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Browse files- README.md +9 -12
- desertIArag/303/263n.png +0 -0
- desertificacion_ai.py +360 -0
- requirements.txt +5 -0
README.md
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@@ -1,12 +1,9 @@
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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# desertificacion-AI-Streamlit
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## AI Saturdays - I Edición Zaragoza - CURSO BÁSICO DE IA
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### Streamlit app
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Enlace para lanzar la aplicación:
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https://share.streamlit.io/desertificacion-ai/desertificacion-ai-streamlit/main/desertificacion_ai.py
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desertIArag/303/263n.png
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desertificacion_ai.py
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import matplotlib.pyplot as plt
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import numpy as np
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import random
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import skimage.io as skio
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import streamlit as st
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from PIL import Image
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from rasterio import plot
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.metrics import mean_squared_error
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# Image.MAX_IMAGE_PIXELS = None
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st.set_page_config(
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page_title='Aplicación megachula',
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page_icon=':cactus:',
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layout='centered'
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)
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# Título
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# ======
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image = Image.open('desertIAragón.png')
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st.image(image, width=700)
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st.title('Desertificación en Aragón')
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st.subheader('AI Saturdays - I Edición Zaragoza - CURSO BÁSICO DE IA')
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# Librerías
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# =========
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st.markdown('---')
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with st.expander('Librerías'):
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st.markdown("""
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* matplotlib   3.5.1
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* numpy     1.22.2
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* PIL         9.0.1
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* rasterio     1.2.10
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* scikit-image   0.19.2
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* scikit-learn   1.0.2
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* streamlit     1.5.1
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""")
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# Imágenes satelitales
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# ====================
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@st.cache
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def lee_imagenes():
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img_name_1 = 'imagen_1.tif'
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img_name_2 = 'imagen_2.tif'
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img_name_3 = 'imagen_3.tif'
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img_name_4 = 'imagen_4.tif'
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img_name_5 = 'imagen_5.tif'
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img_name_6 = 'imagen_6.tif'
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img1 = skio.imread(img_name_1, plugin='pil')
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img2 = skio.imread(img_name_2, plugin='pil')
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img3 = skio.imread(img_name_3, plugin='pil')
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img4 = skio.imread(img_name_4, plugin='pil')
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img5 = skio.imread(img_name_5, plugin='pil')
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img6 = skio.imread(img_name_6, plugin='pil')
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return img1, img2, img3, img4, img5, img6
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img1 = lee_imagenes()[0]
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img2 = lee_imagenes()[1]
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img3 = lee_imagenes()[2]
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img4 = lee_imagenes()[3]
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img5 = lee_imagenes()[4]
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img6 = lee_imagenes()[5]
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# @st.cache
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def muestra_imagenes():
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st.markdown('---')
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st.subheader('Imágenes satelitales')
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col11, col12 = st.columns(2)
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with col11:
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st.write('1 - 20210316 - Tamaño:', img1.shape)
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st.image(img1, width=350, clamp=True)
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with col12:
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st.write('2 - 20210405 - Tamaño:', img1.shape)
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st.image(img2, width=350, clamp=True)
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col21, col22 = st.columns(2)
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with col21:
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st.write('3 - 20210505 - Tamaño:', img1.shape)
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st.image(img3, width=350, clamp=True)
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with col22:
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st.write('4 - 20210525 - Tamaño:', img1.shape)
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st.image(img4, width=350, clamp=True)
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col31, col32 = st.columns(2)
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with col31:
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st.write('5 - 20210614 - Tamaño:', img1.shape)
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st.image(img5, width=350, clamp=True)
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with col32:
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st.write('6 - 20210624 - Tamaño:', img1.shape)
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st.image(img6, width=350, clamp=True)
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muestra_imagenes()
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# Banda lateral
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# =============
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st.sidebar.markdown('# Cuadrícula de estudio')
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st.sidebar.markdown("""
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Cuadrícula de estudio dentro de la imagen, con un ancho y un alto en píxeles según lo indicado.
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La esquina superior izquierda de la cuadrícula se determinará con las coodenadas $x_0$ e $y_0$.
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"""
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)
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# Sidebar
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# -------
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x0 = 0
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x1 = min(img1.shape[1], img2.shape[1], img3.shape[1], img4.shape[1], img5.shape[1], img6.shape[1])
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y0 = 0
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y1 = min(img1.shape[0], img2.shape[0], img3.shape[0], img4.shape[0], img5.shape[0], img6.shape[0])
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coord_x = st.sidebar.slider('Coordenada x.0', x0, x1)
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coord_y = st.sidebar.slider('Coordenada y.0', y0, y1)
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a0 = 1
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a1 = 250
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ancho = st.sidebar.slider('Ancho/Alto de la cuadrícula', a0, a1, value=5)
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if coord_x + ancho > x1:
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coord_x = x1 - ancho
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xn = x1
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else:
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xn = coord_x + ancho
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if coord_y + ancho > y1:
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coord_y = y1 - ancho
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yn = y1
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else:
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yn = coord_y + ancho
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# st.sidebar.write('x0', coord_x)
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# st.sidebar.write('y0', coord_y)
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# st.sidebar.write('xn', xn)
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# st.sidebar.write('yn', yn)
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# st.sidebar.write('ancho', ancho)
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# st.sidebar.write('x1', x1)
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# st.sidebar.write('y1', y1)
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# Cuadrículas
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# ===========
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st.markdown('---')
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st.subheader('Cuadriculas de estudio e índice de vegetación NDVI')
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x0 = coord_x
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y0 = coord_y
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st.markdown(f'Coordenadas de la esquina superior izquierda:   $x_0$ * $y_0$ = {x0} * {y0} px')
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st.markdown(f'Coordenadas de la esquina superior derecha:   $x_n$ * $y_n$ = {xn} * {yn} px')
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cuadricula1 = img1[y0:yn, x0:xn]
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cuadricula2 = img2[y0:yn, x0:xn]
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cuadricula3 = img3[y0:yn, x0:xn]
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cuadricula4 = img4[y0:yn, x0:xn]
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cuadricula5 = img5[y0:yn, x0:xn]
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cuadricula6 = img6[y0:yn, x0:xn]
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cuadriculas = (cuadricula1, cuadricula2, cuadricula3, cuadricula4, cuadricula5, cuadricula6)
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def zona_ndvi(ndvi):
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"""Zona de representación según el rango."""
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if ndvi < 0:
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return 'Zona sin vegetación'
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elif ndvi > 0.3:
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return 'Zona con vegetación'
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else:
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return 'Zona con algo de vegetación'
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i = 0
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for cuadricula in cuadriculas:
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i += 1
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st.markdown(f'')
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st.markdown(f'##### Cuadrícula {i}')
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st.image(cuadricula, width=400, clamp=True)
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st.text('Valor NDVI de cada píxel de la cuadrícula:')
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np_data = np.asarray(cuadricula)
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st.write(np.round(np_data, 3))
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np_data_mean = np_data.mean()
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st.markdown(f'##### Valor promedio NDVI del conjunto de píxeles de la cuadrícula:   {np_data_mean:.3f}')
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zona = zona_ndvi(np_data_mean)
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st.markdown(f'##### Interpretación del valor promedio NDVI de la cuadrícula {i}   →   {zona}')
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# Random Forest
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# =============
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st.markdown('---')
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st.subheader('Predicción de la IA')
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st.markdown(
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"""
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El sistema se entrena con una porción igual de cada una de las imágenes 1 a 5. Esta porción es un recuadro de \
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coordenadas aleatorias y dimensiones igual al ancho/alto de la cuadrícula indicado en la banda lateral.
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La razón de elegir un recuadro reducido es el elevado coste computacional que tiene el entrenamiento del sistema. \
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De esta forma la aplicación puede mostrar unos resultados de una forma relativamente ágil.
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Para el test se ha elegido las imágenes de las cuadrículas 1 a 5 con las que se ha obtenido el índice NDVI.
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La predicción se hace con la cuadrícula 6. Se compara la imagen original con la predicha por la IA.
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"""
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)
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# Imágenes de entrenamiento
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# -------------------------
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numpydata1 = np.asarray(img1)
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numpydata2 = np.asarray(img2)
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numpydata3 = np.asarray(img3)
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numpydata4 = np.asarray(img4)
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numpydata5 = np.asarray(img5)
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# Imágenes para test
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# ------------------
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numpydata6 = np.asarray(img6)
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X = []
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y = []
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dim = ancho
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i0 = random.randint(0, x1 - dim) # Mitad izquierda de la imagen
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j0 = random.randint(0, y1 - dim)
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ik = i0 + dim - 2
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jk = j0 + dim - 2
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for i in range(i0, ik):
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for j in range(j0, jk):
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X.append(
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[
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numpydata1[i, j], numpydata1[i, j + 1], numpydata1[i, j + 2],
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numpydata1[i + 1, j], numpydata1[i + 1, j + 1], numpydata1[i + 1, j + 2],
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numpydata1[i + 2, j], numpydata1[i + 2, j + 1], numpydata1[i + 2, j + 2],
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numpydata2[i, j], numpydata2[i, j + 1], numpydata2[i, j + 2],
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numpydata2[i + 1, j], numpydata2[i + 1, j + 1], numpydata2[i + 1, j + 2],
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numpydata2[i + 2, j], numpydata2[i + 2, j + 1], numpydata2[i + 2, j + 2],
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numpydata3[i, j], numpydata3[i, j + 1], numpydata3[i, j + 2],
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numpydata3[i + 1, j], numpydata3[i + 1, j + 1], numpydata3[i + 1, j + 2],
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247 |
+
numpydata3[i + 2, j], numpydata3[i + 2, j + 1], numpydata3[i + 2, j + 2],
|
248 |
+
numpydata4[i, j], numpydata4[i, j + 1], numpydata4[i, j + 2],
|
249 |
+
numpydata4[i + 1, j], numpydata4[i + 1, j + 1], numpydata4[i + 1, j + 2],
|
250 |
+
numpydata4[i + 2, j], numpydata4[i + 2, j + 1], numpydata4[i + 2, j + 2],
|
251 |
+
numpydata5[i, j], numpydata5[i, j + 1], numpydata5[i, j + 2],
|
252 |
+
numpydata5[i + 1, j], numpydata5[i + 1, j + 1], numpydata5[i + 1, j + 2],
|
253 |
+
numpydata5[i + 2, j], numpydata5[i + 2, j + 1], numpydata5[i + 2, j + 2]
|
254 |
+
]
|
255 |
+
)
|
256 |
+
y.append(numpydata6[i + 1, j + 1])
|
257 |
+
|
258 |
+
# Clasificador
|
259 |
+
# ------------
|
260 |
+
|
261 |
+
cls = RandomForestRegressor()
|
262 |
+
|
263 |
+
# Entrenamiento
|
264 |
+
# -------------
|
265 |
+
|
266 |
+
cls.fit(X, y)
|
267 |
+
|
268 |
+
A = []
|
269 |
+
b = []
|
270 |
+
|
271 |
+
i0 = x0
|
272 |
+
j0 = y0
|
273 |
+
ik = i0 + dim
|
274 |
+
jk = j0 + dim
|
275 |
+
|
276 |
+
for i in range(j0, jk):
|
277 |
+
for j in range(i0, ik):
|
278 |
+
A.append(
|
279 |
+
[
|
280 |
+
numpydata1[i, j], numpydata1[i, j + 1], numpydata1[i, j + 2],
|
281 |
+
numpydata1[i + 1, j], numpydata1[i + 1, j + 1], numpydata1[i + 1, j + 2],
|
282 |
+
numpydata1[i + 2, j], numpydata1[i + 2, j + 1], numpydata1[i + 2, j + 2],
|
283 |
+
numpydata2[i, j], numpydata2[i, j + 1], numpydata2[i, j + 2],
|
284 |
+
numpydata2[i + 1, j], numpydata2[i + 1, j + 1], numpydata2[i + 1, j + 2],
|
285 |
+
numpydata2[i + 2, j], numpydata2[i + 2, j + 1], numpydata2[i + 2, j + 2],
|
286 |
+
numpydata3[i, j], numpydata3[i, j + 1], numpydata3[i, j + 2],
|
287 |
+
numpydata3[i + 1, j], numpydata3[i + 1, j + 1], numpydata3[i + 1, j + 2],
|
288 |
+
numpydata3[i + 2, j], numpydata3[i + 2, j + 1], numpydata3[i + 2, j + 2],
|
289 |
+
numpydata4[i, j], numpydata4[i, j + 1], numpydata4[i, j + 2],
|
290 |
+
numpydata4[i + 1, j], numpydata4[i + 1, j + 1], numpydata4[i + 1, j + 2],
|
291 |
+
numpydata4[i + 2, j], numpydata4[i + 2, j + 1], numpydata4[i + 2, j + 2],
|
292 |
+
numpydata5[i, j], numpydata5[i, j + 1], numpydata5[i, j + 2],
|
293 |
+
numpydata5[i + 1, j], numpydata5[i + 1, j + 1], numpydata5[i + 1, j + 2],
|
294 |
+
numpydata5[i + 2, j], numpydata5[i + 2, j + 1], numpydata5[i + 2, j + 2]
|
295 |
+
]
|
296 |
+
)
|
297 |
+
b.append(numpydata6[i + 1, j + 1])
|
298 |
+
|
299 |
+
b_predicho = cls.predict(A)
|
300 |
+
mse = mean_squared_error(b, b_predicho)
|
301 |
+
|
302 |
+
# Plot
|
303 |
+
# ----
|
304 |
+
|
305 |
+
tfreal = b
|
306 |
+
tfpredicho = b_predicho.tolist()
|
307 |
+
|
308 |
+
anp=np.array(tfpredicho)
|
309 |
+
anp=np.reshape(anp, (ik - i0, jk - j0))
|
310 |
+
org=np.array(tfreal)
|
311 |
+
org=np.reshape(org, (ik - i0, jk - j0))
|
312 |
+
|
313 |
+
st.markdown(f'Coordenadas de la esquina superior izquierda:   $i_0$ * $j_0$ = {i0} * {j0} px')
|
314 |
+
st.markdown(f'Coordenadas de la esquina superior derecha:   $i_n$ * $j_n$ = {ik} * {jk} px')
|
315 |
+
|
316 |
+
st.markdown(f'##### Error cuadrático promedio:   {mse:.5f}')
|
317 |
+
|
318 |
+
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
|
319 |
+
ax1.imshow(org.astype(np.float64), interpolation='nearest')
|
320 |
+
ax2.imshow(anp.astype(np.float64), interpolation='nearest')
|
321 |
+
ax1.set_title('Imagen original')
|
322 |
+
ax2.set_title('Imagen predicha por la IA')
|
323 |
+
st.pyplot(fig)
|
324 |
+
|
325 |
+
np_data_6 = np.asarray(cuadricula6)
|
326 |
+
|
327 |
+
np_data_mean_6 = np_data_6.mean()
|
328 |
+
np_data_mean_anp = anp.mean()
|
329 |
+
|
330 |
+
st.markdown(f'##### Valor promedio NDVI del conjunto de píxeles:')
|
331 |
+
st.markdown(f'##### Imagen original:            {np_data_mean_6:.3f}')
|
332 |
+
st.markdown(f'##### Cuadrícula predicha por la IA:   {np_data_mean_anp:.3f}')
|
333 |
+
|
334 |
+
zona_6 = zona_ndvi(np_data_mean_6)
|
335 |
+
zona_anp = zona_ndvi(np_data_mean_anp)
|
336 |
+
st.markdown(f'##### Interpretación del valor promedio NDVI:')
|
337 |
+
st.markdown(f'##### Imagen original:             →   {zona_6}')
|
338 |
+
st.markdown(f'##### Cuadrícula predicha por la IA:   →   {zona_anp}')
|
339 |
+
|
340 |
+
# Créditos
|
341 |
+
# ========
|
342 |
+
|
343 |
+
st.markdown('---')
|
344 |
+
with st.expander('Créditos'):
|
345 |
+
st.markdown(
|
346 |
+
"""
|
347 |
+
06/03/2022
|
348 |
+
|
349 |
+
Autores:
|
350 |
+
|
351 |
+
* [Eva de Miguel](https://www.linkedin.com/in/eva-de-miguel-morales-a63938a0/)
|
352 |
+
* [Pedro Biel](www.linkedin.com/in/pedrobiel)
|
353 |
+
* [Yinet Castiblanco](https://www.linkedin.com/in/yinethcastiblancorojas/)
|
354 |
+
|
355 |
+
---
|
356 |
+
|
357 |
+
* **Artículo Medium** [Medium](https://medium.com/saturdays-ai/predicci%C3%B3n-de-zonas-de-desertificaci%C3%B3n-en-arag%C3%B3n-usando-ia-ee59c15c12a5)
|
358 |
+
* **Código fuente:** [GitHub](https://github.com/desertificacion-AI/desertificacion-AI)
|
359 |
+
"""
|
360 |
+
)
|
requirements.txt
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
matplotlib==3.5.1
|
2 |
+
rasterio==1.2.10
|
3 |
+
scikit-image==0.19.2
|
4 |
+
scikit-learn==1.0.2
|
5 |
+
streamlit==1.5.1
|