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.
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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
I have implemented a convolutional neural network by transfer learning using VGG19 to classify 5 different traffic signs. It works well with new test images, but when I apply the model upon video streaming it doesn't classify them correctly.
Assuming the neural network works well on images, it should work the same on frames of a video stream. In the end, a video stream is a sequence of images.
The problem is not that it doesn't work on video stream, it simply does not work on the type of images similar to the ones you have in the video stream.
It is hard to exactly find the problem, since the question does not have enough detail. However, some considerations are:
Obviously, there is a problem with the network's ability to generalize. Was the testing performed well? For example, is there a train-validation split of the data?
Does the training error and the validation error indicate any possible issues, such as overfitting?
Is the data used to train the image similar enough to the video frames?
Is the training dataset large enough? Data augmentation might help if there is not enough data.
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 would like to train a CNN for detection and classification of any kind of signs (mainly laboratory and safety markers) using tensorflow.
While I can gather enough training data for the classification training set, using e.g. The Bing API, I‘m struggeling to think about a solution to get enough images for the object detection training set. Since these markers are mostly not public available, I thought I could make a composite of a natrual scene image with the image of the marker itself, to get a training set. Is there any way to do that automatically?
I looked at tensorflow data augmentation class, but it seems it only provides functionality for simpler data augmentation tasks.
You can do it with OpenCV as preprocessing.
The algorithm follows:
Choose a combination of a natural scene image and a sign image randomly.
Sample random position in the natural scene image where the sign image is pasted.
Paste the sign image at the position.
Obtain the pasted image and the position as a part of training data.
Step1 and 2 is done with python standard random module or numpy.
Step3 is done with opencv-python. See overlay a smaller image on a larger image python OpenCv
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I am new by tensorflow. I want to write a Neural network, that gets noisy images from a file and uncorrupted images from another file.
then I want to correct noisy images based on the other images.
What you are talking about is a denoising autoencoder.
This is not my code. It was ranked very high on google search, has several github stars and forkes, all which are great indicators that it is a working and supported implementation.
Actually, I'm trying to train a NN that get corrupted images and based on them the grand truth, remove noise from that images.It must be Network in Network, an another word pixels independent.