clip_raster.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99c2bc93",
   "metadata": {},
   "source": [
    "# Issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc3ec89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio as rst\n",
    "from rasterio.plot import show\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1bbc5d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = r'Classifier1_moja.tiff'\n",
    "img = rst.open(fp)\n",
    "show(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e72f7f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundingBox(left=-106.0, bottom=54.0, right=-105.0, top=55.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aebdf849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838f8017",
   "metadata": {},
   "source": [
    "Issue :   A lot of padding around the area of interest.\n",
    "\n",
    "Task  :   Clip a raster to the area of interest."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d37481b",
   "metadata": {},
   "source": [
    "# Finding geometry for a mask"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877debc7",
   "metadata": {},
   "source": [
    "rasterio.features.dataset_features documentation:\n",
    "https://rasterio.readthedocs.io/en/latest/api/rasterio.features.html#rasterio.features.dataset_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "694f2a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio.features import dataset_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b50befc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset_features yields GeoJSON-like Feature dictionaries for shapes found in the given band\n",
    "geom_gen = dataset_features(img, bidx=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16f8cc89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get bounding boxes from all shapes\n",
    "bboxes = [geom['bbox'] for geom in geom_gen]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62496443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-105.84875, 54.48625, -105.8485, 54.4865],\n",
       " [-105.84725, 54.47775, -105.83225, 54.48775],\n",
       " [-105.873, 54.47575, -105.84875, 54.48625],\n",
       " [-105.85275, 54.47475, -105.84275, 54.48675],\n",
       " [-105.864, 54.47375, -105.84925, 54.482],\n",
       " [-105.84725, 54.4745, -105.83175, 54.4825],\n",
       " [-105.871, 54.471, -105.85875, 54.47925],\n",
       " [-105.84375, 54.469, -105.82925, 54.48025],\n",
       " [-105.86975, 54.46675, -105.8535, 54.4755],\n",
       " [-105.861, 54.4675, -105.84025, 54.476],\n",
       " [-105.86125, 54.46725, -105.861, 54.4675]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bboxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5fcdb1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get extremums from the bounding boxes\n",
    "left   = min([bbox[0] for bbox in bboxes])\n",
    "bottom = min([bbox[1] for bbox in bboxes])\n",
    "right  = max([bbox[2] for bbox in bboxes])\n",
    "top    = max([bbox[3] for bbox in bboxes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3acc6b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'POLYGON ((-105.82925 54.46675, -105.82925 54.48775, -105.873 54.48775, -105.873 54.46675, -105.82925 54.46675))'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from shapely.geometry import box\n",
    "\n",
    "# create a POLYGON geometry for the mask\n",
    "geom = box(left, bottom, right, top)\n",
    "geom.wkt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2952f0e",
   "metadata": {},
   "source": [
    "# Masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80102f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio.mask import mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5c3fad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data for a clipped image\n",
    "out_image, out_transform = mask(img, [geom], crop=True, pad=True)\n",
    "out_meta = img.meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2b1ed20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save clipped image in a new file\n",
    "out_meta.update({\"driver\": \"GTiff\",\n",
    "                 \"height\": out_image.shape[1],\n",
    "                 \"width\": out_image.shape[2],\n",
    "                 \"transform\": out_transform})\n",
    "\n",
    "with rst.open(r'Classifier1_moja_masked.tiff', 'w', **out_meta) as dest:\n",
    "    dest.write(out_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e962816",
   "metadata": {},
   "source": [
    "# Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "677d8cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_result = r'Classifier1_moja_masked.tiff'\n",
    "result = rst.open(fp_result)\n",
    "show(result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}