def test_getLabel(self):
rows, cols = (25600, 25600)
diag = np.zeros((rows, cols), 'uint8')
np.fill_diagonal(diag, 1)
block_size = 256
for i in xrange(0, rows, block_size):
for j in xrange(0, cols, block_size):
if diag[i, j] == 1:
diag[i:i + block_size, j:j + block_size] = 255
thresholds = [0, 0.25, 0.5, 0.75, 1.0]
block_sizes = [2 ** i for i in range(8, 11)]
success = "1"
failure = "0"
for block_size in block_sizes:
for threshold in thresholds:
for i in xrange(0, rows, block_size):
for j in xrange(0, cols, block_size):
temp = diag[i:i + block_size, j:j + block_size]
test_percent = np.count_nonzero(temp) / temp.size
if test_percent >= threshold:
test_label = success
else:
test_label = failure
assert(test_label==le.getLabel(temp, success, success, threshold),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