Utility functions to 1) convert projected shapefiles into annotation format, 2) deepforest predictions to geospatial objects

shapefile_utility.py

import geopandas as gp
import rasterio
import os

def shapefile_to_annotations(shapefile, rgb, savedir="."):
    """
    Convert a shapefile of annotations into annotations csv file for DeepForest training and evaluation
    Args:
        shapefile: Path to a shapefile on disk. If a label column is present, it will be used, else all labels are assumed to be "Tree"
        rgb: Path to the RGB image on disk
        savedir: Directory to save csv files
    Returns:
        None: a csv file is written
    """
    #Read shapefile
    gdf = gp.read_file(shapefile)
    
    #get coordinates
    df = gdf.geometry.bounds
    
    #raster bounds
    with rasterio.open(rgb) as src:
        left, bottom, right, top = src.bounds
        
    #Transform project coordinates to image coordinates
    df["tile_xmin"] = df.minx - left
    df["tile_xmin"] = df["tile_xmin"].astype(int)
    
    df["tile_xmax"] = df.maxx - left
    df["tile_xmax"] = df["tile_xmax"].astype(int)
    
    #UTM is given from the top, but origin of an image is top left
    
    df["tile_ymax"] = top - df.miny 
    df["tile_ymax"] = df["tile_ymax"].astype(int)
    
    df["tile_ymin"] = top - df.maxy
    df["tile_ymin"] = df["tile_ymin"].astype(int)    
    
    #Add labels is they exist
    if "label" in gdf.columns:
        df["label"] = gdf["label"]
    else:
        df["label"] = "Tree"
    
    #add filename
    df["image_path"] = os.path.basename(rgb)
    
    #select columns
    result = df[["image_path","tile_xmin","tile_ymin","tile_xmax","tile_ymax","label"]]
    result = result.rename(columns={"tile_xmin":"xmin","tile_ymin":"ymin","tile_xmax":"xmax","tile_ymax":"ymax"})
    image_name = os.path.splitext(os.path.basename(rgb))[0]
    csv_filename = os.path.join(savedir, "{}.csv".format(image_name))
    
    #ensure no zero area polygons due to rounding to pixel size
    result = result[~(result.xmin == result.xmax)]
    result = result[~(result.ymin == result.ymax)]
    
    #write file
    result.to_csv(csv_filename,index=False)
    
def project(raster_path, boxes):
    """
    Convert image coordinates into a geospatial object to overlap with input image. 
    Args:
        raster_path: path to the raster .tif on disk. Assumed to have a valid spatial projection
        boxes: a prediction pandas dataframe from deepforest.predict_tile()
    Returns:
        a geopandas dataframe with predictions in input projection.
    """
    with rasterio.open(raster_path) as dataset:
        bounds = dataset.bounds
        pixelSizeX, pixelSizeY  = dataset.res

    #subtract origin. Recall that numpy origin is top left! Not bottom left.
    boxes["xmin"] = (boxes["xmin"] *pixelSizeX) + bounds.left
    boxes["xmax"] = (boxes["xmax"] * pixelSizeX) + bounds.left
    boxes["ymin"] = bounds.top - (boxes["ymin"] * pixelSizeY) 
    boxes["ymax"] = bounds.top - (boxes["ymax"] * pixelSizeY)
    
    # combine column to a shapely Box() object, save shapefile
    boxes['geometry'] = boxes.apply(lambda x: shapely.geometry.box(x.xmin,x.ymin,x.xmax,x.ymax), axis=1)
    boxes = geopandas.GeoDataFrame(boxes, geometry='geometry')
    
    boxes.crs = dataset.crs.to_wkt()
    
    #Shapefiles could be written with geopandas boxes.to_file(<filename>, driver='ESRI Shapefile')
    
    return boxes

I have tested this with utm shapefiles from the Northern Hemisphere. It may need to be tweaked for Southern (negative utms)?

Hi there,
I'm just having a play with deepforest and the shapefiles_to_annotations tool.
I had to add a few lines of code to get it to work properly for me (at least, I think it's now working properly):

after line 23)
x_res, y_res = src.res # extract the pixel sizes for each dimension

Then divide through by the pixel sizes, so lines 26-38 become:

    #Transform project coordinates to image coordinates
    df["tile_xmin"] = df.minx - left
    df['tile_xmin'] = df['tile_xmin'] / x_res
    df["tile_xmin"] = df["tile_xmin"].astype(int)
    
    df["tile_xmax"] = df.maxx - left
    df['tile_xmax'] = df['tile_xmax'] / x_res
    df["tile_xmax"] = df["tile_xmax"].astype(int)
    
    #UTM is given from the top, but origin of an image is top left
    
    df["tile_ymax"] = top - df.miny
    df['tile_ymax'] = df['tile_ymax'] / y_res 
    df["tile_ymax"] = df["tile_ymax"].astype(int)
    
    df["tile_ymin"] = top - df.maxy
    df['tile_ymin'] = df['tile_ymin'] / y_res
    df["tile_ymin"] = df["tile_ymin"].astype(int) 

Hopefully this makes sense and is correct!

Thanks for this comment, i'm looking now. Yes, that makes sense. I had already done that in a previous step before this gist. I'll leave this comment here, it will depend on how the data is formatted. Good note. Feel free to drop some comments in issues on any other notes on deepforest, eager to here from users about how to make it most useful.