Color Extractor

This project is both a library and a CLI tool to extract the dominant colors of the main object of an image. Most of the preprocessing steps assume that the images are related to e-commerce, meaning that the objects targeted by the algorithms are supposed to be mostly centered and with a fairly simple background (single color, gradient, low contrast, etc.). The algorithm may still perform if any of those two conditions is not met, but be aware that its precision will certainly be hindered.

A blog post describing this experiment can be found here.

Note: this project is released as-is, and is no longer maintained by us, however feel free to edit the code and use as you see fit.

Installation

The script and the library are currently targeting python 3 and won't work with python 2.

Most of the dependencies can be installed using

pip install -r requirements.txt

Note that library and the CLI tool also depend on opencv 3.1.0 and its python 3 bindings. For Linux users, the steps to install it are available here. For OSX users, the steps to install it are available here.

You then just have to ensure that this repository root is present in your PYTHONPATH.

Color tagging

Searching objects by color is a common practice while browsing e-commerce web sites and relying only on the description and the title of the object may not be enough to provide top-notch relevancy. We propose this tool to automatically associate color tags to an image by trying to guess the main object of the picture and extracting its dominant color(s).

The design of the library can be viewed as a pipeline composed of several sequential processing. Each of these processings accepts several options in order to tune its behavior to better fit your catalog. Those processings are (in order):

1. Resizing and cropping

2. Background detection

3. Skin detection

4. Clustering of remaining pixels

5. Selection of the best clusters

6. Giving color names to clusters

Usage

The library can be used as simply as this:

import cv2
import numpy as np

from color_extractor import ImageToColor

npz = np.load('color_names.npz')
img_to_color = ImageToColor(npz['samples'], npz['labels'])

img = cv2.imread('image.jpg')
print(img_to_color.get(img))

The CLI tool as simply as this:

./color-extractor color_names.npz image.jpg
> red,black

The file color_names.pnz can be found in this repository.

Passing Settings

All algorithms can be used right out of the box thanks to settings tweaked for the larger range of images possible. Because these settings don't target any special kind of catalog, changing them may cause a gain of precision.

Settings can be passed at three different levels.

The lowest level is at the algorithm-level. Each algorithm is embodied by a python class which accepts a settings dictionary. This dictionary is then merged with its default settings. The given settings have precedence over the default one.

A slightly higher level still concerns the library users. The process of chaining all those algorithms together is also embedded in 3 classes called FromJson, FromFile and ImageToColor. Those three classes also take a settings parameter, composed of several dictionary to be forwarded to each algorithm.

The higher level is to pass those settings to the CLI tool. When passing the --settings option with a JSON file the latter is parsed as a dictionary and giving to the underlying FromJson or FromFile object (which in turn will forward to the individual algorithms).

Resizing and Cropping

This step is available as the Resize class.

Pictures with a too high resolution have too much details that can be considered as noise when the goal is to find the most dominant colors. Moreover, smaller images mean faster processing time. Most of the testing has been done on 100x100 images, and it is usually the best compromise between precision and speed. Most of the time the object of the picture is centered, cropping can make sense in order to reduce the quantity of background and ease its removal.

The available settings are:

• 'crop' sets the cropping ratio. A ratio of 1. means no cropping. Default is 0.9.

• 'rows' gives the number of rows to reduce the image to. The columns are computed to keep the same ratio. Default is 100.

Background Detection

This step is available as the Back class.

This algorithm tries to discard the background from the foreground by combining two simple algorithms.

The first algorithm takes the colors of the four corners of the image and treat as background all pixels close to those colors.

The second algorithm uses a Sobel filter to detect edges and then runs a flood fill algorithm from all four corners. All pixels touched by the flood fill are considered background.

The masks created by the two algorithms are then combined together with a logical or.

The available settings are:

• 'max_distance' sets the maximum distance for two colors to be considered close by the first algorithm. A higher value means more pixels will be considered as background. Default is 5.

• 'use_lab' converts pixels to the LAB color space before using the first algorithm. The conversion makes the process a bit more expensive but the computed distances are closer to human perception. Default is True.

Skin Detection

This step is available as the Skin class.

When working with fashion pictures models are usually present in the picture. The main problem is that their skin color can be confused with the object color and yield to incorrect tags. One way to avoid that is to ignore ranges of colors corresponding to common color skins.

The available settings are:

• 'skin_type' The skin type to target. At the moment only 'general' and 'none' are supported. 'none' returns an empty mask every time, deactivating skin detection. Default is 'general'.

Clustering

This step is available as the Cluster class.

As we want to find the most dominant color(s) of an object, grouping them into buckets allows us to retain only a few ones and to have a sense of which are the most present. The clustering is done using the K-Means algorithm. K-Means doesn't result in the most accurate clusterings (compared to Mean Shift for example) but its speed certainly compensate. Before all images are different, it's hard to use a fixed number of clusters for the entire catalog. We implemented a method that tries to find an optimal number of clusters called the jump method.

The available settings are:

• 'min_k' The minimum number of clusters to consider. Default is 2.

• 'max_k' The maximum number of clusters to consider. Allowing more clusters results in greater computing times. Default is 7.

Selection of Clusters

This step is available as the Selector class.

Once clusters are made, all of them may not be worth a color tag: some may be very tiny for example. The purpose of this step is to only keep the clusters that are worth it. We implemented different way of selecting clusters:

• 'all' keeps all clusters.

• 'largest' keeps only the largest cluster.

• 'ratio' keeps the biggest clusters until their total number of pixels exceeds a certain percentage of all clustered pixels.

While the outcome of all is quite obvious, the use of largest versus ratio is trickier. largest will yield very few colors, meaning the chance of assigning a tag not really relevant is greatly diminished. On the other hand objects with two colors in equal quantity will see one of them discarded. It's up to you to decide which one behaves the best with your catalog.

The available settings are:

• 'strategy': The strategy to used among 'all', 'largest' and 'ratio'. Default is 'largest'.

• 'ratio.threshold': The percentage of clustered pixels to target while selecting clusters with the 'ratio' strategy. Default is 0.75.

Naming Color Values

This step is available as the Name class.

The last step is to give human readable color names to RGB values. To solve this last step we use a K Nearest Neighbors algorithm applied to a large dictionary of colors taken from the XKCD color survey. Because of the erratic distribution of colors (some colors are far more represented that others) a KNN behaves in most cases better than more statistical classifiers. The "learning" phase of the classifier is done when the object is built, and requires that two arrays are passed to its constructor: an array of BGR colors and an array of the corresponding names. When using the CLI tool, the path to an .npz numpy archive containing those two matrices must be given.

Even if the algorithm used defaults to KNN, it's still possible to use a custom class to do it. The supplied class must support a fit method in lieu of training phase and a predict method for the actual classification.

The available settings are:

• 'algorithm' The algorithm to use to perform the classification. Must be either 'knn' or 'custom'. If custom is given, 'classifier.class' must also be given. Default is 'knn'

• 'hard_monochrome' Monochrome colors (especially gray) may be hard to classify, this option makes use of a built in way of qualifying colors as "white", "gray" or "black". It uses the rejection of the color vector against the gray axis and uses a threshold to determine whether or not the color can be considered monochrome and the luminance to classify it as "black", "white" or "gray". Default is True.

• '{gray,white,black}_name' When using 'hard_monochrome' changes the name actually given to "gray", "white" and "black" respectively. Useful when wanting color names in another language. Default is "gray", "white" and "black"

• 'classifier.args' Arguments passed to the classifier constructor. Default one are provided for 'knn' being {"n_neighbors": 50, "weights": "distance", "n_jobs": -1}. The possible arguments are the ones available to the scikit-learn implementation of the KNeighborsClassifier.

• 'classifier.scale' Many classification algorithms make strong assumption regarding the distribution of the samples, and may need some kind of standardization of the data to behave better. This settings controls the application of such a standardization before training and prediction. Default is True but is ignored when using 'knn'.

Complete Processing

Instead of instantiating each of the aforementioned classes, you can simply use ImageToColor or FromFile. Those two classes take the same arguments for their construction.

• An array of BGR colors to learn how to associate color names to color values.

• An array of strings corresponding to the labels of the previous array.

• A dictionary of settings to be passed to each processing.

The dictionary can have the following keys:

• 'resize' settings to be given to the Resize object

• 'back' settings to be given to the Back object

• 'skin' settings to be given to the Skin object

• 'cluster' settings to be given to the Cluster object

• 'selector' settings to be given to the Selector object

• 'name' settings to be given to the Name object

The main difference is the source of the image used. ImageToColor expects a numpy array while FromFile expects both a local path or a URL where the image can be (down)loaded from.

Enriching JSON

Because we want Algolia customers to be able to enrich their JSON records easily we provide a class able to stream JSON and add color tags on the fly. The object is initialized with the same arguments as FromFile plus the name of the field where the URI of the images can be found. While reading the JSON file if the given name is encountered the corresponding image is downloaded and its colors computed. Those colors are then added to the JSON object under the field _color_tags. The name of this field can be changed thanks to an optional parameter of the constructor.

Enriching JSON can be used directly from the command line as this:

./color-extractor -j color_names.npz file.json