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- import numpy as np
- import gdal
- import matplotlib.pyplot as plt
- dataset2 = gdal.Open('/Users/SPH_1_RGB.tif')
- band_1 = dataset2.GetRasterBand(1).ReadAsArray()
- band_2 = dataset2.GetRasterBand(2).ReadAsArray()
- band_3 = dataset2.GetRasterBand(3).ReadAsArray()
- array_RGB = np.dstack((band_1, band_2, band_3))
- fig = plt.figure(1)
- ax1 = fig.add_axes([0.0, 0.0, 0.89, 1.0])
- ax1.imshow(array_RGB)
- plt.xticks(())
- plt.yticks(())
- # [left, bottom, width, height]
- ax2 = fig.add_axes([0.0, 0.0, 0.89, 1.0])
- ax2.set_title('Chlorophyll_a')
- dataset = gdal.Open('/Users/SPH_1_Grayscale.tif')
- band = dataset.GetRasterBand(1)
- array0 = band.ReadAsArray(0, 0, band.XSize, band.YSize)
- array_Gray = np.ma.masked_where(array0 == 0.0, array0)
- im = ax2.imshow(array_Gray, interpolation='nearest', cmap='jet')
- cbaxes = fig.add_axes([0.9, 0.25, 0.02, 0.5])
- cbar = plt.colorbar(im, orientation="vertical", cax=cbaxes)
- plt.xticks(())
- plt.yticks(())
- plt.savefig('/Users/SPH_1_Overlap.jpg', dpi=300, bbox_inches='tight')
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