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brookisme/CropAOIs.DEV.ipynb

Created January 17, 2019 01:23
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Crop Isolated AOIs in geotiff
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CropAOIs.DEV.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pdb\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from rasterio.plot import show\n",
"from rasterio.plot import show_hist\n",
"import rasterio as rio\n",
"import geopandas as gpd\n",
"import json\n",
"from rasterio.mask import mask\n",
"import geojson\n",
"from pprint import pprint\n",
"from random import choice\n",
"from rasterio import features\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def image_data(path,src=False):\n",
" if src:\n",
" src=rio.open(path,'r')\n",
" return src, src.read(), src.profile\n",
" else:\n",
" with rio.open(path,'r') as src:\n",
" profile=src.profile\n",
" image=src.read()\n",
" return image, profile\n",
"\n",
"\n",
"def image_write(im,path,profile):\n",
" with rio.open(path,'w',**profile) as dst:\n",
" dst.write(im)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### CREATE MASK TIF"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/brook/anaconda3/lib/python3.7/site-packages/rasterio/__init__.py:160: FutureWarning: GDAL-style transforms are deprecated and will not be supported in Rasterio 1.0.\n",
" transform = guard_transform(transform)\n"
]
}
],
"source": [
"im,p=image_data('aue.tif')\n",
"msk=(im!=255).astype('uint8')\n",
"image_write(msk,'aue-mask.tif',p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### CREATE MASK VECTORS"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output aue-mask.shp of format ESRI Shapefile.\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdal_polygonize.py -8 aue-mask.tif aue-mask.shp -b 1 -f \"ESRI Shapefile\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(119, 2)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DN</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>0</td>\n",
" <td>POLYGON ((241080 1925910, 241100 1925910, 2411...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1</td>\n",
" <td>POLYGON ((219420 1935610, 219490 1935610, 2194...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>0</td>\n",
" <td>POLYGON ((241110 1925730, 241120 1925730, 2411...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DN geometry\n",
"54 0 POLYGON ((241080 1925910, 241100 1925910, 2411...\n",
"14 1 POLYGON ((219420 1935610, 219490 1935610, 2194...\n",
"79 0 POLYGON ((241110 1925730, 241120 1925730, 2411..."
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mask_df=gpd.read_file('aue-mask.shp')\n",
"print(mask_df.shape)\n",
"mask_df.sample(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"H=41\n",
"W=41\n",
" \n",
"def update_profile(profile,w,h,affine):\n",
" profile['width']=w\n",
" profile['height']=h\n",
" profile['affine']=affine\n",
" profile.pop('transform',None)\n",
" return profile\n",
"\n",
"\n",
"def crop_sizes(in_size,out_size):\n",
" assert (in_size>=out_size), 'cropping must be >= 0 ({in_size},{out_size})'\n",
" diff=in_size-out_size\n",
" c_0=diff//2\n",
" c_1=diff-c_0\n",
" return c_0, c_1\n",
"\n",
" \n",
"def crop(src,im,profile,h=H,w=W):\n",
" if im.ndim==3:\n",
" im_h,im_w=im.shape[1:]\n",
" else:\n",
" im_h,im_w=im.shape\n",
" crp_y_0,crp_y_1=crop_sizes(im_h,h)\n",
" crp_x_0,crp_x_1=crop_sizes(im_w,w)\n",
" win=((crp_y_0,crp_x_0),(h,w))\n",
" affine=src.window_transform(win)\n",
" profile=update_profile(profile,h,w,affine)\n",
" if im.ndim==3:\n",
" im=im[:,crp_y_0:crp_y_0+h,crp_x_0:crp_x_0+w] \n",
" else:\n",
" im=im[crp_y_0:crp_y_0+h,crp_x_0:crp_x_0+w] \n",
" return im, profile\n",
"\n",
"\n",
"def cut_img(src,geom):\n",
" im,affine=mask(raster=src, shapes=geom, crop=True)\n",
" profile=src.profile.copy()\n",
" if im.ndim==3:\n",
" _,h,w=im.shape\n",
" else:\n",
" h,w=im.shape\n",
" profile=update_profile(profile,h,w,affine)\n",
" return im, profile\n",
"\n",
"\n",
"def show_data(im,profile,figsize=(8,4),x_max=10,bins=256):\n",
" print(\"IMAGE SHAPE:\",im.shape)\n",
" pprint(profile)\n",
" show(im)\n",
" fig,ax=plt.subplots(1,figsize=figsize)\n",
" ax.set_xlim((0,x_max))\n",
" show_hist(im,ax=ax,bins=bins)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### CLEAN UP GEOMS"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"200.0\n"
]
},
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"241009.2 1925689.2 11.599999999976717 21.600000000093132\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,3851400.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.43200000000186267\" opacity=\"0.6\" d=\"M 241010.0,1925710.0 L 241020.0,1925710.0 L 241020.0,1925690.0 L 241010.0,1925690.0 L 241010.0,1925710.0 z\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.polygon.Polygon at 0x11a029160>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bad=mask_df[mask_df.area<1000].sample(1).iloc[0]\n",
"print(bad.geometry.area)\n",
"bad.geometry"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"MIN_AREA=99000.0\n",
"MAX_AREA=101000.0"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NB AOIs: 78\n"
]
}
],
"source": [
"aois=mask_df[(mask_df.area>MIN_AREA) & (mask_df.area<MAX_AREA)].copy()\n",
"aois.loc[:,'geometry']=aois.geometry.apply(lambda g: g.buffer(25))\n",
"aois=aois.reset_index(drop=True)\n",
"print(\"NB AOIs:\",aois.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
"<shapely.geometry.polygon.Polygon at 0x121fd87b8>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aois.sample(1).iloc[0].geometry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### SET CRS"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"aue_src,aue_im,aue_p=image_data('aue.tif',src=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"aois=aois.to_crs(aue_p['crs'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<shapely.geometry.polygon.Polygon at 0x1220352e8>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aois.sample(1).iloc[0].geometry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### GET GEOMS"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"aois_geojson=json.loads(aois.to_json())\n",
"aois_geoms=[[f['geometry']] for f in aois_geojson['features']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### CUT OUT AOIS"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"aois_ims=[cut_img(aue_src,g) for g in aois_geoms]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"test_im,test_profile=choice(aois_ims)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IMAGE SHAPE: (1, 42, 41)\n",
"{'affine': Affine(10.0, 0.0, 235720.0,\n",
" 0.0, -10.0, 1916740.0),\n",
" 'count': 1,\n",
" 'crs': CRS({'init': 'epsg:32644'}),\n",
" 'driver': 'GTiff',\n",
" 'dtype': 'uint8',\n",
" 'height': 41,\n",
" 'interleave': 'band',\n",
" 'nodata': 255.0,\n",
" 'tiled': False,\n",
" 'width': 42}\n"
]
},
{
"data": {
"image/png": 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bsBn4KaPPTp9u+/g1xP9V4DjwGqN3KNsYfW7aDRwq7pd1HecCzuf3Gb312w/sK26bh3hOwO8BTxfncgD422L7O4HHgcPAfwJv6jrWKc7teuCBac/HM+PMEvDMOLMEnOhmCTjRzRJwopsl4EQ3S8CJbpaAE90sASe6WQL/B2ablR2BO0fwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_data(test_im,test_profile)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"test_im_crp,test_profile_crp=crop(aue_src,test_im,test_profile)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IMAGE SHAPE: (1, 41, 41)\n",
"{'affine': Affine(10.0, 0.0, 195690.0,\n",
" 0.0, -10.0, 1963600.0),\n",
" 'count': 1,\n",
" 'crs': CRS({'init': 'epsg:32644'}),\n",
" 'driver': 'GTiff',\n",
" 'dtype': 'uint8',\n",
" 'height': 41,\n",
" 'interleave': 'band',\n",
" 'nodata': 255.0,\n",
" 'tiled': False,\n",
" 'width': 41}\n"
]
},
{
"data": {
"image/png": 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3/fo1HcNXgHngTQbvZnYw+Ny1FzhS3K+cdpxjHtPvMnireBA4UNy29vm4gN9msOL0IHAI+Jti+3XAU8BR4D+At0w71gmP72bgkUmPyTP8zJLyDD+zpJz8Zkk5+c2ScvKbJeXkN0vKyW+WlJPfLCknv1lS/w8ckJFc5kBaFAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_data(test_im_crp,test_profile_crp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"#### SAVE IMAGES"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def save_all():\n",
" for i,(im,p) in enumerate(aois_ims):\n",
" im,p=crop(aue_src,im,p)\n",
" image_write(im,f'aoi_{i}',p)\n",
" if not i % 5:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"5\n",
"10\n",
"15\n",
"20\n",
"25\n",
"30\n",
"35\n",
"40\n",
"45\n",
"50\n",
"55\n",
"60\n",
"65\n",
"70\n",
"75\n",
"CPU times: user 92.3 ms, sys: 25.9 ms, total: 118 ms\n",
"Wall time: 116 ms\n"
]
}
],
"source": [
"%time save_all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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