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jupyter-notebooks/crop-classification/datasets-prepare.ipynb

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@@ -574,7 +574,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 75,
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"execution_count": 86,
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"metadata": {},
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"outputs": [],
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"source": [
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" warp(filename, projected_filename, dst_srs=dst_srs)\n",
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" \n",
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" # save as jpeg\n",
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" display_image = os.path.join(os.path.dirname(filename), 'display.jpg')\n",
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" display_image = os.path.join('data', 'display.jpg')\n",
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" _save_display_image(projected_filename, display_image)\n",
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" \n",
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" # determine image layer bounds\n",
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},
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{
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"cell_type": "code",
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"execution_count": 76,
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"execution_count": 87,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"execution_count": 88,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"gdalwarp -s_srs epsg:32610 -t_srs epsg:4326 -overwrite pre-data/train_scene_cropped.tif /tmp/tmpdr6u8rec.tif\n"
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"gdalwarp -s_srs epsg:32610 -t_srs epsg:4326 -overwrite pre-data/train_scene_cropped.tif /tmp/tmpplxcjvsq.tif\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "80efff46277845ddac16d49b8461af1a",
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"model_id": "d5def8c273984be59c5acc08292df7e6",
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"version_major": 2,
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"version_minor": 0
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},
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},
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{
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"cell_type": "code",
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"execution_count": 80,
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"execution_count": 89,
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "code",
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"execution_count": 81,
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"execution_count": 90,
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"metadata": {},
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"outputs": [
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{
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"'pre-data/test_scene_cropped_metadata.xml'"
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]
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},
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"execution_count": 81,
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"execution_count": 90,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 82,
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"execution_count": 91,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7f3a9497d748>"
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"<matplotlib.image.AxesImage at 0x7f3a947b8a20>"
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]
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},
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"execution_count": 82,
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"execution_count": 91,
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"metadata": {},
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"output_type": "execute_result"
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},

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