def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
s = min(image.size)
sizeRange = [int(0.1 * s), int(0.4 * s)]
newArray = numpy.array(image.split()[0].getdata())
newArray.resize(image.size[1], image.size[0])
for j in xrange(self.numRectangles):
# Generate random rectange
size = (self.random.randint(sizeRange[0], sizeRange[1]),
self.random.randint(sizeRange[0], sizeRange[1]))
loc = [
self.random.randint(0, image.size[1]),
self.random.randint(0, image.size[0])
]
# Move the location so that the rectangle is centered on it
loc[0] -= size[0] / 2
loc[1] -= size[1] / 2
# Generate random color
color = self.random.randint(0, 255)
# Add the rectangle to the image
newArray[max(0,loc[0]):min(newArray.shape[0], loc[0]+size[0]), \
max(0,loc[1]):min(newArray.shape[1],loc[1]+size[1])] = color
newImage = Image.new("L", image.size)
newImage.putdata([uint(p) for p in newArray.flatten()])
newImage.putalpha(image.split()[1])
return newImage
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
s = min(image.size)
sizeRange = [int(0.1 * s), int(0.4 * s)]
newArray = numpy.array(image.split()[0].getdata())
newArray.resize(image.size[1],image.size[0])
for j in xrange(self.numRectangles):
# Generate random rectange
size = (self.random.randint(sizeRange[0], sizeRange[1]),
self.random.randint(sizeRange[0], sizeRange[1]))
loc = [self.random.randint(0,image.size[1]),
self.random.randint(0,image.size[0])]
# Move the location so that the rectangle is centered on it
loc[0] -= size[0]/2
loc[1] -= size[1]/2
# Generate random color
color = self.random.randint(0,255)
# Add the rectangle to the image
newArray[max(0,loc[0]):min(newArray.shape[0], loc[0]+size[0]), \
max(0,loc[1]):min(newArray.shape[1],loc[1]+size[1])] = color
newImage = Image.new("L", image.size)
newImage.putdata([uint(p) for p in newArray.flatten()])
newImage.putalpha(image.split()[1])
return newImage
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
s = min(image.size)
sizeRange = [0, s]
imageArray = numpy.array(image.split()[0].getdata())
newImage = Image.new("LA", image.size)
newImage.putdata([uint(p) for p in imageArray])
newImage.putalpha(image.split()[1])
for i in xrange(int(self.difficulty * self.maxLines)):
# Generate random line
start = (random.randint(sizeRange[0], sizeRange[1]),
random.randint(sizeRange[0], sizeRange[1]))
end = (random.randint(sizeRange[0], sizeRange[1]),
random.randint(sizeRange[0], sizeRange[1]))
# Generate random color
color = random.randint(0, 255)
# Add the line to the image
draw = ImageDraw.Draw(newImage)
draw.line((start, end), fill=color)
return newImage
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
mode = image.mode;
originalSize = image.size;
sizes = [(int(round(image.size[0]*s)), int(round(image.size[1]*s)))
for s in self.scales]
resizedImages = []
for size in sizes:
if size < image.size:
resizedImage = image.resize(size,Image.ANTIALIAS)
else:
resizedImage = image.resize(size,Image.BICUBIC)
x = (originalSize[0] - size[0])/2
y = (originalSize[1] - size[1])/2
newImage = Image.new(mode,originalSize,self.background)
newImage.paste(resizedImage,(x,y))
resizedImages.append(newImage)
if not self.simultaneous:
return resizedImages
else:
return [resizedImages]
def __init__(self, factor=1.0, scaleTowardCenter=False):
"""
Parameters
----------
factor: float
How much contrast to produce in the output image relative
to the input image. A factor of 0 returns a solid gray image,
a factor of 1.0 returns the original image, and higher values
return higher-contrast images.
scaleTowardCenter: bool
If False (the default), uses PIL.ImageEnhance.Contrast.
If True, scales the pixel values toward 0.5.
"""
BaseFilter.__init__(self)
if factor < 0:
raise ValueError("'factor' must be nonnegative")
self.factor = factor
self.scaleTowardCenter = scaleTowardCenter
if scaleTowardCenter and not (0.0 <= factor <= 1.0):
raise ValueError("scaleTowardCenter only supports contrast factors "
"between 0 and 1, inclusive.")
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
base, alpha = image.split()
if self.scaleTowardCenter:
scale = float(self.factor)
assert base.mode == "L"
maxValue = 255 # TODO: Determine how to get maximum value __allowed__.
offset = ((1.0 - self.factor) / 2.0) * maxValue
newImage = ImageMath.eval(
"convert(convert(gray, 'F') * scale + offset, mode)",
gray=base,
scale=scale,
offset=offset,
mode=base.mode,
)
else:
contrastEnhancer = ImageEnhance.Contrast(image.split()[0])
newImage = contrastEnhancer.enhance(self.factor)
newImage.putalpha(alpha)
return newImage
def __init__(self, xsize, ysize, c, preserveCenterResolution=False, Debug=False):
"""
Initializes the kernel matrices, a one-time cost, which are then applied to
each image.
@param xsize -- The x-dimension size of the desired output
@param ysize -- The y-dimension size of the desired output
@param c -- Paramaterizes how much the image bends (ie. how steep the
fish-eye. c=0 is no distortion. c=3 is severe.
@param preserveCenterResolution -- if True, the resolution of the center of the
image will be preserved and the edges will be sub-sampled.
If False, the center pixels will be blown up to keep the
outside corners at the original resolution.
@param Debug -- Determines whether to compute and save some intermediate
data structures for debugging (deltaxmat, deltaymat, scales).
"""
BaseFilter.__init__(self)
# Init params
self._lastOutputImage = None
self._xsize = xsize
self._ysize = ysize
self._c = c
self._pcr = preserveCenterResolution
self._debug = Debug
self._kernelx = None
self._kernely = None
self._kernel = None
self._imgSize = (-1,-1)
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
# ---------------------------------------------------------------------------
# Create the mask around the source image
mask = image.split()[-1]
if image.mode[-1] != 'A' or isSimpleBBox(mask):
mask = createMask(image, threshold=self._threshold, fillHoles=True,
backgroundColor=self.background, blurRadius=self._blurRadius,
maskScale=self._maskScale)
# ---------------------------------------------------------------------------
# Process each value
newImages = []
for value in self._values:
if value is None:
value = self.background
bg = ImageChops.constant(image, value)
newImage = Image.composite(image.split()[0], bg, mask)
newImage.putalpha(image.split()[-1])
newImages.append(newImage)
if len(newImages) == 1:
return newImages[0]
else:
return newImages
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
# ---------------------------------------------------------------------------
# Create the mask around the source image
mask = image.split()[-1]
if image.mode[-1] != 'A' or isSimpleBBox(mask):
mask = createMask(image, threshold=self._threshold, fillHoles=True,
backgroundColor=self.background, blurRadius=self._blurRadius,
maskScale=self._maskScale)
# ---------------------------------------------------------------------------
# Process each value
newImages = []
for value in self._values:
if value is None:
value = self.background
bg = ImageChops.constant(image, value)
newImage = Image.composite(image.split()[0], bg, mask)
newImage.putalpha(image.split()[-1])
newImages.append(newImage)
if len(newImages) == 1:
return newImages[0]
else:
return newImages
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
base, alpha = image.split()
if self.scaleTowardCenter:
scale = float(self.factor)
assert base.mode == "L"
maxValue = 255 # TODO: Determine how to get maximum value __allowed__.
offset = ((1.0 - self.factor) / 2.0) * maxValue
newImage = ImageMath.eval(
"convert(convert(gray, 'F') * scale + offset, mode)",
gray=base,
scale=scale,
offset=offset,
mode=base.mode,
)
else:
contrastEnhancer = ImageEnhance.Contrast(image.split()[0])
newImage = contrastEnhancer.enhance(self.factor)
newImage.putalpha(alpha)
return newImage
def __init__(self,
xsize,
ysize,
c,
preserveCenterResolution=False,
Debug=False):
"""
Initializes the kernel matrices, a one-time cost, which are then applied to
each image.
@param xsize -- The x-dimension size of the desired output
@param ysize -- The y-dimension size of the desired output
@param c -- Paramaterizes how much the image bends (ie. how steep the
fish-eye. c=0 is no distortion. c=3 is severe.
@param preserveCenterResolution -- if True, the resolution of the center of the
image will be preserved and the edges will be sub-sampled.
If False, the center pixels will be blown up to keep the
outside corners at the original resolution.
@param Debug -- Determines whether to compute and save some intermediate
data structures for debugging (deltaxmat, deltaymat, scales).
"""
BaseFilter.__init__(self)
# Init params
self._lastOutputImage = None
self._xsize = xsize
self._ysize = ysize
self._c = c
self._pcr = preserveCenterResolution
self._debug = Debug
self._kernelx = None
self._kernely = None
self._kernel = None
self._imgSize = (-1, -1)
def __init__(self, noiseLevel=0.0, doForeground=True, doBackground=False,
dynamic=True, noiseThickness=1):
"""
noiseLevel -- Amount of noise to add, from 0 to 1.0. For black and white
images, this means the values of noiseLevel fraction of the pixels will
be flipped (e.g. noiseLevel of 0.2 flips 20 percent of the pixels). For
grayscale images, each pixel will be modified by up to 255 * noiseLevel
(either upwards or downwards).
doForeground -- Whether to add noise to the foreground. For black and white
images, black pixels are foreground and white pixels are background. For
grayscale images, any pixel which does not equal the background color
(the ImageSensor 'background' parameter) is foreground, and the rest is
background.
doBackground -- Whether to add noise to the background (see above).
"""
BaseFilter.__init__(self)
self.noiseLevel = noiseLevel
self.doForeground = doForeground
self.doBackground = doBackground
self.dynamic = dynamic
self.noiseThickness = noiseThickness
# Generate and save our random state
saveState = numpy.random.get_state()
numpy.random.seed(0)
self._randomState = numpy.random.get_state()
numpy.random.set_state(saveState)
def __init__(self, factor=1.0, scaleTowardCenter=False):
"""
Parameters
----------
factor: float
How much contrast to produce in the output image relative
to the input image. A factor of 0 returns a solid gray image,
a factor of 1.0 returns the original image, and higher values
return higher-contrast images.
scaleTowardCenter: bool
If False (the default), uses PIL.ImageEnhance.Contrast.
If True, scales the pixel values toward 0.5.
"""
BaseFilter.__init__(self)
if factor < 0:
raise ValueError("'factor' must be nonnegative")
self.factor = factor
self.scaleTowardCenter = scaleTowardCenter
if scaleTowardCenter and not (0.0 <= factor <= 1.0):
raise ValueError(
"scaleTowardCenter only supports contrast factors "
"between 0 and 1, inclusive.")
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
mode = image.mode
originalSize = image.size
sizes = [(int(round(image.size[0] * s)), int(round(image.size[1] * s)))
for s in self.scales]
resizedImages = []
for size in sizes:
if size < image.size:
resizedImage = image.resize(size, Image.ANTIALIAS)
else:
resizedImage = image.resize(size, Image.BICUBIC)
x = (originalSize[0] - size[0]) / 2
y = (originalSize[1] - size[1]) / 2
newImage = Image.new(mode, originalSize, self.background)
newImage.paste(resizedImage, (x, y))
resizedImages.append(newImage)
if not self.simultaneous:
return resizedImages
else:
return [resizedImages]
def __init__(self,
angles=[0],
expand=False,
targetRatio=None,
highQuality=True):
"""
angles -- List of angles by which to rotate, in degrees.
expand -- Whether to expand the output image to contain the entire
rotated image. If False, the output image will match the dimensions of
the input image, but cropping may occur.
targetRatio -- Ratio of the sensor. If specified, used if expand == False
to grow the image to the target ratio to avoid unnecessary clipping.
highQuality -- Whether to use bicubic interpolation for rotating.
instead of nearest neighbor.
"""
BaseFilter.__init__(self)
self.angles = angles
self.expand = expand
self.targetRatio = targetRatio
self.highQuality = highQuality
if not expand:
for i, angle in enumerate(angles):
if angle != 0 and angle % 90 == 0:
angles[i] -= .01 # Oh, PIL...
def __init__(self,
noiseLevel=0.0,
doForeground=True,
doBackground=False,
dynamic=True,
noiseThickness=1):
"""
noiseLevel -- Amount of noise to add, from 0 to 1.0. For black and white
images, this means the values of noiseLevel fraction of the pixels will
be flipped (e.g. noiseLevel of 0.2 flips 20 percent of the pixels). For
grayscale images, each pixel will be modified by up to 255 * noiseLevel
(either upwards or downwards).
doForeground -- Whether to add noise to the foreground. For black and white
images, black pixels are foreground and white pixels are background. For
grayscale images, any pixel which does not equal the background color
(the ImageSensor 'background' parameter) is foreground, and the rest is
background.
doBackground -- Whether to add noise to the background (see above).
"""
BaseFilter.__init__(self)
self.noiseLevel = noiseLevel
self.doForeground = doForeground
self.doBackground = doBackground
self.dynamic = dynamic
self.noiseThickness = noiseThickness
# Generate and save our random state
saveState = numpy.random.get_state()
numpy.random.seed(0)
self._randomState = numpy.random.get_state()
numpy.random.set_state(saveState)
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
s = min(image.size)
sizeRange = [0, s]
imageArray = numpy.array(image.split()[0].getdata())
newImage = Image.new("LA", image.size)
newImage.putdata([uint(p) for p in imageArray])
newImage.putalpha(image.split()[1])
for i in xrange(int(self.difficulty * self.maxLines)):
# Generate random line
start = (random.randint(sizeRange[0], sizeRange[1]), random.randint(sizeRange[0], sizeRange[1]))
end = (random.randint(sizeRange[0], sizeRange[1]), random.randint(sizeRange[0], sizeRange[1]))
# Generate random color
color = random.randint(0, 255)
# Add the line to the image
draw = ImageDraw.Draw(newImage)
draw.line((start, end), fill=color)
return newImage
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
#Create numpy array from image grayscale data and resize to image dimensions
imageArray = numpy.array(image.split()[0].getdata())
imageArray.resize(image.size[1], image.size[0])
#Calculate offset from difficulty level
offset = self.difficulty * (self.maxOffset)
#Add random change to offset within window size
halfWindowSize = 0.1 * offset
offset = (offset -
halfWindowSize) + halfWindowSize * self.random.random() * (
(-1)**self.random.randint(1, 2))
#Apply random direction
offset *= ((-1)**self.random.randint(1, 2))
imageArray += offset
#Recreate PIL image
newImage = Image.new("L", image.size)
newImage.putdata([uint(p) for p in imageArray.flatten()])
newImage.putalpha(image.split()[1])
return newImage
def __init__(self, shiftSize=1):
"""
@param stepSize -- number of pixels to shift
"""
BaseFilter.__init__(self)
self.shiftSize = shiftSize
def __init__(self, shiftSize=1):
"""
@param stepSize -- number of pixels to shift
"""
BaseFilter.__init__(self)
self.shiftSize = shiftSize
def __init__(self, level=1):
"""
@param level -- Number of times to blur.
"""
BaseFilter.__init__(self)
self.level = level
def __init__(self, level=1):
"""
@param level -- Number of times to blur.
"""
BaseFilter.__init__(self)
self.level = level
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
newImage = ImageOps.flip(image)
return newImage
def __init__(self, difficulty = 0.5, seed=None, reproducible=False):
"""
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
newImage = ImageOps.flip(image)
return newImage
def __init__(self, difficulty=0.5, seed=None, reproducible=False):
"""
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
def process(self, image):
"""
image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
if not self.expand and self.targetRatio:
# Pad the image to the aspect ratio of the sensor
# This allows us to rotate in expand=False without cutting off parts
# of the image unnecessarily
# Unlike expand=True, the object doesn't get smaller
ratio = (image.size[0] / float(image.size[1]))
if ratio < self.targetRatio:
# Make image wider
size = (int(image.size[0] * self.targetRatio / ratio),
image.size[1])
newImage = Image.new('LA', size, (self.background, 0))
newImage.paste(image,
((newImage.size[0] - image.size[0]) / 2, 0))
image = newImage
elif ratio > self.targetRatio:
# Make image taller
size = (image.size[0],
int(image.size[1] * ratio / self.targetRatio))
newImage = Image.new('LA', size, (self.background, 0))
newImage.paste(image,
(0, (newImage.size[1] - image.size[1]) / 2))
image = newImage
if self.highQuality:
resample = Image.BICUBIC
else:
resample = Image.NEAREST
outputs = []
for angle in self.angles:
# Rotate the image, which expands it and pads it with black and a 0
# alpha value
rotatedImage = image.rotate(angle,
resample=resample,
expand=self.expand)
# Create a new larger image to hold the rotated image
# It is filled with the background color and an alpha value of 0
outputImage = Image.new('LA', rotatedImage.size,
(self.background, 0))
# Paste the rotated image into the new image, using the rotated image's
# alpha channel as a mask
# This effectively just fills the area around the rotation with the
# background color, and imports the alpha channel from the rotated image
outputImage.paste(rotatedImage, None, rotatedImage.split()[1])
outputs.append(outputImage)
return outputs
def __init__(self, seed=None, reproducible=False, both=False):
"""
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
both -- Whether to also pass through the original image.
"""
BaseFilter.__init__(self, seed, reproducible)
self.both = both
def __init__(self, seed=None, reproducible=False, both=False):
"""
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
both -- Whether to also pass through the original image.
"""
BaseFilter.__init__(self, seed, reproducible)
self.both = both
def __init__(self, box):
"""
@param box -- 4-tuple specifying the left, top, right, and bottom coords.
"""
BaseFilter.__init__(self)
if box[2] <= box[0] or box[3] <= box[1]:
raise RuntimeError('Specified box has zero width or height')
self.box = box
def __init__(self, box):
"""
@param box -- 4-tuple specifying the left, top, right, and bottom coords.
"""
BaseFilter.__init__(self)
if box[2] <= box[0] or box[3] <= box[1]:
raise RuntimeError('Specified box has zero width or height')
self.box = box
def __init__(self, width, height):
"""
** DEPRECATED **
@param width -- Target width, in pixels.
@param height -- Target height, in pixels.
"""
BaseFilter.__init__(self)
self.width = width
self.height = height
def __init__(self, width, height):
"""
** DEPRECATED **
@param width -- Target width, in pixels.
@param height -- Target height, in pixels.
"""
BaseFilter.__init__(self)
self.width = width
self.height = height
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
newImage = ImageOps.mirror(image)
if not self.both:
return newImage
else:
return [image, newImage]
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
newImage = ImageOps.mirror(image)
if not self.both:
return newImage
else:
return [image, newImage]
def __init__(self, numRectangles=4, seed=None, reproducible=False):
"""
@param numRectangles -- Number of rectangles to add.
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
self.numRectangles = numRectangles
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
if self.box[2] > image.size[0] or self.box[3] > image.size[1]:
raise RuntimeError('Crop coordinates exceed image bounds')
return image.crop(self.box)
def __init__(self, difficulty = 0.5, seed=None, reproducible=False):
"""
@param difficulty -- Value between 0.0 and 1.0 that controls the intensity of the gradient applied.
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
self.difficulty = difficulty
self.types = ('horizontal', 'vertical', 'circular')
self.gradientImages = {}
def __init__(self, difficulty=0.5, seed=None, reproducible=False):
"""
@param difficulty -- Value between 0.0 and 1.0 that controls how many lines to add in image.
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
self.difficulty = difficulty
# Maximum number of lines to add
self.maxLines = 10
def __init__(self, numRectangles=4, seed=None, reproducible=False):
"""
@param numRectangles -- Number of rectangles to add.
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
self.numRectangles = numRectangles
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
brightnessEnhancer = ImageEnhance.Brightness(image.split()[0])
newImage = brightnessEnhancer.enhance(self.factor)
newImage.putalpha(image.split()[1])
return newImage
def __init__(self, factor=1.0):
"""
@param factor -- Factor by which to brighten the image, a nonnegative
number. 0.0 returns a black image, 1.0 returns the original image, and
higher values return brighter images.
"""
BaseFilter.__init__(self)
if factor < 0:
raise ValueError("'factor' must be a nonnegative number")
self.factor = factor
def __init__(self, difficulty=0.5, seed=None, reproducible=False):
"""
@param difficulty -- Value between 0.0 and 1.0 that controls the intensity of the gradient applied.
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
self.difficulty = difficulty
self.types = ('horizontal', 'vertical', 'circular')
self.gradientImages = {}
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
if self.box[2] > image.size[0] or self.box[3] > image.size[1]:
raise RuntimeError('Crop coordinates exceed image bounds')
return image.crop(self.box)
def __init__(self, difficulty=0.5, seed=None, reproducible=False):
"""
@param difficulty -- Value between 0.0 and 1.0 that controls how many lines to add in image.
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
self.difficulty = difficulty
#Maximum number of lines to add
self.maxLines = 10
def __init__(self, scales=[1], simultaneous=False):
"""
** DEPRECATED **
@param scales -- List of factors used for scaling. scales = [.5, 1] returns
two images, one half the size of the original in each dimension, and one
which is the original image.
@param simultaneous -- Whether the images should be sent out of the sensor
simultaneously.
"""
BaseFilter.__init__(self)
self.scales = scales
self.simultaneous = simultaneous
def process(self, image):
"""
image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
if not self.expand and self.targetRatio:
# Pad the image to the aspect ratio of the sensor
# This allows us to rotate in expand=False without cutting off parts
# of the image unnecessarily
# Unlike expand=True, the object doesn't get smaller
ratio = (image.size[0] / float(image.size[1]))
if ratio < self.targetRatio:
# Make image wider
size = (int(image.size[0] * self.targetRatio / ratio), image.size[1])
newImage = Image.new('LA', size, (self.background, 0))
newImage.paste(image, ((newImage.size[0] - image.size[0])/2, 0))
image = newImage
elif ratio > self.targetRatio:
# Make image taller
size = (image.size[0], int(image.size[1] * ratio / self.targetRatio))
newImage = Image.new('LA', size, (self.background, 0))
newImage.paste(image, (0, (newImage.size[1] - image.size[1])/2))
image = newImage
if self.highQuality:
resample = Image.BICUBIC
else:
resample = Image.NEAREST
outputs = []
for angle in self.angles:
# Rotate the image, which expands it and pads it with black and a 0
# alpha value
rotatedImage = image.rotate(angle,
resample=resample,
expand=self.expand)
# Create a new larger image to hold the rotated image
# It is filled with the background color and an alpha value of 0
outputImage = Image.new('LA', rotatedImage.size, (self.background, 0))
# Paste the rotated image into the new image, using the rotated image's
# alpha channel as a mask
# This effectively just fills the area around the rotation with the
# background color, and imports the alpha channel from the rotated image
outputImage.paste(rotatedImage, None, rotatedImage.split()[1])
outputs.append(outputImage)
return outputs
def __init__(self, img=None, threshold=10, maskScale=1.0, blurRadius=0.0):
"""
@param img -- path to background image(s) to use
"""
BaseFilter.__init__(self)
self.bgPath = img
self.bgImgs = None
self._rng = random.Random()
self._rng.seed(42)
self._threshold = threshold
self._maskScale = maskScale
self._blurRadius = blurRadius
def __init__(self, img=None, threshold=10, maskScale=1.0, blurRadius=0.0):
"""
@param img -- path to background image(s) to use
"""
BaseFilter.__init__(self)
self.bgPath = img
self.bgImgs = None
self._rng = random.Random()
self._rng.seed(42)
self._threshold = threshold
self._maskScale = maskScale
self._blurRadius = blurRadius
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
type = self.random.choice(self.types)
#Default matrix
matrix = (1, 0, 0, 0, 1, 0)
size = list(image.size)
newImage = Image.new('LA', size)
if type == 'shear_x':
shear = self.difficulty*self.maxShear - self.difficulty*0.3 + self.difficulty*0.3*self.random.random()
matrix = (1, shear, -shear*size[1], 0, 1, 0)
size[0] += int(shear*size[0])
newImage = image.transform(tuple(size), Image.AFFINE, matrix)
bbox = list(newImage.split()[1].getbbox())
bbox[1] = 0
bbox[3] = size[1]
newImage = newImage.crop(bbox)
elif type == 'shear_y':
shear = self.difficulty*self.maxShear - self.difficulty*0.3 + self.difficulty*0.3*self.random.random()
matrix = (1, 0, 0, shear, 1, -shear*size[0])
size[1] += int(shear*size[1])
newImage = image.transform(tuple(size), Image.AFFINE, matrix)
bbox = list(newImage.split()[1].getbbox())
bbox[0] = 0
bbox[2] = size[0]
newImage = newImage.crop(bbox)
elif type == 'squeeze_x':
squeeze = self.minSqueeze - (self.minSqueeze - self.maxSqueeze)*(self.difficulty - self.difficulty*0.3 + self.difficulty*0.3*self.random.random())
matrix = (1/squeeze, 0, 0, 0, 1, 0)
newImage = ImageChops.offset(image.transform(tuple(size), Image.AFFINE, matrix), int((size[0] - squeeze*size[0])/2), 0)
elif type == 'squeeze_y':
squeeze = self.minSqueeze - (self.minSqueeze - self.maxSqueeze)*(self.difficulty - self.difficulty*0.3 + self.difficulty*0.3*self.random.random())
matrix = (1, 0, 0, 0, 1/squeeze, 0)
newImage = ImageChops.offset(image.transform(tuple(size), Image.AFFINE, matrix), 0, int((size[1] - squeeze*size[1])/2))
#Appropriate sizing
if newImage.size[0] > image.size[0] or newImage.size[1] > image.size[1]:
newImage = newImage.resize(image.size)
elif newImage.size[1] < image.size[1]:
retImage = Image.new('LA', image.size)
retImage.paste(newImage, (0, int((image.size[1] - newImage.size[1])/2.0)))
newImage = retImage
elif newImage.size[0] < image.size[0]:
retImage = Image.new('LA', image.size)
retImage.paste(newImage, (0, int((image.size[0] - newImage.size[0])/2.0)))
return newImage
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
if image.size == (self.width, self.height):
return image
# Resize the image
targetRatio = self.width / float(self.height)
imageRatio = image.size[0] / float(image.size[1])
if self.scaleHeightTo:
ySize = self.scaleHeightTo
scaleFactor = self.scaleHeightTo / float(image.size[1])
xSize = int(scaleFactor * image.size[0])
elif self.scaleWidthTo:
xSize = self.scaleWidthTo
scaleFactor = self.scaleWidthTo / float(image.size[0])
ySize = int(scaleFactor * image.size[1])
else:
if imageRatio > targetRatio:
xSize = self.width
scaleFactor = self.width / float(image.size[0])
ySize = int(scaleFactor * image.size[1])
else:
ySize = self.height
scaleFactor = self.height / float(image.size[1])
xSize = int(scaleFactor * image.size[0])
if (xSize, ySize) < image.size:
image = image.resize((xSize, ySize),Image.ANTIALIAS)
else:
image = image.resize((xSize, ySize),Image.BICUBIC)
# Pad the image if necessary
if self.pad and image.size != (self.width, self.height):
paddedImage = Image.new('L', (self.width, self.height),
self.background)
alpha = Image.new('L', (self.width, self.height))
paddedImage.putalpha(alpha)
paddedImage.paste(image,
((self.width - image.size[0])/2, (self.height - image.size[1])/2))
image = paddedImage
return image
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
if image.size == (self.width, self.height):
return image
# Resize the image
targetRatio = self.width / float(self.height)
imageRatio = image.size[0] / float(image.size[1])
if self.scaleHeightTo:
ySize = self.scaleHeightTo
scaleFactor = self.scaleHeightTo / float(image.size[1])
xSize = int(scaleFactor * image.size[0])
elif self.scaleWidthTo:
xSize = self.scaleWidthTo
scaleFactor = self.scaleWidthTo / float(image.size[0])
ySize = int(scaleFactor * image.size[1])
else:
if imageRatio > targetRatio:
xSize = self.width
scaleFactor = self.width / float(image.size[0])
ySize = int(scaleFactor * image.size[1])
else:
ySize = self.height
scaleFactor = self.height / float(image.size[1])
xSize = int(scaleFactor * image.size[0])
if (xSize, ySize) < image.size:
image = image.resize((xSize, ySize), Image.ANTIALIAS)
else:
image = image.resize((xSize, ySize), Image.BICUBIC)
# Pad the image if necessary
if self.pad and image.size != (self.width, self.height):
paddedImage = Image.new('L', (self.width, self.height),
self.background)
alpha = Image.new('L', (self.width, self.height))
paddedImage.putalpha(alpha)
paddedImage.paste(image, ((self.width - image.size[0]) / 2,
(self.height - image.size[1]) / 2))
image = paddedImage
return image
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
type = self.random.choice(self.types)
#Default matrix
matrix = (1, 0, 0, 0, 1, 0)
size = list(image.size)
newImage = Image.new('LA', size)
if type == 'shear_x':
shear = self.difficulty*self.maxShear - self.difficulty*0.3 + self.difficulty*0.3*self.random.random()
matrix = (1, shear, -shear*size[1], 0, 1, 0)
size[0] += int(shear*size[0])
newImage = image.transform(tuple(size), Image.AFFINE, matrix)
bbox = list(newImage.split()[1].getbbox())
bbox[1] = 0
bbox[3] = size[1]
newImage = newImage.crop(bbox)
elif type == 'shear_y':
shear = self.difficulty*self.maxShear - self.difficulty*0.3 + self.difficulty*0.3*self.random.random()
matrix = (1, 0, 0, shear, 1, -shear*size[0])
size[1] += int(shear*size[1])
newImage = image.transform(tuple(size), Image.AFFINE, matrix)
bbox = list(newImage.split()[1].getbbox())
bbox[0] = 0
bbox[2] = size[0]
newImage = newImage.crop(bbox)
elif type == 'squeeze_x':
squeeze = self.minSqueeze - (self.minSqueeze - self.maxSqueeze)*(self.difficulty - self.difficulty*0.3 + self.difficulty*0.3*self.random.random())
matrix = (1/squeeze, 0, 0, 0, 1, 0)
newImage = ImageChops.offset(image.transform(tuple(size), Image.AFFINE, matrix), int((size[0] - squeeze*size[0])/2), 0)
elif type == 'squeeze_y':
squeeze = self.minSqueeze - (self.minSqueeze - self.maxSqueeze)*(self.difficulty - self.difficulty*0.3 + self.difficulty*0.3*self.random.random())
matrix = (1, 0, 0, 0, 1/squeeze, 0)
newImage = ImageChops.offset(image.transform(tuple(size), Image.AFFINE, matrix), 0, int((size[1] - squeeze*size[1])/2))
#Appropriate sizing
if newImage.size[0] > image.size[0] or newImage.size[1] > image.size[1]:
newImage = newImage.resize(image.size)
elif newImage.size[1] < image.size[1]:
retImage = Image.new('LA', image.size)
retImage.paste(newImage, (0, int((image.size[1] - newImage.size[1])/2.0)))
newImage = retImage
elif newImage.size[0] < image.size[0]:
retImage = Image.new('LA', image.size)
retImage.paste(newImage, (0, int((image.size[0] - newImage.size[0])/2.0)))
return newImage
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
mask = image.split()[1]
for i in xrange(self.level):
sharpness_enhancer = ImageEnhance.Sharpness(image.split()[0])
image = sharpness_enhancer.enhance(0.0)
image.putalpha(mask)
return image
def process(self, image):
"""
@param image -- The image to process.
Returns a single image, or a list containing one or more images.
"""
BaseFilter.process(self, image)
mask = image.split()[1]
for i in xrange(self.level):
sharpness_enhancer = ImageEnhance.Sharpness(image.split()[0])
image = sharpness_enhancer.enhance(0.0)
image.putalpha(mask)
return image
def __init__(self, difficulty=0.5, seed=None, reproducible=False):
"""
@param difficulty -- Controls the amount of stretch and shear applied to the image.
@param seed -- Seed value for random number generator, to produce
reproducible results.
@param reproducible -- Whether to seed the random number generator based
on a hash of the image pixels upon each call to process().
'seed' and 'reproducible' cannot be used together.
"""
BaseFilter.__init__(self, seed, reproducible)
self.difficulty = difficulty
self.maxShear = 1.0
self.maxSqueeze = 0.1
self.minSqueeze = 1.0
self.types = ("shear_x", "shear_y", "squeeze_x", "squeeze_y")
def __init__(self, width, height, scaleHeightTo=None, scaleWidthTo=None, pad=False):
"""
** DEPRECATED **
@param width -- Target width, in pixels.
@param height -- Target height, in pixels.
@param pad -- Whether to pad the image with the background color in order
to fit the specified size exactly.
"""
BaseFilter.__init__(self)
self.width = width
self.height = height
self.pad = pad
self.scaleHeightTo = scaleHeightTo
self.scaleWidthTo = scaleWidthTo