def test_ImageWriter(tmp_path):
d = tmp_path / "sub"
d.mkdir()
p = d / "new.jpg"
image = skimage.data.camera()
with Pipeline() as pipeline:
result = ImageWriter(p, image)
pipeline.run()
"""Process video data using MorphoCut and store frames as individual frames."""
from morphocut import Pipeline
from morphocut.image import ImageWriter
from morphocut.pims import VideoReader
from morphocut.str import Format
with Pipeline() as p:
# Read individual frames from a video file
frame = VideoReader("/path/to/video.avi")
# Format filename
filename = Format("/output/path/frame_#{}.png", frame.frame_no)
# Write individual frames as image files
ImageWriter(filename, frame)
p.run()
# 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)
#Dilate
kernel = Call(cv2.getStructuringElement, cv2.MORPH_ELLIPSE, (15, 15))
img_dilate = Call(cv2.dilate, img_canny, kernel, iterations=2)
#Close
img = RescaleIntensity(img, in_range=(0, 1.1), dtype="uint8")
# Show progress bar for frames
TQDM(Format("Frame {name}", name=name))
# Convert image to uint8 gray
img_gray = RGB2Gray(img)
img_gray = Call(img_as_ubyte, img_gray)
# Apply threshold find objects
threshold = 204 # Call(skimage.filters.threshold_otsu, img_gray)
mask = img_gray < threshold
# Write corrected frames
frame_fn = Format(os.path.join(export_path, "{name}.jpg"), name=name)
ImageWriter(frame_fn, img)
# Find objects
regionprops = FindRegions(mask,
img_gray,
min_area=100,
padding=10,
warn_empty=name)
# For an object, extract a vignette/ROI from the image
roi_orig = ExtractROI(img, regionprops, bg_color=255)
roi_gray = ExtractROI(img_gray, regionprops, bg_color=255)
# Generate an object identifier
i = Enumerate()
object_id = Format("{name}_{i:d}", name=name, i=i)
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()
Call(rgb, 255, 0, 255)
# For an object, extract a vignette/ROI from the image
roi_orig = ExtractROI(img, regionprops, bg_color=255)
# Generate an object identifier
i = Enumerate()
#Call(print,i)
object_id = Format("{name}_{i:d}", name=name, i=i)
#Call(print,object_id)
object_fn = Format(os.path.join("/home/pi/PlanktonScope/", "OBJECTS",
"{name}.jpg"),
name=object_id)
ImageWriter(object_fn, roi_orig)
# Calculate features. The calculated features are added to the global_metadata.
# Returns a Variable representing a dict for every object in the stream.
meta = CalculateZooProcessFeatures(regionprops,
prefix="object_",
meta=global_metadata)
# json_meta = Call(json.dumps,meta, sort_keys=True, default=str)
# Call(client.publish, "receiver/segmentation/metric", json_meta)
# Add object_id to the metadata dictionary
meta["object_id"] = object_id
# Generate object filenames