How can I cartoonize images with the model trained with a custom dataset? There are projects on the internet that cartoonize pictures with library methods without a dataset, but I couldn't find a project source for the purpose I wanted. Could you post resources that might help?
you can try using neural style transfer with PyTorch, use the style image as the cartoon image and the content image as the image that you wish to cartoonize.
https://pytorch.org/tutorials/advanced/neural_style_tutorial.html
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I've been using this tutorial from TensorFlow's site to create a script (python3) that performs a style transfer. I'm trying to train a model on a particular art piece and then apply that style to any random photo. From my understanding of this tutorial the script takes a style image and a content image, runs them through the VGG19 model and spits out a final image (takes about 30min on my machine). But I see no way to save the trained model to apply it to another content photo. This tutorial doesn't use TF's model fit(), predict(), and save() methods like I would expect. It just seems to be applying the prediction to the image as it trains.
How do I save the trained model? Once saved how do I use it on another content photo?
Use model.save() method.
Read this tutorial: https://www.tensorflow.org/guide/keras/save_and_serialize
Currently, I have a CNN model that I developed in Pytorch. I have used hiddenlayer package to create an image like shown in image 1. But, I want to create an image of the model that should look like something in image 2. Is there any package that I can use to achieve this?
image 1:
image 2:
I personally use figma.com and "draw" it myself, but if you want to create it automatically you should check out this github repository, you might find a nice tool.
I have successfully build an model of handwritten digits. How would I load the model and use it with live data coming from a video camera? I would like it to draw a box around the number and label the number.
Your question is very broad however there might be one video to answer all your qestions.
This is how use an ml model on your Android.
I am trying to do handwriting character recognition using Tensorflow in Google-colab.
I have trained and tested model with an accuracy of 91%
I tried it on image given in the tutorial, and it worked correctly.
it was 28*28 resized.
When I wanted to try it on my input-image, it is predicting wrong results as 2,3, but my input-image is of 'digit-6'.
the problem may be in image-operations and before passing to model.
also, further I wanted to pass that image for realtime-recognition.
I am doing resizing, inverting of the image, to make it compatible with my trained labels.
OpenCV input image is represented opposite-notation of tensorflow labels, as the current matrix represents black as 0 and white as 255.
my GitHub Jupyter-notebook file is followed from tutorial of digitalocean's blog
How can I upload an image taken from a phone/webcam and recognize characters from that image?
where I am making mistakes in processing image?
further, I wanted to pass that image in a project - real-time recognition of characters
testing images are
do you know Mnist data set is restricted with padding of images?
appropriate realtime image processing is needed.
This is useful article about that
https://link.medium.com/0ySCmyMpzU
and following is my project about simple mnist game
https://github.com/mym0404/Math-Writer
I am new in scikit-learn, I have a lot of images and images size not all same, A kind of are real scenes image like
cdn.mayike.com/emotion/img/attached/1/image/dt/20170920/12/20170920121356_795.png
cdn.mayike.com/emotion/img/attached/1/image/mainImg/20170916/15/20170916153205_512.png
, another are not real scenes image like
cdn.mayike.com/emotion/img/attached/1/image/dt/20170917/01/20170917011403_856.jpeg
cdn.mayike.com/emotion/img/attached/1/image/dt/20170917/14/20170917145613_197.png
.
I want to use scikit-learn recognizing which not real scenes image, I think it simlar to http://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py. I am totally no idea how to begin.How to creating dateset and extracting features from images? Can someone tell me what should I do?
This seems to not directly be a programming problem here and your questions are related to non-basic 'current' research.
It seems that you should read about Natural Scene (Statistics) and get yourself familiar with one of the current machine learning frameworks like TensorFlow, Caffe.
There are many tutorials out there to get started, for example you could begin with a binary classifier which outputs if the given image shows a natural scene or not.
Your database setup could have a structure like so:
-> Dataset
-> natural_scenes
-> artificial_images
Digits for example can use such a structure to create a dataset and is able to use models designed for Caffe and TensorFlow.
I would also recommend that you read about finetuning nerual networks, as you would need a lot of images in your database if you start training from scratch.
In Caffe you can finetune pretrained models like CaffeNet or GoogeNet.
I think those are some basic information which should get you started.
As of scikit-learn and face-detection: Face-Detection is more looking for local candidates or image patches which could possibly contain a face. Your problem on the other hand is more of a global problem as the whole image is concerned. That said I would start off with a neural network here which is able to extract local and global features for you.