260 lines
29 KiB
Plaintext
260 lines
29 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"from matplotlib import path\n",
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"import matplotlib.patches as patches\n",
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"from skimage import draw\n",
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"import scipy.ndimage as ndimage\n",
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"import Utils\n",
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"import georasters as gr\n",
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"import cv2\n",
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"from Utils import doubleMADsfromMedian\n",
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"from skimage.transform import resize\n",
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"import pickle\n",
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"import simplekml\n"
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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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"# Parameters"
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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": 91,
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"metadata": {},
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"outputs": [],
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"source": [
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"path_T = \"Pampa/THERMAL_06.tif\"\n",
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"path_String = \"Pampa/BP-B-5.tif\"\n",
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"ZonaPV = 'BP-B-5'\n",
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"path_kml_panel = 'Pampa/KML/Paneles_' + ZonaPV +'_classifier.kml'\n",
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"path_dict = 'Pampa/KML/Mesa_' + ZonaPV + '.pickle'\n",
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"\n",
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"GR_String = gr.from_file(path_String)\n",
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"GR_T = gr.from_file(path_T)\n",
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"geot_T = GR_T.geot\n",
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"img_height, img_width = 128,128\n",
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"## Load List in coordinate latitud and longitude ###\n",
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"with open(path_dict, \"rb\") as fp:\n",
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" L_strings_coord = pickle.load(fp)"
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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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"# Classifier each panel"
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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": 92,
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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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"1\n"
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]
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},
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{
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"ename": "NameError",
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"evalue": "name 'classifier' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-92-11368de2a81e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mIm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mIm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mpanel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpanel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'prob'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'problem with coords panel: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_key\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpanel_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'classifier' is not defined"
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]
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}
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],
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"source": [
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"\n",
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"epsilon = 0\n",
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"matrix_expand_bounds = [[-epsilon, -epsilon],[+epsilon, -epsilon], [+epsilon, +epsilon], [-epsilon, +epsilon]]\n",
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"\n",
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"for string_key in ['1', '20']:# L_strings_coord.keys():\n",
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" print(string_key)\n",
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" string = L_strings_coord[string_key]\n",
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" for panel_key in string['panels'].keys():\n",
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" panel = string['panels'][panel_key]\n",
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" Points = Utils.gps2pixel(panel['points'], geot_T) + matrix_expand_bounds\n",
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" \n",
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" if not GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]].size == 0:\n",
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" Im = np.zeros((img_height, img_width, 3))\n",
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" Im[:,:,0] = cv2.resize(GR_T.raster.data[0,Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]], (img_width, img_height))\n",
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" Im[:,:,1] = Im[:,:,0].copy()\n",
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" Im[:,:,2] = Im[:,:,0].copy()\n",
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" panel['status'], panel['prob'] = classifier(Im)\n",
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" else:\n",
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" print('problem with coords panel: ', string_key, '_', panel_key)\n",
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"\n",
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"plt.figure(figsize=(6, 6))\n",
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"plt.imshow(Im.astype(int))"
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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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"panel"
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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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"# Save KML Panels"
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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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"\n",
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"kml=simplekml.Kml()\n",
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"\n",
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"\n",
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"for string_key in L_strings_coord.keys():\n",
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" \n",
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" string = L_strings_coord[string_key]\n",
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" points = string['points']\n",
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" \n",
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" for panel_key in string['panels'].keys():\n",
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" panel = string['panels'][panel_key]\n",
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" points = panel['points']\n",
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" \n",
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" pmt = kml.newpolygon(outerboundaryis = points)\n",
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" pmt.extendeddata.newdata(name= 'Id integer', value= str(string_key).zfill(3) + '_' + str(panel['id']).zfill(3))\n",
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" pmt.extendeddata.newdata(name= 'Id panel', value= str(panel['id']).zfill(3))\n",
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" pmt.extendeddata.newdata(name='Zona PV', value= ZonaPV)\n",
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" pmt.extendeddata.newdata(name='Cód. Fall', value= 0)\n",
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" pmt.extendeddata.newdata(name= 'Tipo falla', value= panel['status'])\n",
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" pmt.extendeddata.newdata(name= 'Mesa', value= string['id'])\n",
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" \n",
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"kml.save(path_kml_panel)\n",
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"print('Listo')"
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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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"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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"cell_type": "code",
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"execution_count": 95,
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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 0x7fd0127a2d10>"
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]
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},
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"execution_count": 95,
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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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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(Im.astype(int))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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": "3.7.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|