Python库 | clustimage-1.3.11.tar.gz

# clustimage [](https://img.shields.io/pypi/pyversions/clustimage) [](https://pypi.org/project/clustimage/) [](https://github.com/erdogant/clustimage/blob/master/LICENSE) [](https://github.com/erdogant/clustimage/network) [](https://github.com/erdogant/clustimage/issues) [](http://www.repostatus.org/#active) [](https://erdogant.github.io/clustimage/) [](https://pepy.tech/project/clustimage/month) [](https://pepy.tech/project/clustimage) [](https://www.buymeacoffee.com/erdogant) [](https://zenodo.org/badge/latestdoi/423822054) [](https://colab.research.google.com/github/erdogant/clustimage/blob/master/notebooks/clustimage.ipynb) [](https://towardsdatascience.com/a-step-by-step-guide-for-clustering-images-4b45f9906128) <!---[](https://erdogant.github.io/donate/?currency=USD&amount=5)--> * [**A step-by-step guide for clustering images**](https://towardsdatascience.com/a-step-by-step-guide-for-clustering-images-4b45f9906128) The aim of ``clustimage`` is to detect natural groups or clusters of images. Image recognition is a computer vision task for identifying and verifying objects/persons on a photograph. We can seperate the image recognition task into the two broad tasks, namely the supervised and unsupervised task. In case of the supervised task, we have to classify an image into a fixed number of learned categories. Most packages rely on (deep) neural networks, and try solve the problem of predicting "whats on the image". In case of the unsupervised task, we do not depend on the fact that training data is required but we can interpret the input data and find natural groups or clusters. However, it can be quit a breath to carefully group similar images in an unsupervised manner, or simply identify the unique images. The aim of ``clustimage`` is to detect natural groups or clusters of images. It works using a multi-step proces of carefully pre-processing the images, extracting the features, and evaluating the optimal number of clusters across the feature space. The optimal number of clusters can be determined using well known methods suchs as *silhouette, dbindex, and derivatives* in combination with clustering methods, such as *agglomerative, kmeans, dbscan and hdbscan*. With ``clustimage`` we aim to determine the most robust clustering by efficiently searching across the parameter and evaluation the clusters. Besides clustering of images, the ``clustimage`` model can also be used to find the most similar images for a new unseen sample. A schematic overview is as following: <p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/schematic_overview.png" width="1000" /> </p> ``clustimage`` overcomess the following challenges: * 1. Robustly groups similar images. * 2. Returns the unique images. * 3. Finds higly similar images for a given input image. ``clustimage`` is fun because: * It does not require a learning proces. * It can group any set of images. * It can return only the unique() images. * it can find highly similar images given an input image. * It provided many plots to improve understanding of the feature-space and sample-sample relationships * It is build on core statistics, such as PCA, HOG and many more, and therefore it does not has a dependency block. * It works out of the box. ### Installation * Install clustimage from PyPI (recommended). clustimage is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows. * A new environment can be created as following: ```bash conda create -n env_clustimage python=3.8 conda activate env_clustimage ``` * Install from pypi ```bash pip install -U clustimage ``` #### Import the clustimage package ```python from clustimage import Clustimage ``` ### Example 1: Digit images. In this example we will be using a flattened grayscale image array loaded from sklearn. The array in NxM, where N are the samples and M the flattened raw rgb/gray image. ```python # Load library import matplotlib.pyplot as plt from clustimage import Clustimage # init cl = Clustimage() # Load example digit data X = cl.import_example(data='mnist') print(X) # Each row is an image that can be plotted after reshaping: plt.imshow(X[0,:].reshape(8,8), cmap='binary') # array([[ 0., 0., 5., ..., 0., 0., 0.], # [ 0., 0., 0., ..., 10., 0., 0.], # [ 0., 0., 0., ..., 16., 9., 0.], # ..., # [ 0., 0., 0., ..., 9., 0., 0.], # [ 0., 0., 0., ..., 4., 0., 0.], # [ 0., 0., 6., ..., 6., 0., 0.]]) # # Preprocessing and feature extraction results = cl.fit_transform(X) # Lets examine the results. print(results.keys()) # ['feat', 'xycoord', 'pathnames', 'filenames', 'labels'] # # feat : Extracted features # xycoord : Coordinates of samples in the embedded space. # filenames : Name of the files # pathnames : Absolute location of the files # labels : Cluster labels in the same order as the input # Get the unique images unique_samples = cl.unique() # print(unique_samples.keys()) # ['labels', 'idx', 'xycoord_center', 'pathnames'] # # Collect the unique images from the input X[unique_samples['idx'],:] ``` ##### Plot the unique images. ```python cl.plot_unique() ``` <p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_unique.png" width="300" /> </p> ##### Scatter samples based on the embedded space. ```python # The scatterplot that is coloured on the clusterlabels. The clusterlabels should match the unique labels. # Cluster 1 contains digit 4 # Cluster 5 contains digit 2 # etc # # No images in scatterplot cl.scatter(zoom=None) # Include images scatterplot cl.scatter(zoom=4) cl.scatter(zoom=8, plt_all=True, figsize=(150,100)) ``` <p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_fig2_tsne.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_fig21_tsne.png" width="400" /> </p> <p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/scatter_mnist_all.png" width="400" /> </p> #### Plot the clustered images ```python # Plot all images per cluster cl.plot(cmap='binary') # Plot the images in a specific cluster cl.plot(cmap='binary', labels=[1,5]) ``` <p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_cluster1.png" width="400" /> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_cluster5.png" width="400" /> </p> #### Dendrogram ```python # The dendrogram is based on the high-dimensional feature space. cl.dendrogram() ``` <p align="center"> <img src="https://github.com/erdogant/clustimage/blob/main/docs/figs/digits_dendrogram.png" width="400" /> </p> #### Make various other plots ```python # Plot the explained variance cl.pca.plot() # Make scatter plot of PC1 vs PC2 cl.pca.scatter(legen