vidnoz.com/face-swap.html #Check by IP, PW

[4] Swap free 8 times/day https://www.miocreate.com/face-swap.html  #Check by IP, PW

[5] https://www.ismartta.com/ #Check by IP,  works more time in private window

 

Compose

[1] Replace hair style https://picsart.com/ai-replace/

Online Machine Learning tools

 

 Multiclass Image Classification

https://teachablemachine.withgoogle.com/train/image

Update Gimp

From 2.8 to 2.10 using apt-get

$ sudo add-apt-repository ppa:otto-kesselgulasch/gimp

$ sudo apt-get update

$ sudo apt-get install gimp

 

References:

https://linuxhint.com/install_gimp_210_ubuntu_1804/

Volume Fractions

Thesis

[1] Smooth Interface Reconstruction from Volume Fraction Data Using Variational
Techniques and Level Set Methods

https://escholarship.org/content/qt2466j56f/qt2466j56f_noSplash_4c9dcb1c6cacafb1bcdc4a7bab220630.pdf?t=odypru

 

Volume Fractions in Rectangular Grids

Analytical Relations Connecting Linear Interfaces and Volume Fractions in Rectangular Grids
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.536.848&rep=rep1&type=pdf
[1] Markers - A front-tracking algorithm for accurate representation of surface tension (Zaleski)
https://hal.archives-ouvertes.fr/hal-01445441/document



[2] Level Set Methods (LSM)
[2.1] Tutorial Level Set vs Marching Methods https://math.berkeley.edu/~sethian/2006/Explanations/level_set_explain.html

[2.2-1] Part I (Def) https://wiseodd.github.io/techblog/2016/11/05/levelset-method/
[2.2-2] Part II(Application for segmentation) https://wiseodd.github.io/techblog/2016/11/20/levelset-segmentation/

Deep Gan Applications

Super Resolution

[0] https://replicate.com/collections/super-resolution 

[1] https://replicate.com/nightmareai/real-esrgan  /require github account

[2] https://replicate.com/mv-lab/swin2sr (alternative) 

[3] Nightmare https://www.nightmare-ai.com/playground

Styles

[1] https://replicate.com/collections/ml-makeovers 

Restoration

[1] 5byday https://www.restorephotos.io/restore  (based on replicate)

[2] https://replicate.com/collections/image-restoration

[3] https://replicate.com/microsoft/bringing-old-photos-back-to-life

Frequence space ( Source codes )

References:

[1] Elliptic Harmonics http://www.cs.utah.edu/~jfishbau/advimproc/project3/



Resources:
 0) Discrete Fourier Transform 1D, source code c/c++/java/python and more https://www.nayuki.io/page/how-to-implement-the-discrete-fourier-transform
1) Discret Sine Tranform  https://people.sc.fsu.edu/~jburkardt/cpp_src/sine_transform/sine_transform.html
  1.1) Python version http://www-personal.umich.edu/~mejn/computational-physics/dcst.py

2) Haar Wavelet compression https://people.math.osu.edu/husen.1/teaching/wi2010/572/572.html

3) Fast Fourier Tranform 1d/2d http://www.kurims.kyoto-u.ac.jp/~ooura/fft.html
4) Elliptic Fourier Transform source code python https://github.com/hbldh/pyefd
5) Spatial Elliptical Fourier Descriptors python https://spatial-efd.readthedocs.io/en/latest/raster_link.html 

6) Morphometric in R https://github.com/MomX/Momocs/

Geo DataScience

Datasets

[1] Flood Mapping https://ieee-dataport.org/competitions/2024-ieee-grss-data-fusion-contest-flood-rapid-mapping

 

[1] datos https://www.datosabiertos.gob.pe/

[2] lat/long Perú https://github.com/jmcastagnetto/ubigeo-peru-aumentado

[3] variables vivienda y conocimiento https://www.redalyc.org/journal/6357/635767693004/html/

CONCLUSIONES

El conocimiento sobre dengue, las viviendas que presentan depósitos de basura y agua acumulada están relacionadas a los casos de Dengue en los distritos de Luyando y Rupa Rupa, en la provincia de Leoncio Prado. Persisten conductas de riesgo a pesar del adecuado conocimiento sobre la enfermedad. No se encontró asociación con el tipo de vivienda, género, material predominante en las viviendas, tipo de abastecimiento de agua y servicios higiénicos.

 

[4] datos amazonia https://www.dge.gob.pe/sala-situacional-dengue/#grafico27

[5] Mapas Perú .shp https://www.geogpsperu.com/2014/03/base-de-datos-peru-shapefile-shp-minam.html

[6] Información Geoespacial Perú https://www.idep.gob.pe/geovisor/descarga/visor.html

[6] Hidrografía https://data.humdata.org/dataset/hidrografia-de-peru?

   https://data.humdata.org/dataset

[7] Áreas  naturales protegidas https://geo.sernanp.gob.pe/visorsernanp/

[8] Biomasa Brasil https://mapbiomas.org/download

Processing 

[1] plotting maps https://ggplot2-book.org/maps.html

[1.1] https://community.rstudio.com/t/geom-label-with-custom-background-and-color-text/45161

[2] poly2nb https://www.rdocumentation.org/packages/spdep/versions/1.2-8/topics/poly2nb

[3] Indicadores demograficos Perú (teoria) https://www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1743/Libro.pdf

Transformers in Computer Vision

 

[1] Review https://sh-tsang.medium.com/review-vision-transformer-vit-406568603de0

[2] Colab ViT Tutorial https://colab.research.google.com/github/hirotomusiker/schwert_colab_data_storage/blob/master/notebook/Vision_Transformer_Tutorial.ipynb

[3] ViT pytorch https://github.com/lucidrains/vit-pytorch

[4] Vit Keras https://wandb.ai/ayush-thakur/keras_cv_vit/reports/Image-Classification-Using-Vision-Transformer-and-KerasCV--VmlldzozNTE4MzMz

[5] Fine tunning https://huggingface.co/blog/fine-tune-vit

[6] Full material https://github.com/cmhungsteve/Awesome-Transformer-Attention

Convert Photo in Cartoon

 

 

[1] https://imglarger.com/Cartoonizer

[2] https://vanceai.com/toongineer-cartoonizer/

[3] Prompt image to cartoon https://deepai.org/machine-learning-model/image-editor

[4] https://www.befunky.com/create/photo-to-cartoon/

[5] Head+ [Body models]  https://imagetocartoon.com/ 10/week

[6] 4Adults AI https://metaroids.com/lists/adult-ai-art-tools-that-can-generate-nsfw-ai-images/

2D and 3D Moments resources

Theory

[1] Binary Image http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/OWENS/LECT2/node3.html

[2] https://towardsdatascience.com/a-visual-introduction-to-binary-image-processing-part-1-d2fba9f102a4

[3] Hu Moments code and theory https://www.learnopencv.com/shape-matching-using-hu-moments-c-python

[4] PCA Analysis and code https://towardsdatascience.com/visualizing-principal-component-analysis-with-matrix-transforms-d17dabc8230e

[3] 3D moments http://147.231.16.195/3DRotationInvariants

Codes

[1] OpenCV moments https://github.com/krutikabapat/Shape-Matching-using-Hu-Moments

[2] Hu Moments https://github.com/adailtonjn68/hu_moments_in_python

Books

[1] 2D and 3D Image Analysis by Moments https://b-ok.lat/book/4985123/51d687?regionChanged=&redirect=34651345

Applications

[1] Tracking http://raphael.candelier.fr/?blog=Image%20Moments

 

 

 

 

 

Anisotropic Diffusion

[1] Perona Malik Diffusion for Edge Detection
https://github.com/fubel/PeronaMalikDiffusion/blob/master/main.py

[2] https://github.com/pastapleton/Perona-Malik
[3] https://pastebin.com/sBsPX4Y7

Image Segmentation using Deep Learning resources

[1] https://www.nature.com/articles/s41699-020-0137-z.pdf

https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5729043&blobtype=pdf

https://borda.github.io/pyImSegm/

http://vision.cs.utexas.edu/projects/keysegments/keysegments.html

Coffee Rust

Based on Deep Learning

[1] Deep Learning for Classification and Severity Estimation of Coffee Leaf BioticStresshttps://arxiv.org/pdf/1907.11561.pdf



Datasets Coffee Rust
[1] Coffe leaves https://github.com/esgario/lara2018/tree/master/classifier
[2] https://www.digipathos-rep.cnptia.embrapa.br/

Photo3D Resources

[1] Convert image + depth map in 3D animation http://depthy.me
[2] depth map generator https://3dphoto.io/uploader/
[3] Manual depth creation https://triaxes.com/articles/manual-depth-map-creation/
[4] Complete pipeline https://www.omnivirt.com/3d-photo/
[5] https://www.brainfevermedia.com/DepthCamProTutorial.html

https://github.com/alexjc/neural-enhance
https://github.com/idealo/image-super-resolution

Anti Aliasing Lectures

Lectures
[1] http://cs248.stanford.edu/winter19content/lectures/02_drawtriangle/02_drawtriangle_slides.pdf
[2] http://graphics.stanford.edu/courses/cs348b-19-spring-content/lectures/08_sampling/08_sampling_slides.pdf
[3] http://www8.cs.umu.se/kurser/TDBC07/HT04/handouts/HO-lecture5.pdf


References:
[1] Super Sample http://hhoppe.com/proj/supersample/
Amortized Supersampling  http://hhoppe.com/supersample.pdf
[2] Sampling https://noahmjacobs.com/graphics/rasterizer/
[3] MultiSampling https://learnopengl.com/Advanced-OpenGL/Anti-Aliasing
[4] Anti Aliasing https://vr.arvilab.com/blog/anti-aliasing

Super-sampling Anti-aliasing Analyzed https://pdfs.semanticscholar.org/ebd9/ddb08c4244fc7df00672cacb420212cdde54.pdf

Comparison for R.T. Applications http://jacoblongazo.com/documents/Anti-Aliasing%20Paper.pdf


Related:
[1] Deep Learning Super resolution (2019) https://arxiv.org/pdf/1808.03344.pdf

Mathematical Morphology on Graphs

References:
[1] A graph-based mathematical morphology reader https://arxiv.org/pdf/1404.7748.pdf
[2] Morphological filtering on graphs https://hal.archives-ouvertes.fr/hal-00700784v1/document

Gaussian blur/filter

Resources:
OpenCL:

$clinfo

 [1] 2D Gauss+SIPL https://www.eriksmistad.no/gaussian-blur-using-opencl-and-the-built-in-images-textures/


[2] Gaussian blur  https://github.com/mnmnc/gaussian_blur_opencl
$locate CL/cl.h
$sudo apt install ocl-icd-opencl-dev
$g++ main.cpp lodepng.cpp -lOpenCL -std=c++11 -o gb -I /usr/include





Cuda:
[1] Compile by Cuda 8 https://github.com/yanji84/cuda-blur
$nvcc -c -arch=sm_20 blur.cu -I /usr/local/cuda-8.0/samples/common/inc/
$g++ main.cpp reference_cal.cpp util.cpp -I /usr/local/cuda-8.0/samples/common/inc/ `pkg-config --cflags --libs opencv` -lcuda  -I /usr/local/cuda-8.0/targets/x86_64-linux/include/ blur.o  -std=c++11 -L /usr/local/cuda-8.0/targets/x86_64-linux/lib/ -lcudart


[2] Gaussian kd-tree https://melhorum.blogspot.com/search?q=Gaussian

Cuda Makefile:

all: program

program: cudacode.o
    g++ -o program -L/usr/local/cuda/lib64 -lcuda -lcudart main.cpp  cudacode.o 

cudacode.o:
    nvcc -c -arch=sm_20 cudacode.cu 

clean: rm -rf *o program

Cuda Tutorials:
[1] Hello https://cuda-tutorial.readthedocs.io/en/latest/tutorials/tutorial01/
[2] Summary http://www.icl.utk.edu/~mgates3/docs/cuda.html
[1] locate convolutionSeparable #example from cuda
[2] Samples https://docs.nvidia.com/cuda/cuda-samples/index.html#cuda-separable-convolution
[3] Compiling https://devblogs.nvidia.com/easy-introduction-cuda-c-and-c/
[4] Find Cuda https://github.com/dusty-nv/jetson-utils/tree/master/cuda
[5] Image to GL https://github.com/zchee/cuda-sample/tree/master/3_Imaging/simpleCUDA2GL
[6] Image processing http://developer.download.nvidia.com/compute/DevZone/C/html/Image_Processing.html

References:

Interactive Maps and D3 and others

References:
[1] Image to map by Deep Learning https://ai.facebook.com/blog/mapping-roads-through-deep-learning-and-weakly-supervised-training/

Resources

[1] Just a map https://codepen.io/manishgolcha/post/world-map-using-d3-js
[2] Datamaps http://datamaps.github.io/
     Tutorial https://github.com/markmarkoh/datamaps/blob/master/README.md#getting-started

[3] Worldmap by cuntry tooltip http://bl.ocks.org/micahstubbs/8e15870eb432a21f0bc4d3d527b2d14f
[4] d3-zoom https://github.com/d3/d3-zoom


Free geocode
[1] Works using open street map https://opencagedata.com/api
      https://api.opencagedata.com/geocode/v1/json?q=São Paulo&key=8c0e3ccc191e41f38dd211fe50fb9eab

[2] Limited https://developer.here.com/documentation/maps/dev_guide/topics/quick-start.html

[3] Google not recommended https://developers.google.com/maps/documentation/geocoding/intro


Testing
https://blockbuilder.org/Ayumix01/984d70d411ea1d29f48b3963881a5618
 https://medium.com/@ttemplier/map-visualization-of-open-data-with-d3-part3-db98e8b346b3
http://datawanderings.com/2018/10/28/making-a-map-in-d3-js-v-5/
https://github.com/ivan-ha/d3-hk-map

Offline Handwritten Signature Verification - Resources

Source code:

[1] https://github.com/adarshsrivastava11/sbi


[2] Kaggle https://www.kaggle.com/divyanshrai/training-using-greyscale

[3] Dataset https://www.kaggle.com/divyanshrai/handwritten-signatures


Scanned Documents
[1] Tobacco-800  https://github.com/prittt/YACCLAB


Object Detection
[1] https://pjreddie.com/darknet/yolo/

Related
[1] https://medium.com/@everisUS/handwritten-signature-verification-with-neural-networks-94da5cbe0c5f