#!/usr/bin/env python
# coding: utf-8

# # Machine Learning with Earth Engine - Unsupervised Classification

# ## Unsupervised classification algorithms available in Earth Engine
# 
# Source: https://developers.google.com/earth-engine/clustering
# 
# The `ee.Clusterer` package handles unsupervised classification (or clustering) in Earth Engine. These algorithms are currently based on the algorithms with the same name in [Weka](http://www.cs.waikato.ac.nz/ml/weka/). More details about each Clusterer are available in the reference docs in the Code Editor.
# 
# Clusterers are used in the same manner as classifiers in Earth Engine. The general workflow for clustering is:
# 
# 1. Assemble features with numeric properties in which to find clusters.
# 2. Instantiate a clusterer. Set its parameters if necessary.
# 3. Train the clusterer using the training data.
# 4. Apply the clusterer to an image or feature collection.
# 5. Label the clusters.
# 
# The training data is a `FeatureCollection` with properties that will be input to the clusterer. Unlike classifiers, there is no input class value for an `Clusterer`. Like classifiers, the data for the train and apply steps are expected to have the same number of values. When a trained clusterer is applied to an image or table, it assigns an integer cluster ID to each pixel or feature.
# 
# Here is a simple example of building and using an ee.Clusterer:
# 

# ![](https://i.imgur.com/IcBapEx.png)

# ## Step-by-step tutorial
# 
# ### Import libraries

# In[ ]:


import ee
import geemap


# ### Create an interactive map

# In[ ]:


Map = geemap.Map()
Map


# ### Add data to the map

# In[ ]:


# point = ee.Geometry.Point([-122.4439, 37.7538])
point = ee.Geometry.Point([-87.7719, 41.8799])

image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') \
    .filterBounds(point) \
    .filterDate('2019-01-01', '2019-12-31') \
    .sort('CLOUD_COVER') \
    .first() \
    .select('B[1-7]')

vis_params = {
    'min': 0,
    'max': 3000,
    'bands': ['B5', 'B4', 'B3']
}

Map.centerObject(point, 8)
Map.addLayer(image, vis_params, "Landsat-8")


# ### Check image properties

# In[ ]:


props = geemap.image_props(image)
props.getInfo()


# In[ ]:


props.get('IMAGE_DATE').getInfo()


# In[ ]:


props.get('CLOUD_COVER').getInfo()


# ### Make training dataset
# 
# There are several ways you can create a region for generating the training dataset.
# 
# - Draw a shape (e.g., rectangle) on the map and the use `region = Map.user_roi`
# - Define a geometry, such as `region = ee.Geometry.Rectangle([-122.6003, 37.4831, -121.8036, 37.8288])`
# - Create a buffer zone around a point, such as `region = ee.Geometry.Point([-122.4439, 37.7538]).buffer(10000)`
# - If you don't define a region, it will use the image footprint by default

# In[ ]:


# region = Map.user_roi
# region = ee.Geometry.Rectangle([-122.6003, 37.4831, -121.8036, 37.8288])
# region = ee.Geometry.Point([-122.4439, 37.7538]).buffer(10000)


# In[ ]:


# Make the training dataset.
training = image.sample(**{
#     'region': region,
    'scale': 30,
    'numPixels': 5000,
    'seed': 0,
    'geometries': True  # Set this to False to ignore geometries
})

Map.addLayer(training, {}, 'training', False)
Map


# ### Train the clusterer

# In[ ]:


# Instantiate the clusterer and train it.
n_clusters = 5
clusterer = ee.Clusterer.wekaKMeans(n_clusters).train(training)


# ### Classify the image

# In[ ]:


# Cluster the input using the trained clusterer.
result = image.cluster(clusterer)

# # Display the clusters with random colors.
Map.addLayer(result.randomVisualizer(), {}, 'clusters')
Map


# ### Label the clusters

# In[ ]:


legend_keys = ['One', 'Two', 'Three', 'Four', 'ect']
legend_colors = ['#8DD3C7', '#FFFFB3', '#BEBADA', '#FB8072', '#80B1D3']

# Reclassify the map
result = result.remap([0, 1, 2, 3, 4], [1, 2, 3, 4, 5])

Map.addLayer(result, {'min': 1, 'max': 5, 'palette': legend_colors}, 'Labelled clusters')
Map.add_legend(legend_keys=legend_keys, legend_colors=legend_colors, position='bottomright')
Map


# ### Visualize the result

# In[ ]:


print('Change layer opacity:')
cluster_layer = Map.layers[-1]
cluster_layer.interact(opacity=(0, 1, 0.1))


# ### Export the result
# 
# Export the result directly to your computer:

# In[ ]:


import os
out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')
out_file = os.path.join(out_dir, 'cluster.tif')


# In[ ]:


geemap.ee_export_image(result, filename=out_file, scale=90)


# Export the result to Google Drive:

# In[ ]:


geemap.ee_export_image_to_drive(result, description='clusters', folder='export', scale=90)