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)
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)
#Type of gradient?
type = self.random.choice(self.types)
gradientImage = self.gradientImages.get((image.size, type))
if not gradientImage:
#Gradient image, used as mask
gradientImage = Image.new("L", image.size)
gradientArray = numpy.array(gradientImage.split()[0].getdata())
gradientArray.resize(image.size[1], image.size[0])
#Calculate gradient
opacity = self.difficulty - self.difficulty * .2 + self.random.random(
) * self.difficulty * .2
for i in xrange(image.size[1]):
for j in xrange(image.size[0]):
if type == 'horizontal':
gradientArray[i][j] = int(
float(j) / image.size[0] * 255 / opacity)
elif type == 'vertical':
gradientArray[i][j] = int(
float(i) / image.size[1] * 255 / opacity)
elif type == 'circular':
gradientArray[i][j] = int(
math.sqrt((i - image.size[1] / 2)**2 +
(j - image.size[0] / 2)**2) /
math.sqrt((image.size[1] / 2)**2 +
(image.size[0] / 2)**2) * 255 / opacity)
gradientImage.putdata([uint(p) for p in gradientArray.flatten()])
#Add gradient image to dictionary
self.gradientImages[(image.size, type)] = gradientImage
#Image to composite with for brightness
whiteImage = Image.new("LA", image.size)
whiteArray = numpy.array(whiteImage.split()[0].getdata())
whiteArray += 255
whiteImage.putdata([uint(p) for p in whiteArray])
newImage = Image.composite(image, whiteImage, gradientImage)
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)
#Type of gradient?
type = self.random.choice(self.types)
gradientImage = self.gradientImages.get((image.size, type))
if not gradientImage:
#Gradient image, used as mask
gradientImage = Image.new("L", image.size)
gradientArray = numpy.array(gradientImage.split()[0].getdata())
gradientArray.resize(image.size[1], image.size[0])
#Calculate gradient
opacity = self.difficulty - self.difficulty*.2 + self.random.random()*self.difficulty*.2
for i in xrange(image.size[1]):
for j in xrange(image.size[0]):
if type == 'horizontal':
gradientArray[i][j] = int(float(j)/image.size[0]*255/opacity)
elif type == 'vertical':
gradientArray[i][j] = int(float(i)/image.size[1]*255/opacity)
elif type == 'circular':
gradientArray[i][j] = int(math.sqrt((i - image.size[1]/2)**2 + (j - image.size[0]/2)**2)/math.sqrt((image.size[1]/2)**2 + (image.size[0]/2)**2)*255/opacity)
gradientImage.putdata([uint(p) for p in gradientArray.flatten()])
#Add gradient image to dictionary
self.gradientImages[(image.size, type)] = gradientImage
#Image to composite with for brightness
whiteImage = Image.new("LA", image.size)
whiteArray = numpy.array(whiteImage.split()[0].getdata())
whiteArray += 255
whiteImage.putdata([uint(p) for p in whiteArray])
newImage = Image.composite(image, whiteImage, gradientImage)
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 self.mode != 'gray':
raise RuntimeError("EqualizeHistogram only supports grayscale images.")
if self.region == 'bbox':
bbox = image.split()[1].getbbox()
croppedImage = image.crop(bbox)
croppedImage.load()
alpha = croppedImage.split()[1]
croppedImage = ImageOps.equalize(croppedImage.split()[0])
croppedImage.putalpha(alpha)
image.paste(croppedImage, bbox)
elif self.region == 'mask':
bbox = image.split()[1].getbbox()
croppedImage = image.crop(bbox)
croppedImage.load()
alpha = croppedImage.split()[1]
# Fill in the part of the cropped image outside the bounding box with
# uniformly-distributed noise
noiseArray = \
numpy.random.randint(0, 255, croppedImage.size[0]*croppedImage.size[1])
noiseImage = Image.new('L', croppedImage.size)
noiseImage.putdata([uint(p) for p in noiseArray])
compositeImage = Image.composite(croppedImage, noiseImage, alpha)
# Equalize the composite image
compositeImage = ImageOps.equalize(compositeImage.split()[0])
# Paste the part of the equalized image within the mask back
# into the cropped image
croppedImage = Image.composite(compositeImage, croppedImage, alpha)
croppedImage.putalpha(alpha)
# Paste the cropped image back into the full image
image.paste(croppedImage, bbox)
elif self.region == 'all':
alpha = image.split()[1]
image = ImageOps.equalize(image.split()[0])
image.putalpha(alpha)
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)
#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 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 _processImage(self, image, bbox):
"""Return a single image, or a list containing one or more images.
@param image -- The image to process.
"""
BaseFilter.process(self, image)
inWidth, inHeight = image.size
# Get output dims and buffers (cached)
outWidth, outHeight, inBuffer, outBuffer, mask = self._prepare(image.size)
# Ask the sub-class the build the filter bank
self._buildFilterBank()
inputOffset = 0
outputOffset = 0
data = image.split()[0]
inputVector = numpy.asarray(data, dtype=numpy.float32)
inputVector.shape = (inHeight, inWidth)
# If we are using "color-key" mode, then detect the value of
# the upper-left pixel and use it as the value of
# 'offImagePixelValue'
if self._offImagePixelValue in ('colorKey', u'colorKey'):
offImagePixelValue = inputVector[0, 0]
else:
offImagePixelValue = self._offImagePixelValue
result = []
# Compute the convolution responses
# Determine proper input/output dimensions
outputSize = outHeight * outWidth * self._outputPlaneCount
# Locate correct portion of output
outputVector = numpy.zeros((outHeight,
outWidth,
self._outputPlaneCount),
dtype=numpy.float32)
outputVector.shape = (self._outputPlaneCount, outHeight, outWidth)
# Compute the bounding box to use for our C implementation
imageBox = numpy.array([0, 0, inWidth, inHeight], dtype=numpy.int32)
## --- DEBUG CODE ----
#global id
#o = inputVector
#f = os.path.abspath('convolution_input_%d.txt' % id)
#print f
#numpy.savetxt(f, o)
#id += 1
##from dbgp.client import brk; brk(port=9019)
## --- DEBUG CODE END ----
# Call the fast convolution C code
self._convolve(inputVector,
bbox,
imageBox,
outputVector,
offImagePixelValue,
inBuffer,
outBuffer)
outputVector = numpy.rollaxis(outputVector, 0, 3)
outputVector = outputVector.reshape(outWidth * outHeight,
self._outputPlaneCount).flatten()
assert outputVector.dtype == numpy.float32
locationCount = len(outputVector) / self._outputPlaneCount
response = outputVector.reshape(locationCount, self._outputPlaneCount)
## --- DEBUG CODE ----
#global id
#o = outputVector.flatten()
##print outputVector.shape, len(o)
#f = os.path.abspath('convolution_output_%d.txt' % id)
#print f
#numpy.savetxt(f, o)
#id += 1
##from dbgp.client import brk; brk(port=9019)
## --- DEBUG CODE END ----
# Convert the reponses to images
result = []
for i in range(response.shape[1]):
newImage = Image.new('L', (outWidth, outHeight))
#data = (self._gainConstant * 255.0 * response[:,i]).clip(min=0.0, max=255.0).astype(numpy.uint8)
data = (255.0 * response[:,i]).clip(min=0.0, max=255.0).astype(numpy.uint8)
newImage.putdata([uint(p) for p in data])
newImage.putalpha(mask)
result.append(newImage)
return (result, outputVector)
def _processImage(self, image, bbox):
"""Return a single image, or a list containing one or more images.
@param image -- The image to process.
"""
BaseFilter.process(self, image)
inWidth, inHeight = image.size
# Get output dims and buffers (cached)
outWidth, outHeight, inBuffer, outBuffer, mask = self._prepare(image.size)
# Ask the sub-class the build the filter bank
self._buildFilterBank()
inputOffset = 0
outputOffset = 0
data = image.split()[0]
inputVector = numpy.asarray(data, dtype=numpy.float32)
inputVector.shape = (inHeight, inWidth)
# If we are using "color-key" mode, then detect the value of
# the upper-left pixel and use it as the value of
# 'offImagePixelValue'
if self._offImagePixelValue in ('colorKey', u'colorKey'):
offImagePixelValue = inputVector[0, 0]
else:
offImagePixelValue = self._offImagePixelValue
result = []
# Compute the convolution responses
# Determine proper input/output dimensions
outputSize = outHeight * outWidth * self._outputPlaneCount
# Locate correct portion of output
outputVector = numpy.zeros((outHeight,
outWidth,
self._outputPlaneCount),
dtype=numpy.float32)
outputVector.shape = (self._outputPlaneCount, outHeight, outWidth)
# Compute the bounding box to use for our C implementation
imageBox = numpy.array([0, 0, inWidth, inHeight], dtype=numpy.int32)
## --- DEBUG CODE ----
#global id
#o = inputVector
#f = os.path.abspath('convolution_input_%d.txt' % id)
#print f
#numpy.savetxt(f, o)
#id += 1
##from dbgp.client import brk; brk(port=9019)
## --- DEBUG CODE END ----
# Call the fast convolution C code
self._convolve(inputVector,
bbox,
imageBox,
outputVector,
offImagePixelValue,
inBuffer,
outBuffer)
outputVector = numpy.rollaxis(outputVector, 0, 3)
outputVector = outputVector.reshape(outWidth * outHeight,
self._outputPlaneCount).flatten()
assert outputVector.dtype == numpy.float32
locationCount = len(outputVector) / self._outputPlaneCount
response = outputVector.reshape(locationCount, self._outputPlaneCount)
## --- DEBUG CODE ----
#global id
#o = outputVector.flatten()
##print outputVector.shape, len(o)
#f = os.path.abspath('convolution_output_%d.txt' % id)
#print f
#numpy.savetxt(f, o)
#id += 1
##from dbgp.client import brk; brk(port=9019)
## --- DEBUG CODE END ----
# Convert the reponses to images
result = []
for i in range(response.shape[1]):
newImage = Image.new('L', (outWidth, outHeight))
#data = (self._gainConstant * 255.0 * response[:,i]).clip(min=0.0, max=255.0).astype(numpy.uint8)
data = (255.0 * response[:,i]).clip(min=0.0, max=255.0).astype(numpy.uint8)
newImage.putdata([uint(p) for p in data])
newImage.putalpha(mask)
result.append(newImage)
return (result, outputVector)