Merge pull request #104 from developmentseed/bug/78-render-boundary

Bug/78 render boundary

Author: wronk

README.md

@@ -44,7 +44,7 @@ Before running any commands, it is necessary to create a `config.json` file to s
   - `name`: The class name
   - `filter`: A [Mapbox GL Filter](https://www.mapbox.com/mapbox-gl-js/style-spec#other-filter) to define any vector features matching this class. Filters are applied with the standalone [featureFilter](https://github.com/mapbox/mapbox-gl-js/tree/master/src/style-spec/feature_filter) from Mapbox GL JS.
   - `buffer`: The number of pixels to buffer the geometry by. This is an optional parameter to buffer the label for `object-detection` and `segmentation` tasks. Accepts any number (positive or negative). It uses [Shapely `object.buffer`](https://shapely.readthedocs.io/en/latest/manual.html#object.buffer) to calculate the final geometry. You can verify that your buffer options create the desired labels by inspecting the files created in `data/labels/` after running the `labels` command.
-- `imagery`: One of:
+- `imagery`: Label Maker expects to receive imagery tiles that are 256 x 256 pixels. You can specific the source of the imagery with one of:
   - A template string for a tiled imagery service. Note that you will generally need an API key to obtain images and there may be associated costs. The above example requires a [Mapbox access token](https://www.mapbox.com/help/how-access-tokens-work/)
   - A GeoTIFF file location. Works with both local and remote files. Ex: `'http://oin-hotosm.s3.amazonaws.com/593ede5ee407d70011386139/0/3041615b-2bdb-40c5-b834-36f580baca29.tif'`
   - A [WMS endpoint](http://www.opengeospatial.org/standards/wms) `GetMap` request. Fill out all necessary parameters except `bbox` which should be set as `{bbox}`. Ex:

label_maker/label.py

@@ -9,7 +9,7 @@
 import numpy as np
 import mapbox_vector_tile
 import pyproj
-from shapely.geometry import shape, mapping
+from shapely.geometry import shape, mapping, Polygon
 from rasterio.features import rasterize
 from geojson import Feature, FeatureCollection as fc
 from mercantile import tiles, feature, Tile
@@ -24,6 +24,9 @@
 # declare a global accumulator so the workers will have access
 tile_results = dict()
 
+# clip all geometries to a tile
+clip_mask = Polygon(((0, 0), (0, 255), (255, 255), (255, 0), (0, 0)))
+
 def make_labels(dest_folder, zoom, country, classes, ml_type, bounding_box, sparse, **kwargs):
     """Create label data from OSM QA tiles for specified classes
 
@@ -215,6 +218,7 @@ def _mapper(x, y, z, data, args):
                     if ff(feat):
                         feat['geometry']['coordinates'] = _convert_coordinates(feat['geometry']['coordinates'])
                         geo = shape(feat['geometry'])
+                        geo = geo.intersection(clip_mask)
                         if cl.get('buffer'):
                             geo = geo.buffer(cl.get('buffer'), 4)
                         geos.append((mapping(geo), i + 1))
@@ -238,21 +242,27 @@ def _pixel_bbox(bb):
 
 def _buffer_bbox(bb, buffer=4):
     """Buffer a bounding box in pixel coordinates"""
-    return [
-        bb[0] - buffer,
-        bb[1] - buffer,
-        bb[2] + buffer,
-        bb[3] + buffer
-    ]
+    return list(map(
+        _clamp,
+        [
+            bb[0] - buffer,
+            bb[1] - buffer,
+            bb[2] + buffer,
+            bb[3] + buffer
+        ]
+    ))
+
+def _clamp(coordinate):
+    """restrict a single coordinate to 0-255"""
+    return max(0, min(255, coordinate))
 
 def _pixel_bounds_convert(x):
     """Convert a single 0-4096 coordinate to a pixel coordinate"""
     (i, b) = x
     # input bounds are in the range 0-4096 by default: https://github.com/tilezen/mapbox-vector-tile
     # we want them to match our fixed imagery size of 256
     pixel = round(b * 255. / 4096) # convert to tile pixels
-    flipped = pixel if (i % 2 == 0) else 255 - pixel # flip the y axis
-    return max(0, min(255, flipped)) # clamp to the correct range
+    return pixel if (i % 2 == 0) else 255 - pixel # flip the y axis
 
 def _callback(tile_label):
     """Attach tile labels to a global tile_results dict"""

test/integration/test_object_labels.py

@@ -29,44 +29,44 @@ def test_cli(self):
         expected_bboxes = dict()
         expected_bboxes['62092-50162-17'] = np.empty((0, 5))
         expected_bboxes['62092-50163-17'] = np.array([
-            [209, 192, 259, 259, 6], [251, 251, 259, 259, 6]
+            [209, 192, 255, 255, 6], [253, 251, 255, 255, 6]
         ])
         expected_bboxes['62092-50164-17'] = np.array([
-            [209, -4, 250, 28, 6], [242, -4, 259, 28, 6],
-            [222, 13, 235, 66, 6], [87, 20, 250, 259, 6]
+            [209, 0, 250, 28, 6], [242, 0, 255, 28, 6],
+            [222, 13, 235, 66, 6], [87, 20, 250, 255, 6]
         ])
         expected_bboxes['62093-50162-17'] = np.array([
-            [81, 145, 128, 259, 6], [124, -4, 218, 259, 6],
-            [207, -4, 247, 153, 6], [140, 108, 193, 259, 6],
-            [125, 236, 152, 259, 6], [162, 177, 176, 216, 6],
-            [170, 151, 214, 179, 6], [141, 166, 244, 259, 6],
-            [203, 88, 259, 186, 6]
+            [81, 145, 128, 255, 6], [124, 0, 218, 255, 6],
+            [207, 0, 247, 153, 6], [140, 108, 193, 255, 6],
+            [125, 236, 152, 255, 6], [162, 177, 176, 216, 6],
+            [170, 151, 214, 179, 6], [141, 166, 244, 255, 6],
+            [203, 88, 255, 186, 6]
         ])
         expected_bboxes['62093-50163-17'] = np.array([
-            [81, -4, 125, 15, 6], [117, -4, 133, 17, 6],
-            [119, -4, 151, 36, 6], [125, -4, 140, 7, 6],
-            [141, -4, 187, 7, 6], [64, 32, 91, 60, 4],
+            [81, 0, 125, 15, 6], [117, 0, 133, 17, 6],
+            [119, 0, 151, 36, 6], [125, 0, 140, 7, 6],
+            [141, 0, 187, 7, 6], [64, 32, 91, 60, 4],
             [84, 50, 106, 64, 6], [111, 9, 127, 26, 6],
             [111, 18, 127, 35, 6], [84, 15, 119, 52, 6],
             [74, 6, 129, 69, 5], [93, 24, 123, 46, 6],
-            [88, 27, 127, 93, 6], [-4, 85, 96, 213, 6],
-            [-2, 85, 96, 259, 6], [115, 38, 259, 100, 6]
+            [88, 27, 127, 93, 6], [0, 85, 96, 213, 6],
+            [0, 85, 96, 255, 6], [115, 38, 255, 100, 6]
         ])
         expected_bboxes['62094-50162-17'] = np.array([
-            [67, -4, 172, 248, 6], [-4, 172, 90, 259, 6],
-            [91, 170, 259, 227, 6]
+            [67, 0, 172, 248, 6], [0, 172, 90, 255, 6],
+            [91, 170, 255, 227, 6]
         ])
         expected_bboxes['62093-50164-17'] = np.array([
-            [-4, -4, 12, 22, 6], [207, 158, 259, 195, 6]
+            [0, 0, 12, 22, 6], [207, 158, 255, 195, 6]
         ])
         expected_bboxes['62094-50163-17'] = np.array([
-            [73, -4, 259, 78, 6], [30, 166, 60, 196, 1],
-            [30, 166, 60, 196, 2], [203, 129, 259, 259, 6],
-            [-4, 90, 259, 138, 6]
+            [73, 0, 255, 78, 6], [30, 166, 60, 196, 1],
+            [30, 166, 60, 196, 2], [203, 129, 255, 255, 6],
+            [0, 90, 255, 138, 6]
         ])
         expected_bboxes['62094-50164-17'] = np.array([
-            [158, -4, 216, 82, 6], [-4, 108, 147, 173, 6],
-            [139, 74, 254, 143, 6], [240, 90, 259, 232, 6]
+            [158, 0, 216, 82, 6], [0, 108, 147, 173, 6],
+            [139, 74, 254, 143, 6], [240, 90, 255, 232, 6]
         ])
 
         self.assertEqual(len(labels.files), len(expected_bboxes.keys())) # First check the number of tiles

test/integration/test_segmentation_labels.py

@@ -27,14 +27,14 @@ def test_cli(self):
         # our labels should look like this
         expected_sums = {
             '62092-50162-17': 0,
-            '62092-50163-17': 2760,
-            '62092-50164-17': 13728,
-            '62093-50162-17': 38004,
-            '62093-50164-17': 2688,
-            '62094-50162-17': 21630,
-            '62094-50164-17': 19440,
-            '62094-50163-17': 21895,
-            '62093-50163-17': 32198
+            '62092-50163-17': 0,
+            '62092-50164-17': 13500,
+            '62093-50162-17': 36822,
+            '62093-50164-17': 2400,
+            '62094-50162-17': 21234,
+            '62094-50164-17': 19146,
+            '62094-50163-17': 21613,
+            '62093-50163-17': 31568
         }
 
         labels = np.load('integration-sg/labels.npz')

test/integration/test_segmentation_labels_sparse.py

@@ -26,14 +26,13 @@ def test_cli(self):
 
         # our labels should look like this
         expected_sums = {
-            '62092-50163-17': 2760,
-            '62092-50164-17': 13728,
-            '62093-50162-17': 38004,
-            '62093-50164-17': 2688,
-            '62094-50162-17': 21630,
-            '62094-50164-17': 19440,
-            '62094-50163-17': 21895,
-            '62093-50163-17': 32198
+            '62092-50164-17': 13500,
+            '62093-50162-17': 36822,
+            '62093-50164-17': 2400,
+            '62094-50162-17': 21234,
+            '62094-50164-17': 19146,
+            '62094-50163-17': 21613,
+            '62093-50163-17': 31568
         }
 
         labels = np.load('integration-sg/labels.npz')