def test_extract_points_from_polygon(tanzania_example_image):
"""Test a polygon point extraction.
Within a 1000x1000 pixel original image, consider 500x500 tiles, and more
specifically the right-bottom tile. One wants to retrieve a triangle
whose coordinates are as follows:
- (image_width/2, image_height/2)
- (image_width/2, image_height)
- (image_width*3/4, image_height/2)
The point coordinate representation must be inverted between georeferenced
points and 2D-'numpy.array' pixel points: in the latter, the first
(resp. the second) dimension corresponds to rows (resp.columns).
"""
ds = gdal.Open(str(tanzania_example_image))
geofeatures = get_image_features(ds)
min_x = min_y = 500
x1 = geofeatures["west"] + (geofeatures["east"] - geofeatures["west"]) / 2
y1 = (geofeatures["south"] +
(geofeatures["north"] - geofeatures["south"]) / 2)
x2 = x1 + (geofeatures["east"] - x1) / 2
y2 = geofeatures["south"]
polygon = Polygon(((x1, y1), (x1, y2), (x2, y1), (x1, y1)))
points = extract_points_from_polygon(polygon, geofeatures, min_x, min_y)
expected_points = np.array([[0, 0], [500, 0], [0, 250], [0, 0]])
assert np.all(points == expected_points)
def test_extract_tile_items(tanzania_example_image, tanzania_example_labels):
"""Test the extraction of polygons that overlap a given squared tile, based
on a reference test image (see 'tests/data/tanzania/input/training/').
The tests check that:
- the example image contains 7 valid items
- the items are 'Polygon' (in opposition to 'MultiPolygon')
- the item union is contained into the tile footprint (overlapping items
are cutted out so as out-of-image parts are removed)
"""
ds = gdal.Open(str(tanzania_example_image))
geofeatures = get_image_features(ds)
labels = gpd.read_file(tanzania_example_labels)
labels = labels.loc[~labels.geometry.isna(), ["condition", "geometry"]]
none_mask = [lc is None for lc in labels.condition]
labels.loc[none_mask, "condition"] = "Complete"
tile_items = extract_tile_items(geofeatures, labels, 0, 0, 1000, 1000)
expected_items = 7
assert tile_items.shape[0] == expected_items
assert np.all([geom.is_valid for geom in tile_items["geometry"]])
assert np.all(
[geom.geom_type == "Polygon" for geom in tile_items["geometry"]])
item_bounds = tile_items.unary_union.bounds
assert (item_bounds[0] >= geofeatures["west"]
and item_bounds[0] <= geofeatures["east"])
assert (item_bounds[1] >= geofeatures["south"]
and item_bounds[1] <= geofeatures["north"])
assert (item_bounds[2] >= geofeatures["west"]
and item_bounds[2] <= geofeatures["east"])
assert (item_bounds[3] >= geofeatures["south"]
and item_bounds[3] <= geofeatures["north"])
def test_extract_empty_tile_items(tanzania_example_image,
tanzania_example_labels):
"""Test the extraction of polygons that overlap a given squared tile, based
on a reference test image (see 'tests/data/tanzania/input/training/').
The tests is focused on an empty tile, that must provide an empty item set.
"""
ds = gdal.Open(str(tanzania_example_image))
geofeatures = get_image_features(ds)
labels = gpd.read_file(tanzania_example_labels)
labels = labels.loc[~labels.geometry.isna(), ["condition", "geometry"]]
none_mask = [lc is None for lc in labels.condition]
labels.loc[none_mask, "condition"] = "Complete"
empty_tile_items = extract_tile_items(geofeatures, labels, 450, 450, 100,
100)
assert empty_tile_items.shape[0] == 0
def test_square_tile_footprint(tanzania_example_image):
"""Test a tile footprint recovery, based on the reference test image (see
'tests/data/tanzania/input/training/').
The full image is considered as the tile, its bounds must equal the image
coordinates.
"""
ds = gdal.Open(str(tanzania_example_image))
geofeatures = get_image_features(ds)
min_x = min_y = 0
tile_width = ds.RasterXSize
tile_footprint = get_tile_footprint(geofeatures, min_x, min_y, tile_width)
assert tile_footprint.is_valid
tile_bounds = tile_footprint.bounds
assert geofeatures["north"] in tile_bounds
assert geofeatures["south"] in tile_bounds
assert geofeatures["east"] in tile_bounds
assert geofeatures["west"] in tile_bounds
def test_get_image_features(tanzania_example_image):
"""Test the image geographic feature recovering:
- 'south', 'north', 'west' and 'east' are the image geographic coordinates,
hence floating numbers
- west is smaller than east
- south is smaller than north
- srid is an integer geocode
- width and height are strictly positive int, as they represent the image
size, in pixels
"""
ds = gdal.Open(str(tanzania_example_image))
geofeatures = get_image_features(ds)
assert isinstance(geofeatures["south"], float)
assert isinstance(geofeatures["north"], float)
assert isinstance(geofeatures["east"], float)
assert isinstance(geofeatures["west"], float)
assert geofeatures["west"] < geofeatures["east"]
assert geofeatures["south"] < geofeatures["north"]
assert isinstance(geofeatures["srid"], int)
assert isinstance(geofeatures["width"], int)
assert isinstance(geofeatures["height"], int)
assert geofeatures["width"] > 0
assert geofeatures["height"] > 0
def test_rectangle_tile_footprint(tanzania_example_image):
"""Test a tile footprint recovery, based on the reference test image (see
'tests/data/tanzania/input/training/').
The considered tile is the top-half of the image, its bounds must equal
the image coordinates, except the south bound that must equal the mean
between north and south coordinates.
"""
ds = gdal.Open(str(tanzania_example_image))
geofeatures = get_image_features(ds)
min_x = min_y = 0
tile_width = ds.RasterXSize
tile_height = int(ds.RasterYSize / 2)
tile_footprint = get_tile_footprint(geofeatures, min_x, min_y, tile_width,
tile_height)
assert tile_footprint.is_valid
tile_bounds = tile_footprint.bounds
tile_south = (geofeatures["south"] +
(geofeatures["north"] - geofeatures["south"]) / 2)
assert tile_south in tile_bounds
assert geofeatures["north"] in tile_bounds
assert geofeatures["east"] in tile_bounds
assert geofeatures["west"] in tile_bounds