geo-referenced inference

geoinference.py

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+''' Runs a georeferenced inference on a given GeoTIFF image.
+
+example:
+$ python geoinference.py --checkpoint_path checkpoints/cp.135.ckpt \
+    --image_path sample_data/sentinel2_example.tif --save_path water_map.tif
+'''
+
+# Uncomment this to run inference on CPU if your GPU runs out of memory
+# import os
+# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+import argparse
+import deepwatermap
+import tifffile as tiff
+import numpy as np
+import cv2
+import rasterio as rio
+
+def find_padding(v, divisor=32):
+    v_divisible = max(divisor, int(divisor * np.ceil( v / divisor )))
+    total_pad = v_divisible - v
+    pad_1 = total_pad // 2
+    pad_2 = total_pad - pad_1
+    return pad_1, pad_2
+
+def main(checkpoint_path, image_path, save_path):
+    # load the model
+    model = deepwatermap.model()
+    model.load_weights(checkpoint_path)
+
+    # load and preprocess the input image
+    src = rio.open(image_path)
+    image = src.read()
+    image = np.moveaxis(image, [0,1,2], [2,0,1])
+
+    pad_r = find_padding(image.shape[0])
+    pad_c = find_padding(image.shape[1])
+    image = np.pad(image, ((pad_r[0], pad_r[1]), (pad_c[0], pad_c[1]), (0, 0)), 'reflect')
+
+    # solve no-pad index issue after inference
+    if pad_r[1] == 0:
+        pad_r = (pad_r[0], 1)
+    if pad_c[1] == 0:
+        pad_c = (pad_c[0], 1)
+
+    image = image.astype(np.float32)
+
+    # remove nans (and infinity) - replace with 0s
+    image = np.nan_to_num(image, copy=False, nan=0.0, posinf=0.0, neginf=0.0)
+
+    image = image - np.min(image)
+    image = image / np.maximum(np.max(image), 1)
+
+    # run inference
+    image = np.expand_dims(image, axis=0)
+    dwm = model.predict(image)
+    dwm = np.squeeze(dwm)
+    dwm = dwm[pad_r[0]:-pad_r[1], pad_c[0]:-pad_c[1]]
+
+    # soft threshold
+    dwm = 1./(1+np.exp(-(16*(dwm-0.5))))
+    dwm = np.clip(dwm, 0, 1)
+
+    dwm = np.expand_dims(dwm*255, axis=0)
+
+    # get image metadata
+    metadata = src.meta
+    metadata.update({
+    'dtype': image.dtype,
+    'count': image.shape[0],
+    'compress': 'lzw'})
+
+    # save the output as GeoTIFF
+    with rio.open(save_path, 'w', **metadata) as dst:
+        dst.write(dwm)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--checkpoint_path', type=str,
+                        help="Path to the dir where the checkpoints are stored")
+    parser.add_argument('--image_path', type=str, help="Path to the input GeoTIFF image")
+    parser.add_argument('--save_path', type=str, help="Path where the output map will be saved")
+    args = parser.parse_args()
+    main(args.checkpoint_path, args.image_path, args.save_path)
+