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)