I am creating binary images (i.e., with only black and white and no other colors) of different shapes using matplotlib and PIL libraries. I have constraints on the number of pixels as 64x64, and the image has to be binary. At this low pixel range, I am getting pixelated images as shown below. I am searching for an approach where I can minimize the protrusions of white pixels and make their variation more uniform and gradual.

I have used the following piece of code to generate this image.

import matplotlib.pyplot as plt
import numpy as np
import io
from PIL import Image

def get_image_array(_fig):
    io_buffer = io.BytesIO()
    plt.savefig(io_buffer, format="raw")
    io_buffer.seek(0)
    _image_array = np.reshape(
        np.frombuffer(io_buffer.getvalue(), dtype=np.uint8),
        newshape=(int(_fig.bbox.bounds[3]), int(_fig.bbox.bounds[2]), -1)
    )
    io_buffer.close()
    return _image_array

def draw_box_and_circle(bbox, xc, yc, r, pixels=(100, 100), angular_parts=100):
    fig = plt.figure(figsize=(pixels[0]*0.01, pixels[1]*0.01))
    fig.add_axes(plt.Axes(fig, [0., 0., 1., 1.]))
    # draw bbox
    x0, y0, x1, y1 = bbox
    plt.fill([x0, x1, x1, x0], [y0, y0, y1, y1], color='0')
    # draw circle
    theta = np.linspace(0.0, 2.0*np.pi, angular_parts)
    x = xc   (r*np.cos(theta))
    y = yc   (r*np.sin(theta))
    plt.fill(x, y, color='1')
    #
    plt.axis('off')
    plt.axis('equal')
    plt.xlim([BBOX[0], BBOX[2]])
    plt.ylim([BBOX[1], BBOX[3]])
    image_array = get_image_array(fig)
    print(image_array.shape)
    image = Image.fromarray(image_array)
    image.save("before_conversion.png")
    image = image.convert(mode="1")
    image.save("after_conversion.png")
    print(np.array(image).shape)    
    return
   

BBOX = (0.0, 0.0, 1.0, 1.0)
draw_box_and_circle(BBOX, 0.5, 0.5, 0.25)

CodePudding user response：

You probably want to disable dithering in the conversion from RGB, use:

image = image.convert(mode="1", dither=0)

You could also consider generating a binary shape directly, instead of "thresholding" the anti-aliassed shape from Matplotlib.

For example using something like:

xc = 0.5
yc = 0.5
r = 0.25

n_pixels = 100
dxy = 1/n_pixels
hdxy = dxy/2

yy, xx = np.mgrid[hdxy:1-hdxy:dxy, hdxy:1-hdxy:dxy]

# subtract the center
xx = xx - xc
yy = yy - yc

# treshold distance from center to create 
# the (binary) circle
circle = np.sqrt(xx**2   yy**2) <= r
circle = (circle*255).astype(np.uint8)

And plot the array with plt.imshow(circle, resample="nearest") instead. It might give you finer control at the pixel level. Relying on Matplotlibs anti-aliassing might make your solution backend specific?