pix2pix-tensorflow | Tensorflow port of Image-to-Image Translation | Computer Vision library

 by   affinelayer JavaScript Version: Current License: MIT

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kandi X-RAY | pix2pix-tensorflow Summary

kandi X-RAY | pix2pix-tensorflow Summary

pix2pix-tensorflow is a JavaScript library typically used in Artificial Intelligence, Computer Vision, Tensorflow applications. pix2pix-tensorflow has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets
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              pix2pix-tensorflow has a medium active ecosystem.
              It has 5044 star(s) with 1307 fork(s). There are 183 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 132 open issues and 52 have been closed. On average issues are closed in 99 days. There are 11 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pix2pix-tensorflow is current.

            kandi-Quality Quality

              pix2pix-tensorflow has 0 bugs and 0 code smells.

            kandi-Security Security

              pix2pix-tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pix2pix-tensorflow code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pix2pix-tensorflow is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              pix2pix-tensorflow releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              pix2pix-tensorflow saves you 966 person hours of effort in developing the same functionality from scratch.
              It has 2200 lines of code, 61 functions and 15 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            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.
            • Emit a node from a single node .
            • Generate random entropy
            • Decode a Matrix3 .
            • Encode an array of RGBA blocks
            • Get nodes from the tape
            • Compiles a program .
            • Computes the 2D - position information .
            • Generate a sampler3D
            • Creates a new pool
            • Parse a raw image
            Get all kandi verified functions for this library.

            pix2pix-tensorflow Key Features

            No Key Features are available at this moment for pix2pix-tensorflow.

            pix2pix-tensorflow Examples and Code Snippets

            No Code Snippets are available at this moment for pix2pix-tensorflow.

            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.

            ...

            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).

            ...

            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

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

            Vulnerabilities

            No vulnerabilities reported

            Install pix2pix-tensorflow

            The test run will output an HTML file at facades_test/index.html that shows input/output/target image sets.

            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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