from __future__ import print_function

from SimpleCV import Camera, Display, Image

##import arcpy

##arcpy.CheckOutExtension("Spatial")

import matplotlib.pyplot as plt

import numpy as np

from skimage import io

from skimage.segmentation import quickshift

# The input 4-band NAIP image

river = r'C:\path\to\naip_image.tif'

# Convert image to numpy array

img = io.imread(river)

# Run the quick shift segmentation

segments = quickshift(img, kernel_size=3, convert2lab=False, max_dist=6, ratio=0.5)

print("Quickshift number of segments: %d" % len(np.unique(segments)))

# View the segments via Python

plt.imshow(segments)

### Get raster metrics for coordinate info

##myRaster = arcpy.sa.Raster(river)

##

### Lower left coordinate of block (in map units)

##mx = myRaster.extent.XMin

##my = myRaster.extent.YMin

##sr = myRaster.spatialReference

##

### Note the use of arcpy to convert numpy array to raster

##seg = arcpy.NumPyArrayToRaster(segments, arcpy.Point(mx, my),

## myRaster.meanCellWidth,

## myRaster.meanCellHeight)

##

##outRaster = r'C:\path\to\segments.tif'

##seg_temp = seg.save(outRaster)

##arcpy.DefineProjection_management(outRaster, sr)

##

### Calculate NDVI from bands 4 and 3

##b4 = arcpy.sa.Raster(r'C:\path\to\naip_image.tif\Band_4')

##b3 = arcpy.sa.Raster(r'C:\path\to\naip_image.tif\Band_3')

##ndvi = arcpy.sa.Float(b4-b3) / arcpy.sa.Float(b4+b3)

##

### Extract NDVI values based on image object boundaries

##zones = arcpy.sa.ZonalStatistics(outRaster, "VALUE", ndvi, "MEAN")

##zones.save(r'C:\path\to\zones.tif')

##

### Classify the segments based on NDVI values

##binary = arcpy.sa.Con(zones < 20, 1, 0)

##binary.save(r'C:\path\to\classified_image_objects.tif')