Update morphocut.py

morphocut/morphocut.py

@@ -22,25 +22,56 @@ 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"
+from skimage.feature import canny
-import_path = "/home/pi/Desktop/PlanktonScope_acquisition/01_16_2020/afternoon/14_1"
+from skimage.color import rgb2gray, label2rgb
-export_path = "/home/pi/Desktop/PlanktonScope_acquisition/01_16_2020/14_1_export"
+from skimage.morphology import disk
-archive_fn = os.path.join(export_path, "14_1_morphocut_processed.zip")
+from skimage.morphology import erosion, dilation, closing
+from skimage.measure import label, regionprops
+
+
+import_path = "/media/tpollina/rootfs/home/pi/Desktop/PlanktonScope_acquisition/01_17_2020/RAW"
+export_path = "/media/tpollina/rootfs/home/pi/Desktop/PlanktonScope_acquisition/01_17_2020/"
+CLEAN = os.path.join(export_path, "CLEAN")
+os.makedirs(CLEAN, 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
 }
 
-if __name__ == "__main__":
+# Define processing pipeline
-    print("Processing images under {}...".format(import_path))
+with Pipeline() as p:
-
-    # 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)
@@ -60,33 +91,49 @@ if __name__ == "__main__":
     # Rescale intensities and convert to uint8 to speed up calculations
     img = RescaleIntensity(img, in_range=(0, 1.1), dtype="uint8")
 
+    # Convert image to uint8 gray
+    img_gray = RGB2Gray(img)
+
+    img_gray = Call(img_as_ubyte, img_gray)
+
+    img_canny = Call(canny, img_gray, sigma=0.3)
+
+    img_dilate = Call(dilation, img_canny)
+
+    img_closing = Call(closing, img_dilate)
+
+    mask = Call(erosion, img_closing)
+
     # 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)
+    #threshold = 204  # Call(skimage.filters.threshold_otsu, img_gray)
-        mask = img_gray < threshold
+    #mask = img_gray < threshold
 
     # Write corrected frames
-        frame_fn = Format(os.path.join(export_path, "{name}.jpg"), name=name)
+    frame_fn = Format(os.path.join(CLEAN, "{name}.jpg"), name=name)
     ImageWriter(frame_fn, img)
 
     # Find objects
     regionprops = FindRegions(
-            mask, img_gray, min_area=100, padding=10, warn_empty=name
+        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)
-        roi_gray = ExtractROI(img_gray, regionprops, bg_color=255)
 
+    roi_orig
     # 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.
@@ -102,15 +149,21 @@ if __name__ == "__main__":
 
     # 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)
+    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()
 
+import datetime
+
+BEGIN = datetime.datetime.now()
+# Execute pipeline
+p.run() 
+
+END = datetime.datetime.now()
+
+print("MORPHOCUT    :"+str(END-BEGIN))