def test_getTraining(self):
test = np.array(Image.open(self.filename), 'uint8')
train = np.array(Image.open(self.filename), 'uint8')
np.bitwise_xor(test,train,test)
image = np.ones(test.shape,test.dtype)
le.getTraining(image,self.filename, self.filename)
assert((test == image).all(), True)
def get_labeled_data(filename, training_file, block_size=32):
"""Read input-array (image) and label-images and return it as list of tuples. """
rows,cols = load_extension.getDims(filename)
print rows,cols
image = np.ones((rows, cols), 'uint8')
label_image = np.ones((rows, cols), 'uint8')
# x is a dummy to use as a form of error checking will return false on error
x = load_extension.getImage(image, filename)
x = load_extension.getTraining(label_image, filename, training_file)
X = []
y = []
for i in xrange(0,rows,block_size):
for j in xrange(0,cols,block_size):
try:
X.append(image[i:i + block_size, j:j + block_size].reshape(1, block_size * block_size))
y.append(int(load_extension.getLabel(label_image[i:i + block_size, j:j + block_size], "1", "0", 0.75)))
except ValueError:
continue
X = np.array(X).astype(np.float32)
label_blocks = np.array(y).astype(np.int32)
test_blocks = X.reshape(-1, 1, block_size, block_size)
return test_blocks, label_blocks
def get_labeled_data(filename, training_file):
"""Read input-array (image) and label-images and return it as list of tuples. """
rows,cols = load_extension.get_dims(filename)
print rows,cols
image = np.ones((rows,cols),'uint8')
label_image = np.ones((rows,cols),'uint8')
# x is a dummy to use as a form of error checking will return false on error
x = load_extension.getImage(image ,filename)
x = load_extension.getTraining(label_image,filename, training_file)
#Seperate Image and Label into blocks
test_blocks, blocks = create_image_blocks(24,11543,12576,rows,cols,image)
label_blocks, blocks = create_image_blocks(24,11543,12576,rows,cols,label_image)
#Used to Write image blocks to folder
# for i in range(blocks):
# im = Image.fromarray(test_blocks[i])
# im.save(str(i) +"label.tif")
return test_blocks, label_blocks
def get_labeled_data(filename, training_file):
"""Read input-array (image) and label-images and return it as list of tuples. """
rows, cols = load_extension.getDims(filename)
print rows, cols
image = np.ones((rows, cols), 'uint8')
label_image = np.ones((rows, cols), 'uint8')
# x is a dummy to use as a form of error checking will return false on error
x = load_extension.getImage(image, filename)
x = load_extension.getTraining(label_image, filename, training_file)
#Seperate Image and Label into blocks
#test_blocks,blocks = create_image_blocks(768, 393,11543,rows,cols,image)
#label_blocks, blocks = create_image_blocks(768, 393,11543,rows,cols,label_image)
test_blocks, blocks = load4d(4096, 8, 8, rows, cols, image)
label_blocks, blocks = load4d(4096, 8, 8, rows, cols, label_image)
#Used to Write image blocks to folder
#or i in range(blocks):
#im = Image.fromarray(test_blocks[i][i])
#im.save(str(i) +"label.tif")
return test_blocks, label_blocks
