shapevolve package

Submodules

shapevolve.adjusters module

Adjusters that modify the source image during evolution, but are undone for the final product.

shapevolve.adjusters.strong_dark_adjust(original_image)

A function that applies a strong brightening filter to a dark image.

Parameters

original_image (ndarray) – The original image.

Returns

The image with the adjustment applied.

Return type

ndarray

shapevolve.adjusters.strong_dark_unadjust(adjusted_image)

A function that reverses a strong brightening filter to a dark image.

Parameters

adjusted_image (ndarray) – The original image.

Returns

The image with the adjustment applied.

Return type

ndarray

shapevolve.adjusters.weak_dark_adjust(original_image)

A function that applies a weak brightening filter to a dark image.

Parameters

original_image (ndarray) – The original image.

Returns

The image with the adjustment applied.

Return type

ndarray

shapevolve.adjusters.weak_dark_unadjust(adjusted_image)

A function that reverses a strong brightening filter to a dark image.

Parameters

adjusted_image (ndarray) – The original image.

Returns

The image with the adjustment applied.

Return type

ndarray

shapevolve.callbacks module

Callbacks that can be given to the Evolver class, to be run when a successful change is applied to the genome.

class shapevolve.callbacks.CSVLogger(csv_filepath, frequency=1)

Bases: object

A class that defines callbacks which record statistics into a CSV file.

A constructor for the class that stores a path to a CSV file.

Parameters

• csv_filepath (str) – The filepath of the CSV file that statistics will be written to.

• frequency (int) – The frequency at which stats will be saved.

callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

A callback that stores offspring, generation, loop index, and error into the class’s CSV file.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

class shapevolve.callbacks.GenomeSaver(genome_root, frequency=1)

Bases: object

A class that defines a callback where genomes are saved to files.

Constructs a genome saver class that takes a given root file path.

Parameters

• genome_root (str) – The file path where genomes should be saved.

• frequency (int) – The frequency at which files will be saved.

callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

A callback that saves genomes to a filepath.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

class shapevolve.callbacks.HighQualityImageSaver(image_root, frequency=1)

Bases: shapevolve.callbacks.ImageSaver

A subclass of ImageSaver that scales images to the original resolution before saving them.

A constructor for the class that defines a root filepath for saved images.

Parameters

• image_root (str) – The root filepath for saved images.

• frequency (int) – The frequency at which files will be saved.

callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

A callback that scales an image before providing it to the equivalent method in ImageSaver.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

class shapevolve.callbacks.ImageSaver(image_root, frequency=1)

Bases: object

A class that defines a callback where images built during evolution are saved.

A constructor for the class that defines a root filepath for saved images.

Parameters

• image_root (str) – The root filepath for saved images.

• frequency (int) – The frequency at which files will be saved.

callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

A callback that saves the best image so far into a png file.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

class shapevolve.callbacks.MatplotImageSaver(image_root, frequency=1)

Bases: shapevolve.callbacks.ImageSaver

A class that defines a callback where images shown by matplotlib during evolution are saved. Must be used with a variant of visual_callback in the same list.

A constructor for the class that defines a root filepath for saved images.

Parameters

• image_root (str) – The root filepath for saved images.

• frequency (int) – The frequency at which files will be saved.

callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

A callback that saves the best image so far into a png file.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

shapevolve.callbacks.default_callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

The default callback that the Evolver class uses. Currently visual_callback.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

shapevolve.callbacks.quiet_verbose_callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

The same thing as verbose_callback, but this only runs every 50 generations.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

shapevolve.callbacks.quiet_visual_callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

The same thing as visual_callback, but this only runs every 50 generations.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

shapevolve.callbacks.verbose_callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

A callback that prints the status of the evolution into standard output.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

shapevolve.callbacks.visual_callback(offspring, changes, loop_index, num_mutation_type_switches, error, complex_mutation, best_image, genome)

A simple callback that uses matplotlib to provide a live update of the image on the screen.

Parameters

• offspring (int) – The total number of offspring already processed.

• changes (int) – The total number of changes already applied to the genome.

• loop_index (int) – The total number of loops already performed.

• num_mutation_type_switches (int) – The total number of switches between simple and complex mutations so far.

• error (float) – The error of the current genome compared to the base image.

• complex_mutation (bool) – Whether complex mutations are being applied or not.

• best_image (ndarray) – The best image so far.

• genome (Genome) – The genome for the best image so far.

shapevolve.drawers module

Defines functions that can be used to draw shapes on an image based on genetic information by the evolver class.

shapevolve.drawers.add_circle(image, gene, palette)

Adds a circle to an image based on info from a gene.

Parameters

• image (ndarray) – The base image to draw the circle on.

• gene (Gene) – The gene that specifies how to draw the circle.

• palette (List[Tuple[int, int, int]]) – The color palette where colors are referenced from.

shapevolve.drawers.add_square(image, gene, palette)

Adds a square to an image based on info from a gene.

Parameters

• image (ndarray) – The base image to draw the square on.

• gene (Gene) – The gene that specifies how to draw the square.

• palette (List[Tuple[int, int, int]]) – The color palette where colors are referenced from.

shapevolve.drawers.draw_overlay(image, overlay, alpha)

Draws an overlay over an image at a specified alpha.

Parameters

• image (ndarray) – The base image.

• overlay (ndarray) – The overlay image.

• alpha (float) – The opacity, from 0 to 1.

shapevolve.error_metrics module

Defines error metrics that can be used to evaluate similarity between two images.

shapevolve.error_metrics.mean_squared_error(image, source)

Calculates the mean squared error between two images.

Parameters

• image (ndarray) – The constructed image.

• source (ndarray) – The original image.

Returns

The mean squared error between the two images.

Return type

float

shapevolve.error_metrics.structural_similarity_error(image, source)

Calculates the opposite of the structural similarity between two images.

Parameters

• image (ndarray) – The constructed image.

• source (ndarray) – The original image.

Returns

The opposite of the structural similarity between the two images.

Return type

float

shapevolve.evolver module

A class that manages evolving a genome based on certain specifications.

class shapevolve.evolver.Evolver(base_image, saved_genome=None, num_shapes=1000, num_colors=256, target_resolution=250, adjusters=None, preprocesses=None, draw=<function add_circle>, calculate_error=<function mean_squared_error>)

Bases: object

A class that manages evolving a genome based on certain specifications.

A constructor that specifies base images, a saved genome, and settings for the evolution.

Parameters

• base_image (Image) – The source PIL image object that the evolution will be performed against.

• saved_genome (Genome) – An optional saved genome. If unspecified, a genome will be randomly generated.

• num_shapes (int) – The number of shapes to be used in the genome construction.

• num_colors (int) – The number of colors that should be used by the shapes.

• target_resolution (int) – The resolution that images will be resized to during the evolution.

• adjusters (List[Dict[str, Callable]]) – A list of reversible adjusters that can be applied during the evolution. See adjusters module.

• preprocesses (List[Callable]) – A list of preprocessors that will be applied to the image before the evolution. See preprocessors module.

• draw (Callable) – The function used to draw shapes on the image. See drawers module.

• calculate_error (Callable) – The function used to calculate errors between images. See error_metrics module.

evolve(num_generations=5000, callbacks=None, silent=False)

Evolves a genome, and returns it after evolution.

Parameters

• num_generations (int) – The number of generations to evolve to. Note: early stoppages are possible.

• callbacks (List[Callable]) – Callbacks that can be run when a new generation is evolved.

• silent (bool) – Whether callbacks will be run at all.

Returns

Genome

shapevolve.gene module

A Gene class that defines the properties of a single shape on an image.

class shapevolve.gene.Gene(max_radius, min_radius, height, width, num_colors, radius=None, center=None, color=None, alpha=None)

Bases: object

A Gene class that defines the properties of a single shape on an image.

Constructs a gene and its initial properties.

Parameters

• max_radius (int) – The maximum possible radius the gene can have.

• min_radius (int) – The minimum possible radius the gene can have.

• height (int) – The height of the image that the gene is drawing on.

• width (int) – The width of the image that the gene is drawing on.

• num_colors (int) – The number of colours the gene can have.

• radius (int) – The current radius of the gene.

• center (Tuple[int, int]) – The coordinates of the center of the gene’s shape.

• color (int) – The current color of the gene, as an index of an external color palette list.

• alpha (float) – The current opacity of the gene, between 0 and 1.

clone()

Clones itself.

completely_randomize(initialization=False)

Completely randomizes the properties of the gene.

Parameters

initialization (bool) – Whether this is being called from the constructor.

get_scaled_version(ratio, fill_gaps)

Creates a scaled version of the gene, according to some ratio (original dimension / scaled dimension)

Parameters

• ratio – (original dimension) / (scaled dimension)

• fill_gaps – Whether the radii should be slightly expanded so gaps don’t form between shapes.

Returns

The scaled gene.

Return type

Gene

mutate()

Mutates the gene randomly.

randomize_alpha()

Randomizes the opacity of the shape.

randomize_center()

Randomizes the center of the shape.

randomize_color()

Randomizes the color of the shape.

randomize_radius()

Randomizes the radius.

revert()

Reverts the gene’s properties to before it was last modified.

shapevolve.gene.random() → x in the interval [0, 1).

shapevolve.genome module

The Genome class represents a shape sequence and its various properties, from which an image can be built.

class shapevolve.genome.Genome(sequence, ratio, height, width, background_color, adjusters, palette, draw)

Bases: object

This class represents a shape sequence and its various properties, from which an image can be built.

Constructs a genome based on certain properties.

Parameters

• sequence (List[Gene]) – A sequence of Gene objects that represent the shapes in the image.

• ratio (float) – The ratio between the evolution image dimensions and the original image dimensions.

• height (int) – The height of the image.

• width (int) – The width of the image.

• background_color (Tuple[int, int, int]) – The background color of the image.

• adjusters (List[Dict[str, Callable]]) – Reversible image adjusters that can be applied and unapplied to the image.

• palette (List[Tuple[int, int, int]]) – A list of RGB color tuples that colors are referenced from.

• draw (Callable) – A function that is used to draw an image according to a Gene object.

render_image(height, width, sequence, after_unadjustment)

Renders an image according to certain parameters.

Parameters

• height (int) – The height of the desired image.

• width (int) – The width of the desired image.

• sequence (List[Gene]) – The gene sequence to draw on the image.

• after_unadjustment (bool) – Whether the image has already been un-adjusted.

Returns

The rendered image.

Return type

ndarray

render_raw_image(after_unadjustment=False)

Renders the image without any scaling.

Parameters

after_unadjustment (bool) – Whether the image has already been un-adjusted.

Returns

The rendered image.

Return type

ndarray

render_scaled_image(after_unadjustment=False, fill_gaps=True)

Renders a scaled version of an image according to the genome’s ratio field.

Parameters

• after_unadjustment (bool) – Whether the image has already been un-adjusted.

• fill_gaps (bool) – Whether gaps should be filled in the scaled image.

Returns

The scaled image.

Return type

ndarray

save_genome(filename)

Saves the genome to a file using pickle.

Parameters

filename (str) – The filename to save the genome to.

scale_sequence(fill_gaps)

Generates a gene sequence with each gene scaled by the genome’s ratio field.

Parameters

fill_gaps (bool) – Whether the gene’s radius should be adjusted to fill new gaps in the scaled image.

Returns

The new Gene sequence.

Return type

List[Gene]

exception shapevolve.genome.MismatchedGenomeError

Bases: Exception

An error that occurs when two genomes do not share the same properties with one another.

shapevolve.genome.is_compatible(genome1, genome2)

Check whether two genomes could describe the same base image.

Parameters

• genome1 (Genome) – Genome 1, to be compared.

• genome2 (Genome) – Genome 2, to be compared.

Returns

Whether they are matched.

Return type

bool

Raises

MismatchedGenomeError: If the genomes do not match.

shapevolve.genome.load_genome(filename)

Loads a pickled Genome file.

Parameters

filename (str) – The filename of the genome to be loaded.

Returns

The loaded genome.

Return type

Genome

shapevolve.main module

The main CLI interface for the standalone app.

shapevolve.main.imwrite(filename, img[, params]) → retval

. @brief Saves an image to a specified file. . . The function imwrite saves the image to the specified file. The image format is chosen based on the . filename extension (see cv::imread for the list of extensions). In general, only 8-bit . single-channel or 3-channel (with ‘BGR’ channel order) images . can be saved using this function, with these exceptions: . . - 16-bit unsigned (CV_16U) images can be saved in the case of PNG, JPEG 2000, and TIFF formats . - 32-bit float (CV_32F) images can be saved in PFM, TIFF, OpenEXR, and Radiance HDR formats; . 3-channel (CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding . (4 bytes per pixel) . - PNG images with an alpha channel can be saved using this function. To do this, create . 8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels . should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below). . . If the format, depth or channel order is different, use . Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O . functions to save the image to XML or YAML format. . . The sample below shows how to create a BGRA image and save it to a PNG file. It also demonstrates how to set custom . compression parameters: . @include snippets/imgcodecs_imwrite.cpp . @param filename Name of the file. . @param img Image to be saved. . @param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, … .) see cv::ImwriteFlags

shapevolve.main.main()

Run the application.

shapevolve.mutations module

Defines mutations.

shapevolve.mutations.complex_mutation(ancestor_image, base_image, sequence, num_shapes, calculate_error, palette, draw)

Computes the effects of a complex mutation.

Parameters

• ancestor_image (ndarray) – An image with the solid background color only.

• base_image (ndarray) – The original image for comparison.

• sequence (List[Gene]) – The existing gene sequence.

• num_shapes (int) – The number of shapes to draw.

• calculate_error (Callable) – The error metric method.

• palette (List[Tuple[int, int, int]]) – The color palette to use.

• draw (Callable) – The method to draw the shape according to the gene.

Returns

The error, the mutated gene, the new image, the index of the mutated gene, whether mutated gene was placed on top.

Return type

Tuple[float, Gene, ndarray, int, bool]

shapevolve.mutations.random() → x in the interval [0, 1).

shapevolve.mutations.simple_mutation(image, base_image, sequence, calculate_error, palette, draw)

Computes the effects of a simple mutation.

Parameters

• image (ndarray) – The image to draw the gene on.

• base_image (ndarray) – The original image to compare to.

• sequence (List[Gene]) – The gene sequence.

• calculate_error (Callable) – The error metric method.

• palette (List[Tuple[int, int, int]]) – The color palette to use.

• draw (Callable) – The method to draw the shape according to the gene.

Returns

The error, the mutated gene, and the new image.

Return type

Tuple[float, Gene, ndarray]

shapevolve.preprocessors module

shapevolve.preprocessors.brighten_preprocess(image)

Increases brightness of an image.

Parameters

image (Image) – The image to process.

Returns

The processed image.

Return type

Image

shapevolve.preprocessors.contrast_preprocess(image)

Increases contrast of an image.

Parameters

image (Image) – The image to process.

Returns

The processed image.

Return type

Image

shapevolve.preprocessors.debrighten_preprocess(image)

Decreases brightness of an image.

Parameters

image (Image) – The image to process.

Returns

The processed image.

Return type

Image

shapevolve.preprocessors.decontrast_preprocess(image)

Decreases contrast of an image.

Parameters

image (Image) – The image to process.

Returns

The processed image.

Return type

Image

shapevolve.preprocessors.desaturate_preprocess(image)

Desaturates an image.

Parameters

image (Image) – The image to desaturate.

Returns

The desaturated image.

Return type

Image

shapevolve.preprocessors.saturate_preprocess(image)

Saturates an image.

Parameters

image (Image) – The image to saturate.

Returns

The saturated image.

Return type

Image

shapevolve.preprocessors.smooth_preprocess(image)

Smooths an image.

Parameters

image (Image) – The image to smooth.

Returns

The smoothed image.

Return type

Image

shapevolve.samples module

Some sample methods to show the functionality of the module.

shapevolve.samples.adjustment_sample(filepath)

A sample function that illustrates how to use adjusters.

Parameters

filepath (str) – The absolute filepath of an input image.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.callback_sample(filepath_source, filepath_csv)

A sample function that illustrates how to add callbacks to the evolve method.

Parameters

• filepath_source (str) – The absolute filepath of an input image.

• filepath_csv (str) – The filepath to save CSV statistics to.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.draw_shape_sample(filepath)

A sample function that illustrates how to use custom shape drawing methods.

Parameters

filepath (str) – The absolute filepath of an input image.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.error_metric_sample(filepath)

A sample function that illustrates how to use custom error metrics.

Parameters

filepath (str) – The absolute filepath of an input image.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.load_genome_sample(filepath_source, filepath_genome)

A sample function that illustrates how to load previously saved genomes.

Parameters

• filepath_source (str) – The absolute filepath of an input image.

• filepath_genome (str) – The filepath to load the pre-evolved genome.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.preprocess_sample(filepath)

A sample function that illustrates how to use preprocesses.

Parameters

filepath (str) – The absolute filepath of an input image.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.save_genome_sample(filepath_source, filepath_to_save_to)

A sample function that illustrates how to save genomes.

Parameters

• filepath_source (str) – The absolute filepath of an input image.

• filepath_to_save_to (str) – The filepath to save the genome.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.save_image_sample(filepath_source, filepath_to_save_to)

A sample function that illustrates how to save images from genomes.

Parameters

• filepath_source (str) – The absolute filepath of an input image.

• filepath_to_save_to (str) – The filepath to save the evolved image to.

Returns

The evolved genome.

Return type

Genome

shapevolve.samples.standard_sample(filepath)

A standard sample function that illustrates how to use the module.

Parameters

filepath (str) – The absolute filepath of an input image.

Returns

The evolved genome.

Return type

Genome

shapevolve.utils module

Utility methods.

class shapevolve.utils.ColorThiefFromImage(image)

Bases: colorthief.ColorThief

This class extends ColorThief to support providing images as-is instead of filenames.

Builds the ColorThief object with an image field.

Parameters

image (Image) – The image to build colors from.

shapevolve.utils.adjust(original_image, adjusters)

Adjusts an image based on adjusters.

Parameters

• original_image (ndarray) – The original image before adjustments.

• adjusters (List[Dict[str, Callable]]) – Adjusters to apply.

Returns

The adjusted image.

Return type

ndarray

shapevolve.utils.convert_RGB_to_BGR(color)

Converts a RGB color to a BGR color.

Parameters

color (Tuple[int, int, int]) – The RGB color.

Returns

The corresponding BGR color.

Return type

Tuple[int, int, int]

shapevolve.utils.get_image(path)

A shortcut to get an image from a filepath.

Parameters

path (str) – The filepath of the image.

Returns

The image object.

Return type

Image

shapevolve.utils.get_image_path(name, directory)

Gets the path of an image based on a directory in the script’s folder and a name.

Parameters

• name (str) – The name of the image.

• directory (str) – The directory name, in the same folder as this script.

Returns

The filepath.

Return type

string

shapevolve.utils.get_rescale_ratio(image, target)

Gets the required ratio to rescale an image to a target resolution

Parameters

• image (Image) – An image to resize.

• target (int) – The target resolution to resize to.

Returns

The min of the ratio between the target resolution and the image’s smallest dimension, and 1.

Return type

float

shapevolve.utils.show_image(image, generation, display=None, adjusters=None)

Uses matplotlib to show an image.

Parameters

• image (ndarray) – The image to show.

• generation (int) – What generation the image corresponds to.

• display (AxesImage) – The window the image should display on.

• adjusters (Dict[str, Callable]) – Any adjusters that should be reversed for the image.

Returns

The window where the image was displayed.

Return type

AxesImage

shapevolve.utils.unadjust(adjusted_image, adjusters)

Reverses an adjustment on an image based on adjusters.

Parameters

• adjusted_image (ndarray) – The image after adjustments.

• adjusters (List[Dict[str, Callable]]) – Adjusters to reverse.

Returns

The unadjusted image.

Return type

ndarray

Module contents

Shapevolve: A genetic algorithm to recreate artworks using simple shapes, with Python 3.