pix2pix-tensorflow | use general purpose implementation of pix2pix model | Computer Vision library

by Cartmanishere Python Version: Current License: No License

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kandi X-RAY | pix2pix-tensorflow Summary

pix2pix-tensorflow is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, Generative adversarial networks applications. pix2pix-tensorflow has no bugs, it has no vulnerabilities and it has low support. However pix2pix-tensorflow build file is not available. You can download it from GitHub.

This is a general purpose implementation of the pix2pix algorithm for image to image translation. This algorithm is based on pix2pix by Isola et al. Code in this repo has been heavily borrowed from this implementation of the pix2pix tensorflow. But the linked code is not easy to use as I have experienced it first hand while working on a related project. This repo is an attempt to make the pix2pix implementation easily approachable for training and testing. A guide on how to use this code for image to image translation is provided.

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pix2pix-tensorflow has no bugs reported.

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pix2pix-tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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pix2pix-tensorflow releases are not available. You will need to build from source code and install.

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Top functions reviewed by kandi - BETA

kandi has reviewed pix2pix-tensorflow and discovered the below as its top functions. This is intended to give you an instant insight into pix2pix-tensorflow implemented functionality, and help decide if they suit your requirements.

Train the model
Save images to files
Get the path to the checkpoint
Append a list of files to the output directory
Build the graph
Resize image
ResNet resnet
Create a convolutional model
Augment image with brightness
Convert a lab to RGB
Deprocessing labels
Parse the parsed arguments

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pix2pix-tensorflow Key Features

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pix2pix-tensorflow Examples and Code Snippets

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Community Discussions

Trending Discussions on pix2pix-tensorflow

error UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte

Resize float32 array with K-nearest neighbour in the same way as scipy.misc.imresize or tf.image.resize

What if Batch Normalization is used in training mode when testing?

QUESTION

error UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte

Asked 2020-May-19 at 07:48

https://github.com/affinelayer/pix2pix-tensorflow/tree/master/tools

An error occurred when compiling "process.py" on the above site.

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ANSWER

Answered 2018-Nov-20 at 09:21

Python tries to convert a byte-array (a bytes which it assumes to be a utf-8-encoded string) to a unicode string (str). This process of course is a decoding according to utf-8 rules. When it tries this, it encounters a byte sequence which is not allowed in utf-8-encoded strings (namely this 0xff at position 0).

Since you did not provide any code we could look at, we only could guess on the rest.

From the stack trace we can assume that the triggering action was the reading from a file (contents = open(path).read()). I propose to recode this in a fashion like this:

Source https://stackoverflow.com/questions/42339876

QUESTION

Resize float32 array with K-nearest neighbour in the same way as scipy.misc.imresize or tf.image.resize

Asked 2019-Oct-15 at 11:48

I am to create a network using much of the same characteristics as pix2pix: https://github.com/affinelayer/pix2pix-tensorflow.

My adjustment is that I will not be using images, but matrices with float32 values. This introduces a lot of problems and there is a lot to rewrite. Most of the code can easily be rewritten, but I've encountered a problem.

The network has a separable convolutional layer where the image is resized using tf.image.resize. This function uses different resize methods, such as K-Nearest Neighbors, and I don't want to loose that feature. Both scipy.misc.imresize and tf.image.resize are limited to int values and does not support any higher than uint16. If I were to transform the data to said formats, I will loose precision.

Is there a way to create this efficiently in numpy (or any equivalent) supporting float32?

Sorry for not introducing any code, but the problem more or less explains itself without (I hope).

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ANSWER

Answered 2019-Oct-15 at 11:48

Try using scipy.ndimage.interpolation.zoom. This works for float number images. Use it as below:

image = scipy.ndimage.interpolation.zoom(image, 0.5)

Source https://stackoverflow.com/questions/58134946

QUESTION

What if Batch Normalization is used in training mode when testing?

Asked 2019-Jan-07 at 09:51

Batch Normalization has different behavior in training phase and testing phase.

For example, when using tf.contrib.layers.batch_norm in tensorflow, we should set different value for is_training in different phase.

My qusetion is: what if I still set is_training=True when testing? That is to say what if I still use the training mode in testing phase?

The reason why I come up with this question is that, the released code of both Pix2Pix and DualGAN don't set is_training=False when testing. And it seems that if is_training=False is set when testing, the quality of generated images could be very bad.

Is there someone could please explain this? thanks.

...

ANSWER

Answered 2019-Jan-07 at 09:51

During training, the BatchNorm-layer tries to do two things:

estimate the mean and variance of the entire training set (population statistics)
normalize the inputs mean and variance, such that they behave like a Gaussian

In the ideal case, one would use the population statistic of the entire dataset in the second point. However, these are unknown and change during training. There are also some other issues with this.

A work-around is doing the normalization of the input by

Source https://stackoverflow.com/questions/46290930

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Vulnerabilities

No vulnerabilities reported

Install pix2pix-tensorflow

The goal in image to image translation is to convert the input image A to target image B. You can also specify the mapping AtoB or BtoA in config.py. For training, you should generate such images and put all the training images in the train_data folder in the root directory. Alternatively, you can also use --input-dir flag to set your custom input directory. For more control over the training, refer to config.py which contains all the configurable settings to be used. You'll find comments there to help you out. For generating output samples using your trained model, you should follow this pattern.
Create folder inputs inside folder test_data inside the project root. (Note: You can change this in the config.py file)
Put your input images inside the inputs folder and run test.py.
You can specify the --checkpoint flag to point to the folder where model checkpoints are saved.

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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