def analyze_color_calibration_target(self):
"""
Find whether there is a color calibration strip on top of the image.
"""
grey_array = self.fetch('ndarray_grey')
image_array = self.fetch('ndarray')
if grey_array is None:
return {}
# For the images we're testing, the IT8 bar takes about 20% of the
# image and also in the 20% we need the mid area
bary = int(0.2 * grey_array.shape[0])
def bar_intensity(x):
sampley = max(int(0.1 * x.shape[0]), 2)
return numpy.mean(
x[(x.shape[0] - sampley) // 2:(x.shape[0] + sampley) // 2,
:, ...],
axis=0)
topbar = bar_intensity(grey_array[:bary, :, ...])
botbar = bar_intensity(grey_array[-bary:, :, ...])
def _merge_near(arr):
out = []
last_elem = arr[0]
out.append(last_elem)
for elem in arr[1:]:
if elem != last_elem:
out.append(elem)
last_elem = elem
return numpy.asarray(out)
# Bottom bars seem to have smaller intensity because of the background
# Hence, we set a smaller threshold for peaks in bottom bars.
bot_spikes = _merge_near((numpy.diff(botbar)) > -2.5).sum()
top_spikes = _merge_near((numpy.diff(topbar)) < 3).sum()
top_grey_mse, bot_grey_mse = 0, 0
if image_array.ndim == 3:
for chan in range(image_array.shape[2]):
top_grey_mse += (
(image_array[bary:, :, chan] -
grey_array[bary:]) ** 2).mean()
bot_grey_mse += (
(image_array[-bary, :, chan] -
grey_array[-bary]) ** 2).mean()
top_grey_mse /= 3.0
bot_grey_mse /= 3.0
data = {}
if 15 < top_spikes < 25:
data['Color:IT8TopBar'] = top_spikes
data['Color:IT8TopBarGreyMSE'] = top_grey_mse
if 15 < bot_spikes < 25:
data['Color:IT8BottomBar'] = bot_spikes
data['Color:IT8BottomBarGreyMSE'] = bot_grey_mse
return data
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