Create morphocut_opencv.py

morphocut/morphocut_opencv.py

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+"""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, FilterVariables
+
+from skimage.feature import canny
+from skimage.color import rgb2gray, label2rgb
+from skimage.morphology import disk
+from skimage.morphology import erosion, dilation, closing
+from skimage.measure import label, regionprops
+import cv2
+
+import_path = "/home/tpollina/Desktop/JUPYTER/IMAGES/RAW"
+export_path = "/home/tpollina/Desktop/JUPYTER/IMAGES/"
+
+CLEAN = os.path.join(export_path, "CLEAN")
+os.makedirs(CLEAN, exist_ok=True)
+
+
+ANNOTATED = os.path.join(export_path, "ANNOTATED")
+os.makedirs(ANNOTATED, exist_ok=True)
+
+
+OBJECTS = os.path.join(export_path, "OBJECTS")
+os.makedirs(OBJECTS, exist_ok=True)
+
+archive_fn = os.path.join(export_path, "ecotaxa_export.zip")
+
+# Meta data that is added to every object
+global_metadata = {
+    "acq_instrument": "Planktoscope",
+    "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",
+    "sample_time":150000,
+    "sample_date":16112020,
+    "object_lat": 43.696146,
+    "object_lon": 7.308359,
+    "acq_fnumber_objective": 16,
+    "acq_celltype": 200,
+    "process_pixel": 1.19,
+    "acq_camera": "Pi Camera V2.1",
+    "acq_instrument": "PlanktonScope V2.1",
+    "acq_software": "Node-RED Dashboard and raw python",
+    "acq_instrument_ID": "copepode",
+    "acq_volume": 24,
+    "acq_flowrate": "Unknown",
+    "acq_camera.resolution" : "(3280, 2464)",
+    "acq_camera.iso" : 60,
+    "acq_camera.shutter_speed" : 100,
+    "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)
+    #
+    frame_fn = Format(os.path.join(CLEAN, "{name}.jpg"), name=name)
+    ImageWriter(frame_fn, img)
+    
+    # Convert image to uint8 gray
+    img_gray = RGB2Gray(img)
+    
+    # ?
+    img_gray = Call(img_as_ubyte, img_gray)
+
+    #Canny edge detection
+    img_canny = Call(cv2.Canny, img_gray, 50,100)
+
+    #Dilate
+    kernel = Call(cv2.getStructuringElement, cv2.MORPH_ELLIPSE, (15, 15))
+    img_dilate = Call(cv2.dilate, img_canny, kernel, iterations=2)
+    
+    #Close
+    kernel = Call(cv2.getStructuringElement, cv2.MORPH_ELLIPSE, (5, 5))
+    img_close = Call(cv2.morphologyEx, img_dilate, cv2.MORPH_CLOSE, kernel, iterations=1)
+     
+    #Erode
+    kernel = Call(cv2.getStructuringElement, cv2.MORPH_ELLIPSE, (15, 15))
+    mask = Call(cv2.erode, img_close, kernel, iterations=2)
+    
+    frame_fn = Format(os.path.join(ANNOTATED, "{name}.jpg"), name=name)
+    ImageWriter(frame_fn, mask)
+    
+    # Find objects
+    regionprops = FindRegions(
+        mask, img_gray, min_area=1000, padding=10, warn_empty=name
+    )
+    # For an object, extract a vignette/ROI from the image
+    roi_orig = ExtractROI(img, regionprops, bg_color=255)
+    
+    # For an object, extract a vignette/ROI from the image
+    roi_mask = ExtractROI(mask, regionprops, bg_color=255)
+
+
+    # Generate an object identifier
+    i = Enumerate()
+    #Call(print,i)
+
+    object_id = Format("{name}_{i:d}", name=name, i=i)
+    #Call(print,object_id)
+    
+    object_fn = Format(os.path.join(OBJECTS, "{name}.jpg"), name=object_id)
+    ImageWriter(object_fn, roi_orig)
+
+
+    # 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
+    )
+    
+    # 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)
+
+    # 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))
+
+
+import datetime
+
+BEGIN = datetime.datetime.now()
+# Execute pipeline
+p.run() 
+
+END = datetime.datetime.now()
+
+print("MORPHOCUT    :"+str(END-BEGIN))