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Prediction through DeepForest

Prediction

DeepForest allows the prediction of the new data with the prebuilt model or we can also use our custom trained models. 


Predict a single image

DeepForest allows to predict single images with predict_image function which can read an image from memory or file provided by user, which returns the bounding boxes of the predicted trees in image.

 
image_path = get_data("OSBS_029.png")
boxes = model.predict_image(path=image_path, return_plot = False)


 

 
boxes.head()
xmin ymin xmax ymax label scores
0 334.708405 342.333954 375.941376 392.187531 0 0.736650
1 295.990601 371.456604 331.521240 400.000000 0 0.714327
2 216.828201 207.996216 245.123276 240.167023 0 0.691064
3 276.206848 330.758636 303.309631 363.038422 0 0.690987
4 328.604736 45.947182 361.095276 80.635254 0 0.638212
 

The current DeepForest has release model there is a category named "Tree", which has the label as numeric 0 and there is also a bird release model which has the label as numeric 1.

Predict a tile

Large tiles covering large geographic areas will not fit in memory during prediction, and results will be poor due to the density of bounding boxes. Remote sensing data is typically provided as a geospatial .tif file and is best suited for the predict_tile function, which divides the tile into overlapping windows, performs predictions on each window, and reassembles the resulting annotations.

 
raster_path = get_data("OSBS_029.tif")
# Window size of 300px with an overlap of 25% among windows for this small tile.
predicted_raster = model.predict_tile(raster_path, return_plot = True, 
                                              patch_size=300,patch_overlap=0.25)
 

 

Predict a set of annotations

When evaluating ground truth data, it is helpful to predict a sequence of images and combine them into a data frame. The predict_generator method allows the user to point to an annotated file and return predictions for all images. The image path is a relative path to the root dir. What we generally do is save the .csv file alongside the images.

 
csv_file = get_data("testfile_deepforest.csv")
boxes = model.predict_file(csv_file=csv_file, 
                                root_dir = os.path.dirname(csv_file),savedir=".")
 


We can also customize the appeaance of bounding box created like color and thickness by providing these arguments a value while calling predict_image function.

 
image_path = get_data("OSBS_029.png")
boxes = model.predict_image(path=image_path, return_plot = True, 
                                               color=(0, 165, 255), thickness=3)
 

 

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