Create Morphocut_segmentation.py

scripts/Morphocut_segmentation.py

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+
+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()