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import numpy as np
import pandas as pd
import h5py
from trimesh.voxel.runlength import dense_to_brle
from pathlib import Path
from collections import defaultdict
from typing import Any, Union, Dict, Literal
from numpy.typing import NDArray
# class RandomModel:
# def __init__(self, shape):
# self.shape = shape
# return
# def __call__(self, input):
# # input is ignored, just generate some random predictions
# return np.random.randint(0, 2, size=self.shape, dtype=bool)
class FixedModel:
def __init__(self, shape) -> None:
self.shape = shape
return
def __call__(self, input) -> Any:
# input is ignored, just generate a mask filled with zeros, with fixed pixels set to 1
mask = np.zeros(self.shape, dtype=bool)
mask[100:250, 100:250] = True
return mask
def retrieve_validation_fold(path: Union[str, Path]) -> Dict[str, NDArray]:
result = defaultdict(dict)
with h5py.File(path, 'r') as fp:
for uuid, values in fp.items():
if values.attrs['fold'] != 0:
continue
result[uuid]['post'] = values['post_fire'][...]
# result[uuid]['pre'] = values['pre_fire'][...]
return dict(result)
def compute_submission_mask(id: str, mask: NDArray):
brle = dense_to_brle(mask.astype(bool).flatten())
return {"id": id, "rle_mask": brle, "index": np.arange(len(brle))}
if __name__ == '__main__':
validation_fold = retrieve_validation_fold('train_eval.hdf5')
# use a list to accumulate results
result = []
# instantiate the model
model = FixedModel(shape=(512, 512))
for uuid in validation_fold:
input_images = validation_fold[uuid]
# perform the prediction
predicted = model(input_images)
# convert the prediction in RLE format
encoded_prediction = compute_submission_mask(uuid, predicted)
result.append(pd.DataFrame(encoded_prediction))
# concatenate all dataframes
submission_df = pd.concat(result)
submission_df.to_csv('predictions.csv', index=False) |