def __init__(self, Sfilename, model='Net1_FFN_v7', verbose=False):
self.index = 0
self.settingfuncs = [self.setting1, self.setting2, self.setting3,
self.setting4, self.setting5, self.setting6,
self.setting7, self.setting8]
if isinstance(model, str):
self.modelname = model
self.model = FFN(model)
self.model.Load(verbose=verbose)
elif isinstance(model, FFN):
self.model = model
self.modelname = self.model.name
mask1, pmask1 = self.model.apply_mask(Sfilename)
rgb, self.TitleStr = Vis.FalseColour(Sfilename, False)
scn = DL.scene_loader(Sfilename)
scn.load(['bayes_in', 'probability_cloud_single_in'])
bmask = DL.upscale_repeat(scn['bayes_in'].values).astype('int')
bmask = 1 - ((bmask & 2) / 2)
bpmask = DL.upscale_repeat(scn['probability_cloud_single_in'].values)
self.im1 = plt.imshow(rgb)
plt.title('False colour image\n' + self.TitleStr)
self.im2 = plt.imshow(mask1, cmap='Blues')
self.im2.set_visible(False)
bmask = DL.extract_mask(Sfilename, 'bayes_in', 2)
self.im3 = plt.imshow(bmask, cmap='Reds')
self.im3.set_visible(False)
mask1 = mask1.astype('bool')
temp = np.copy(rgb)
temp[~mask1, :] = 254 / 255, 253 / 255, 185 / 255
self.im4 = plt.imshow(temp)
self.im4.set_visible(False)
rgb[mask1, :] = 74 / 255, 117 / 255, 50 / 255
self.im5 = plt.imshow(rgb)
self.im5.set_visible(False)
self.im6 = plt.imshow(1 - pmask1, cmap='Oranges')
self.im6.set_visible(False)
self.im7 = plt.imshow(1 - bpmask, cmap='Reds')
self.im7.set_visible(False)
maskdiff = bmask - mask1
self.im8 = plt.imshow(maskdiff, cmap='bwr')
self.im8.set_visible(False)
self.cbset = False
self.cb = None
def mask_debug(Sfilename, model, verbose):
if isinstance(model, str):
modelname = model
model = FFN(model)
model.Load(verbose=verbose)
elif isinstance(model, FFN):
modelname = model.name
mask1, pmask = model.apply_mask(Sfilename)
rgb, TitleStr = Vis.FalseColour(Sfilename, False)
plt.figure()
plt.imshow(rgb)
plt.title('False colour image\n' + TitleStr)
plt.xlabel('km')
plt.ylabel('km')
plt.xticks([0, 500, 1000, 1500, 2000, 2500, 3000],
[0, 250, 500, 750, 1000, 1250, 1500])
plt.yticks([0, 500, 1000, 1500, 2000], [0, 250, 500, 750, 1000])
plt.figure()
plt.imshow(mask1, cmap='Blues')
plt.title(modelname + ' mask\n' + TitleStr)
plt.xlabel('km')
plt.ylabel('km')
plt.xticks([0, 500, 1000, 1500, 2000, 2500, 3000],
[0, 250, 500, 750, 1000, 1250, 1500])
plt.yticks([0, 500, 1000, 1500, 2000], [0, 250, 500, 750, 1000])
plt.figure()
bmask = DL.extract_mask(Sfilename, 'bayes_in', 2)
plt.imshow(bmask, cmap='Reds')
plt.title('Bayesian mask\n' + TitleStr)
plt.xlabel('km')
plt.ylabel('km')
plt.xticks([0, 500, 1000, 1500, 2000, 2500, 3000],
[0, 250, 500, 750, 1000, 1250, 1500])
plt.yticks([0, 500, 1000, 1500, 2000], [0, 250, 500, 750, 1000])
plt.figure()
mask1 = mask1.astype('bool')
rgb[~mask1, :] = 254 / 255, 253 / 255, 185 / 255
plt.imshow(rgb)
plt.title(modelname + ' masked false colour image\n' + TitleStr)
plt.xlabel('km')
plt.ylabel('km')
plt.xticks([0, 500, 1000, 1500, 2000, 2500, 3000],
[0, 250, 500, 750, 1000, 1250, 1500])
plt.yticks([0, 500, 1000, 1500, 2000], [0, 250, 500, 750, 1000])
plt.figure()
ax = plt.gca()
im5 = plt.imshow(pmask, cmap='Oranges_r')
plt.title(modelname + ' model output\n' + TitleStr)
plt.xlabel('km')
plt.ylabel('km')
plt.xticks([0, 500, 1000, 1500, 2000, 2500, 3000],
[0, 250, 500, 750, 1000, 1250, 1500])
plt.yticks([0, 500, 1000, 1500, 2000], [0, 250, 500, 750, 1000])
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
plt.colorbar(im5, cax=cax)
plt.figure()
maskdiff = mask1 - bmask
ax = plt.gca()
im6 = plt.imshow(maskdiff, cmap='bwr_r')
plt.title(modelname + ' mask - Bayesian mask\n' + TitleStr)
plt.xlabel('km')
plt.ylabel('km')
plt.xticks([0, 500, 1000, 1500, 2000, 2500, 3000],
[0, 250, 500, 750, 1000, 1250, 1500])
plt.yticks([0, 500, 1000, 1500, 2000], [0, 250, 500, 750, 1000])
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
plt.colorbar(im6, cax=cax)
plt.show()
