Source code for wordcloud.wordcloud

# coding=utf-8
# Author: Andreas Christian Mueller <>
# (c) 2012
# Modified by: Paul Nechifor <>
# License: MIT

from __future__ import division

import warnings
from random import Random
import io
import os
import re
import base64
import sys
import colorsys
import matplotlib
import numpy as np
from operator import itemgetter
from xml.sax import saxutils

from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFilter
from PIL import ImageFont

from .query_integral_image import query_integral_image
from .tokenization import unigrams_and_bigrams, process_tokens

FILE = os.path.dirname(__file__)
FONT_PATH = os.environ.get('FONT_PATH', os.path.join(FILE, 'DroidSansMono.ttf'))
STOPWORDS = set(map(str.strip, open(os.path.join(FILE, 'stopwords')).readlines()))

class IntegralOccupancyMap(object):
    def __init__(self, height, width, mask):
        self.height = height
        self.width = width
        if mask is not None:
            # the order of the cumsum's is important for speed ?!
            self.integral = np.cumsum(np.cumsum(255 * mask, axis=1),
            self.integral = np.zeros((height, width), dtype=np.uint32)

    def sample_position(self, size_x, size_y, random_state):
        return query_integral_image(self.integral, size_x, size_y,

    def update(self, img_array, pos_x, pos_y):
        partial_integral = np.cumsum(np.cumsum(img_array[pos_x:, pos_y:],
                                               axis=1), axis=0)
        # paste recomputed part into old image
        # if x or y is zero it is a bit annoying
        if pos_x > 0:
            if pos_y > 0:
                partial_integral += (self.integral[pos_x - 1, pos_y:]
                                     - self.integral[pos_x - 1, pos_y - 1])
                partial_integral += self.integral[pos_x - 1, pos_y:]
        if pos_y > 0:
            partial_integral += self.integral[pos_x:, pos_y - 1][:, np.newaxis]

        self.integral[pos_x:, pos_y:] = partial_integral

[docs]def random_color_func(word=None, font_size=None, position=None, orientation=None, font_path=None, random_state=None): """Random hue color generation. Default coloring method. This just picks a random hue with value 80% and lumination 50%. Parameters ---------- word, font_size, position, orientation : ignored. random_state : random.Random object or None, (default=None) If a random object is given, this is used for generating random numbers. """ if random_state is None: random_state = Random() return "hsl(%d, 80%%, 50%%)" % random_state.randint(0, 255)
class colormap_color_func(object): """Color func created from matplotlib colormap. Parameters ---------- colormap : string or matplotlib colormap Colormap to sample from Example ------- >>> WordCloud(color_func=colormap_color_func("magma")) """ def __init__(self, colormap): import matplotlib.pyplot as plt self.colormap = def __call__(self, word, font_size, position, orientation, random_state=None, **kwargs): if random_state is None: random_state = Random() r, g, b, _ = np.maximum(0, 255 * np.array(self.colormap( random_state.uniform(0, 1)))) return "rgb({:.0f}, {:.0f}, {:.0f})".format(r, g, b)
[docs]def get_single_color_func(color): """Create a color function which returns a single hue and saturation with. different values (HSV). Accepted values are color strings as usable by PIL/Pillow. >>> color_func1 = get_single_color_func('deepskyblue') >>> color_func2 = get_single_color_func('#00b4d2') """ old_r, old_g, old_b = ImageColor.getrgb(color) rgb_max = 255. h, s, v = colorsys.rgb_to_hsv(old_r / rgb_max, old_g / rgb_max, old_b / rgb_max) def single_color_func(word=None, font_size=None, position=None, orientation=None, font_path=None, random_state=None): """Random color generation. Additional coloring method. It picks a random value with hue and saturation based on the color given to the generating function. Parameters ---------- word, font_size, position, orientation : ignored. random_state : random.Random object or None, (default=None) If a random object is given, this is used for generating random numbers. """ if random_state is None: random_state = Random() r, g, b = colorsys.hsv_to_rgb(h, s, random_state.uniform(0.2, 1)) return 'rgb({:.0f}, {:.0f}, {:.0f})'.format(r * rgb_max, g * rgb_max, b * rgb_max) return single_color_func
[docs]class WordCloud(object): r"""Word cloud object for generating and drawing. Parameters ---------- font_path : string Font path to the font that will be used (OTF or TTF). Defaults to DroidSansMono path on a Linux machine. If you are on another OS or don't have this font, you need to adjust this path. width : int (default=400) Width of the canvas. height : int (default=200) Height of the canvas. prefer_horizontal : float (default=0.90) The ratio of times to try horizontal fitting as opposed to vertical. If prefer_horizontal < 1, the algorithm will try rotating the word if it doesn't fit. (There is currently no built-in way to get only vertical words.) mask : nd-array or None (default=None) If not None, gives a binary mask on where to draw words. If mask is not None, width and height will be ignored and the shape of mask will be used instead. All white (#FF or #FFFFFF) entries will be considerd "masked out" while other entries will be free to draw on. [This changed in the most recent version!] contour_width: float (default=0) If mask is not None and contour_width > 0, draw the mask contour. contour_color: color value (default="black") Mask contour color. scale : float (default=1) Scaling between computation and drawing. For large word-cloud images, using scale instead of larger canvas size is significantly faster, but might lead to a coarser fit for the words. min_font_size : int (default=4) Smallest font size to use. Will stop when there is no more room in this size. font_step : int (default=1) Step size for the font. font_step > 1 might speed up computation but give a worse fit. max_words : number (default=200) The maximum number of words. stopwords : set of strings or None The words that will be eliminated. If None, the build-in STOPWORDS list will be used. Ignored if using generate_from_frequencies. background_color : color value (default="black") Background color for the word cloud image. max_font_size : int or None (default=None) Maximum font size for the largest word. If None, height of the image is used. mode : string (default="RGB") Transparent background will be generated when mode is "RGBA" and background_color is None. relative_scaling : float (default='auto') Importance of relative word frequencies for font-size. With relative_scaling=0, only word-ranks are considered. With relative_scaling=1, a word that is twice as frequent will have twice the size. If you want to consider the word frequencies and not only their rank, relative_scaling around .5 often looks good. If 'auto' it will be set to 0.5 unless repeat is true, in which case it will be set to 0. .. versionchanged: 2.0 Default is now 'auto'. color_func : callable, default=None Callable with parameters word, font_size, position, orientation, font_path, random_state that returns a PIL color for each word. Overwrites "colormap". See colormap for specifying a matplotlib colormap instead. To create a word cloud with a single color, use ``color_func=lambda *args, **kwargs: "white"``. The single color can also be specified using RGB code. For example ``color_func=lambda *args, **kwargs: (255,0,0)`` sets color to red. regexp : string or None (optional) Regular expression to split the input text into tokens in process_text. If None is specified, ``r"\w[\w']+"`` is used. Ignored if using generate_from_frequencies. collocations : bool, default=True Whether to include collocations (bigrams) of two words. Ignored if using generate_from_frequencies. .. versionadded: 2.0 colormap : string or matplotlib colormap, default="viridis" Matplotlib colormap to randomly draw colors from for each word. Ignored if "color_func" is specified. .. versionadded: 2.0 normalize_plurals : bool, default=True Whether to remove trailing 's' from words. If True and a word appears with and without a trailing 's', the one with trailing 's' is removed and its counts are added to the version without trailing 's' -- unless the word ends with 'ss'. Ignored if using generate_from_frequencies. repeat : bool, default=False Whether to repeat words and phrases until max_words or min_font_size is reached. include_numbers : bool, default=False Whether to include numbers as phrases or not. min_word_length : int, default=0 Minimum number of letters a word must have to be included. collocation_threshold: int, default=30 Bigrams must have a Dunning likelihood collocation score greater than this parameter to be counted as bigrams. Default of 30 is arbitrary. See Manning, C.D., Manning, C.D. and Sch├╝tze, H., 1999. Foundations of Statistical Natural Language Processing. MIT press, p. 162 Attributes ---------- ``words_`` : dict of string to float Word tokens with associated frequency. .. versionchanged: 2.0 ``words_`` is now a dictionary ``layout_`` : list of tuples (string, int, (int, int), int, color)) Encodes the fitted word cloud. Encodes for each word the string, font size, position, orientation and color. Notes ----- Larger canvases with make the code significantly slower. If you need a large word cloud, try a lower canvas size, and set the scale parameter. The algorithm might give more weight to the ranking of the words than their actual frequencies, depending on the ``max_font_size`` and the scaling heuristic. """
[docs] def __init__(self, font_path=None, width=400, height=200, margin=2, ranks_only=None, prefer_horizontal=.9, mask=None, scale=1, color_func=None, max_words=200, min_font_size=4, stopwords=None, random_state=None, background_color='black', max_font_size=None, font_step=1, mode="RGB", relative_scaling='auto', regexp=None, collocations=True, colormap=None, normalize_plurals=True, contour_width=0, contour_color='black', repeat=False, include_numbers=False, min_word_length=0, collocation_threshold=30): if font_path is None: font_path = FONT_PATH if color_func is None and colormap is None: version = matplotlib.__version__ if version[0] < "2" and version[2] < "5": colormap = "hsv" else: colormap = "viridis" self.colormap = colormap self.collocations = collocations self.font_path = font_path self.width = width self.height = height self.margin = margin self.prefer_horizontal = prefer_horizontal self.mask = mask self.contour_color = contour_color self.contour_width = contour_width self.scale = scale self.color_func = color_func or colormap_color_func(colormap) self.max_words = max_words self.stopwords = stopwords if stopwords is not None else STOPWORDS self.min_font_size = min_font_size self.font_step = font_step self.regexp = regexp if isinstance(random_state, int): random_state = Random(random_state) self.random_state = random_state self.background_color = background_color self.max_font_size = max_font_size self.mode = mode if relative_scaling == "auto": if repeat: relative_scaling = 0 else: relative_scaling = .5 if relative_scaling < 0 or relative_scaling > 1: raise ValueError("relative_scaling needs to be " "between 0 and 1, got %f." % relative_scaling) self.relative_scaling = relative_scaling if ranks_only is not None: warnings.warn("ranks_only is deprecated and will be removed as" " it had no effect. Look into relative_scaling.", DeprecationWarning) self.normalize_plurals = normalize_plurals self.repeat = repeat self.include_numbers = include_numbers self.min_word_length = min_word_length self.collocation_threshold = collocation_threshold
def fit_words(self, frequencies): """Create a word_cloud from words and frequencies. Alias to generate_from_frequencies. Parameters ---------- frequencies : dict from string to float A contains words and associated frequency. Returns ------- self """ return self.generate_from_frequencies(frequencies) def generate_from_frequencies(self, frequencies, max_font_size=None): # noqa: C901 """Create a word_cloud from words and frequencies. Parameters ---------- frequencies : dict from string to float A contains words and associated frequency. max_font_size : int Use this font-size instead of self.max_font_size Returns ------- self """ # make sure frequencies are sorted and normalized frequencies = sorted(frequencies.items(), key=itemgetter(1), reverse=True) if len(frequencies) <= 0: raise ValueError("We need at least 1 word to plot a word cloud, " "got %d." % len(frequencies)) frequencies = frequencies[:self.max_words] # largest entry will be 1 max_frequency = float(frequencies[0][1]) frequencies = [(word, freq / max_frequency) for word, freq in frequencies] if self.random_state is not None: random_state = self.random_state else: random_state = Random() if self.mask is not None: boolean_mask = self._get_bolean_mask(self.mask) width = self.mask.shape[1] height = self.mask.shape[0] else: boolean_mask = None height, width = self.height, self.width occupancy = IntegralOccupancyMap(height, width, boolean_mask) # create image img_grey ="L", (width, height)) draw = ImageDraw.Draw(img_grey) img_array = np.asarray(img_grey) font_sizes, positions, orientations, colors = [], [], [], [] last_freq = 1. if max_font_size is None: # if not provided use default font_size max_font_size = self.max_font_size if max_font_size is None: # figure out a good font size by trying to draw with # just the first two words if len(frequencies) == 1: # we only have one word. We make it big! font_size = self.height else: self.generate_from_frequencies(dict(frequencies[:2]), max_font_size=self.height) # find font sizes sizes = [x[1] for x in self.layout_] try: font_size = int(2 * sizes[0] * sizes[1] / (sizes[0] + sizes[1])) # quick fix for if self.layout_ contains less than 2 values # on very small images it can be empty except IndexError: try: font_size = sizes[0] except IndexError: raise ValueError( "Couldn't find space to draw. Either the Canvas size" " is too small or too much of the image is masked " "out.") else: font_size = max_font_size # we set self.words_ here because we called generate_from_frequencies # above... hurray for good design? self.words_ = dict(frequencies) if self.repeat and len(frequencies) < self.max_words: # pad frequencies with repeating words. times_extend = int(np.ceil(self.max_words / len(frequencies))) - 1 # get smallest frequency frequencies_org = list(frequencies) downweight = frequencies[-1][1] for i in range(times_extend): frequencies.extend([(word, freq * downweight ** (i + 1)) for word, freq in frequencies_org]) # start drawing grey image for word, freq in frequencies: if freq == 0: continue # select the font size rs = self.relative_scaling if rs != 0: font_size = int(round((rs * (freq / float(last_freq)) + (1 - rs)) * font_size)) if random_state.random() < self.prefer_horizontal: orientation = None else: orientation = Image.ROTATE_90 tried_other_orientation = False while True: # try to find a position font = ImageFont.truetype(self.font_path, font_size) # transpose font optionally transposed_font = ImageFont.TransposedFont( font, orientation=orientation) # get size of resulting text box_size = draw.textsize(word, font=transposed_font) # find possible places using integral image: result = occupancy.sample_position(box_size[1] + self.margin, box_size[0] + self.margin, random_state) if result is not None or font_size < self.min_font_size: # either we found a place or font-size went too small break # if we didn't find a place, make font smaller # but first try to rotate! if not tried_other_orientation and self.prefer_horizontal < 1: orientation = (Image.ROTATE_90 if orientation is None else Image.ROTATE_90) tried_other_orientation = True else: font_size -= self.font_step orientation = None if font_size < self.min_font_size: # we were unable to draw any more break x, y = np.array(result) + self.margin // 2 # actually draw the text draw.text((y, x), word, fill="white", font=transposed_font) positions.append((x, y)) orientations.append(orientation) font_sizes.append(font_size) colors.append(self.color_func(word, font_size=font_size, position=(x, y), orientation=orientation, random_state=random_state, font_path=self.font_path)) # recompute integral image if self.mask is None: img_array = np.asarray(img_grey) else: img_array = np.asarray(img_grey) + boolean_mask # recompute bottom right # the order of the cumsum's is important for speed ?! occupancy.update(img_array, x, y) last_freq = freq self.layout_ = list(zip(frequencies, font_sizes, positions, orientations, colors)) return self def process_text(self, text): """Splits a long text into words, eliminates the stopwords. Parameters ---------- text : string The text to be processed. Returns ------- words : dict (string, int) Word tokens with associated frequency. ..versionchanged:: 1.2.2 Changed return type from list of tuples to dict. Notes ----- There are better ways to do word tokenization, but I don't want to include all those things. """ flags = (re.UNICODE if sys.version < '3' and type(text) is unicode # noqa: F821 else 0) pattern = r"\w[\w']*" if self.min_word_length <= 1 else r"\w[\w']+" regexp = self.regexp if self.regexp is not None else pattern words = re.findall(regexp, text, flags) # remove 's words = [word[:-2] if word.lower().endswith("'s") else word for word in words] # remove numbers if not self.include_numbers: words = [word for word in words if not word.isdigit()] # remove short words if self.min_word_length: words = [word for word in words if len(word) >= self.min_word_length] stopwords = set([i.lower() for i in self.stopwords]) if self.collocations: word_counts = unigrams_and_bigrams(words, stopwords, self.normalize_plurals, self.collocation_threshold) else: # remove stopwords words = [word for word in words if word.lower() not in stopwords] word_counts, _ = process_tokens(words, self.normalize_plurals) return word_counts def generate_from_text(self, text): """Generate wordcloud from text. The input "text" is expected to be a natural text. If you pass a sorted list of words, words will appear in your output twice. To remove this duplication, set ``collocations=False``. Calls process_text and generate_from_frequencies. ..versionchanged:: 1.2.2 Argument of generate_from_frequencies() is not return of process_text() any more. Returns ------- self """ words = self.process_text(text) self.generate_from_frequencies(words) return self def generate(self, text): """Generate wordcloud from text. The input "text" is expected to be a natural text. If you pass a sorted list of words, words will appear in your output twice. To remove this duplication, set ``collocations=False``. Alias to generate_from_text. Calls process_text and generate_from_frequencies. Returns ------- self """ return self.generate_from_text(text) def _check_generated(self): """Check if ``layout_`` was computed, otherwise raise error.""" if not hasattr(self, "layout_"): raise ValueError("WordCloud has not been calculated, call generate" " first.") def to_image(self): self._check_generated() if self.mask is not None: width = self.mask.shape[1] height = self.mask.shape[0] else: height, width = self.height, self.width img =, (int(width * self.scale), int(height * self.scale)), self.background_color) draw = ImageDraw.Draw(img) for (word, count), font_size, position, orientation, color in self.layout_: font = ImageFont.truetype(self.font_path, int(font_size * self.scale)) transposed_font = ImageFont.TransposedFont( font, orientation=orientation) pos = (int(position[1] * self.scale), int(position[0] * self.scale)) draw.text(pos, word, fill=color, font=transposed_font) return self._draw_contour(img=img) def recolor(self, random_state=None, color_func=None, colormap=None): """Recolor existing layout. Applying a new coloring is much faster than generating the whole wordcloud. Parameters ---------- random_state : RandomState, int, or None, default=None If not None, a fixed random state is used. If an int is given, this is used as seed for a random.Random state. color_func : function or None, default=None Function to generate new color from word count, font size, position and orientation. If None, self.color_func is used. colormap : string or matplotlib colormap, default=None Use this colormap to generate new colors. Ignored if color_func is specified. If None, self.color_func (or self.color_map) is used. Returns ------- self """ if isinstance(random_state, int): random_state = Random(random_state) self._check_generated() if color_func is None: if colormap is None: color_func = self.color_func else: color_func = colormap_color_func(colormap) self.layout_ = [(word_freq, font_size, position, orientation, color_func(word=word_freq[0], font_size=font_size, position=position, orientation=orientation, random_state=random_state, font_path=self.font_path)) for word_freq, font_size, position, orientation, _ in self.layout_] return self def to_file(self, filename): """Export to image file. Parameters ---------- filename : string Location to write to. Returns ------- self """ img = self.to_image(), optimize=True) return self def to_array(self): """Convert to numpy array. Returns ------- image : nd-array size (width, height, 3) Word cloud image as numpy matrix. """ return np.array(self.to_image()) def __array__(self): """Convert to numpy array. Returns ------- image : nd-array size (width, height, 3) Word cloud image as numpy matrix. """ return self.to_array() def to_html(self): raise NotImplementedError("FIXME!!!") def to_svg(self, embed_font=False, optimize_embedded_font=True, embed_image=False): """Export to SVG. Font is assumed to be available to the SVG reader. Otherwise, text coordinates may produce artifacts when rendered with replacement font. It is also possible to include a subset of the original font in WOFF format using ``embed_font`` (requires `fontTools`). Note that some renderers do not handle glyphs the same way, and may differ from ``to_image`` result. In particular, Complex Text Layout may not be supported. In this typesetting, the shape or positioning of a grapheme depends on its relation to other graphemes. Pillow, since version 4.2.0, supports CTL using ``libraqm``. However, due to dependencies, this feature is not always enabled. Hence, the same rendering differences may appear in ``to_image``. As this rasterized output is used to compute the layout, this also affects the layout generation. Use ``PIL.features.check`` to test availability of ``raqm``. Consistant rendering is therefore expected if both Pillow and the SVG renderer have the same support of CTL. Contour drawing is not supported. Parameters ---------- embed_font : bool, default=False Whether to include font inside resulting SVG file. optimize_embedded_font : bool, default=True Whether to be aggressive when embedding a font, to reduce size. In particular, hinting tables are dropped, which may introduce slight changes to character shapes (w.r.t. `to_image` baseline). embed_image : bool, default=False Whether to include rasterized image inside resulting SVG file. Useful for debugging. Returns ------- content : string Word cloud image as SVG string """ # TODO should add option to specify URL for font (i.e. WOFF file) # Make sure layout is generated self._check_generated() # Get output size, in pixels if self.mask is not None: width = self.mask.shape[1] height = self.mask.shape[0] else: height, width = self.height, self.width # Get max font size if self.max_font_size is None: max_font_size = max(w[1] for w in self.layout_) else: max_font_size = self.max_font_size # Text buffer result = [] # Get font information font = ImageFont.truetype(self.font_path, int(max_font_size * self.scale)) raw_font_family, raw_font_style = font.getname() # TODO properly escape/quote this name? font_family = repr(raw_font_family) # TODO better support for uncommon font styles/weights? raw_font_style = raw_font_style.lower() if 'bold' in raw_font_style: font_weight = 'bold' else: font_weight = 'normal' if 'italic' in raw_font_style: font_style = 'italic' elif 'oblique' in raw_font_style: font_style = 'oblique' else: font_style = 'normal' # Add header result.append( '<svg' ' xmlns=""' ' width="{}"' ' height="{}"' '>' .format( width * self.scale, height * self.scale ) ) # Embed font, if requested if embed_font: # Import here, to avoid hard dependency on fonttools import fontTools import fontTools.subset # Subset options options = fontTools.subset.Options( # Small impact on character shapes, but reduce size a lot hinting=not optimize_embedded_font, # On small subsets, can improve size desubroutinize=optimize_embedded_font, # Try to be lenient ignore_missing_glyphs=True, ) # Load and subset font ttf = fontTools.subset.load_font(self.font_path, options) subsetter = fontTools.subset.Subsetter(options) characters = {c for item in self.layout_ for c in item[0][0]} text = ''.join(characters) subsetter.populate(text=text) subsetter.subset(ttf) # Export as WOFF # TODO is there a better method, i.e. directly export to WOFF? buffer = io.BytesIO() ttf.saveXML(buffer) woff = fontTools.ttLib.TTFont(flavor='woff') woff.importXML(buffer) # Create stylesheet with embedded font face buffer = io.BytesIO() data = base64.b64encode(buffer.getbuffer()).decode('ascii') url = 'data:application/font-woff;charset=utf-8;base64,' + data result.append( '<style>' '@font-face{{' 'font-family:{};' 'font-weight:{};' 'font-style:{};' 'src:url("{}")format("woff");' '}}' '</style>' .format( font_family, font_weight, font_style, url ) ) # Select global style result.append( '<style>' 'text{{' 'font-family:{};' 'font-weight:{};' 'font-style:{};' '}}' '</style>' .format( font_family, font_weight, font_style ) ) # Add background if self.background_color is not None: result.append( '<rect' ' width="100%"' ' height="100%"' ' style="fill:{}"' '>' '</rect>' .format(self.background_color) ) # Embed image, useful for debug purpose if embed_image: image = self.to_image() data = io.BytesIO(), format='JPEG') data = base64.b64encode(data.getbuffer()).decode('ascii') result.append( '<image' ' width="100%"' ' height="100%"' ' href="data:image/jpg;base64,{}"' '/>' .format(data) ) # For each word in layout for (word, count), font_size, (y, x), orientation, color in self.layout_: x *= self.scale y *= self.scale # Get text metrics font = ImageFont.truetype(self.font_path, int(font_size * self.scale)) (size_x, size_y), (offset_x, offset_y) = font.font.getsize(word) ascent, descent = font.getmetrics() # Compute text bounding box min_x = -offset_x max_x = size_x - offset_x max_y = ascent - offset_y # Compute text attributes attributes = {} if orientation == Image.ROTATE_90: x += max_y y += max_x - min_x transform = 'translate({},{}) rotate(-90)'.format(x, y) else: x += min_x y += max_y transform = 'translate({},{})'.format(x, y) # Create node attributes = ' '.join('{}="{}"'.format(k, v) for k, v in attributes.items()) result.append( '<text' ' transform="{}"' ' font-size="{}"' ' style="fill:{}"' '>' '{}' '</text>' .format( transform, font_size * self.scale, color, saxutils.escape(word) ) ) # TODO draw contour # Complete SVG file result.append('</svg>') return '\n'.join(result) def _get_bolean_mask(self, mask): """Cast to two dimensional boolean mask.""" if mask.dtype.kind == 'f': warnings.warn("mask image should be unsigned byte between 0" " and 255. Got a float array") if mask.ndim == 2: boolean_mask = mask == 255 elif mask.ndim == 3: # if all channels are white, mask out boolean_mask = np.all(mask[:, :, :3] == 255, axis=-1) else: raise ValueError("Got mask of invalid shape: %s" % str(mask.shape)) return boolean_mask def _draw_contour(self, img): """Draw mask contour on a pillow image.""" if self.mask is None or self.contour_width == 0: return img mask = self._get_bolean_mask(self.mask) * 255 contour = Image.fromarray(mask.astype(np.uint8)) contour = contour.resize(img.size) contour = contour.filter(ImageFilter.FIND_EDGES) contour = np.array(contour) # make sure borders are not drawn before changing width contour[[0, -1], :] = 0 contour[:, [0, -1]] = 0 # use gaussian to change width, divide by 10 to give more resolution radius = self.contour_width / 10 contour = Image.fromarray(contour) contour = contour.filter(ImageFilter.GaussianBlur(radius=radius)) contour = np.array(contour) > 0 contour = np.dstack((contour, contour, contour)) # color the contour ret = np.array(img) * np.invert(contour) if self.contour_color != 'black': color =, img.size, self.contour_color) ret += np.array(color) * contour return Image.fromarray(ret)