def test_RescaleIntensity():
image = skimage.data.camera()
with Pipeline() as pipeline:
result = RescaleIntensity(image, in_range=(0, 200), dtype=np.uint8)
pipeline.run()
name = Call(lambda p: os.path.splitext(os.path.basename(p))[0], abs_path)
# Read image
img = ImageReader(abs_path)
# Show progress bar for frames
#TQDM(Format("Frame {name}", name=name))
# Apply running median to approximate the background image
flat_field = RunningMedian(img, 5)
# Correct image
img = img / flat_field
# Rescale intensities and convert to uint8 to speed up calculations
img = RescaleIntensity(img, in_range=(0, 1.1), dtype="uint8")
FilterVariables(name,img)
#
frame_fn = Format(os.path.join(CLEAN, "{name}.jpg"), name=name)
ImageWriter(frame_fn, img)
# Convert image to uint8 gray
img_gray = RGB2Gray(img)
# ?
img_gray = Call(img_as_ubyte, img_gray)
#Canny edge detection
img_canny = Call(cv2.Canny, img_gray, 50,100)
from morphocut.image import ImageWriter, RescaleIntensity
from morphocut.pims import BioformatsReader
from morphocut.str import Format
from morphocut.stream import TQDM, Slice, Pack
if __name__ == "__main__":
with Pipeline() as p:
input_fn = "/home/moi/Work/0-Datasets/06_CD20_brightfield_6.cif"
frame, series = BioformatsReader(input_fn, meta=False)
# Every second frame is in fact a mask
# TODO: Batch consecutive objects in the stream
frame, mask = Pack(2, frame).unpack(2)
image_fn = Format("/tmp/cif/{}-img.png", series)
mask_fn = Format("/tmp/cif/{}-mask.png", series)
frame = RescaleIntensity(frame, dtype=np.uint8)
mask = RescaleIntensity(mask, dtype=np.uint8)
ImageWriter(image_fn, frame)
ImageWriter(mask_fn, mask)
TQDM()
print(p)
p.run()