Also, the threshold and noise settings are hard coded - if the object being scanned doesn't effect this too much, we might be able to get away with just having these values hard coded for whatever makes sense for the scanner.
Also, here is another version that doesn't use open_cv, is faster, and produces somewhat cleaner output:
- Code: Select all
from glob import glob
from os.path import join
from tempfile import mkstemp
from PIL import Image, ImageOps, ImageChops, ImageFilter
THRESHOLD = 26
def threshold(img, t=127, invert=False):
Your standard threshold function.
def clamp (p):
return 0 if p<t else 255
def invert_clamp (p):
return 0 if p>=t else 255
gray = img.convert("L")
clamp_func = clamp if not invert else invert_clamp
return gray.point(clamp_func, 'L')
if __name__ == "__main__":
# Load up images.
search_dir = "fuzzy"
scan_path = glob(join(search_dir, "scan.*???"))
bg_path = glob(join(search_dir, "bg.*???"))
scan, bg = map(Image.open, [scan_path, bg_path])
# Create difference map
diff = ImageChops.difference(bg, scan)
# Create noisey image mask
clamped = threshold(diff, THRESHOLD)
# Blur and then clamp to eliminate noise
smudge = clamped.filter(ImageFilter.GaussianBlur(5))
smudge_mask = threshold(smudge, invert=True)
# Use smudge mask to remove most noise but leave original clamp.
result = clamped.convert("RGB")
result.paste((0,0,0), None, mask=smudge_mask)
And the resulting output: