Reading in Data#
The most time-consuming part of many open-source projects is getting the data in and out. This is because there are so many formats and ways a user might interact with the package. DeepForest has collated many use cases into a single read_file function that will attempt to read many common data formats, both projected and unprojected, and create a dataframe ready for DeepForest functions.
Annotation Geometries and Coordinate Systems#
DeepForest was originally designed for bounding box annotations. As of DeepForest 1.4.0, point and polygon annotations are also supported. There are two ways to format annotations, depending on the annotation platform you are using. read_file can read points, polygons, and boxes, in both image coordinate systems (relative to image origin at top-left 0,0) as well as projected coordinates on the Earth’s surface.
Note: For CSV files, coordinates are expected to be in the image coordinate system, not projected coordinates (such as latitude/longitude or UTM).
Boxes#
CSV#
Here, the annotations are in plain CSV files, with coordinates relative to the image origin.
image_path,xmin,ymin,xmax,ymax,label
OSBS_029.tif,203,67,227,90,Tree
OSBS_029.tif,256,99,288,140,Tree
OSBS_029.tif,166,253,225,304,Tree
OSBS_029.tif,365,2,400,27,Tree
OSBS_029.tif,312,13,349,47,Tree
OSBS_029.tif,365,21,400,70,Tree
OSBS_029.tif,278,1,312,37,Tree
OSBS_029.tif,364,204,400,246,Tree
filename = get_data("OSBS_029.csv")
utilities.read_file(filename)
Example output:
image_path xmin ymin xmax ymax label geometry
0 OSBS_029.tif 203 67 227 90 Tree POLYGON ((227.000 67.000, 227.000 90.000, 203....
1 OSBS_029.tif 256 99 288 140 Tree POLYGON ((288.000 99.000, 288.000 140.000, 256...
2 OSBS_029.tif 166 253 225 304 Tree POLYGON ((225.000 253.000, 225.000 304.000, 16...
3 OSBS_029.tif 365 2 400 27 Tree POLYGON ((400.000 2.000, 400.000 27.000, 365.0...
4 OSBS_029.tif 312 13 349 47 Tree POLYGON ((349.000 13.000, 349.000 47.000, 312....
Note: To maintain continuity with versions < 1.4.0, the function for boxes continues to output xmin, ymin, xmax, and ymax columns as individual columns as well.
Shapefiles#
Geographic data can also be saved as shapefiles with projected coordinates.
Example:
gdf.iloc[0]
geometry POLYGON ((404222.4 3285121.5, 404222.4 3285122...
label Tree
image_path /Users/benweinstein/Documents/DeepForest/deepf...
Name: 0, dtype: object
These coordinates are made relative to the image origin when the file is read.
shp = utilities.read_file(input="/path/to/boxes_shapefile.shp")
shp.head()
Example output:
label image_path geometry
0 Tree OSBS_029.tif POLYGON ((105.000 214.000, 95.000 214.000, 95....
1 Tree OSBS_029.tif POLYGON ((205.000 214.000, 195.000 214.000, 19...
Points#
CSV#
Example:
x,y,label
10,20,Tree
15,30,Tree
Shapefile#
shp = utilities.read_file(input="/path/to/points_shapefile.shp")
annotations.head()
Example output:
label image_path geometry
0 Tree OSBS_029.tif POINT (100.000 209.000)
1 Tree OSBS_029.tif POINT (200.000 209.000)
Polygons#
CSV#
Polygons are expressed in well-known-text (WKT) format. Learn more about WKT.
"POLYGON ((0 0, 0 2, 1 1, 1 0, 0 0))",Tree,OSBS_029.png
"POLYGON ((2 2, 2 4, 3 3, 3 2, 2 2))",Tree,OSBS_029.png
Shapefile#
shp = utilities.read_file(input="/path/to/polygons_shapefile.shp")
annotations.head()
Example output:
label image_path geometry
0 Tree OSBS_029.png POLYGON ((0.00000 0.00000, 0.00000 2.00000, 1....
1 Tree OSBS_029.png POLYGON ((2.00000 2.00000, 2.00000 4.00000, 3....
Writing Data#
Objects read in by deepforest.utilities.read_file are geopandas GeoDataFrames. They can be exported as CSV files or shapefiles.
annotations.to_csv("annotations.csv")
annotations.to_file("annotations.shp")
Converting from Image to Geographic Coordinates#
Standard computer vision models have no concept of geographic information, which is why we use the image coordinate system to represent coordinates within DeepForest. If you want to convert predictions back into geographic coordinates, we provide a utility to go from image to geo coordinates based on the coordinate reference system of the file in the image_path column.
from deepforest import get_data
import rasterio as rio
import geopandas as gpd
from matplotlib import pyplot as plt
annotations = get_data("2018_SJER_3_252000_4107000_image_477.csv")
path_to_raster = get_data("2018_SJER_3_252000_4107000_image_477.tif")
src = rio.open(path_to_raster)
original = utilities.read_file(annotations)
geo_coords = utilities.image_to_geo_coordinates(original, root_dir=os.path.dirname(path_to_raster))
src_window = geometry.box(*src.bounds)
fig, ax = plt.subplots(figsize=(10, 10))
gpd.GeoSeries(src_window).plot(ax=ax, color="blue", alpha=0.5)
geo_coords.plot(ax=ax, color="red")
plt.show()