def __init__(self,
image,
background,
dpi,
precision=16,
deskew=True,
contrast=10):
self.contrast = contrast
self.image = image
self.dpi = dpi
self.width, self.height = image.size
self.precision = precision
self.deskew = deskew
self.samples = PixelSampler(image, dpi, precision)
self.background = background
self.sections = self._find_sections()
class MultiPartImage(object):
"""Object for handling images that contain multiple subimages.
This is used, for example, to detect and access multiple photos that were
scanned simultaneously in a flat-bed scanner. """
def __init__(self, image, background, dpi, precision=16, deskew=True, contrast=10):
self.contrast = contrast
self.image = image
self.dpi = dpi
self.width, self.height = image.size
self.precision = precision
self.deskew = deskew
self.samples = PixelSampler(image, dpi, precision)
self.background = background
self.sections = self._find_sections()
def __iter__(self):
for section in self.sections:
image = self.image.crop((section.left, section.top, section.right, section.bottom))
if self.deskew:
skew = SkewedImage(image, self.background, self.contrast)
image = skew.correct()
yield image
def __len__(self):
return len(self.sections)
def _find_sections(self):
sections = []
for (x, y, red, green, blue) in self.samples:
# Skip if the sample is background or is already in a section.
if self.background.matches(red, green, blue, self.contrast):
continue
if True in (section.contains(x, y) for section in sections):
continue
# Find contiguous samples. This works like a 4-way flood fill, but
# instead of changing the color we just collect the coordinates.
seeds = [(x, y)]
pixels = set(seeds)
for coords in iter(seeds):
for x, y, r, g, b in self.samples.around(*coords):
if (x, y) not in pixels:
pixels.add((x, y))
if not self.background.matches(r, g, b, self.contrast):
seeds.append((x, y))
new_section = ImageSection(pixels)
if True in (s.merge_if_overlapping(new_section) for s in sections):
continue
sections.append(new_section)
# Filter out sections smaller than 1 square inch before returning.
return [s for s in sections if s > self.dpi ** 2]
def __init__(self, image, background, dpi, precision=16, deskew=True, contrast=10):
self.contrast = contrast
self.image = image
self.dpi = dpi
self.width, self.height = image.size
self.precision = precision
self.deskew = deskew
self.samples = PixelSampler(image, dpi, precision)
self.background = background
self.sections = self._find_sections()
def __init__(self, image, background, contrast=10):
self.image = image
self.width, self.height = image.size
self.background = background
self.contrast = contrast
sampler = PixelSampler(image, dpi=1, precision=1)
self.sides = (
Left(sampler),
Top(sampler),
Right(sampler),
Bottom(sampler),
)
def load_from_image(self, image, dpi):
"""Determine background stats by examining a blank scan.
"""
sampler = PixelSampler(image, dpi, precision=4)
reds, greens, blues = zip(*[sample[2:] for sample in sampler])
self.medians = {
'red': numpy.median(reds),
'green': numpy.median(greens),
'blue': numpy.median(blues),
}
self.std_devs = {
'red': numpy.std(reds),
'green': numpy.std(greens),
'blue': numpy.std(blues),
}
return self
class MultiPartImage(object):
"""Object for handling images that contain multiple subimages.
This is used, for example, to detect and access multiple photos that were
scanned simultaneously in a flat-bed scanner. """
def __init__(self,
image,
background,
dpi,
precision=16,
deskew=True,
contrast=10):
self.contrast = contrast
self.image = image
self.dpi = dpi
self.width, self.height = image.size
self.precision = precision
self.deskew = deskew
self.samples = PixelSampler(image, dpi, precision)
self.background = background
self.sections = self._find_sections()
def __iter__(self):
for section in self.sections:
image = self.image.crop(
(section.left, section.top, section.right, section.bottom))
if self.deskew:
skew = SkewedImage(image, self.background, self.contrast)
image = skew.correct()
yield image
def __len__(self):
return len(self.sections)
def _find_sections(self):
sections = []
for (x, y, red, green, blue) in self.samples:
# Skip if the sample is background or is already in a section.
if self.background.matches(red, green, blue, self.contrast):
continue
if True in (section.contains(x, y) for section in sections):
continue
# Find contiguous samples. This works like a 4-way flood fill, but
# instead of changing the color we just collect the coordinates.
seeds = [(x, y)]
pixels = set(seeds)
for coords in iter(seeds):
for x, y, r, g, b in self.samples.around(*coords):
if (x, y) not in pixels:
pixels.add((x, y))
if not self.background.matches(r, g, b, self.contrast):
seeds.append((x, y))
new_section = ImageSection(pixels)
if True in (s.merge_if_overlapping(new_section) for s in sections):
continue
sections.append(new_section)
# Filter out sections smaller than 1 square inch before returning.
return [s for s in sections if s > self.dpi**2]