From dd79fb6e68ec0eb9bfed4274fcc6f160fb137dba Mon Sep 17 00:00:00 2001 From: Starbeamrainbowlabs Date: Thu, 5 Jan 2023 17:09:09 +0000 Subject: [PATCH] fixup --- aimodel/src/deeplabv3_plus_test_rainfall.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/aimodel/src/deeplabv3_plus_test_rainfall.py b/aimodel/src/deeplabv3_plus_test_rainfall.py index b058377..21f0518 100755 --- a/aimodel/src/deeplabv3_plus_test_rainfall.py +++ b/aimodel/src/deeplabv3_plus_test_rainfall.py @@ -18,7 +18,7 @@ import tensorflow as tf IMAGE_SIZE = int(os.environ["IMAGE_SIZE"]) if "IMAGE_SIZE" in os.environ else 128 # was 512; 128 is the highest power of 2 that fits the data BATCH_SIZE = int(os.environ["BATCH_SIZE"]) if "BATCH_SIZE" in os.environ else 64 NUM_CLASSES = 2 -DIR_DATA_TF = os.environ["DATA_DIR_TF"] +DIR_RAINFALLWATER = os.environ["DIR_RAINFALLWATER"] PATH_HEIGHTMAP = os.environ["PATH_HEIGHTMAP"] PATH_COLOURMAP = os.environ["COLOURMAP"] STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"]) if "STEPS_PER_EPOCH" in os.environ else None @@ -29,17 +29,16 @@ if not os.path.exists(DIR_OUTPUT): os.makedirs(DIR_OUTPUT) logger.info("DeepLabV3+ rainfall radar TEST") -logger.info(f"> NUM_BATCHES {NUM_BATCHES}") logger.info(f"> BATCH_SIZE {BATCH_SIZE}") -logger.info(f"> DIR_DATA_TF {DIR_DATA_TF}") +logger.info(f"> DIR_RAINFALLWATER {DIR_RAINFALLWATER}") logger.info(f"> PATH_HEIGHTMAP {PATH_HEIGHTMAP}") logger.info(f"> PATH_COLOURMAP {PATH_COLOURMAP}") -logger.info(f"> DIR_OUTPUT {DIR_OUTPUT}") logger.info(f"> STEPS_PER_EPOCH {STEPS_PER_EPOCH}") +logger.info(f"> DIR_OUTPUT {DIR_OUTPUT}") dataset_train, dataset_validate = dataset_mono( - dirpath_input=DIR_DATA, + dirpath_input=DIR_RAINFALLWATER, batch_size=BATCH_SIZE, water_threshold=0.1, rainfall_scale_up=2, # done BEFORE cropping to the below size @@ -141,8 +140,8 @@ model.compile( metrics=["accuracy"], ) logger.info(">>> Beginning training") -history = model.fit(train_dataset, - validation_data=val_dataset, +history = model.fit(dataset_train, + validation_data=dataset_validate, epochs=25, callbacks=[ tf.keras.callbacks.CSVLogger( @@ -219,10 +218,10 @@ def decode_segmentation_masks(mask, colormap, n_classes): return rgb -def get_overlay(image, colored_mask): +def get_overlay(image, coloured_mask): image = tf.keras.preprocessing.image.array_to_img(image) image = np.array(image).astype(np.uint8) - overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0) + overlay = cv2.addWeighted(image, 0.35, coloured_mask, 0.65, 0) return overlay