Merge pull request #171 from developmentseed/endpoint

tms tile image format

Author: martham93

docs/parameters.rst

@@ -44,7 +44,7 @@ Here is the full list of configuration parameters you can specify in a ``config.
 One of ``'classification'``, ``'object-detection'``, or ``'segmentation'``. This defines the output format for the final label numpy arrays (``y_train`` and ``y_test``).
 
 	``'classification'``
-	Output is an array of ``len(classes) + 1``. Each array value will be either `1` or `0` based on whether it matches the class at the same index. The additional array element belongs to the background class, which will always be the first element. 
+	Output is an array of ``len(classes) + 1``. Each array value will be either `1` or `0` based on whether it matches the class at the same index. The additional array element belongs to the background class, which will always be the first element.
 
 	``'object-detection'``
 	Output is an array of bounding boxes of the form ``[xmin, ymin, width, height, class_index]``. In this case, the values are pixel values measured from the upper left-hand corner (not latitude and longitude values). Each feature is tested against each class, so if a feature matches two or more classes, it will have the corresponding number of bounding boxes created.
@@ -65,3 +65,6 @@ Here is the full list of configuration parameters you can specify in a ``config.
 
 **imagery_offset**: list of ints
 An optional list of integers representing the number of pixels to offset imagery. For example ``[15, -5]`` will move the images 15 pixels right and 5 pixels up relative to the requested tile bounds.
+
+**tms_image_format**: string
+ An option string that has the downloaded imagery's format such as `.jpg` or `.png` when it isn't provided by the endpoint

label_maker/package.py

@@ -6,7 +6,7 @@
 import numpy as np
 from PIL import Image
 
-from label_maker.utils import is_tif
+from label_maker.utils import is_tif, get_image_format
 
 
 def package_directory(dest_folder, classes, imagery, ml_type, seed=False,
@@ -72,10 +72,12 @@ def package_directory(dest_folder, classes, imagery, ml_type, seed=False,
  y_vals = []
 
  # open the images and load those plus the labels into the final arrays
- o = urlparse(imagery)
- _, image_format = op.splitext(o.path)
  if is_tif(imagery): # if a TIF is provided, use jpg as tile format
  image_format = '.jpg'
+
+ else:
+ image_format = get_image_format(imagery, kwargs)
+
  for tile in tiles:
  image_file = op.join(dest_folder, 'tiles', '{}{}'.format(tile, image_format))
  try:

label_maker/utils.py

@@ -27,10 +27,19 @@ def class_match(ml_type, label, i):
  return np.count_nonzero(label == i)
  return None
 
+def get_image_format(imagery, kwargs):
+ if kwargs.get('tms_image_format'):
+ image_format = kwargs.get('tms_image_format')
+ else:
+ o = urlparse(imagery)
+ _, image_format = op.splitext(o.path)
+ return image_format
+
 def download_tile_tms(tile, imagery, folder, kwargs):
  """Download a satellite image tile from a tms endpoint"""
- o = urlparse(imagery)
- _, image_format = op.splitext(o.path)
+
+ image_format = get_image_format(imagery, kwargs)
+
  r = requests.get(url(tile.split('-'), imagery),
  auth=kwargs.get('http_auth'))
  tile_img = op.join(folder, '{}{}'.format(tile, image_format))

label_maker/validate.py

@@ -33,5 +33,6 @@
  'seed': {'type': 'integer'},
  'imagery_offset': {'type': 'list', 'schema': {'type': 'integer'}, 'minlength': 2, 'maxlength': 2},
  'split_vals': {'type': 'list', 'schema': {'type': 'float'}},
- 'split_names': {'type': 'list', 'schema': {'type': 'string'}}
+ 'split_names': {'type': 'list', 'schema': {'type': 'string'}},
+ 'tms_image_format': {'type': 'string'}
 }

test/fixtures/integration/config_tms_format_img.json

@@ -0,0 +1,13 @@
+{
+ "country": "togo",
+ "bounding_box": [1.09725, 6.05520, 1.34582, 6.30915],
+ "zoom": 12,
+ "classes": [
+ { "name": "Roads", "filter": ["has", "highway"] },
+ { "name": "Buildings", "filter": ["has", "building"] }
+ ],
+ "imagery": "http://a.tiles.mapbox.com/v4/mapbox.satellite/{z}/{x}/{y}.jpg?access_token=ACCESS_TOKEN",
+ "background_ratio": 1,
+ "ml_type": "classification",
+ "tms_image_format": ".png"
+}