Mercurial > repos > imgteam > points2labelimage
changeset 6:22bb32eae6a1 draft default tip
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/points2labelimage/ commit edac062b00490276ef00d094e0594abdb3a3f23c
| author | imgteam |
|---|---|
| date | Thu, 06 Nov 2025 09:59:34 +0000 |
| parents | 4a49f74a3c14 |
| children | |
| files | REAME.md creators.xml points2label.py points2label.xml test-data/input5.tsv test-data/input6.tsv test-data/output2.tiff test-data/output4.tiff test-data/output5.tiff test-data/output6.tiff test-data/output7.tiff test-data/output8.tiff test-data/rois-illegal1.geojson test-data/rois-illegal2.geojson test-data/rois-noname.geojson test-data/rois.geojson test_utils.py |
| diffstat | 17 files changed, 404 insertions(+), 333 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/REAME.md Thu Nov 06 09:59:34 2025 +0000 @@ -0,0 +1,5 @@ +# points2labelimage + +Unit-testable functions are implemented in utils.py. + +Run `python -m unittest` for the unit tests (for regression testing).
--- a/creators.xml Mon May 12 14:01:26 2025 +0000 +++ b/creators.xml Thu Nov 06 09:59:34 2025 +0000 @@ -30,4 +30,9 @@ <yield/> </xml> + <xml name="creators/tuncK"> + <person givenName="Tunc" familyName="Kayikcioglu"/> + <yield/> + </xml> + </macros>
--- a/points2label.py Mon May 12 14:01:26 2025 +0000 +++ b/points2label.py Thu Nov 06 09:59:34 2025 +0000 @@ -1,12 +1,11 @@ import argparse import json -import os import warnings from typing import ( + Any, Dict, - List, + Optional, Tuple, - Union, ) import giatools.pandas @@ -14,219 +13,276 @@ import numpy.typing as npt import pandas as pd import scipy.ndimage as ndi +import skimage.draw import skimage.io import skimage.segmentation -def is_rectangular(points: Union[List[Tuple[float, float]], npt.NDArray]) -> bool: - points = np.asarray(points) - - # Rectangle must have 5 points, where first and last are identical - if len(points) != 5 or not (points[0] == points[-1]).all(): - return False - - # Check that all edges align with the axes - edges = points[1:] - points[:-1] - if any((edge == 0).sum() != 1 for edge in edges): - return False - - # All checks have passed, the geometry is rectangular - return True +def get_list_depth(nested_list: Any) -> int: + if isinstance(nested_list, list): + if len(nested_list) > 0: + return 1 + max(map(get_list_depth, nested_list)) + else: + return 1 + else: + return 0 -def geojson_to_tabular(geojson: Dict): - rows = [] - labels = [] - for feature in geojson['features']: - assert feature['geometry']['type'].lower() == 'polygon', ( - f'Unsupported geometry type: "{feature["geometry"]["type"]}"' - ) - coords = feature['geometry']['coordinates'][0] +class AutoLabel: + """ + Creates a sequence of unique labels (non-negative values). + """ - # Properties and name (label) are optional - try: - label = feature['properties']['name'] - except KeyError: - label = max(labels, default=0) + 1 - labels.append(label) + def __init__(self, reserved_labels): + self.reserved_labels = reserved_labels + self.next_autolabel = 0 - # Read geometry - xs = [pt[0] for pt in coords] - ys = [pt[1] for pt in coords] + def next(self): + """ + Retrieve the next auto-label (post-increment). + """ + # Fast-forward `next_autolabel` to the first free label + while self.next_autolabel in self.reserved_labels: + self.next_autolabel += 1 - x = min(xs) - y = min(ys) + # Return the free label, then advance `next_autolabel` + try: + return self.next_autolabel + finally: + self.next_autolabel += 1 - width = max(xs) + 1 - x - height = max(ys) + 1 - y - - # Validate geometry (must be rectangular) - assert is_rectangular(list(zip(xs, ys))) - # Append the rectangle - rows.append({ - 'pos_x': x, - 'pos_y': y, - 'width': width, - 'height': height, - 'label': label, - }) - df = pd.DataFrame(rows) - point_file = './point_file.tabular' - df.to_csv(point_file, sep='\t', index=False) - return point_file +def get_feature_label(feature: Dict) -> Optional[int]: + """ + Get the label of a GeoJSON feature, or `None` if there is no proper label. + """ + label = feature.get('properties', {}).get('name', None) + if label is None: + return None + + # If the `label` is given as a string, try to parse as integer + if isinstance(label, str): + try: + label = int(label) + except ValueError: + pass + + # Finally, if `label` is an integer, only use it if it is non-negative + if isinstance(label, int) and label >= 0: + return label + else: + return None -def rasterize(point_file, out_file, shape, has_header=False, swap_xy=False, bg_value=0, fg_value=None): +def rasterize( + geojson: Dict, + shape: Tuple[int, int], + bg_value: int = 0, + fg_value: Optional[int] = None, +) -> npt.NDArray: + """ + Rasterize GeoJSON into a pixel image, that is returned as a NumPy array. + """ - img = np.full(shape, dtype=np.uint16, fill_value=bg_value) - if os.path.exists(point_file) and os.path.getsize(point_file) > 0: + # Determine which labels are reserved (not used by auto-label) + reserved_labels = [bg_value] + if fg_value is None: + for feature in geojson['features']: + label = get_feature_label(feature) + if label is not None: + reserved_labels.append(label) - # Read the tabular file with information from the header - if has_header: - df = pd.read_csv(point_file, delimiter='\t') + # Convert `reserved_labels` into a `set` for faster look-ups + reserved_labels = frozenset(reserved_labels) + + # Define routine to retrieve the next auto-label + autolabel = AutoLabel(reserved_labels) - pos_x_column = giatools.pandas.find_column(df, ['pos_x', 'POS_X']) - pos_y_column = giatools.pandas.find_column(df, ['pos_y', 'POS_Y']) - pos_x_list = df[pos_x_column].round().astype(int) - pos_y_list = df[pos_y_column].round().astype(int) - assert len(pos_x_list) == len(pos_y_list) + # Rasterize the image + img = np.full(shape, dtype=np.uint16, fill_value=bg_value) + for feature in geojson['features']: + geom_type = feature['geometry']['type'].lower() + coords = feature['geometry']['coordinates'] + + # Rasterize a `mask` separately for each feature + if geom_type == 'polygon': + + # Normalization: Let there always be a list of polygons + if get_list_depth(coords) == 2: + coords = [coords] - try: - radius_column = giatools.pandas.find_column(df, ['radius', 'RADIUS']) - radius_list = df[radius_column] - assert len(pos_x_list) == len(radius_list) - except KeyError: - radius_list = [0] * len(pos_x_list) + # Rasterize each polygon separately, then join via XOR + mask = np.zeros(shape, dtype=bool) + for polygon_coords in coords: + polygon_mask = skimage.draw.polygon2mask( + shape, + [point[::-1] for point in polygon_coords], + ) + mask = np.logical_xor(mask, polygon_mask) + + elif geom_type == 'point': + mask = np.zeros(shape, dtype=bool) + mask[coords[1], coords[0]] = True + radius = feature.get('properties', {}).get('radius', 0) + if radius > 0: + mask = (ndi.distance_transform_edt(~mask) <= radius) + + else: + raise ValueError( + f'Unsupported geometry type: "{feature["geometry"]["type"]}"', + ) - try: - width_column = giatools.pandas.find_column(df, ['width', 'WIDTH']) - height_column = giatools.pandas.find_column(df, ['height', 'HEIGHT']) - width_list = df[width_column] - height_list = df[height_column] - assert len(pos_x_list) == len(width_list) - assert len(pos_x_list) == len(height_list) - except KeyError: - width_list = [0] * len(pos_x_list) - height_list = [0] * len(pos_x_list) + # Determine the `label` for the current `mask` + if fg_value is None: + label = get_feature_label(feature) + if label is None: + label = autolabel.next() + else: + label = fg_value + + # Blend the current `mask` with the rasterized image + img[mask] = label + + # Return the rasterized image + return img + + +def convert_tabular_to_geojson( + tabular_file: str, + has_header: bool, +) -> dict: + """ + Read a tabular file and convert it to GeoJSON. + + The GeoJSON data is returned as a dictionary. + """ + + # Read the tabular file with information from the header + if has_header: + df = pd.read_csv(tabular_file, delimiter='\t') - try: - label_column = giatools.pandas.find_column(df, ['label', 'LABEL']) - label_list = df[label_column] - assert len(pos_x_list) == len(label_list) - except KeyError: - label_list = list(range(1, len(pos_x_list) + 1)) + pos_x_column = giatools.pandas.find_column(df, ['pos_x', 'POS_X']) + pos_y_column = giatools.pandas.find_column(df, ['pos_y', 'POS_Y']) + pos_x_list = df[pos_x_column].round().astype(int) + pos_y_list = df[pos_y_column].round().astype(int) + assert len(pos_x_list) == len(pos_y_list) + + try: + radius_column = giatools.pandas.find_column(df, ['radius', 'RADIUS']) + radius_list = df[radius_column] + assert len(pos_x_list) == len(radius_list) + except KeyError: + radius_list = [0] * len(pos_x_list) - # Read the tabular file without header - else: - df = pd.read_csv(point_file, header=None, delimiter='\t') - pos_x_list = df[0].round().astype(int) - pos_y_list = df[1].round().astype(int) - assert len(pos_x_list) == len(pos_y_list) - radius_list, width_list, height_list = [[0] * len(pos_x_list)] * 3 + try: + width_column = giatools.pandas.find_column(df, ['width', 'WIDTH']) + height_column = giatools.pandas.find_column(df, ['height', 'HEIGHT']) + width_list = df[width_column] + height_list = df[height_column] + assert len(pos_x_list) == len(width_list) + assert len(pos_x_list) == len(height_list) + except KeyError: + width_list = [0] * len(pos_x_list) + height_list = [0] * len(pos_x_list) + + try: + label_column = giatools.pandas.find_column(df, ['label', 'LABEL']) + label_list = df[label_column] + assert len(pos_x_list) == len(label_list) + except KeyError: label_list = list(range(1, len(pos_x_list) + 1)) - # Optionally swap the coordinates - if swap_xy: - pos_x_list, pos_y_list = pos_y_list, pos_x_list - - # Perform the rasterization - for y, x, radius, width, height, label in zip( - pos_y_list, pos_x_list, radius_list, width_list, height_list, label_list, - ): - if fg_value is not None: - label = fg_value - - if y < 0 or x < 0 or y >= shape[0] or x >= shape[1]: - raise IndexError(f'The point x={x}, y={y} exceeds the bounds of the image (width: {shape[1]}, height: {shape[0]})') + # Read the tabular file without header + else: + df = pd.read_csv(tabular_file, header=None, delimiter='\t') + pos_x_list = df[0].round().astype(int) + pos_y_list = df[1].round().astype(int) + assert len(pos_x_list) == len(pos_y_list) + radius_list, width_list, height_list = [[0] * len(pos_x_list)] * 3 + label_list = list(range(1, len(pos_x_list) + 1)) - # Rasterize circle and distribute overlapping image area - # Rasterize primitive geometry - if radius > 0 or (width > 0 and height > 0): - - # Rasterize circle - if radius > 0: - mask = np.ones(shape, dtype=bool) - mask[y, x] = False - mask = (ndi.distance_transform_edt(mask) <= radius) - else: - mask = np.zeros(shape, dtype=bool) - - # Rasterize rectangle - if width > 0 and height > 0: - mask[ - y:min(shape[0], y + width), - x:min(shape[1], x + height) - ] = True + # Convert to GeoJSON + features = [] + geojson = { + 'type': 'FeatureCollection', + 'features': features, + } + for y, x, radius, width, height, label in zip( + pos_y_list, pos_x_list, radius_list, width_list, height_list, label_list, + ): + if radius > 0 and width > 0 and height > 0: + raise ValueError('Ambiguous shape type (circle or rectangle)') - # Compute the overlap (pretend there is none if the rasterization is binary) - if fg_value is None: - overlap = np.logical_and(img > 0, mask) - else: - overlap = np.zeros(shape, dtype=bool) + # Create a rectangle + if width > 0 and height > 0: + geom_type = 'Polygon' + coords = [ + [x, y], + [x + width - 1, y], + [x + width - 1, y + height - 1], + [x, y + height - 1], + ] - # Rasterize the part of the circle which is disjoint from other foreground. - # - # In the current implementation, the result depends on the order of the rasterized circles if somewhere - # more than two circles overlap. This is probably negligable for most applications. To achieve results - # that are invariant to the order, first all circles would need to be rasterized independently, and - # then blended together. This, however, would either strongly increase the memory consumption, or - # require a more complex implementation which exploits the sparsity of the rasterized masks. - # - disjoint_mask = np.logical_xor(mask, overlap) - if disjoint_mask.any(): - img[disjoint_mask] = label + # Create a point or circle + else: + geom_type = 'Point' + coords = [x, y] - # Distribute the remaining part of the circle - if overlap.any(): - dist = ndi.distance_transform_edt(overlap) - foreground = (img > 0) - img[overlap] = 0 - img = skimage.segmentation.watershed(dist, img, mask=foreground) + # Create a GeoJSON feature + feature = { + 'type': 'Feature', + 'geometry': { + 'type': geom_type, + 'coordinates': coords, + }, + 'properties': { + 'name': label, + }, + } + if radius > 0: + feature['properties']['radius'] = radius + feature['properties']['subType'] = 'Circle' + features.append(feature) - # Rasterize point (there is no overlapping area to be distributed) - else: - img[y, x] = label - - else: - raise Exception('{} is empty or does not exist.'.format(point_file)) # appropriate built-in error? - - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - skimage.io.imsave(out_file, img, plugin='tifffile') # otherwise we get problems with the .dat extension + # Return the GeoJSON object + return geojson if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('in_file', type=argparse.FileType('r'), help='Input point file or GeoJSON file') - parser.add_argument('out_file', type=str, help='out file (TIFF)') - parser.add_argument('shapex', type=int, help='shapex') - parser.add_argument('shapey', type=int, help='shapey') - parser.add_argument('--has_header', dest='has_header', default=False, help='set True if point file has header') + parser.add_argument('in_ext', type=str, help='Input file format') + parser.add_argument('in_file', type=str, help='Input file path (tabular or GeoJSON)') + parser.add_argument('out_file', type=str, help='Output file path (TIFF)') + parser.add_argument('shapex', type=int, help='Output image width') + parser.add_argument('shapey', type=int, help='Output image height') + parser.add_argument('--has_header', dest='has_header', default=False, help='Set True if tabular file has a header') parser.add_argument('--swap_xy', dest='swap_xy', default=False, help='Swap X and Y coordinates') parser.add_argument('--binary', dest='binary', default=False, help='Produce binary image') - args = parser.parse_args() - point_file = args.in_file.name - has_header = args.has_header + # Validate command-line arguments + assert args.in_ext in ('tabular', 'geojson'), ( + f'Unexpected input file format: {args.in_ext}' + ) - try: - with open(args.in_file.name, 'r') as f: - content = json.load(f) - if isinstance(content, dict) and content.get('type') == 'FeatureCollection' and isinstance(content.get('features'), list): - point_file = geojson_to_tabular(content) - has_header = True # header included in the converted file - else: - raise ValueError('Input is a JSON file but not a valid GeoJSON file') - except json.JSONDecodeError: - print('Input is not a valid JSON file. Assuming it a tabular file.') + # Load the GeoJSON data (if the input file is tabular, convert to GeoJSON) + if args.in_ext == 'tabular': + geojson = convert_tabular_to_geojson(args.in_file, args.has_header) + else: + with open(args.in_file) as f: + geojson = json.load(f) - rasterize( - point_file, - args.out_file, - (args.shapey, args.shapex), - has_header=has_header, - swap_xy=args.swap_xy, + # Rasterize the image from GeoJSON + shape = (args.shapey, args.shapex) + img = rasterize( + geojson, + shape if not args.swap_xy else shape[::-1], fg_value=0xffff if args.binary else None, ) + if args.swap_xy: + img = img.T + + # Write the rasterized image as TIFF + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + skimage.io.imsave(args.out_file, img, plugin='tifffile') # otherwise we get problems with the .dat extension
--- a/points2label.xml Mon May 12 14:01:26 2025 +0000 +++ b/points2label.xml Thu Nov 06 09:59:34 2025 +0000 @@ -1,10 +1,9 @@ -<tool id="ip_points_to_label" name="Convert coordinates to label map" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05"> +<tool id="ip_points_to_label" name="Convert coordinates to label map" version="@TOOL_VERSION@" profile="20.05"> <description></description> <macros> <import>creators.xml</import> <import>tests.xml</import> - <token name="@TOOL_VERSION@">0.4.1</token> - <token name="@VERSION_SUFFIX@">1</token> + <token name="@TOOL_VERSION@">0.5.0</token> </macros> <creator> <expand macro="creators/bmcv" /> @@ -16,15 +15,16 @@ <xref type="bio.tools">galaxy_image_analysis</xref> </xrefs> <requirements> - <requirement type="package" version="0.21">scikit-image</requirement> - <requirement type="package" version="1.26.4">numpy</requirement> - <requirement type="package" version="1.2.4">pandas</requirement> - <requirement type="package" version="2024.6.18">tifffile</requirement> + <requirement type="package" version="0.25.2">scikit-image</requirement> + <requirement type="package" version="2.3.4">numpy</requirement> + <requirement type="package" version="2.3.3">pandas</requirement> + <requirement type="package" version="2025.10.16">tifffile</requirement> <requirement type="package" version="0.3.1">giatools</requirement> </requirements> <command detect_errors="aggressive"><![CDATA[ python '$__tool_directory__/points2label.py' + '$input.file_ext' '$input' '$output' $shapex @@ -35,12 +35,12 @@ ]]></command> <inputs> - <param name="input" type="data" format="tabular,geojson" label="List of points in tabular or geojson format"/> - <param name="shapex" type="integer" value="500" min="1" label="Width of output image" /> - <param name="shapey" type="integer" value="500" min="1" label="Height of output image" /> - <param name="has_header" type="boolean" checked="true" truevalue="--has_header True" falsevalue="" optional="true" label="Tabular list of points has header" help="Turning this off will ignore the first row and assume that the X and Y coordinates correspond to the first and second column, respectively. Ignored, if GeoJSON is used for input." /> - <param name="swap_xy" type="boolean" checked="false" falsevalue="" truevalue="--swap_xy True" optional="true" label="Swap X and Y coordinates" help="Swap the X and Y coordinates, regardless of whether the tabular list has a header or not." /> - <param name="binary" type="boolean" checked="false" truevalue="--binary True" falsevalue="" optional="true" label="Produce binary image" help="Use the same label for all points (65535)." /> + <param name="input" type="data" format="tabular,geojson" label="Shapes to be rasterized"/> + <param name="shapex" type="integer" value="500" min="1" label="Width of the output image" /> + <param name="shapey" type="integer" value="500" min="1" label="Height of the output image" /> + <param name="has_header" type="boolean" checked="true" truevalue="--has_header True" falsevalue="" optional="true" label="Tabular list of shapes has header" help="Only used if the input is a tabular file (ignored for GeoJSON). Turning this off will interpret the tabular file as a list of points, where the X and Y coordinates correspond to the first and second column, respectively." /> + <param name="swap_xy" type="boolean" checked="false" falsevalue="" truevalue="--swap_xy True" optional="true" label="Swap X and Y coordinates" help="Swap the X and Y coordinates before rasterization. The width and height of the output image is not affected." /> + <param name="binary" type="boolean" checked="false" truevalue="--binary True" falsevalue="" optional="true" label="Produce binary image" help="Use the same label for all shapes (65535)." /> </inputs> <outputs> <data name="output" format="tiff" /> @@ -106,7 +106,29 @@ <param name="binary" value="false" /> <expand macro="tests/label_image_diff" name="output" value="output4.tiff" ftype="tiff" /> </test> - <!-- Labeled / ROI / Rectangles --> + <!-- Labeled / TSV with header / TSV with labels / Circles + Rectangles --> + <test> + <param name="input" value="input5.tsv" /> + <param name="shapex" value="200" /> + <param name="shapey" value="100" /> + <param name="has_header" value="true" /> + <param name="swap_xy" value="false" /> + <param name="binary" value="false" /> + <expand macro="tests/label_image_diff" name="output" value="output8.tiff" ftype="tiff" /> + </test> + <test expect_failure="true"> + <!-- The test should fail because simultaneous usage of radius and width/height on a row is forbidden --> + <param name="input" value="input6.tsv" /> + <param name="shapex" value="200" /> + <param name="shapey" value="100" /> + <param name="has_header" value="true" /> + <param name="swap_xy" value="false" /> + <param name="binary" value="false" /> + <assert_stderr> + <has_text text="Ambiguous shape type (circle or rectangle)" /> + </assert_stderr> + </test> + <!-- Labeled / GeoJSON / mixed name and no-name --> <test> <param name="input" value="rois.geojson" /> <param name="shapex" value="300" /> @@ -117,15 +139,6 @@ <expand macro="tests/label_image_diff" name="output" value="output5.tiff" ftype="tiff" /> </test> <test> - <param name="input" value="rois-noname.geojson" /> - <param name="shapex" value="300" /> - <param name="shapey" value="300" /> - <param name="has_header" value="false" /> - <param name="swap_xy" value="false" /> - <param name="binary" value="false" /> - <expand macro="tests/label_image_diff" name="output" value="output5.tiff" ftype="tiff" /> - </test> - <test> <param name="input" value="rois.geojson" /> <param name="shapex" value="300" /> <param name="shapey" value="300" /> @@ -134,48 +147,57 @@ <param name="binary" value="false" /> <expand macro="tests/label_image_diff" name="output" value="output6.tiff" ftype="tiff" /> </test> - <test expect_failure="true"> - <!-- The test should fail because the GeoJSON is not rectangular --> - <param name="input" value="rois-illegal1.geojson" /> + <!-- Labeled / GeoJSON / no-name --> + <test> + <param name="input" value="rois-noname.geojson" /> <param name="shapex" value="300" /> <param name="shapey" value="300" /> <param name="has_header" value="false" /> <param name="swap_xy" value="false" /> <param name="binary" value="false" /> + <expand macro="tests/label_image_diff" name="output" value="output7.tiff" ftype="tiff" /> </test> <test expect_failure="true"> - <!-- The test should fail because the GeoJSON is not of `Polygon` type --> + <!-- The test should fail because the GeoJSON uses the `LineString` primitive type that is not supported yet --> <param name="input" value="rois-illegal2.geojson" /> <param name="shapex" value="300" /> <param name="shapey" value="300" /> <param name="has_header" value="false" /> <param name="swap_xy" value="false" /> <param name="binary" value="false" /> + <assert_stderr> + <has_text text='Unsupported geometry type: "LineString"' /> + </assert_stderr> </test> </tests> <help> - **Converts a list of points to a label map by rasterizing the coordinates.** + **Converts a list of shapes to a label map via rasterization.** - The created image is a single-channel image with 16 bits per pixel (unsigned integer). The points are - rasterized with unique labels, or the value 65535 (white) for binary image output. Pixels not corresponding to - any points in the tabular file are assigned the value 0 (black). + The created image is a single-channel image with 16 bits per pixel (unsigned integers). The shapes are + rasterized with unique labels, explicitly given labels (custom), or the value 65535 (white) for binary image + output. Pixels not corresponding to any shapes are assigned the value 0 (black). - **Using a tabular input file:** The tabular list of points can either be header-less. In this case, the first - and second columns are expected to be the X and Y coordinates, respectively. Otherwise, if a header is present, - it is searched for the following column names: + **Using a GeoJSON input file (recommended).** Only features (shape specifications) of `Polygon` and `Point` + type are supported. In conjunction with the `radius` property, a `Point` type feature can also be used to + represent circles. Custom labels can be encoded in the `name` property (must be numeric and integer), and + different features are allowed to use the same labels. - - ``pos_x`` or ``POS_X``: This column corresponds to the X coordinates. - - ``pos_y`` or ``POS_Y``: This column corresponds to the Y coordinates. - - If a ``radius`` or ``RADIUS`` column is present, then the points will be rasterized as circles of the - corresponding radii. - - If ``width`` or ``WIDTH`` and ``height`` or ``HEIGHT`` columns are present, then the points will be rasterized - as rectangles of the corresponding size. + **Using a tabular input file (deprecated).** The tabular list of points can either be header-less. In this + case, the first and second columns are expected to be the X and Y coordinates, respectively, and each row + corresponds to a single point. Otherwise, if a header is present, it is searched for the following column + names: + + - Column ``pos_x`` or ``POS_X`` is mandatory and corresponds to the X coordinates. + - Column ``pos_y`` or ``POS_Y`` is mandatory and corresponds to the Y coordinates. + - If a ``radius`` or ``RADIUS`` column is present and the value in a row is positive, then the row will be + rasterized as a circle of the corresponding size, centered at the given X and Y coordinates. + - If ``width`` or ``WIDTH`` and ``height`` or ``HEIGHT`` columns are present and the values in a row are + positive, then the rows will be rasterized as rectangles of the corresponding size, with the upper-left + corner given by the X and Y coordinates. - If a ``label`` or ``LABEL`` column is present, then the corresponding labels will be used for rasterization - (unless "Produce binary image" is activated). Different points are allowed to use the same label. If used, the - label must be numeric and integer. - - **Using a GeoJSON input file:** Only rectangular specifications of `Polygon` type geometry is supported. + (unless "Produce binary image" is activated). Different rows are allowed to use the same label. If used, + the label must be numeric and integer. </help> <citations>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input5.tsv Thu Nov 06 09:59:34 2025 +0000 @@ -0,0 +1,4 @@ +pos_x pos_y radius width height label +20 20 10 0 0 1 +50 50 0 30 40 2 +150 50 0 60 40 3
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input6.tsv Thu Nov 06 09:59:34 2025 +0000 @@ -0,0 +1,4 @@ +pos_x pos_y radius width height label +20 20 10 0 0 1 +50 50 10 30 40 2 +150 50 0 60 40 3
--- a/test-data/rois-illegal1.geojson Mon May 12 14:01:26 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,40 +0,0 @@ -{ - "type": "FeatureCollection", - "features": [ - { - "type": "Feature", - "id": "a5d9de43-1a4a-4194-b06d-a6c6d0f81f91", - "geometry": { - "type": "Polygon", - "coordinates": [ - [ - [ - 201, - 48 - ], - [ - 292, - 48 - ], - [ - 292, - 184 - ], - [ - 201, - 48 - ], - [ - 201, - 48 - ] - ] - ] - }, - "properties": { - "objectType": "annotation", - "name": "1" - } - } - ] -}
--- a/test-data/rois-illegal2.geojson Mon May 12 14:01:26 2025 +0000 +++ b/test-data/rois-illegal2.geojson Thu Nov 06 09:59:34 2025 +0000 @@ -5,29 +5,27 @@ "type": "Feature", "id": "a5d9de43-1a4a-4194-b06d-a6c6d0f81f91", "geometry": { - "type": "Point", + "type": "LineString", "coordinates": [ [ - [ - 201, - 48 - ], - [ - 292, - 48 - ], - [ - 292, - 184 - ], - [ - 201, - 184 - ], - [ - 201, - 48 - ] + 201, + 48 + ], + [ + 292, + 48 + ], + [ + 292, + 184 + ], + [ + 201, + 184 + ], + [ + 201, + 48 ] ] },
--- a/test-data/rois-noname.geojson Mon May 12 14:01:26 2025 +0000 +++ b/test-data/rois-noname.geojson Thu Nov 06 09:59:34 2025 +0000 @@ -3,6 +3,18 @@ "features": [ { "type": "Feature", + "id": "9ef0487b-29a5-4e53-8eca-5c9dbf7bfc81", + "geometry": { + "type": "Point", + "coordinates": [100, 50] + }, + "properties": { + "objectType": "annotation", + "radius": 100 + } + }, + { + "type": "Feature", "id": "9ef0487b-29a5-4e53-8eca-5c9dbf7bfc80", "geometry": { "type": "Polygon", @@ -17,11 +29,11 @@ 149 ], [ - 183, + 163, 275 ], [ - 124, + 104, 275 ], [ @@ -64,6 +76,9 @@ ] ] ] + }, + "properties": { + "objectType": "annotation" } }, { @@ -73,31 +88,18 @@ "type": "Polygon", "coordinates": [ [ - [ - 151, - 95 - ], - [ - 260, - 95 - ], - [ - 260, - 162 - ], - [ - 151, - 162 - ], - [ - 151, - 95 - ] + [151, 95], + [260, 95], + [260, 162], + [151, 162] + ], + [ + [156, 100], + [255, 100], + [255, 157], + [156, 157] ] ] - }, - "properties": { - "objectType": "annotation" } } ]
--- a/test-data/rois.geojson Mon May 12 14:01:26 2025 +0000 +++ b/test-data/rois.geojson Thu Nov 06 09:59:34 2025 +0000 @@ -3,6 +3,18 @@ "features": [ { "type": "Feature", + "id": "9ef0487b-29a5-4e53-8eca-5c9dbf7bfc81", + "geometry": { + "type": "Point", + "coordinates": [100, 50] + }, + "properties": { + "objectType": "annotation", + "radius": 100 + } + }, + { + "type": "Feature", "id": "9ef0487b-29a5-4e53-8eca-5c9dbf7bfc80", "geometry": { "type": "Polygon", @@ -17,11 +29,11 @@ 149 ], [ - 183, + 163, 275 ], [ - 124, + 104, 275 ], [ @@ -78,26 +90,16 @@ "type": "Polygon", "coordinates": [ [ - [ - 151, - 95 - ], - [ - 260, - 95 - ], - [ - 260, - 162 - ], - [ - 151, - 162 - ], - [ - 151, - 95 - ] + [151, 95], + [260, 95], + [260, 162], + [151, 162] + ], + [ + [156, 100], + [255, 100], + [255, 157], + [156, 157] ] ] }, @@ -107,4 +109,4 @@ } } ] -} \ No newline at end of file +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test_utils.py Thu Nov 06 09:59:34 2025 +0000 @@ -0,0 +1,13 @@ +import unittest + +import points2label + + +class get_list_depth(unittest.TestCase): + + def test(self): + self.assertEqual(points2label.get_list_depth(1234), 0) + self.assertEqual(points2label.get_list_depth([]), 1) + self.assertEqual(points2label.get_list_depth([1, 2, 3]), 1) + self.assertEqual(points2label.get_list_depth([1, [2, 3]]), 2) + self.assertEqual(points2label.get_list_depth([[1], [2, 3]]), 2)