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@ -162,12 +162,22 @@ for i in range(N):
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# plot rgb image
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# plot rgb image
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plt.figure()
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plt.figure()
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plt.subplot(121)
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plt.axis('off')
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plt.axis('off')
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plt.imshow(im_display)
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plt.imshow(im_display)
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# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
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# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
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im_classif, im_labels = sds.classify_image_NN_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
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im_classif, im_labels = sds.classify_image_NN_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
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plt.subplot(122)
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plt.axis('off')
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im = np.copy(im_display)
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colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
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for k in range(0,im_labels.shape[2]):
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im[im_labels[:,:,k],0] = colours[k,0]
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im[im_labels[:,:,k],1] = colours[k,1]
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im[im_labels[:,:,k],2] = colours[k,2]
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plt.imshow(im)
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# store the data
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# store the data
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cloud_cover_ts.append(cloud_cover)
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cloud_cover_ts.append(cloud_cover)
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acc_georef_ts.append(metadata[satname]['acc_georef'][i])
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acc_georef_ts.append(metadata[satname]['acc_georef'][i])
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