From 2815656944a0b332ea2410c3f600b087e15b9639 Mon Sep 17 00:00:00 2001 From: tpollina Date: Thu, 16 Jan 2020 16:19:03 +0100 Subject: [PATCH] Create morphocut.py --- morphocut/morphocut.py | 116 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 morphocut/morphocut.py diff --git a/morphocut/morphocut.py b/morphocut/morphocut.py new file mode 100644 index 0000000..2fbd9f3 --- /dev/null +++ b/morphocut/morphocut.py @@ -0,0 +1,116 @@ +"""Experiment on processing KOSMOS data using MorphoCut.""" + +import datetime +import os + +from skimage.util import img_as_ubyte + +from morphocut import Call +from morphocut.contrib.ecotaxa import EcotaxaWriter +from morphocut.contrib.zooprocess import CalculateZooProcessFeatures +from morphocut.core import Pipeline +from morphocut.file import Find +from morphocut.image import ( + ExtractROI, + FindRegions, + ImageReader, + ImageWriter, + RescaleIntensity, + RGB2Gray, +) +from morphocut.stat import RunningMedian +from morphocut.str import Format +from morphocut.stream import TQDM, Enumerate + +# import_path = "/data-ssd/mschroeder/Datasets/Pyrocystis_noctiluca/RAW" +import_path = "/home/pi/Desktop/PlanktonScope_acquisition/01_16_2020/afternoon/14_1" +export_path = "/home/pi/Desktop/PlanktonScope_acquisition/01_16_2020/14_1_export" +archive_fn = os.path.join(export_path, "14_1_morphocut_processed.zip") + +# Meta data that is added to every object +global_metadata = { + "acq_instrument": "Planktoscope", + "process_datetime": datetime.datetime.now(), +} + +if __name__ == "__main__": + print("Processing images under {}...".format(import_path)) + + # Create export_path in case it doesn't exist + os.makedirs(export_path, exist_ok=True) + + # 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) + + # Apply running median to approximate the background image + flat_field = RunningMedian(img, 10) + + # 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") + + # 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) + + # 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 + ) + # If CalculateZooProcessFeatures is not used, we need to copy global_metadata into the stream: + # meta = Call(lambda: global_metadata.copy()) + # https://github.com/morphocut/morphocut/issues/51 + + # Add object_id to the metadata dictionary + meta["object_id"] = object_id + + # Generate object filenames + orig_fn = Format("{object_id}.jpg", object_id=object_id) + gray_fn = Format("{object_id}-gray.jpg", object_id=object_id) + + # Write objects to an EcoTaxa archive: + # roi image in original color, roi image in grayscale, metadata associated with each object + EcotaxaWriter(archive_fn, [(orig_fn, roi_orig), (gray_fn, roi_gray)], meta) + + # Progress bar for objects + TQDM(Format("Object {object_id}", object_id=object_id)) + + # Execute pipeline + p.run() +