From c474b3a7e69812ae9fdba0ad7145eb7f9e0f5cbf Mon Sep 17 00:00:00 2001
From: tpollina <thibaut.pollina@gmail.com>
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()