385 lines
33 KiB
Plaintext
385 lines
33 KiB
Plaintext
{
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"cells": [
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{
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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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/dlsaavedra/anaconda3/envs/new/lib/python3.7/site-packages/pysal/explore/segregation/network/network.py:16: UserWarning: You need pandana and urbanaccess to work with segregation's network module\n",
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"You can install them with `pip install urbanaccess pandana` or `conda install -c udst pandana urbanaccess`\n",
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" \"You need pandana and urbanaccess to work with segregation's network module\\n\"\n",
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"/home/dlsaavedra/anaconda3/envs/new/lib/python3.7/site-packages/pysal/model/spvcm/abstracts.py:10: UserWarning: The `dill` module is required to use the sqlite backend fully.\n",
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" from .sqlite import head_to_sql, start_sql\n"
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]
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}
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],
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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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"\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"import time\n",
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"import tensorflow as tf\n",
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"\n",
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"\n",
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"def classifier(base_model, recognizer, labels, image_input):\n",
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" weight, height, dim = base_model.input.shape[1], base_model.input.shape[2], base_model.input.shape[3]\n",
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" Im_resize = cv2.resize(image_input, (weight, height), interpolation = cv2.INTER_AREA)\n",
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" vec = base_model.predict(tf.keras.backend.expand_dims(Im_resize,0)).flatten()\n",
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" prob = recognizer.predict_proba([vec])[0]\n",
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" return labels.classes_[np.argmax(prob)], prob[np.argmax(prob)]\n",
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" "
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"path_T = \"Los_Loros/TH_02_index_thermal_ir.tif\"\n",
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"\n",
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"\n",
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"ZonaPV = 'Test'\n",
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"path_kml_panel = 'Los_Loros/KML/Paneles_' + ZonaPV +'.kml'\n",
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"path_kml_mesa ='Los_Loros/KML/Mesa_' + ZonaPV +'.kml'\n",
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"path_dict = 'Los_Loros/KML/Mesa_' + ZonaPV + '.pickle'\n",
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"path_new_dict = 'Los_Loros/KML/Mesa_' + ZonaPV + '_classifier.pickle'\n",
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"\n",
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"\n",
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"GR_T.raster.data[GR_T.raster.data == -10000] = 0\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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"## 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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"# Load Classifier"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"path_dataset = './Classifier/Data_set_2/Data_prueba_0/' \n",
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"output_recognizer = path_dataset + \"model_SVM/recognizer.pickle\"\n",
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"output_label = path_dataset + \"model_SVM/le.pickle\"\n",
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"\n",
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"\n",
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"img_width, img_height = 224, 224\n",
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"base_model = tf.keras.applications.Xception(input_shape=(img_height, img_width, 3), weights='imagenet', include_top=False)\n",
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"\n",
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"x = base_model.output\n",
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"x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
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"base_model = tf.keras.models.Model(inputs=base_model.input, outputs=x)\n",
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"recognizer_SVM = pickle.loads(open(output_recognizer, \"rb\").read())\n",
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"le = pickle.loads(open(output_label, \"rb\").read())"
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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": 17,
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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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"2\n",
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"3\n",
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"4\n",
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"5\n",
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"6\n",
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"7\n",
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"8\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7fdc745c1110>"
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]
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},
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"execution_count": 17,
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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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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"epsilon = -2\n",
|
|
"matrix_expand_bounds = [[-epsilon, -epsilon],[+epsilon, -epsilon], [+epsilon, +epsilon], [-epsilon, +epsilon]]\n",
|
|
"\n",
|
|
"for string_key in L_strings_coord.keys():\n",
|
|
" print(string_key)\n",
|
|
" string = L_strings_coord[string_key]\n",
|
|
" for panel_key in string['panels'].keys():\n",
|
|
" panel = string['panels'][panel_key]\n",
|
|
" Points = Utils.gps2pixel(panel['points'], geot_T) + matrix_expand_bounds\n",
|
|
" \n",
|
|
" if not GR_T.raster.data[Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]].size == 0:\n",
|
|
" Im = np.zeros((img_height, img_width, 3))\n",
|
|
" Im[:,:,0] = cv2.resize(GR_T.raster.data[Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]], (img_width, img_height))\n",
|
|
" Im[:,:,1] = Im[:,:,0].copy()\n",
|
|
" Im[:,:,2] = Im[:,:,0].copy()\n",
|
|
" panel['status'], panel['prob'] = classifier(base_model, recognizer_SVM, le, Im)\n",
|
|
" else:\n",
|
|
" print('problem with coords panel: ', string_key, '_', panel_key)\n",
|
|
"\n",
|
|
"plt.figure(figsize=(6, 6))\n",
|
|
"plt.imshow(Im.astype(int))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.image.AxesImage at 0x7fdc7464dbd0>"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"epsilon = 10\n",
|
|
"matrix_expand_bounds = [[-epsilon, -epsilon],[+epsilon, -epsilon], [+epsilon, +epsilon], [-epsilon, +epsilon]]\n",
|
|
"\n",
|
|
"\n",
|
|
"panel = string['panels']['2']\n",
|
|
"Points = Utils.gps2pixel(panel['points'], geot_T) + matrix_expand_bounds\n",
|
|
"plt.figure(figsize=(6, 6))\n",
|
|
"plt.imshow(GR_T.raster.data[Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]],cmap = 'gray')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Save KML Panels"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Listo\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"kml=simplekml.Kml()\n",
|
|
"\n",
|
|
"\n",
|
|
"for string_key in L_strings_coord.keys():\n",
|
|
" \n",
|
|
" string = L_strings_coord[string_key]\n",
|
|
" points = string['points']\n",
|
|
" \n",
|
|
" for panel_key in string['panels'].keys():\n",
|
|
" panel = string['panels'][panel_key]\n",
|
|
" points = panel['points']\n",
|
|
" \n",
|
|
" pmt = kml.newpolygon(outerboundaryis = points)\n",
|
|
" pmt.extendeddata.newdata(name= 'Id integer', value= str(string_key).zfill(3) + '_' + str(panel['id']).zfill(3))\n",
|
|
" pmt.extendeddata.newdata(name= 'Id panel', value= str(panel['id']).zfill(3))\n",
|
|
" pmt.extendeddata.newdata(name='Zona PV', value= ZonaPV)\n",
|
|
" pmt.extendeddata.newdata(name='Cód. Fall', value= 0)\n",
|
|
" pmt.extendeddata.newdata(name= 'Tipo falla', value= panel['status'])\n",
|
|
" pmt.extendeddata.newdata(name= 'Mesa', value= string['id'])\n",
|
|
" pmt.extendeddata.newdata(name= 'T°', value= panel['T'])\n",
|
|
" \n",
|
|
"kml.save(path_kml_panel)\n",
|
|
"## Save List in coordinate latitud and longitude ###\n",
|
|
"with open(path_new_dict, 'wb') as handle:\n",
|
|
" pickle.dump(L_strings_coord, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
|
|
"print('Listo')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.image.AxesImage at 0x7fdc74d9a8d0>"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.imshow(GR_T.raster.data[Points[0][1] : Points[2][1], Points[0][0]: Points[2][0]].astype(int), cmap = 'gray')"
|
|
]
|
|
},
|
|
{
|
|
"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
|
|
}
|