tree_classification_datapre.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmath\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mrasterio\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtqdm\u001b[39;00m \u001b[39mimport\u001b[39;00m tqdm\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
     ]
    }
   ],
   "source": [
    "# import the libraries you need\n",
    "\n",
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import rasterio\n",
    "from tqdm import tqdm\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import matplotlib.image as mpimg\n",
    "from matplotlib.image import imread\n",
    "from itertools import product\n",
    "from PIL import Image\n",
    "from itertools import chain\n",
    "import json\n",
    "from jsonpath import jsonpath \n",
    "\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.utils.multiclass import type_of_target\n",
    "\n",
    "import tensorflow as tf\n",
    "import torch\n",
    "# Folium setup.\n",
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "B11\n",
      "B11_1\n",
      "B11_2\n",
      "B11_3\n",
      "B12\n",
      "B12_1\n",
      "B12_2\n",
      "B12_3\n",
      "B2\n",
      "B2_1\n",
      "B2_2\n",
      "B2_3\n",
      "B3\n",
      "B3_1\n",
      "B3_2\n",
      "B3_3\n",
      "B4\n",
      "B4_1\n",
      "B4_2\n",
      "B4_3\n",
      "B5\n",
      "B5_1\n",
      "B5_2\n",
      "B5_3\n",
      "B6\n",
      "B6_1\n",
      "B6_2\n",
      "B6_3\n",
      "B7\n",
      "B7_1\n",
      "B7_2\n",
      "B7_3\n",
      "B8\n",
      "B8A\n",
      "B8A_1\n",
      "B8A_2\n",
      "B8A_3\n",
      "B8_1\n",
      "B8_2\n",
      "B8_3\n",
      "(9, 9)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x28de09d00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "path = '/Users/siruiwang/Documents/ESPACE-LECTURE/3rd_semester/Data science of earth observation/Project_data_science/data0113/GeoJson/untitled folder/Betula_pendula_0113.geojson'\n",
    "index=5\n",
    "tree_type = 'Betula_pendula'\n",
    "kernel=9\n",
    "bands=40\n",
    "with open(path) as f:\n",
    "    data = json.load(f)\n",
    "for feature in data['features']:\n",
    "    for properties in feature['properties']:\n",
    "        print(properties)\n",
    "\n",
    "B8 = jsonpath(data, \"$..B8\") \n",
    "\n",
    "B8data=np.array(B8[0])\n",
    "print(B8data.shape)\n",
    "plt.imshow(B8data) \n",
    "#print(ddate) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "B2 = jsonpath(data, \"$..B2\") \n",
    "B3 = jsonpath(data, \"$..B3\") \n",
    "B4 = jsonpath(data, \"$..B4\") \n",
    "B5 = jsonpath(data, \"$..B5\") \n",
    "B6 = jsonpath(data, \"$..B6\") \n",
    "B7 = jsonpath(data, \"$..B7\") \n",
    "B8 = jsonpath(data, \"$..B8\") \n",
    "B8A = jsonpath(data, \"$..B8A\") \n",
    "B11 = jsonpath(data, \"$..B11\") \n",
    "B12 = jsonpath(data, \"$..B12\") \n",
    "B2_1 = jsonpath(data, \"$..B2_1\") \n",
    "B3_1 = jsonpath(data, \"$..B3_1\") \n",
    "B4_1 = jsonpath(data, \"$..B4_1\") \n",
    "B5_1 = jsonpath(data, \"$..B5_1\") \n",
    "B6_1 = jsonpath(data, \"$..B6_1\") \n",
    "B7_1 = jsonpath(data, \"$..B7_1\") \n",
    "B8_1 = jsonpath(data, \"$..B8_1\") \n",
    "B8A_1 = jsonpath(data, \"$..B8A_1\") \n",
    "B11_1 = jsonpath(data, \"$..B11_1\") \n",
    "B12_1 = jsonpath(data, \"$..B12_1\") \n",
    "B2_2 = jsonpath(data, \"$..B2_2\") \n",
    "B3_2 = jsonpath(data, \"$..B3_2\") \n",
    "B4_2 = jsonpath(data, \"$..B4_2\") \n",
    "B5_2 = jsonpath(data, \"$..B5_2\") \n",
    "B6_2 = jsonpath(data, \"$..B6_2\") \n",
    "B7_2 = jsonpath(data, \"$..B7_2\") \n",
    "B8_2 = jsonpath(data, \"$..B8_2\") \n",
    "B8A_2 = jsonpath(data, \"$..B8A_2\") \n",
    "B11_2 = jsonpath(data, \"$..B11_2\") \n",
    "B12_2 = jsonpath(data, \"$..B12_2\") \n",
    "B2_3 = jsonpath(data, \"$..B2_3\") \n",
    "B3_3 = jsonpath(data, \"$..B3_3\") \n",
    "B4_3 = jsonpath(data, \"$..B4_3\") \n",
    "B5_3 = jsonpath(data, \"$..B5_3\") \n",
    "B6_3 = jsonpath(data, \"$..B6_3\") \n",
    "B7_3 = jsonpath(data, \"$..B7_3\") \n",
    "B8_3 = jsonpath(data, \"$..B8_3\") \n",
    "B8A_3 = jsonpath(data, \"$..B8A_3\") \n",
    "B11_3 = jsonpath(data, \"$..B11_3\") \n",
    "B12_3 = jsonpath(data, \"$..B12_3\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(564, 9, 9, 40)\n"
     ]
    }
   ],
   "source": [
    "number_samples = np.size(B8,0)\n",
    "dataset_spring= np.zeros((number_samples,   kernel,kernel,10), dtype=float)\n",
    "dataset_summer= np.zeros((number_samples,   kernel,kernel,10), dtype=float)\n",
    "dataset_autumn= np.zeros((number_samples,   kernel,kernel,10), dtype=float)\n",
    "dataset_winter= np.zeros((number_samples,   kernel,kernel,10), dtype=float)\n",
    "dataset= np.zeros((number_samples,   kernel,kernel,bands), dtype=float)\n",
    "for i in range(0,number_samples-1):\n",
    "      dataset_spring[i]=np.dstack((np.array(B2[i]),np.array(B3[i]),np.array(B4[i]),np.array(B5[i]),np.array(B6[i]),np.array(B7[i]),np.array(B8[i]),np.array(B8A[i]),np.array(B11[i]),np.array(B12[i])))\n",
    "      dataset_summer[i]=np.dstack((np.array(B2_1[i]),np.array(B3_1[i]),np.array(B4_1[i]),np.array(B5_1[i]),np.array(B6_1[i]),np.array(B7_1[i]),np.array(B8_1[i]),np.array(B8A_1[i]),np.array(B11_1[i]),np.array(B12_1[i])))\n",
    "      dataset_autumn[i]=np.dstack((np.array(B2_2[i]),np.array(B3_2[i]),np.array(B4_2[i]),np.array(B5_2[i]),np.array(B6_2[i]),np.array(B7_2[i]),np.array(B8_2[i]),np.array(B8A_2[i]),np.array(B11_2[i]),np.array(B12_2[i])))\n",
    "      dataset_winter[i]=np.dstack((np.array(B2_3[i]),np.array(B3_3[i]),np.array(B4_3[i]),np.array(B5_3[i]),np.array(B6_3[i]),np.array(B7_3[i]),np.array(B8_3[i]),np.array(B8A_3[i]),np.array(B11_3[i]),np.array(B12_3[i])))\n",
    "      dataset[i]=np.dstack((np.array(dataset_spring[i]),np.array(dataset_summer[i]),np.array(dataset_autumn[i]),np.array(dataset_winter[i])))\n",
    "print(np.array(dataset).shape)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def navie_sample(src_image, label):\n",
    "    # forest-1, debris-2, water-3\n",
    "    classes = label\n",
    "    # get the feature space from drone image\n",
    "    #with rasterio.open(src_image) as src_ds:\n",
    "    #    src = src_ds.read()\n",
    "       \n",
    "    yield (src_image, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(563, 1, 2)\n"
     ]
    }
   ],
   "source": [
    "########### define the result train/valid file #############\n",
    "### Here the tiles are then saved into numpy arraies\n",
    "root = r\"/Users/siruiwang/Documents/ESPACE-LECTURE/3rd_semester/Data science of earth observation/Project_data_science/data0113/npy/\"\n",
    "\n",
    "# training samples\n",
    "t_root = root + tree_type + \"_samples.npy\"\n",
    "t_sample = []\n",
    "num_sam = dataset.shape[0]\n",
    "for i in range(0,num_sam-1):\n",
    "    result = list(navie_sample(dataset[i], index))\n",
    "    t_sample.append(result)\n",
    "t_sample_array = np.array(t_sample, dtype=object)\n",
    "print(t_sample_array.shape)\n",
    "np.save(t_root, t_sample_array)"
   ]
  }
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