Flower-Recognition | Image recognition tool for flower clasification | Computer Vision library

by mgmacias95 Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Flower-Recognition Summary

Flower-Recognition is a Python library typically used in Artificial Intelligence, Computer Vision, Nodejs, OpenCV applications. Flower-Recognition has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However Flower-Recognition build file is not available. You can download it from GitHub.

Image recognition tool for flower clasification.

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Support

Flower-Recognition has a low active ecosystem.

It has 14 star(s) with 9 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 868 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Flower-Recognition is current.

Quality

Flower-Recognition has no bugs reported.

Security

Flower-Recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Flower-Recognition is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

Flower-Recognition has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Train a K - Means model
Cross validation
Generate the ROC curve
Generate training and test masks
Compute SVM cross validation
Create a test subset
Calculates ROC curve
Create a bag of words with K - Means
Compute a BOW - force response
Convert list of images to HSV
Show an image
Train and train both two images
Paint the ROOC curve
Train random forest classifier
Fits the model and returns the score
Train the SVM model
Generate num_labels

Get all kandi verified functions for this library.

Flower-Recognition Key Features

No Key Features are available at this moment for Flower-Recognition.

Flower-Recognition Examples and Code Snippets

No Code Snippets are available at this moment for Flower-Recognition.

Community Discussions

Trending Discussions on Flower-Recognition

Incompatible inputs of layers (ndim=4, found ndim=3)

QUESTION

Incompatible inputs of layers (ndim=4, found ndim=3)

Asked 2020-Jun-16 at 00:54

I tried to recreate this Flower Recognition CNN in Keras. The model seems to work, at least in the notebook (while getting predictions on the validation set), but I need to use the model somewhere else. Photos are 150x150 and this is how I build the CNN:

...

ANSWER

Answered 2020-Jun-16 at 00:54

Make sure you have the first dimension as 1 (1 image to predict). Your model is expecting the None dimension to be number of samples, 2nd + 3rd to be the image resolution, and 4th to be your RGB channels for the image.

data = np.expand_dims(data, axis=0)

Will add an extra dimension to your first axis.

See: How can I add new dimensions to a Numpy array?

Source https://stackoverflow.com/questions/62399263

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

Vulnerabilities

No vulnerabilities reported

Install Flower-Recognition

You can download it from GitHub.
You can use Flower-Recognition like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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