def preprocess_image(self, i):
downscale = self.downscale
lab_frame_image = freeimage.read(self.timepoint_list[i].image_path('bf'))
lab_frame_image = lab_frame_image.astype(numpy.float32)
height, width = lab_frame_image.shape[:2]
try:
metadata = self.timepoint_list[i].position.experiment.metadata
optocoupler = metadata['optocoupler']
except KeyError:
optocoupler = 1
mode = process_images.get_image_mode(lab_frame_image, optocoupler=optocoupler)
#### DownSample the image
if downscale > 0 and downscale != 1:#and set_name!='train':
#t_size = (int(width / downscale), int(height / downscale))
shrink_image = pyramid.pyr_down(lab_frame_image, downscale=downscale)
#shrink_image = numpy.clip(shrink_image, 0, 40000)
else:
shrink_image = lab_frame_image
shrink_image = shrink_image.astype(numpy.float32)
## scale the image pixel value into a trainable range
# map image image intensities in range (100, 2*mode) to range (0, 2)
bf = colorize.scale(shrink_image, min=100, max=2*mode, output_max=2)
# now shift range to (-1, 1)
bf -= 1
return bf
def normalized_bf_image(self, i):
bf = freeimage.read(self.timepoint_list[i].image_path('bf'))
mode = process_images.get_image_mode(bf, optocoupler=self.timepoint_list.optocoupler(i))
# map image image intensities in range (100, 2*mode) to range (0, 2)
bf = colorize.scale(bf, min=100, max=2*mode, output_max=2)
# now shift range to (-1, 1)
bf -= 1
return bf
def normalized_bf_image(timepoint):
"""Given a timepoint, return a normalized brightfield image."""
bf = freeimage.read(timepoint.image_path('bf'))
mode = process_images.get_image_mode(
bf, optocoupler=timepoint.position.experiment.metadata['optocoupler'])
# map image image intensities in range (100, 2*mode) to range (0, 2)
bf = colorize.scale(bf, min=100, max=2 * mode, output_max=2)
# now shift range to (-1, 1)
bf -= 1
return bf
def scale_image(image,
optocoupler=1,
new_mode=24000,
new_max=26000,
new_min=600,
gamma=0.72):
# step 1: make an image that has a modal value of 24000, yielding a known intensity distribution
noise_floor = 100
image_mode = process_images.get_image_mode(image, optocoupler=optocoupler)
image = image.astype(np.float32)
image -= noise_floor
image *= (new_mode - noise_floor) / (image_mode - noise_floor)
image += noise_floor
# step 2: scale that known distribution down to [0,1] using empirical parameters
image.clip(min=new_min, max=new_max, out=image)
image -= new_min
image /= new_max - new_min
# now image is in the range [0, 1]
image **= gamma # optional nonlinear gamma transform
return image
def preprocess_image(timepoint, downscale):
img_path = timepoint.image_path('bf')
lab_frame_image = freeimage.read(img_path)
lab_frame_image = lab_frame_image.astype(numpy.float32)
height, width = lab_frame_image.shape[:2]
objective, optocoupler, magnification, temp = get_metadata(timepoint)
mode = process_images.get_image_mode(lab_frame_image,
optocoupler=optocoupler)
#### DownSample the image
if downscale > 0 and downscale != 1: #and set_name!='train':
shrink_image = pyramid.pyr_down(lab_frame_image, downscale=downscale)
#shrink_image = numpy.clip(shrink_image, 0, 40000)
else:
shrink_image = lab_frame_image
shrink_image = shrink_image.astype(numpy.float32)
## scale the image pixel value into a trainable range
# map image image intensities in range (100, 2*mode) to range (0, 2)
bf = colorize.scale(shrink_image, min=100, max=2 * mode, output_max=2)
# now shift range to (-1, 1)
bf -= 1
return bf