def test_vectorize_mask(tanzania_raw_image_size):
"""Test the mask vectorization operation, that transform raster mask into a
MultiPolygon.
Test a simple case with two expected polygons. The considered function uses
OpenCV library to find polygon contours, and some approximated methods are
implied. Hence exact polygon vertice coordinates are not tested.
"""
label_dicts = [{
"id": 1,
"color": [50, 200, 50]
}, {
"id": 2,
"color": [200, 50, 50]
}]
mask = np.zeros([tanzania_raw_image_size, tanzania_raw_image_size],
dtype=np.uint8)
data = np.zeros([tanzania_raw_image_size, tanzania_raw_image_size, 3],
dtype=np.uint8)
empty_labels, empty_multipolygon = vectorize_mask(mask, data, label_dicts)
assert len(empty_labels) == 0
assert len(empty_multipolygon) == 0
x1, y1 = 100, 200
x2, y2 = 300, 400
x3, y3 = 300, 600
x4, y4 = 700, 900
mask[y1:y2, x1:x2] = label_dicts[0]["id"]
mask[y3:y4, x3:x4] = label_dicts[1]["id"]
data[y1:y2, x1:x2] = label_dicts[0]["color"]
data[y3:y4, x3:x4] = label_dicts[1]["color"]
labels, multipolygon = vectorize_mask(mask, data, label_dicts)
assert len(labels) == 2
assert np.sum(labels == 1) == 1
assert np.sum(labels == 2) == 1
assert len(multipolygon) == 2
def test_vectorize_mask(tanzania_raw_image_size):
"""Test the mask vectorization operation, that transform raster mask into a
MultiPolygon.
Test a simple case with two expected polygons. The considered function uses
OpenCV library to find polygon contours, and some approximated methods are
implied. Hence exact polygon vertice coordinates are not tested.
"""
mask = np.zeros([tanzania_raw_image_size, tanzania_raw_image_size],
dtype=np.uint8)
empty_multipolygon = vectorize_mask(mask)
assert len(empty_multipolygon) == 0
x1, y1 = 100, 200
x2, y2 = 300, 400
x3, y3 = 300, 600
x4, y4 = 700, 900
mask[y1:y2, x1:x2] = 1
mask[y3:y4, x3:x4] = 1
multipolygon = vectorize_mask(mask)
assert len(multipolygon) == 2
def main(args):
features = get_image_features(args.datapath, args.dataset,
args.image_basename)
img_width, img_height = features["width"], features["height"]
logger.info("Raw image size: %s, %s" % (img_width, img_height))
image_paths = get_image_paths(args.datapath, args.dataset, args.image_size,
args.image_basename)
logger.info("The image will be splitted into %s tiles" % len(image_paths))
images = extract_images(image_paths)
coordinates = extract_coordinates_from_filenames(image_paths)
labels = get_labels(args.datapath, args.dataset, args.image_size)
model = get_trained_model(args.datapath, args.dataset, args.image_size,
len(labels))
data = build_full_labelled_image(
images,
coordinates,
model,
args.image_size,
img_width,
img_height,
args.batch_size,
)
logger.info("Labelled image dimension: %s, %s" %
(data.shape[0], data.shape[1]))
colored_data = assign_label_colors(data, labels)
colored_data = draw_grid(colored_data, img_width, img_height,
args.image_size)
predicted_label_folder = os.path.join(args.datapath, args.dataset,
"output", "semseg",
"predicted_labels")
os.makedirs(predicted_label_folder, exist_ok=True)
predicted_label_file = os.path.join(
predicted_label_folder,
args.image_basename + "_" + str(args.image_size) + ".png",
)
Image.fromarray(colored_data).save(predicted_label_file)
vectorized_data = geometries.vectorize_mask(data)
gdf = gpd.GeoDataFrame({"geometry": vectorized_data})
predicted_geom_folder = os.path.join(
args.datapath,
args.dataset,
"output",
"semseg",
"predicted_geometries",
)
os.makedirs(predicted_geom_folder, exist_ok=True)
predicted_geom_file = os.path.join(
predicted_geom_folder,
args.image_basename + "_" + str(args.image_size) + ".geojson",
)
if not os.path.isfile(predicted_geom_file):
gdf.to_file(predicted_geom_file, driver="GeoJSON")
rasterized_data = geometries.rasterize_polygons(vectorized_data,
img_height, img_width)
colored_raster_data = assign_label_colors(rasterized_data, labels)
colored_raster_data = draw_grid(colored_raster_data, img_width, img_height,
args.image_size)
predicted_raster_folder = os.path.join(
args.datapath,
args.dataset,
"output",
"semseg",
"predicted_rasters",
)
os.makedirs(predicted_raster_folder, exist_ok=True)
predicted_raster_file = os.path.join(
predicted_raster_folder,
args.image_basename + "_" + str(args.image_size) + ".png",
)
Image.fromarray(colored_raster_data).save(predicted_raster_file)
def main(args):
logger.info("Postprocess %s...", args.image_basename)
features = get_image_features(
args.datapath, args.dataset, args.image_basename
)
img_width, img_height = features["width"], features["height"]
logger.info("Raw image size: %s, %s" % (img_width, img_height))
prepro_folder = utils.prepare_preprocessed_folder(args.datapath, args.dataset, args.image_size)
image_paths = get_image_paths(prepro_folder["testing"], args.image_basename)
logger.info("The image will be splitted into %s tiles" % len(image_paths))
images = extract_images(image_paths)
coordinates = extract_coordinates_from_filenames(image_paths)
labels = get_labels(args.datapath, args.dataset, args.image_size)
output_folder = utils.prepare_output_folder(
args.datapath, args.dataset, args.image_size, "semseg"
)
model = get_trained_model(
output_folder["best-model"], args.image_size, len(labels)
)
logger.info("Predict labels for input images...")
data = build_full_labelled_image(
images,
coordinates,
model,
args.image_size,
img_width,
img_height,
args.batch_size,
)
logger.info(
"Labelled image dimension: %s, %s" % (data.shape[0], data.shape[1])
)
colored_data = assign_label_colors(data, labels)
if args.draw_grid:
colored_data = draw_grid(
colored_data, img_width, img_height, args.image_size
)
predicted_label_file = os.path.join(
output_folder["labels"],
args.image_basename + "_" + str(args.image_size) + ".png",
)
Image.fromarray(colored_data).save(predicted_label_file)
vectorized_labels, vectorized_data = geometries.vectorize_mask(
data, colored_data, labels
)
gdf = gpd.GeoDataFrame(
{"labels": vectorized_labels, "geometry": vectorized_data}
)
predicted_geom_file = os.path.join(
output_folder["geometries"],
args.image_basename + "_" + str(args.image_size) + ".geojson",
)
if os.path.isfile(predicted_geom_file):
os.remove(predicted_geom_file)
gdf.to_file(predicted_geom_file, driver="GeoJSON")
rasterized_data = geometries.rasterize_polygons(
vectorized_data, vectorized_labels, img_height, img_width
)
colored_raster_data = assign_label_colors(rasterized_data, labels)
if args.draw_grid:
colored_raster_data = draw_grid(
colored_raster_data, img_width, img_height, args.image_size
)
predicted_raster_file = os.path.join(
output_folder["rasters"],
args.image_basename + "_" + str(args.image_size) + ".png",
)
Image.fromarray(colored_raster_data).save(predicted_raster_file)