From c474b3a7e69812ae9fdba0ad7145eb7f9e0f5cbf Mon Sep 17 00:00:00 2001 From: tpollina Date: Tue, 14 Jul 2020 16:56:17 +0200 Subject: [PATCH] Delete Morphocut_segmentation.py --- scripts/Morphocut_segmentation.py | 140 ------------------------------ 1 file changed, 140 deletions(-) delete mode 100644 scripts/Morphocut_segmentation.py diff --git a/scripts/Morphocut_segmentation.py b/scripts/Morphocut_segmentation.py deleted file mode 100644 index 94531a5..0000000 --- a/scripts/Morphocut_segmentation.py +++ /dev/null @@ -1,140 +0,0 @@ - -import datetime -import os - -from skimage.util import img_as_ubyte -from skimage.filters import threshold_otsu - - -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 = "/home/pi/Desktop/PlanktonScope_acquisition/01_17_2020/16_2" -export_path = "/home/pi/Desktop/PlanktonScope_acquisition/01_17_2020/16" -archive_fn = os.path.join(export_path, "17_morphocut_processed.zip") - -# Meta data that is added to every object -global_metadata = { - "process_datetime": datetime.datetime.now(), - "sample_project": "PlanktonScope Villefranche", - "sample_ship": "Kayak de Fabien", - "sample_operator": "Thibaut Pollina", - "sample_id": "Flowcam_PlanktonScope_comparison", - "sample_sampling_gear": "net", - "object_date": 20200117, - "object_time": 150000, - "object_lat": 43.696146, - "object_lon": 7.308359, - "object_depth_min": 0, - "object_depth_max": 1, - "acq_fnumber_objective": 16, - "acq_celltype": 400, - "acq_camera": "Pi Camera V2.1", - "acq_instrument": "PlanktonScope V2.1", - "acq_software": "Node-RED Dashboard and raw python", - "acq_instrument_ID": "copepode", - "acq_camera_resolution" : "(3280, 2464)", - "acq_camera_iso" : 60, - "acq_camera_shutter_speed" : 100, - "acq_camera_exposure_mode" : "off", - "acq_camera_awb_mode" : "off", - "process_pixel": 1.19 - -} - -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) - - # Show progress bar for frames - TQDM(Format("Frame {name}", name=name)) - - # Read image - img = ImageReader(abs_path) - - - # Convert image to uint8 gray - img_gray = RGB2Gray(img) - - #img_gray = Call(img_as_ubyte, img_gray) - - # Apply threshold find objects - - #threshold = 200 # - #threshold = Call(threshold_otsu, img_gray) - threshold = 180 # - - mask = img_gray < threshold - - - # Write corrected frames - - ImageWriter(frame_fn, mask) - - # Find objects - regionprops = FindRegions( - mask, img_gray, min_area=300, 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(os.path.join(export_path, "{object_id}.jpg"), object_id=object_id) - #gray_fn = Format("{object_id}-gray.jpg", object_id=object_id) - - ImageWriter(orig_fn, roi_orig) - - # 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)], meta) - - # Progress bar for objects - TQDM(Format("Object {object_id}", object_id=object_id)) - - # Execute pipeline - p.run()