117 lines
4 KiB
Python
117 lines
4 KiB
Python
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"""Experiment on processing KOSMOS data using MorphoCut."""
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import datetime
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import os
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from skimage.util import img_as_ubyte
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from morphocut import Call
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from morphocut.contrib.ecotaxa import EcotaxaWriter
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from morphocut.contrib.zooprocess import CalculateZooProcessFeatures
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from morphocut.core import Pipeline
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from morphocut.file import Find
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from morphocut.image import (
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ExtractROI,
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FindRegions,
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ImageReader,
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ImageWriter,
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RescaleIntensity,
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RGB2Gray,
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)
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from morphocut.stat import RunningMedian
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from morphocut.str import Format
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from morphocut.stream import TQDM, Enumerate
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# import_path = "/data-ssd/mschroeder/Datasets/Pyrocystis_noctiluca/RAW"
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import_path = "/home/pi/Desktop/PlanktonScope_acquisition/01_16_2020/afternoon/14_1"
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export_path = "/home/pi/Desktop/PlanktonScope_acquisition/01_16_2020/14_1_export"
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archive_fn = os.path.join(export_path, "14_1_morphocut_processed.zip")
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# Meta data that is added to every object
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global_metadata = {
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"acq_instrument": "Planktoscope",
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"process_datetime": datetime.datetime.now(),
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}
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if __name__ == "__main__":
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print("Processing images under {}...".format(import_path))
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# Create export_path in case it doesn't exist
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os.makedirs(export_path, exist_ok=True)
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# Define processing pipeline
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with Pipeline() as p:
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# Recursively find .jpg files in import_path.
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# Sort to get consective frames.
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abs_path = Find(import_path, [".jpg"], sort=True, verbose=True)
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# Extract name from abs_path
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name = Call(lambda p: os.path.splitext(os.path.basename(p))[0], abs_path)
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# Read image
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img = ImageReader(abs_path)
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# Apply running median to approximate the background image
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flat_field = RunningMedian(img, 10)
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# Correct image
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img = img / flat_field
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# Rescale intensities and convert to uint8 to speed up calculations
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img = RescaleIntensity(img, in_range=(0, 1.1), dtype="uint8")
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# Show progress bar for frames
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TQDM(Format("Frame {name}", name=name))
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# Convert image to uint8 gray
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img_gray = RGB2Gray(img)
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img_gray = Call(img_as_ubyte, img_gray)
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# Apply threshold find objects
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threshold = 204 # Call(skimage.filters.threshold_otsu, img_gray)
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mask = img_gray < threshold
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# Write corrected frames
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frame_fn = Format(os.path.join(export_path, "{name}.jpg"), name=name)
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ImageWriter(frame_fn, img)
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# Find objects
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regionprops = FindRegions(
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mask, img_gray, min_area=100, padding=10, warn_empty=name
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)
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# For an object, extract a vignette/ROI from the image
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roi_orig = ExtractROI(img, regionprops, bg_color=255)
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roi_gray = ExtractROI(img_gray, regionprops, bg_color=255)
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# Generate an object identifier
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i = Enumerate()
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object_id = Format("{name}_{i:d}", name=name, i=i)
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# Calculate features. The calculated features are added to the global_metadata.
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# Returns a Variable representing a dict for every object in the stream.
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meta = CalculateZooProcessFeatures(
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regionprops, prefix="object_", meta=global_metadata
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)
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# If CalculateZooProcessFeatures is not used, we need to copy global_metadata into the stream:
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# meta = Call(lambda: global_metadata.copy())
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# https://github.com/morphocut/morphocut/issues/51
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# Add object_id to the metadata dictionary
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meta["object_id"] = object_id
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# Generate object filenames
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orig_fn = Format("{object_id}.jpg", object_id=object_id)
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gray_fn = Format("{object_id}-gray.jpg", object_id=object_id)
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# Write objects to an EcoTaxa archive:
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# roi image in original color, roi image in grayscale, metadata associated with each object
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EcotaxaWriter(archive_fn, [(orig_fn, roi_orig), (gray_fn, roi_gray)], meta)
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# Progress bar for objects
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TQDM(Format("Object {object_id}", object_id=object_id))
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# Execute pipeline
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p.run()
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