def test_ImageReader(data_path):
d = data_path / "images/test_image_3.png"
with Pipeline() as pipeline:
image = ImageReader(d)
pipeline.run()
"acq_camera.exposure_mode" : 'off',
"acq_camera.awb_mode" : 'off',
"acq_nb_frames" : 1000
}
# Define processing pipeline
with Pipeline() as p:
# Recursively find .jpg files in import_path.
# Sort to get consective frames.
abs_path = Find(import_path, [".jpg"], sort=True, verbose=True)
# Extract name from abs_path
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)
#
# Call calls regular Python functions.
# Here, a subpath is appended to base_path.
pattern = Call(os.path.join, base_path, "subpath/to/input/files/*.jpg")
# Corresponds to `for path in glob(pattern):`
path = Glob(pattern)
# Remove path and extension from the filename
source_basename = Call(lambda x: os.path.splitext(os.path.basename(x))[0], path)
with ParallelPipeline():
# The following operations are distributed among multiple
# worker processes to speed up the calculations.
# Read the image
image = ImageReader(path)
# Do some thresholding
mask = image < 128
# Find regions in the image
region = FindRegions(mask, image)
# Extract just the object
roi_image = region.intensity_image
# An object is identified by its label
roi_label = region.label
# Calculate a filename for the ROI image:
# "RUNNING_NUMBER-SOURCE_BASENAME-ROI_LABEL"