def test_create_tif(ground_truth_raster, tmp_path):
src = rasterio.open(ground_truth_raster)
data = src.read()
filename = "{}/test.tif".format(tmp_path)
resample.create_tif(source_tif=ground_truth_raster, filename=filename, numpy_array=data)
#Assert the original and saved are identical
saved_src = rasterio.open(filename)
saved_data = saved_src.read()
assert saved_src.shape == src.shape
np.testing.assert_array_almost_equal(data, saved_data)
def test_resample(ground_truth_raster, training_raster, tmp_path):
"""Assert that resampled training raster has the shape of the ground truth raster"""
src = rasterio.open(training_raster)
data = src.read(1)
data = np.expand_dims(data,0)
filename = "{}/test.tif".format(tmp_path)
resample.create_tif(source_tif=training_raster, filename=filename, numpy_array=data)
resampled_filename = resample.resample(filename)
resampled_src = rasterio.open(resampled_filename)
ground_truth_raster_src = rasterio.open(ground_truth_raster)
resampled_data = resampled_src.read()
assert ground_truth_raster_src.shape == resampled_src.shape
assert ground_truth_raster_src.bounds == resampled_src.bounds
#Predict
predict_tfrecords = glob.glob(
"/orange/ewhite/b.weinstein/Houston2018/tfrecords/predict/*.tfrecord")
results = model.predict_raster(predict_tfrecords, batch_size=512)
#predicted classes
print(results.label.unique())
predicted_raster = visualize.create_raster(results)
print(np.unique(predicted_raster))
experiment.log_image(name="Prediction",
image_data=predicted_raster,
image_colormap=visualize.discrete_cmap(20,
base_cmap="jet"))
#Save as tif for resampling
prediction_path = os.path.join(save_dir, "prediction.tif")
predicted_raster = np.expand_dims(predicted_raster, 0)
resample.create_tif(
"/home/b.weinstein/DeepTreeAttention/data/processed/20170218_UH_CASI_S4_NAD83.tif",
filename=prediction_path,
numpy_array=predicted_raster)
filename = resample.resample(prediction_path)
experiment.log_image(name="Resampled Prediction",
image_data=filename,
image_colormap=visualize.discrete_cmap(20,
base_cmap="jet"))
#Save model
model.model.save("{}/{}.h5".format(save_dir, timestamp))