def save_annotation(label,
save_dir,
filename,
add_colormap=True,
colormap_type=get_dataset_colormap.get_pascal_name()):
"""Saves the given label to image on disk.
Args:
label: The numpy array to be saved. The data will be converted
to uint8 and saved as png image.
save_dir: The directory to which the results will be saved.
filename: The image filename.
add_colormap: Add color map to the label or not.
colormap_type: Colormap type for visualization.
"""
# Add colormap for visualizing the prediction.
if add_colormap:
colored_label = get_dataset_colormap.label_to_color_image(
label, colormap_type)
else:
colored_label = label
pil_image = img.fromarray(colored_label.astype(dtype=np.uint8))
with tf.gfile.Open('%s/%s.png' % (save_dir, filename), mode='w') as f:
pil_image.save(f, 'PNG')
def testLabelToPASCALColorImage(self):
"""Test the value of the converted label value."""
label = np.array([[0, 16, 16], [52, 7, 52]])
expected_result = np.array([
[[0, 0, 0], [0, 64, 0], [0, 64, 0]],
[[0, 64, 192], [128, 128, 128], [0, 64, 192]]
])
colored_label = get_dataset_colormap.label_to_color_image(
label, get_dataset_colormap.get_pascal_name())
self.assertTrue(np.array_equal(expected_result, colored_label))
def save_annotation(label,
save_dir,
filename,
add_colormap=True,
normalize_to_unit_values=False,
scale_values=False,
colormap_type=get_dataset_colormap.get_pascal_name()):
"""Saves the given label to image on disk.
Args:
label: The numpy array to be saved. The data will be converted
to uint8 and saved as png image.
save_dir: String, the directory to which the results will be saved.
filename: String, the image filename.
add_colormap: Boolean, add color map to the label or not.
normalize_to_unit_values: Boolean, normalize the input values to [0, 1].
scale_values: Boolean, scale the input values to [0, 255] for visualization.
colormap_type: String, colormap type for visualization.
"""
# Add colormap for visualizing the prediction.
if add_colormap:
colored_label = get_dataset_colormap.label_to_color_image(
label, colormap_type)
else:
colored_label = label
if normalize_to_unit_values:
min_value = np.amin(colored_label)
max_value = np.amax(colored_label)
range_value = max_value - min_value
if range_value != 0:
colored_label = (colored_label - min_value) / range_value
if scale_values:
colored_label = 255. * colored_label
pil_image = img.fromarray(colored_label.astype(dtype=np.uint8))
with tf.gfile.Open('%s/%s.png' % (save_dir, filename), mode='w') as f:
pil_image.save(f, 'PNG')
def testUnExpectedLabelDimensionForLabelToPASCALColorImage(self):
"""Raise ValueError if input dimension is not correct."""
label = np.array([120])
with self.assertRaises(ValueError):
get_dataset_colormap.label_to_color_image(
label, get_dataset_colormap.get_pascal_name())
def testUnExpectedLabelValueForLabelToPASCALColorImage(self):
"""Raise ValueError when input value exceeds range."""
label = np.array([[120], [300]])
with self.assertRaises(ValueError):
get_dataset_colormap.label_to_color_image(
label, get_dataset_colormap.get_pascal_name())
def testUnExpectedLabelDimensionForLabelToPASCALColorImage(self):
"""Raise ValueError if input dimension is not correct."""
label = np.array([120])
with self.assertRaises(ValueError):
get_dataset_colormap.label_to_color_image(
label, get_dataset_colormap.get_pascal_name())
def testUnExpectedLabelValueForLabelToPASCALColorImage(self):
"""Raise ValueError when input value exceeds range."""
label = np.array([[120], [300]])
with self.assertRaises(ValueError):
get_dataset_colormap.label_to_color_image(
label, get_dataset_colormap.get_pascal_name())
## Helper methods
LABEL_NAMES = np.asarray([
'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
'train', 'tv'
])
FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
FULL_COLOR_MAP = get_dataset_colormap.label_to_color_image(FULL_LABEL_MAP)
TEST_IMAGE_PATHS = [f for f in glob.glob(PATH_TO_TEST_IMAGES_DIR + "/**/*.jpg", recursive=True)]
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
resized_im, seg_map = model.run(image)
seg_image = get_dataset_colormap.label_to_color_image(
seg_map, get_dataset_colormap.get_pascal_name()).astype(np.uint8)
basename = image_path.replace(PATH_TO_TEST_IMAGES_DIR,"")
open_cv_image = np.array(seg_image)
open_cv_image = open_cv_image[:, :, ::-1].copy()
os.makedirs(OUTPUT_PATH + basename.replace(ntpath.basename(image_path), ""),
exist_ok=True)
cv2.imwrite(OUTPUT_PATH + basename, open_cv_image)