Example cinderella

example_cinderella.py

from Auto2DSelect.auto_2d_select import Auto2DSelectNet # Import cinderella
from Auto2DSelect import results_writer # Import writer

batch_size =4 # If you have memory problems, choose a smaller one
input_path = "path/to/classes.hdf" # path to your classes
weights_path = "path/to/weights.h5" # path to your trained model

# You can read the input size from the trained model or just set it to size used during training.
with h5py.File(weights_path, mode="r") as f:
        try:
            import numpy as np
            input_size = tuple(f["input_size"])
        except KeyError:
            pass
# Create network object
auto2dnet = Auto2DSelectNet(batch_size, input_size) #Create object

#Apply it to your data:
result = auto2dnet.predict(input_path, weights_path, good_thresh=threshold)

#Write the results to disk. It will write hdf or mrcs depending on your input
results_writer.write_labeled_hdf(
        result, output_path, os.path.basename(input_path).split(".")[0]
    )