From 04e6853b81dbbf1ec253e70c41c1dd6f41155a28 Mon Sep 17 00:00:00 2001 From: tpollina Date: Sat, 1 Feb 2020 13:20:44 -0800 Subject: [PATCH] Create morphocut_opencv.py --- morphocut/morphocut_opencv.py | 181 ++++++++++++++++++++++++++++++++++ 1 file changed, 181 insertions(+) create mode 100644 morphocut/morphocut_opencv.py diff --git a/morphocut/morphocut_opencv.py b/morphocut/morphocut_opencv.py new file mode 100644 index 0000000..9acf73a --- /dev/null +++ b/morphocut/morphocut_opencv.py @@ -0,0 +1,181 @@ +"""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))