Refactor Model
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f37c9da47a
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178
demo.ipynb
178
demo.ipynb
@ -13,17 +13,7 @@
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"cell_type": "code",
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"execution_count": 1,
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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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"<Figure size 432x288 with 0 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"outputs": [],
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"source": [
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"import rasterio as rio\n",
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"import sap\n",
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@ -33,16 +23,26 @@
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"import ipywidgets as ipw\n",
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"import higra as hg\n",
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"import numpy as np\n",
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"from pathlib import Path\n",
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"\n",
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"plt.style.use('dark_background')\n",
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"plt.set_cmap('plasma')"
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"plt.set_cmap('plasma')\n",
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"plt.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load DTM"
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"## Load dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"dtm = rio.open('data/dsm_vox_50cm_tile_-12_0.tif').read()[0]#[-500:,-500:].copy()\n",
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"dtm.shape"
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]
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},
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{
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@ -51,19 +51,40 @@
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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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"(2001, 2001)"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"918 tiles loaded.\n"
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]
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}
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],
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"source": [
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"dtm = rio.open('data/dsm_vox_50cm_tile_-12_0.tif').read()[0]#[-500:,-500:].copy()\n",
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"dtm.shape"
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"data_dir = Path('data')\n",
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"tiles = list(data_dir.glob('**/*.tif'))\n",
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"\n",
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"print('{} tiles loaded.'.format(len(tiles)))"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"lidar_features = list(set([str(x.stem).split('_vox_')[0] for x in tiles]))\n",
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"tile_names = list(set(['_'.join(str(x.stem).split('_')[-2:]) for x in tiles]))\n",
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"\n",
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"def get_tile(feature, name):\n",
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" return list(data_dir.glob('**/{}_vox_50cm_tile_{}.tif'.format(feature, name)))[0]\n",
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"\n",
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"def load_tile(feature, name):\n",
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" model['im'] = rio.open(get_tile(feature, name)).read()[0]\n",
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" model['im_hs'] = ui8_clip(hillshades(model['im']), -1, 1)\n",
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" model['im_hs_alpha'] = ((model['im_hs'].astype(float) - 127) / 127) ** 2 * .4\n",
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" kwargs = {}\n",
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" kwargs['vmin'], kwargs['vmax'] = np.quantile(model['im'], (.01, .99))\n",
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" model['im_kwargs'] = kwargs\n",
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" model['im_mesh'] = np.meshgrid(np.arange(model['im'].shape[1]), np.arange(model['im'].shape[0]))"
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]
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},
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{
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@ -75,11 +96,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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},
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"tags": []
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},
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"outputs": [],
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@ -124,14 +142,71 @@
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" hg.Accumulators.min).astype(np.bool)\n",
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" return node_mask\n",
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"\n",
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"dtm_hs = ui8_clip(hillshades(dtm), -1, 1)\n",
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"cm_hud = mpl.colors.LinearSegmentedColormap.from_list('GreenHUD', [(0.,0.,0.,0.), (0.,1.,0.,1.)], 256)\n",
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"alpha = ((dtm_hs.astype(float) - 127) / 127) ** 2 * .4\n",
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"cX, cY = np.meshgrid(np.arange(dtm.shape[1]), np.arange(dtm.shape[0]))\n",
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"kwargs = {}\n",
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"kwargs['vmin'], kwargs['vmax'] = np.quantile(dtm, (.01, .99))\n",
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"\n",
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"model = {}"
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"#dtm_hs = ui8_clip(hillshades(dtm), -1, 1)\n",
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"#cm_hud = mpl.colors.LinearSegmentedColormap.from_list('GreenHUD', [(0.,0.,0.,0.), (0.,1.,0.,1.)], 256)\n",
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"#alpha = ((dtm_hs.astype(float) - 127) / 127) ** 2 * .4\n",
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"#cX, cY = np.meshgrid(np.arange(dtm.shape[1]), np.arange(dtm.shape[0]))\n",
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"#kwargs = {}\n",
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"#kwargs['vmin'], kwargs['vmax'] = np.quantile(dtm, (.01, .99))"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Traceback (most recent call last):\n",
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" File \"/usr/lib/python3.9/site-packages/matplotlib/cbook/__init__.py\", line 224, in process\n",
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" func(*args, **kwargs)\n",
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" File \"/usr/lib/python3.9/site-packages/matplotlib/widgets.py\", line 1555, in release\n",
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" self._release(event)\n",
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" File \"/usr/lib/python3.9/site-packages/matplotlib/widgets.py\", line 2112, in _release\n",
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" self.onselect(self.eventpress, self.eventrelease)\n",
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" File \"<ipython-input-6-6d10fd3db2e6>\", line 27, in line_select_callback\n",
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" filter_dtm()\n",
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" File \"<ipython-input-6-6d10fd3db2e6>\", line 84, in filter_dtm\n",
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" t = model['tree_cache']\n",
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"KeyError: 'tree_cache'\n",
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"<ipython-input-6-6d10fd3db2e6>:30: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
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"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
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" model['dtm_y1'], model['dtm_x1'] = np.int(eclick.xdata), np.int(eclick.ydata)\n",
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"<ipython-input-6-6d10fd3db2e6>:31: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
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"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
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" model['dtm_y2'], model['dtm_x2'] = np.int(erelease.xdata), np.int(erelease.ydata)\n",
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"<ipython-input-6-6d10fd3db2e6>:38: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
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"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
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" pmask = np.zeros_like(model['im'], dtype=np.bool)\n",
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"Traceback (most recent call last):\n",
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" File \"/usr/lib/python3.9/site-packages/matplotlib/cbook/__init__.py\", line 224, in process\n",
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" func(*args, **kwargs)\n",
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" File \"/usr/lib/python3.9/site-packages/matplotlib/widgets.py\", line 1555, in release\n",
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" self._release(event)\n",
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" File \"/usr/lib/python3.9/site-packages/matplotlib/widgets.py\", line 2112, in _release\n",
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" self.onselect(self.eventpress, self.eventrelease)\n",
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" File \"<ipython-input-6-6d10fd3db2e6>\", line 32, in dtm_select_callback\n",
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" filter_nodes()\n",
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" File \"<ipython-input-6-6d10fd3db2e6>\", line 42, in filter_nodes\n",
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" show_spectrum()\n",
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" File \"<ipython-input-6-6d10fd3db2e6>\", line 45, in show_spectrum\n",
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" x, y = model['x'], model['y']\n",
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"KeyError: 'x'\n"
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]
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},
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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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"HI!\n"
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]
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}
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],
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"source": [
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"model = {}\n",
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"load_tile(lidar_features[-1], tile_names[-1])"
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]
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},
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{
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@ -143,18 +218,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 8,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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},
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"tags": []
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},
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"outputs": [
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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": "81086011013945a497e4ffb7163998f9",
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"model_id": "baf2371d25d543739572153d8e331efa",
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"version_major": 2,
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"version_minor": 0
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},
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@ -171,7 +243,7 @@
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"<function __main__.spectrum_widget(tree=<class 'sap.trees.MaxTree'>, x='area', y='compactness', x_log=True, y_log=False, hillshade_overlay=True, highlight=False)>"
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]
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},
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"execution_count": 4,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -211,10 +283,9 @@
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" filter_nodes()\n",
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" \n",
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"def filter_nodes():\n",
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" print('HI!')\n",
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" x1, x2 = model['dtm_x1'], model['dtm_x2']\n",
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" y1, y2 = model['dtm_y1'], model['dtm_y2']\n",
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" pmask = np.zeros_like(dtm, dtype=np.bool)\n",
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" pmask = np.zeros_like(model['im'], dtype=np.bool)\n",
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" pmask[x1:x2,y1:y2] = True\n",
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" model['pixel_mask'] = pmask\n",
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" model['highlight'] = True\n",
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@ -252,10 +323,10 @@
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" # No filter\n",
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" if x1 == x2 == y1 == y2 == 0:\n",
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" if not model['overlay']:\n",
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" model['ax_dtm'].imshow(dtm, **kwargs)\n",
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" model['ax_dtm'].imshow(model['im'], **model['im_kwargs'])\n",
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" else:\n",
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" model['ax_dtm'].imshow(dtm, **kwargs)\n",
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" model['ax_dtm'].imshow(dtm_hs, cmap=plt.cm.Greys, alpha=alpha)\n",
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" model['ax_dtm'].imshow(model['im'], **model['im_kwargs'])\n",
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" model['ax_dtm'].imshow(model['im_hs'], cmap=plt.cm.Greys, alpha=model['im_hs_alpha'])\n",
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"\n",
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" return\n",
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" \n",
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@ -270,9 +341,9 @@
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" if not model['overlay']:\n",
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" model['ax_dtm'].imshow(img)\n",
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" else:\n",
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" model['ax_dtm'].imshow(dtm, **kwargs)\n",
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" model['ax_dtm'].imshow(dtm_hs, cmap=plt.cm.Greys, alpha=alpha)\n",
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" model['ax_dtm'].contour(cX, cY, img, [model['tree_cache']._alt[-1]], colors='lime')\n",
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" model['ax_dtm'].imshow(model['im'], **model['im_kwargs'])\n",
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" model['ax_dtm'].imshow(model['im_hs'], cmap=plt.cm.Greys, alpha=model['im_hs_alpha'])\n",
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" model['ax_dtm'].contour(*model['im_mesh'], img, [model['tree_cache']._alt[-1]], colors='lime')\n",
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"\n",
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" plt.gcf().canvas.draw()\n",
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"\n",
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@ -288,7 +359,7 @@
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" model.update({'x': x, 'y': y, 'x_log': x_log, 'y_log': y_log, 'highlight': highlight})\n",
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" if not 'tree_cache' in model or not isinstance(model['tree_cache'], tree):\n",
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" print('Computing the {} of the DTM...'.format(tree))\n",
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" model['tree_cache'] = tree(dtm)\n",
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" model['tree_cache'] = tree(model['im'])\n",
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" \n",
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" show_spectrum()\n",
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" \n",
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@ -336,6 +407,13 @@
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" x=sap.available_attributes().keys(),\n",
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" y=sap.available_attributes().keys())"
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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": null,
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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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"metadata": {
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