MultiBands, and tools improvements (mostly)

AUTHORS.md

@@ -1,3 +1,5 @@
 Daniel J. Hofmann <[email protected]> https://github.com/daniel-j-h
 
 Bhargav Kowshik <[email protected]> https://github.com/bkowshik
+
+Olivier Courtin <[email protected]> https://github.com/ocourtin

README.md

@@ -87,7 +87,7 @@ The following describes the installation from scratch.
 - Install native system dependencies required for Python 3 bindings
 
 ```bash
-apt-get install build-essential libboost-python-dev libexpat1-dev zlib1g-dev libbz2-dev libspatialindex-dev
+apt-get install build-essential libboost-python-dev libexpat1-dev zlib1g-dev libbz2-dev libspatialindex-dev libjpeg-turbo8-dev libwebp-dev
 ```
 
 - Use a virtualenv for installing this project locally

config/config.toml

@@ -0,0 +1,55 @@
+# RoboSat Configuration
+# For syntax see: https://github.com/toml-lang/toml#table-of-contents
+
+[dataset]
+  # The slippy map dataset's base directory.
+  path = '/tmp/slippy-map-dir/'
+
+  # Dataset specific class weights computes on the training data.
+  # Needed by 'mIoU' and 'CrossEntropy' losses to deal with unbalanced classes.
+  # Note: use `./rs weights -h` to compute these for new datasets.
+  weights = [1.6248, 5.762827]
+
+
+[classes]
+  # Human representation for classes.
+  titles = ['background', 'parking']
+
+  # Color map for visualization and representing classes in masks.
+  # Note: available colors are either CSS3 colors names or #RRGGBB hexadecimal representation.
+  colors  = ['denim', 'orange']
+
+
+# Channels configuration let your indicate wich dataset sub-directory and bands to take as input
+# You could so, add several channels blocks to compose your input Tensor. Orders are meaningful.
+[[channels]]
+sub = "images"
+bands = [1,2,3]
+
+
+# Model specific attributes.
+[model]
+
+  # Batch size for training.
+  batch_size = 2
+
+  # Image side size in pixels.
+  image_size = 512
+
+  # Total number of epochs to train for.
+  epochs     = 10
+
+  # Learning rate for the optimizer.
+  lr         = 0.0001
+
+  # Weight decay l2 penalty for the optimizer
+  decay      = 0.0001
+
+  # Loss function name (e.g 'Lovasz', 'mIoU' or 'CrossEntropy')
+  loss = 'Lovasz'
+
+  # Data augmentation, Flip or Rotate probability
+  data_augmentation = 0.75
+
+  # Use ImageNet weights pretraining
+  pretrained = true

deps/requirements-lock.txt

@@ -28,7 +28,7 @@ pyparsing==2.2.2
 pyproj==1.9.5.1
 pytest==3.9.1
 python-dateutil==2.7.3
-rasterio==1.0.8
+rasterio==1.0.10
 requests==2.20.0
 Rtree==0.8.3
 scikit-learn==0.20.0
@@ -43,3 +43,4 @@ torchvision==0.2.1
 tqdm==4.27.0
 urllib3==1.24
 Werkzeug==0.14.1
+webcolors==1.8.1

deps/requirements.txt

@@ -1,6 +1,6 @@
 torchvision
 numpy
-pillow
+pillow-simd
 scipy
 opencv-contrib-python
 tqdm
@@ -17,3 +17,4 @@ rtree
 pyproj
 toml
 pytest
+webcolors

docker/Dockerfile.cpu

@@ -6,7 +6,7 @@ FROM ubuntu:16.04
 # See: https://github.com/skvark/opencv-python/issues/90
 RUN apt-get update -qq && \
     apt-get install -qq -y -o quiet=1 \
-    python3 python3-dev python3-tk python3-pip build-essential libboost-python-dev libexpat1-dev zlib1g-dev libbz2-dev libspatialindex-dev libsm6
+    python3 python3-dev python3-tk python3-pip build-essential libboost-python-dev libexpat1-dev zlib1g-dev libbz2-dev libspatialindex-dev libsm6 libwebp-dev libjpeg-turbo8-dev
 
 WORKDIR /app
 ADD . /app

docker/Dockerfile.gpu

@@ -6,7 +6,7 @@ FROM nvidia/cuda:9.1-cudnn7-runtime-ubuntu16.04
 # See: https://github.com/skvark/opencv-python/issues/90
 RUN apt-get update -qq && \
     apt-get install -qq -y -o quiet=1 \
-    python3 python3-dev python3-tk python3-pip build-essential libboost-python-dev libexpat1-dev zlib1g-dev libbz2-dev libspatialindex-dev libsm6
+    python3 python3-dev python3-tk python3-pip build-essential libboost-python-dev libexpat1-dev zlib1g-dev libbz2-dev libspatialindex-dev libsm6 libwebp-dev libjpeg-turbo8-dev
 
 WORKDIR /app
 ADD . /app

robosat/colors.py

@@ -2,76 +2,30 @@
 """
 
 import colorsys
-
-from enum import Enum, unique
-
-
-# Todo: user should be able to bring her own color palette.
-# Functions need to account for that and not use one palette.
-
-
-def _rgb(v):
-    r, g, b = v[1:3], v[3:5], v[5:7]
-    return int(r, 16), int(g, 16), int(b, 16)
-
-
-@unique
-class Mapbox(Enum):
-    """Mapbox-themed colors.
-
-    See: https://www.mapbox.com/base/styling/color/
-    """
-
-    dark = _rgb("#404040")
-    gray = _rgb("#eeeeee")
-    light = _rgb("#f8f8f8")
-    white = _rgb("#ffffff")
-    cyan = _rgb("#3bb2d0")
-    blue = _rgb("#3887be")
-    bluedark = _rgb("#223b53")
-    denim = _rgb("#50667f")
-    navy = _rgb("#28353d")
-    navydark = _rgb("#222b30")
-    purple = _rgb("#8a8acb")
-    teal = _rgb("#41afa5")
-    green = _rgb("#56b881")
-    yellow = _rgb("#f1f075")
-    mustard = _rgb("#fbb03b")
-    orange = _rgb("#f9886c")
-    red = _rgb("#e55e5e")
-    pink = _rgb("#ed6498")
+import webcolors
+import numpy as np
 
 
 def make_palette(*colors):
-    """Builds a PIL-compatible color palette from color names.
+    """Builds a PIL-compatible color palette from CSS3 color names, or hex values patterns as #RRGGBB
 
     Args:
       colors: variable number of color names.
     """
 
-    rgbs = [Mapbox[color].value for color in colors]
-    flattened = sum(rgbs, ())
-    return list(flattened)
-
-
-def color_string_to_rgb(color):
-    """Convert color string to a list of RBG integers.
+    assert 0 < len(colors) <= 256
 
-    Args:
-      color: the string color value for example "250,0,0"
-
-    Returns:
-      color: as a list of RGB integers for example [250,0,0]
-    """
+    hexs = [webcolors.CSS3_NAMES_TO_HEX[color] if color[0] != "#" else color for color in colors]
+    rgbs = [(int(h[1:3], 16), int(h[3:5], 16), int(h[5:7], 16)) for h in hexs]
 
-    return [*map(int, color.split(","))]
+    return list(sum(rgbs, ()))
 
 
 def continuous_palette_for_color(color, bins=256):
     """Creates a continuous color palette based on a single color.
 
     Args:
-      color: the rgb color tuple to create a continuous palette for.
+      color: the CSS3 color name or it's hex values as #RRGGBB, to create a continuous palette for.
       bins: the number of colors to create in the continuous palette.
 
     Returns:
@@ -81,15 +35,29 @@ def continuous_palette_for_color(color, bins=256):
     # A quick and dirty way to create a continuous color palette is to convert from the RGB color
     # space into the HSV color space and then only adapt the color's saturation (S component).
 
-    r, g, b = [v / 255 for v in Mapbox[color].value]
+    hexs = webcolors.CSS3_NAMES_TO_HEX[color] if color[0] != "#" else color
+    r, g, b = [(int(h[1:3], 16), int(h[3:5], 16), int(h[5:7], 16)) for h in hexs]
     h, s, v = colorsys.rgb_to_hsv(r, g, b)
 
-    palette = []
+    assert 0 < bins <= 256
 
+    palette = []
     for i in range(bins):
-        ns = (1 / bins) * (i + 1)
-        palette.extend([int(v * 255) for v in colorsys.hsv_to_rgb(h, ns, v)])
-
-    assert len(palette) // 3 == bins
+        r, g, b = [int(v * 255) for v in colorsys.hsv_to_rgb(h, (1 / bins) * (i + 1), v)]
+        palette.extend(r, g, b)
 
     return palette
+
+
+def complementary_palette(palette):
+    """Creates a PIL complementary colors palette based on an initial PIL palette"""
+
+    comp_palette = []
+    colors = [palette[i : i + 3] for i in range(0, len(palette), 3)]
+
+    for color in colors:
+        r, g, b = [v for v in color]
+        h, s, v = colorsys.rgb_to_hsv(r, g, b)
+        comp_palette.extend(map(int, colorsys.hsv_to_rgb((h + 0.5) % 1, s, v)))
+
+    return comp_palette