rift_sensor_convert.py

#!/bin/env python3

# uvccapture -d/dev/video0 -x2560 -y720 -osnap.jpg
# ./this.py snap.jpg
# -> output.jpg

from PIL import Image, ImageFilter
import numpy as np
import sys

im = Image.open(sys.argv[1])

width = im.size[0]
height = im.size[1]

def get_array(i):
    # let's ignore red and blue channels, they look empty and noisy.
    # probably filtered by the hardware
    (r, g, b) = i.split()
    return np.array(g)

# Lower half is always black
# Left and right look to be the same, merged for good measure
left_im = im.crop((0, 0, width / 2, height / 2))
right_im = im.crop((width / 2, 0, width, height / 2))

# Extract useful data
left_arr = get_array(left_im)
right_arr = get_array(right_im)

# Merge sides
arr = (left_arr + right_arr) / 2

# Normalization/leveling (`a` makes it darker)
limits = (arr.min(), arr.max())
print("limits:", limits)
a = 65

arr = np.interp(arr, (arr.min() + a, arr.max()), (0, 255))
arr = np.rint(arr)
arr = arr.astype('uint8')

# Back to an Image, some smoothing, and horizontal scaling
g = Image.fromarray(arr, 'L')

scaled_w = int(round(width / 4))
scaled_h = int(round(height / 2))

g = g.filter(ImageFilter.GaussianBlur(radius=1))
g = g.resize((scaled_w, scaled_h), Image.LANCZOS)

g.save('output.jpg')