Remove Freckles from Simple Binary Image - python-3.x

Question

I have the following NumPy array of a running man, which you can download here:
https://drive.google.com/file/d/1SfIEqGsBV_vA7iP4UjLdklLJlLdDzozL/view?usp=sharing
To display it, use this code:
import numpy as np
import matplotlib.pyplot as plt
# load data
data = np.load('running_man.npy')
# plot data
plt.imshow(data)
As you can see there is a lot of noise (freckles) in the image. I would like to get rid of it and retrieve a clean image of the runner. Any idea of how to do it?
This is what I have done so far:
from skimage import measure
# Find contours at a constant value of 1
contours = measure.find_contours(data, 1, fully_connected='high')
# Select the largest contiguous contour
contour = sorted(contours, key=lambda x: len(x))[-1]
# Create an empty image to store the masked array
r_mask = np.zeros_like(data, dtype='bool')
# Create a contour image by using the contour coordinates rounded to their nearest integer value
r_mask[np.round(contour[:, 0]).astype('int'), np.round(contour[:, 1]).astype('int')] = 1
# Fill in the hole created by the contour boundary
r_mask = ndimage.binary_fill_holes(r_mask)
# Invert the mask since one wants pixels outside of the region
r_mask = ~r_mask
plt.imshow(r_mask)
... but as you can see the outline is very rough !
What works well is to upload the image to an online jpg to SVG converter -> this makes the lines super smooth. ... but I want to be able to do it in python.
Idea:
I am looking for something that can keep the sharp corners, maybe something that detects the gradient along the edge and only keeps the point where the gradient is above a certain threshold...

Answer 1

For this specific image you can just use numpy:
import numpy as np
import matplotlib.pyplot as plt
data = np.load('running_man.npy')
data[data > 1] = 0
plt.xticks([])
plt.yticks([])
plt.imshow(data)

Answer 2

For a method that preserves the corners better, we can use median filters, but force the preservation of corners.
Masked Image
Mask after filtering
Recolored
import cv2
import numpy as np
# load image
img = cv2.imread("run.png");
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY);
# make mask
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU);
# median filter
med = cv2.medianBlur(thresh, 11);
med[thresh == 255] = 255;
# inverse filter
mask = cv2.bitwise_not(med);
med = cv2.medianBlur(mask, 3);
med[mask == 255] = 255;
# recolor
color = np.zeros_like(img);
color[med == 0] = (66, 239, 245);
color[med == 255] = (92, 15, 75);
# show
cv2.imshow("colored", color);
cv2.waitKey(0);