Workaround for storing an image in very low resolution with matplotlib and python

Question

I am trying to create an occupancy grid map by exporting an higher resolution image of the map to a very low resolution.

In most basic form an occupancy grid is a 2 dimensional binary array. The values stored in array denotes free(0) or occupied(1). Each value corresponds to a discrete location of the physical map (the following image depicts an area)

As seen in the above image each array location is a cell of physical world.

I have a 5 meter x 5 meter World, it is then discretized into cells of 5cm x 5cm. The world is thus 100 x 100 cells corresponding to 5m x 5m physical world.

The obstacle re randomly generated circular disks at location (x,y) and of a random radius r like follows:

I need to covert this (above) image into an array of size 100x100. That means evaluating if each cell is actually in the region of a obstacle or free.

To speed things, I have found the following workaround:

Create matplotlib figure populated with obstacles with figsize=(5,5) and save the image with dpi=20 in bmp format and finally import the bmp image as an numpy array. Alas, matplotlib does not support bmp. If I save the image in jpeg using plt.savefig('map.jpg', dpi=20, quality=100) or other formats then the cell's boundary becomes blurred and flows into other cells. Shown in this image :

So my question: How to save a scaled-down image from matplotlib that preserves the cell sharpness of image (akin to bmp).

Answer 1

Nice hack. However, I would rather compute the boolean mask corresponding to your discretized circles explicitly. One simple way to get such a boolean map is by using the contains_points method of matplotlib artists such as a Circle patch.

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle

world_width = 100 # x
world_height = 100 # y
minimum_radius = 1
maximum_radius = 10
total_circles = 5

# create circle patches
x = np.random.randint(0, world_width, size=total_circles)
y = np.random.randint(0, world_height, size=total_circles)
r = minimum_radius + (maximum_radius - minimum_radius) * np.random.rand(total_circles)
circles = [Circle((xx,yy), radius=rr) for xx, yy, rr in zip(x, y, r)]

# for each circle, create a boolean mask where each cell element is True
# if its center is within that circle and False otherwise
X, Y = np.meshgrid(np.arange(world_width) + 0.5, np.arange(world_height) + 0.5)

masks = np.zeros((total_circles, world_width, world_height), dtype=bool)
for ii, circle in enumerate(circles):
    masks[ii] = circle.contains_points(np.c_[X.ravel(), Y.ravel()]).reshape(world_width, world_height)

combined_mask = np.sum(masks, axis=0)
plt.imshow(combined_mask, cmap='gray_r')
plt.show()

Answer 2

If I have understood correctly, I think this can be done quite simply with PIL, specifically with the Image.resize fucntion. For example, does this do what you asked:

import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageDraw

# Make a dummy image with some black circles on a white background
image = Image.new('RGBA', (1000, 1000), color="white")
draw = ImageDraw.Draw(image)
draw.ellipse((20, 20, 180, 180), fill = 'black', outline ='black')
draw.ellipse((500, 500, 600, 600), fill = 'black', outline ='black')
draw.ellipse((100, 800, 250, 950), fill = 'black', outline ='black')
draw.ellipse((750, 300, 800, 350), fill = 'black', outline ='black')
image.save('circles_full_res.png')

# Resize the image with nearest neighbour interpolation to preserve grid sharpness
image_lo = image.resize((100,100), resample=0)
image_lo.save("circles_low_res.png")