Small improvements to gitignore, tests, and docs

Author: drewbo

.circleci/config.yml

@@ -41,8 +41,6 @@ jobs:
             label-maker package --dest integration-cl --config test/fixtures/integration/config.integration.json
             python test/verify-package-cl.py
 
-
-
             # segmentation integration tests
             mkdir integration-sg
             cp test/fixtures/integration/portugal-z17.mbtiles integration-sg/portugal-z17.mbtiles

.gitignore

@@ -10,5 +10,5 @@ MANIFEST
 .cache
 data
 config.json
-stdout
-integration*
+stdout*
+/integration*

README.md

@@ -43,7 +43,7 @@ Before running any commands, it is necessary to create a `config.json` file to s
 - `classes`: An array of classes for machine learning training. Each class is defined as an object with two required properties:
   - `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
+  - `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`: 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/)
 - `background_ratio`: For single-class classification problems, we need to download images with no matching class. We will download `background_ratio` times the number of images matching the one class.
 - `ml_type`: One of `"classification"`, `"object-detection"`, or `"segmentation"`. For the final label numpy arrays (`y_train` and `y_test`), we will produce a different label depending upon the `type`.

test/verify-labels-sg.py

@@ -16,5 +16,7 @@
 }
 
 labels = np.load('integration-sg/labels.npz')
+assert len(labels.files) == len(expected_sums.keys())  # First check number of tiles
+
 for tile in labels.files:
     assert expected_sums[tile] == np.sum(labels[tile])