938 lines
39 KiB
Python
938 lines
39 KiB
Python
################################################################################
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# Practical Libraries
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################################################################################
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# Logger library compatible with multiprocessing
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from loguru import logger
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# Library to get date and time for folder name and filename
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import datetime
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# Library to be able to sleep for a given duration
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import time
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# Libraries manipulate json format, execute bash commands
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import json, shutil, os
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# Library for starting processes
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import multiprocessing
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import io
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import threading
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import functools
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import select
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# Basic planktoscope libraries
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import planktoscope.mqtt
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import planktoscope.segmenter.operations
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import planktoscope.segmenter.encoder
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import planktoscope.segmenter.streamer
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import planktoscope.segmenter.ecotaxa
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################################################################################
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# Morphocut Libraries
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################################################################################
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# import morphocut
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# import morphocut.file
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# import morphocut.image
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# import morphocut.stat
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# import morphocut.stream
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# import morphocut.str
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# import morphocut.contrib.zooprocess
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################################################################################
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# Other image processing Libraries
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################################################################################
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import skimage.util
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import skimage.transform
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import skimage.measure
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import cv2
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import scipy.stats
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import numpy as np
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import PIL.Image
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import math
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logger.info("planktoscope.segmenter is loaded")
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################################################################################
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# Main Segmenter class
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################################################################################
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class SegmenterProcess(multiprocessing.Process):
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"""This class contains the main definitions for the segmenter of the PlanktoScope"""
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@logger.catch
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def __init__(self, event, data_path):
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"""Initialize the Segmenter class
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Args:
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event (multiprocessing.Event): shutdown event
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"""
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super(SegmenterProcess, self).__init__(name="segmenter")
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logger.info("planktoscope.segmenter is initialising")
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self.stop_event = event
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self.__pipe = None
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self.segmenter_client = None
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# Where captured images are saved
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self.__img_path = os.path.join(data_path, "img/")
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# To save export folders
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self.__export_path = os.path.join(data_path, "export/")
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# To save objects to export
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self.__objects_root = os.path.join(data_path, "objects/")
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# To save debug masks
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self.__debug_objects_root = os.path.join(data_path, "clean/")
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self.__ecotaxa_path = os.path.join(self.__export_path, "ecotaxa")
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self.__global_metadata = None
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# path for current folder being segmented
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self.__working_path = ""
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# combination of self.__objects_root and actual sample folder name
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self.__working_obj_path = ""
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# combination of self.__ecotaxa_path and actual sample folder name
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self.__working_ecotaxa_path = ""
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# combination of self.__debug_objects_root and actual sample folder name
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self.__working_debug_path = ""
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self.__archive_fn = ""
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self.__flat = None
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self.__mask_array = None
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self.__mask_to_remove = None
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self.__save_debug_img = True
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# create all base path
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for path in [
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self.__ecotaxa_path,
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self.__objects_root,
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self.__debug_objects_root,
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]:
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if not os.path.exists(path):
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# create the path!
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os.makedirs(path)
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logger.success("planktoscope.segmenter is initialised and ready to go!")
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def _find_files(self, path, extension):
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for _, _, filenames in os.walk(path, topdown=True):
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if filenames:
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filenames = sorted(filenames)
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return [fn for fn in filenames if fn.endswith(extension)]
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def _manual_median(self, array_of_5):
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array_of_5.sort(axis=0)
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return array_of_5[2]
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def _save_image(self, image, path):
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PIL.Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).save(path)
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def _save_mask(self, mask, path):
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PIL.Image.fromarray(mask).save(path)
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def _calculate_flat(self, images_list, images_number, images_root_path):
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# TODO make this calculation optional if a flat already exists
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# make sure image number is smaller than image list
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if images_number > len(images_list):
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logger.error(
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"The image number can't be bigger than the lenght of the provided list!"
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)
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images_number = len(images_list)
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logger.debug("Opening images")
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# start = time.monotonic()
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# Read images and build array
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images_array = np.array(
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[
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cv2.imread(
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os.path.join(images_root_path, images_list[i]),
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)
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for i in range(images_number)
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]
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)
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# logger.debug(time.monotonic() - start)
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logger.success("Opening images")
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logger.info("Manual median calc")
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# start = time.monotonic()
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self.__flat = self._manual_median(images_array)
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# self.__flat = _numpy_median(images_array)
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# logger.debug(time.monotonic() - start)
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logger.success("Manual median calc")
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# cv2.imshow("flat_color", self.__flat.astype("uint8"))
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# cv2.waitKey(0)
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return self.__flat
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def _open_and_apply_flat(self, filepath, flat_ref):
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logger.info("Opening images")
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start = time.monotonic()
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# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
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# Read images
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image = cv2.imread(filepath)
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# print(image)
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# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
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# logger.debug(time.monotonic() - start)
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logger.success("Opening images")
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logger.info("Flat calc")
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# start = time.monotonic()
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# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
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# Correct image
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image = image / self.__flat
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# adding one black pixel top left
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image[0][0] = [0, 0, 0]
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# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
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# logger.debug(time.monotonic() - start)
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image = skimage.exposure.rescale_intensity(
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image, in_range=(0, 1.04), out_range="uint8"
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)
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# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
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logger.debug(time.monotonic() - start)
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logger.success("Flat calc")
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# cv2.imshow("img", img.astype("uint8"))
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# cv2.waitKey(0)
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if self.__save_debug_img:
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self._save_image(
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image,
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os.path.join(self.__working_debug_path, "cleaned_image.jpg"),
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)
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return image
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def _create_mask(self, img, debug_saving_path):
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logger.info("Starting the mask creation")
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pipeline = [
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"adaptative_threshold",
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"remove_previous_mask",
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"erode",
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"dilate",
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"close",
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"erode2",
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]
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mask = img
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for i, transformation in enumerate(pipeline):
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function = getattr(
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planktoscope.segmenter.operations, transformation
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) # Retrieves the actual operation
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mask = function(mask)
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# cv2.imshow(f"mask {transformation}", mask)
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# cv2.waitKey(0)
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if self.__save_debug_img:
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PIL.Image.fromarray(mask).save(
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os.path.join(debug_saving_path, f"mask_{i}_{transformation}.jpg")
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)
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logger.success("Mask created")
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return mask
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def _get_color_info(self, bgr_img, mask):
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# bgr_mean, bgr_stddev = cv2.meanStdDev(bgr_img, mask=mask)
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# (b_channel, g_channel, r_channel) = cv2.split(bgr_img)
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quartiles = [0, 0.05, 0.25, 0.50, 0.75, 0.95, 1]
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# b_quartiles = np.quantile(b_channel, quartiles)
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# g_quartiles = np.quantile(g_channel, quartiles)
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# r_quartiles = np.quantile(r_channel, quartiles)
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hsv_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2HSV)
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(h_channel, s_channel, v_channel) = cv2.split(hsv_img)
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# hsv_mean, hsv_stddev = cv2.meanStdDev(hsv_img, mask=mask)
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h_mean = np.mean(h_channel, where=mask)
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s_mean = np.mean(s_channel, where=mask)
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v_mean = np.mean(v_channel, where=mask)
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h_stddev = np.std(h_channel, where=mask)
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s_stddev = np.std(s_channel, where=mask)
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v_stddev = np.std(v_channel, where=mask)
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# TODO Add skewness and kurtosis calculation (with scipy) here
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# using https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html#scipy.stats.skew
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# and https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kurtosis.html#scipy.stats.kurtosis
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# h_quartiles = np.quantile(h_channel, quartiles)
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# s_quartiles = np.quantile(s_channel, quartiles)
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# v_quartiles = np.quantile(v_channel, quartiles)
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return {
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# "object_MeanRedLevel": bgr_mean[2][0],
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# "object_MeanGreenLevel": bgr_mean[1][0],
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# "object_MeanBlueLevel": bgr_mean[0][0],
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# "object_StdRedLevel": bgr_stddev[2][0],
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# "object_StdGreenLevel": bgr_stddev[1][0],
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# "object_StdBlueLevel": bgr_stddev[0][0],
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# "object_minRedLevel": r_quartiles[0],
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# "object_Q05RedLevel": r_quartiles[1],
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# "object_Q25RedLevel": r_quartiles[2],
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# "object_Q50RedLevel": r_quartiles[3],
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# "object_Q75RedLevel": r_quartiles[4],
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# "object_Q95RedLevel": r_quartiles[5],
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# "object_maxRedLevel": r_quartiles[6],
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# "object_minGreenLevel": g_quartiles[0],
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# "object_Q05GreenLevel": g_quartiles[1],
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# "object_Q25GreenLevel": g_quartiles[2],
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# "object_Q50GreenLevel": g_quartiles[3],
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# "object_Q75GreenLevel": g_quartiles[4],
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# "object_Q95GreenLevel": g_quartiles[5],
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# "object_maxGreenLevel": g_quartiles[6],
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# "object_minBlueLevel": b_quartiles[0],
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# "object_Q05BlueLevel": b_quartiles[1],
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# "object_Q25BlueLevel": b_quartiles[2],
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# "object_Q50BlueLevel": b_quartiles[3],
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# "object_Q75BlueLevel": b_quartiles[4],
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# "object_Q95BlueLevel": b_quartiles[5],
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# "object_maxBlueLevel": b_quartiles[6],
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"MeanHue": h_mean,
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"MeanSaturation": s_mean,
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"MeanValue": v_mean,
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"StdHue": h_stddev,
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"StdSaturation": s_stddev,
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"StdValue": v_stddev,
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# "object_minHue": h_quartiles[0],
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# "object_Q05Hue": h_quartiles[1],
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# "object_Q25Hue": h_quartiles[2],
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# "object_Q50Hue": h_quartiles[3],
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# "object_Q75Hue": h_quartiles[4],
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# "object_Q95Hue": h_quartiles[5],
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# "object_maxHue": h_quartiles[6],
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# "object_minSaturation": s_quartiles[0],
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# "object_Q05Saturation": s_quartiles[1],
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# "object_Q25Saturation": s_quartiles[2],
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# "object_Q50Saturation": s_quartiles[3],
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# "object_Q75Saturation": s_quartiles[4],
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# "object_Q95Saturation": s_quartiles[5],
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# "object_maxSaturation": s_quartiles[6],
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# "object_minValue": v_quartiles[0],
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# "object_Q05Value": v_quartiles[1],
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# "object_Q25Value": v_quartiles[2],
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# "object_Q50Value": v_quartiles[3],
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# "object_Q75Value": v_quartiles[4],
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# "object_Q95Value": v_quartiles[5],
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# "object_maxValue": v_quartiles[6],
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}
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def _extract_metadata_from_regionprop(self, prop):
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return {
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"label": prop.label,
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# width of the smallest rectangle enclosing the object
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"width": prop.bbox[3] - prop.bbox[1],
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# height of the smallest rectangle enclosing the object
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"height": prop.bbox[2] - prop.bbox[0],
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# X coordinates of the top left point of the smallest rectangle enclosing the object
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"bx": prop.bbox[1],
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# Y coordinates of the top left point of the smallest rectangle enclosing the object
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"by": prop.bbox[0],
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# circularity : (4∗π ∗Area)/Perim^2 a value of 1 indicates a perfect circle, a value approaching 0 indicates an increasingly elongated polygon
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"circ.": (4 * np.pi * prop.filled_area) / prop.perimeter ** 2,
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# Surface area of the object excluding holes, in square pixels (=Area*(1-(%area/100))
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"area_exc": prop.area,
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# Surface area of the object in square pixels
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"area": prop.filled_area,
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# Percentage of object’s surface area that is comprised of holes, defined as the background grey level
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"%area": 1 - (prop.area / prop.filled_area),
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# Primary axis of the best fitting ellipse for the object
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"major": prop.major_axis_length,
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# Secondary axis of the best fitting ellipse for the object
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"minor": prop.minor_axis_length,
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# Y position of the center of gravity of the object
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"y": prop.centroid[0],
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# X position of the center of gravity of the object
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"x": prop.centroid[1],
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# The area of the smallest polygon within which all points in the objet fit
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"convex_area": prop.convex_area,
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# # Minimum grey value within the object (0 = black)
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# "min": prop.min_intensity,
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# # Maximum grey value within the object (255 = white)
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# "max": prop.max_intensity,
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# # Average grey value within the object ; sum of the grey values of all pixels in the object divided by the number of pixels
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# "mean": prop.mean_intensity,
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# # Integrated density. The sum of the grey values of the pixels in the object (i.e. = Area*Mean)
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# "intden": prop.filled_area * prop.mean_intensity,
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# The length of the outside boundary of the object
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"perim.": prop.perimeter,
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# major/minor
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"elongation": np.divide(prop.major_axis_length, prop.minor_axis_length),
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# max-min
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# "range": prop.max_intensity - prop.min_intensity,
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# perim/area_exc
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"perimareaexc": prop.perimeter / prop.area,
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# perim/major
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"perimmajor": prop.perimeter / prop.major_axis_length,
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# (4 ∗ π ∗ Area_exc)/perim 2
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"circex": np.divide(4 * np.pi * prop.area, prop.perimeter ** 2),
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# Angle between the primary axis and a line parallel to the x-axis of the image
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"angle": prop.orientation / np.pi * 180 + 90,
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# # X coordinate of the top left point of the image
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# 'xstart': data_object['raw_img']['meta']['xstart'],
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# # Y coordinate of the top left point of the image
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# 'ystart': data_object['raw_img']['meta']['ystart'],
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# Maximum feret diameter, i.e. the longest distance between any two points along the object boundary
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# 'feret': data_object['raw_img']['meta']['feret'],
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# feret/area_exc
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# 'feretareaexc': data_object['raw_img']['meta']['feret'] / property.area,
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# perim/feret
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# 'perimferet': property.perimeter / data_object['raw_img']['meta']['feret'],
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"bounding_box_area": prop.bbox_area,
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"eccentricity": prop.eccentricity,
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"equivalent_diameter": prop.equivalent_diameter,
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"euler_number": prop.euler_number,
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"extent": prop.extent,
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"local_centroid_col": prop.local_centroid[1],
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"local_centroid_row": prop.local_centroid[0],
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"solidity": prop.solidity,
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}
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def _stream(self, img):
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def pipe_full(conn):
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r, w, x = select.select([], [conn], [], 0.0)
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return len(w) == 0
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|
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img_object = io.BytesIO()
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PIL.Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)).save(
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img_object, format="JPEG"
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)
|
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logger.debug("Sending the object in the pipe!")
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if not pipe_full(planktoscope.segmenter.streamer.sender):
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planktoscope.segmenter.streamer.sender.send(img_object)
|
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def _slice_image(self, img, name, mask, start_count=0):
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"""Slice a given image using give mask
|
||
|
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Args:
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img (img array): Image to slice
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name (string): name of the original image
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mask (mask binary array): mask to use slice with
|
||
start_count (int, optional): count start to number the objects, so each one is unique. Defaults to 0.
|
||
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||
Returns:
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tuple: (Number of saved objects, original number of objects before size filtering)
|
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"""
|
||
|
||
def __augment_slice(dim_slice, max_dims, size=10):
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||
# transform tuple in list
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||
dim_slice = list(dim_slice)
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# dim_slice[0] is the vertical component
|
||
# dim_slice[1] is the horizontal component
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||
# dim_slice[1].start,dim_slice[0].start is the top left corner
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for i in range(2):
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if dim_slice[i].start < size:
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dim_slice[i] = slice(0, dim_slice[i].stop)
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else:
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dim_slice[i] = slice(dim_slice[i].start - size, dim_slice[i].stop)
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# dim_slice[1].stop,dim_slice[0].stop is the bottom right corner
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for i in range(2):
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if dim_slice[i].stop + size == max_dims[i]:
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dim_slice[i] = slice(dim_slice[i].start, max_dims[i])
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else:
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dim_slice[i] = slice(dim_slice[i].start, dim_slice[i].stop + size)
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||
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# transform back list in tuple
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||
dim_slice = tuple(dim_slice)
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return dim_slice
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|
||
# TODO retrieve here all those from the global metadata
|
||
minESD = 40 # microns
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||
minArea = math.pi * (minESD / 2) * (minESD / 2)
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||
pixel_size = 1.01 # to be retrieved from metadata
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||
# minsizepix = minArea / pixel_size / pixel_size
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minsizepix = (minESD / pixel_size) ** 2
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||
|
||
labels, nlabels = skimage.measure.label(mask, return_num=True)
|
||
regionprops = skimage.measure.regionprops(labels)
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||
regionprops_filtered = [
|
||
region for region in regionprops if region.bbox_area >= minsizepix
|
||
]
|
||
object_number = len(regionprops_filtered)
|
||
logger.debug(f"Found {nlabels} labels, or {object_number} after size filtering")
|
||
|
||
for (i, region) in enumerate(regionprops_filtered):
|
||
region.label = i + start_count
|
||
|
||
# Publish the object_id to via MQTT to Node-RED
|
||
self.segmenter_client.client.publish(
|
||
"status/segmenter/object_id",
|
||
f'{{"object_id":"{region.label}"}}',
|
||
)
|
||
|
||
# First extract to get all the metadata about the image
|
||
obj_image = img[region.slice]
|
||
colors = self._get_color_info(obj_image, region.filled_image)
|
||
metadata = self._extract_metadata_from_regionprop(region)
|
||
|
||
# Second extract to get a bigger image for saving
|
||
obj_image = img[__augment_slice(region.slice, labels.shape, 10)]
|
||
object_id = f"{name}_{i}"
|
||
object_fn = os.path.join(self.__working_obj_path, f"{object_id}.jpg")
|
||
|
||
self._save_image(obj_image, object_fn)
|
||
self._stream(obj_image)
|
||
|
||
if self.__save_debug_img:
|
||
self._save_mask(
|
||
region.filled_image,
|
||
os.path.join(self.__working_debug_path, f"obj_{i}_mask.jpg"),
|
||
)
|
||
|
||
object_metadata = {
|
||
"name": f"{object_id}",
|
||
"metadata": {**metadata, **colors},
|
||
}
|
||
|
||
# publish metrics about the found object
|
||
self.segmenter_client.client.publish(
|
||
"status/segmenter/metric",
|
||
json.dumps(
|
||
object_metadata, cls=planktoscope.segmenter.encoder.NpEncoder
|
||
),
|
||
)
|
||
|
||
if "objects" in self.__global_metadata:
|
||
self.__global_metadata["objects"].append(object_metadata)
|
||
else:
|
||
self.__global_metadata.update({"objects": [object_metadata]})
|
||
|
||
# TODO make the TSV for ecotaxa
|
||
if self.__save_debug_img:
|
||
if object_number:
|
||
for region in regionprops_filtered:
|
||
tagged_image = cv2.drawMarker(
|
||
img,
|
||
(int(region.centroid[1]), int(region.centroid[0])),
|
||
(0, 0, 255),
|
||
cv2.MARKER_CROSS,
|
||
)
|
||
tagged_image = cv2.rectangle(
|
||
img,
|
||
pt1=region.bbox[-3:-5:-1],
|
||
pt2=region.bbox[-1:-3:-1],
|
||
color=(150, 0, 200),
|
||
thickness=1,
|
||
)
|
||
|
||
# contours = [region.bbox for region in regionprops_filtered]
|
||
# for contour in contours:
|
||
# tagged_image = cv2.rectangle(
|
||
# img, pt1=(contours[0][1],contours[0][0]), pt2=(contours[0][3],contours[0][2]), color=(0, 0, 255), thickness=2
|
||
# )
|
||
# contours = [region.coords for region in regionprops_filtered]
|
||
# for contour in contours:
|
||
# tagged_image = cv2.drawContours(
|
||
# img_erode_2, contour, -1, color=(0, 0, 255), thickness=2
|
||
# )
|
||
|
||
# cv2.imshow("tagged_image", tagged_image.astype("uint8"))
|
||
# cv2.waitKey(0)
|
||
self._save_image(
|
||
tagged_image,
|
||
os.path.join(self.__working_debug_path, "tagged.jpg"),
|
||
)
|
||
else:
|
||
self._save_image(
|
||
img,
|
||
os.path.join(self.__working_debug_path, "tagged.jpg"),
|
||
)
|
||
return (object_number, len(regionprops))
|
||
|
||
def _pipe(self, ecotaxa_export):
|
||
logger.info("Finding images")
|
||
images_list = self._find_files(
|
||
self.__working_path, ("JPG", "jpg", "JPEG", "jpeg")
|
||
)
|
||
|
||
logger.debug(f"Images found are {images_list}")
|
||
images_count = len(images_list)
|
||
logger.debug(f"We found {images_count} images, good luck!")
|
||
|
||
first_start = time.monotonic()
|
||
self.__mask_to_remove = None
|
||
average = 0
|
||
total_objects = 0
|
||
average_objects = 0
|
||
recalculate_flat = True
|
||
# TODO check image list here to find if a flat exists
|
||
# we recalculate the flat every 10 pictures
|
||
if recalculate_flat:
|
||
self.segmenter_client.client.publish(
|
||
"status/segmenter", '{"status":"Calculating flat"}'
|
||
)
|
||
if images_count < 10:
|
||
self._calculate_flat(
|
||
images_list[0:images_count], images_count, self.__working_path
|
||
)
|
||
else:
|
||
self._calculate_flat(images_list[0:10], 10, self.__working_path)
|
||
recalculate_flat = False
|
||
|
||
if self.__save_debug_img:
|
||
self._save_image(
|
||
self.__flat,
|
||
os.path.join(self.__working_debug_path, "flat_color.jpg"),
|
||
)
|
||
|
||
average_time = 0
|
||
|
||
# TODO here would be a good place to parallelize the computation
|
||
for (i, filename) in enumerate(images_list):
|
||
name = os.path.splitext(filename)[0]
|
||
|
||
# Publish the object_id to via MQTT to Node-RED
|
||
self.segmenter_client.client.publish(
|
||
"status/segmenter", f'{{"status":"Segmenting image {filename}"}}'
|
||
)
|
||
|
||
# we recalculate the flat if the heuristics detected we should
|
||
if recalculate_flat: # not i % 10 and i < (images_count - 10)
|
||
if i > (len(images_list) - 11):
|
||
# We are too close to the end of the list, take the previous 10 images instead of the next 10
|
||
flat = self._calculate_flat(
|
||
images_list[i - 10 : i], 10, self.__working_path
|
||
)
|
||
else:
|
||
flat = self._calculate_flat(
|
||
images_list[i : i + 10], 10, self.__working_path
|
||
)
|
||
recalculate_flat = False
|
||
if self.__save_debug_img:
|
||
self._save_image(
|
||
self.__flat,
|
||
os.path.join(
|
||
os.path.dirname(self.__working_debug_path),
|
||
f"flat_color_{i}.jpg",
|
||
),
|
||
)
|
||
|
||
self.__working_debug_path = os.path.join(
|
||
self.__debug_objects_root,
|
||
self.__working_path.split(self.__img_path)[1].strip(),
|
||
name,
|
||
)
|
||
|
||
logger.debug(f"The debug objects path is {self.__working_debug_path}")
|
||
# Create the debug objects path if needed
|
||
if self.__save_debug_img and not os.path.exists(self.__working_debug_path):
|
||
# create the path!
|
||
os.makedirs(self.__working_debug_path)
|
||
|
||
start = time.monotonic()
|
||
logger.info(f"Starting work on {name}, image {i+1}/{images_count}")
|
||
|
||
img = self._open_and_apply_flat(
|
||
os.path.join(self.__working_path, images_list[i]), self.__flat
|
||
)
|
||
|
||
# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
|
||
# logger.debug(time.monotonic() - start)
|
||
|
||
# start = time.monotonic()
|
||
# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
|
||
|
||
mask = self._create_mask(img, self.__working_debug_path)
|
||
|
||
# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
|
||
# logger.debug(time.monotonic() - start)
|
||
|
||
# start = time.monotonic()
|
||
# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
|
||
|
||
objects_count, _ = self._slice_image(img, name, mask, total_objects)
|
||
total_objects += objects_count
|
||
# Simple heuristic to detect a movement of the flow cell and a change in the resulting flat
|
||
if objects_count > average_objects + 20:
|
||
logger.debug(
|
||
f"We need to recalculate a flat since we have {objects_count} new objects instead of the average of {average_objects}"
|
||
)
|
||
recalculate_flat = True
|
||
average_objects = (average_objects * i + objects_count) / (i + 1)
|
||
|
||
# logger.debug(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
|
||
# logger.debug(time.monotonic() - start)
|
||
delay = time.monotonic() - start
|
||
average_time = (average_time * i + delay) / (i + 1)
|
||
logger.success(
|
||
f"Work on {name} is OVER! Done in {delay}s, average time is {average_time}s, average number of objects is {average_objects}"
|
||
)
|
||
logger.success(
|
||
f"We also found {objects_count} objects in this image, at a rate of {objects_count / delay} objects per second"
|
||
)
|
||
logger.success(f"So far we found {total_objects} objects")
|
||
|
||
total_duration = (time.monotonic() - first_start) / 60
|
||
logger.success(
|
||
f"{images_count} images done in {total_duration} minutes, or an average of {average_time}s per image or {total_duration*60/images_count}s per image"
|
||
)
|
||
logger.success(
|
||
f"We also found {total_objects} objects, or an average of {total_objects / (total_duration * 60)}objects per second"
|
||
)
|
||
|
||
if ecotaxa_export:
|
||
if "objects" in self.__global_metadata:
|
||
if planktoscope.segmenter.ecotaxa.ecotaxa_export(
|
||
self.__archive_fn,
|
||
self.__global_metadata,
|
||
self.__working_obj_path,
|
||
keep_files=True,
|
||
):
|
||
logger.error("The ecotaxa export could not be completed")
|
||
else:
|
||
logger.succes("Ecotaxa archive export completed for this folder")
|
||
else:
|
||
logger.info("There are no objects to export")
|
||
else:
|
||
logger.info("We are not creating the ecotaxa output archive for this folder")
|
||
|
||
# cleanup
|
||
# we're done free some mem
|
||
self.__flat = None
|
||
|
||
def segment_all(self, paths: list, force, ecotaxa_export):
|
||
"""Starts the segmentation in all the folders given recursively
|
||
|
||
Args:
|
||
paths (list, optional): path list to recursively explore. Defaults to [self.__img_path].
|
||
"""
|
||
img_paths = []
|
||
for path in paths:
|
||
for x in os.walk(path):
|
||
if x[0] not in img_paths:
|
||
img_paths.append(x[0])
|
||
self.segment_list(img_paths, force, ecotaxa_export)
|
||
|
||
def segment_list(self, path_list: list, force=True, ecotaxa_export=True):
|
||
"""Starts the segmentation in the folders given
|
||
|
||
Args:
|
||
path_list (list): [description]
|
||
"""
|
||
logger.info(f"The pipeline will be run in {len(path_list)} directories")
|
||
logger.debug(f"Those are {path_list}")
|
||
for path in path_list:
|
||
logger.debug(f"{path}: Checking for the presence of metadata.json")
|
||
if os.path.exists(os.path.join(path, "metadata.json")):
|
||
# The file exists, let's check if we force or not
|
||
if force:
|
||
# forcing, let's gooooo
|
||
if not self.segment_path(path, ecotaxa_export):
|
||
logger.error(f"There was en error while segmenting {path}")
|
||
else:
|
||
# we need to check for the presence of done.txt in each folder
|
||
logger.debug(f"{path}: Checking for the presence of done.txt")
|
||
if os.path.exists(os.path.join(path, "done.txt")):
|
||
logger.debug(
|
||
f"Moving to the next folder, {path} has already been segmented"
|
||
)
|
||
else:
|
||
if not self.segment_path(path, ecotaxa_export):
|
||
logger.error(f"There was en error while segmenting {path}")
|
||
else:
|
||
logger.debug(f"Moving to the next folder, {path} has no metadata.json")
|
||
# Publish the status "Done" to via MQTT to Node-RED
|
||
self.segmenter_client.client.publish("status/segmenter", '{"status":"Done"}')
|
||
|
||
def segment_path(self, path, ecotaxa_export):
|
||
"""Starts the segmentation in the given path
|
||
|
||
Args:
|
||
path (string): path of folder to do segmentation in
|
||
"""
|
||
logger.info(f"Loading the metadata file for {path}")
|
||
with open(os.path.join(path, "metadata.json"), "r") as config_file:
|
||
self.__global_metadata = json.load(config_file)
|
||
logger.debug(f"Configuration loaded is {self.__global_metadata}")
|
||
|
||
# Remove all the key,value pairs that don't start with acq, sample, object or process (for Ecotaxa)
|
||
self.__global_metadata = dict(
|
||
filter(
|
||
lambda item: item[0].startswith(("acq", "sample", "object", "process")),
|
||
self.__global_metadata.items(),
|
||
)
|
||
)
|
||
|
||
project = self.__global_metadata["sample_project"].replace(" ", "_")
|
||
date = datetime.datetime.utcnow().isoformat()
|
||
sample = self.__global_metadata["sample_id"].replace(" ", "_")
|
||
|
||
# TODO Add process informations to metadata here
|
||
|
||
# Define the name of the .zip file that will contain the images and the .tsv table for EcoTaxa
|
||
self.__archive_fn = os.path.join(
|
||
self.__ecotaxa_path,
|
||
# filename includes project name, timestamp and sample id
|
||
f"export_{project}_{date}_{sample}.zip",
|
||
)
|
||
|
||
self.__working_path = path
|
||
|
||
# recreate the subfolder img architecture of this folder inside objects
|
||
# when we split the working path with the base img path, we get the date/sample architecture back
|
||
# os.path.relpath("/home/pi/data/img/2020-10-17/5/5","/home/pi/data/img/") => '2020-10-17/5/5'
|
||
|
||
sample_path = os.path.relpath(self.__working_path, self.__img_path)
|
||
|
||
logger.debug(f"base obj path is {self.__objects_root}")
|
||
logger.debug(f"sample path is {sample_path}")
|
||
|
||
self.__working_obj_path = os.path.join(self.__objects_root, sample_path)
|
||
|
||
logger.debug(f"The working objects path is {self.__working_obj_path}")
|
||
|
||
self.__working_debug_path = os.path.join(self.__debug_objects_root, sample_path)
|
||
|
||
logger.debug(f"The debug objects path is {self.__working_debug_path}")
|
||
|
||
# Create the paths
|
||
for path in [self.__working_obj_path, self.__working_debug_path]:
|
||
if not os.path.exists(path):
|
||
# create the path!
|
||
os.makedirs(path)
|
||
|
||
logger.debug(f"The archive folder is {self.__archive_fn}")
|
||
|
||
logger.info(f"Starting the pipeline in {path}")
|
||
|
||
try:
|
||
self._pipe(ecotaxa_export)
|
||
except Exception as e:
|
||
logger.exception(f"There was an error in the pipeline {e}")
|
||
return False
|
||
|
||
# Add file 'done' to path to mark the folder as already segmented
|
||
with open(os.path.join(self.__working_path, "done.txt"), "w") as done_file:
|
||
done_file.writelines(datetime.datetime.utcnow().isoformat())
|
||
logger.info(f"Pipeline has been run for {path}")
|
||
return True
|
||
|
||
@logger.catch
|
||
def treat_message(self):
|
||
last_message = {}
|
||
if self.segmenter_client.new_message_received():
|
||
logger.info("We received a new message")
|
||
last_message = self.segmenter_client.msg["payload"]
|
||
logger.debug(last_message)
|
||
self.segmenter_client.read_message()
|
||
|
||
if "action" in last_message:
|
||
# If the command is "segment"
|
||
if last_message["action"] == "segment":
|
||
path = None
|
||
recursive = True
|
||
force = False
|
||
ecotaxa_export = True
|
||
# {"action":"segment"}
|
||
if "settings" in last_message:
|
||
if "force" in last_message["settings"]:
|
||
# force rework of already done folder
|
||
force = last_message["settings"]["force"]
|
||
if "recursive" in last_message["settings"]:
|
||
# parse folders recursively starting from the given parameter
|
||
recursive = last_message["settings"]["recursive"]
|
||
if "ecotaxa" in last_message["settings"]:
|
||
# generate ecotaxa output archive
|
||
ecotaxa_export = last_message["settings"]["ecotaxa"]
|
||
if "keep" in last_message["settings"]:
|
||
# keep debug images
|
||
self.__save_debug_img = last_message["settings"]["keep"]
|
||
# TODO eventually add customisation to segmenter parameters here
|
||
|
||
if "path" in last_message:
|
||
path = last_message["path"]
|
||
|
||
# Publish the status "Started" to via MQTT to Node-RED
|
||
self.segmenter_client.client.publish(
|
||
"status/segmenter", '{"status":"Started"}'
|
||
)
|
||
if path:
|
||
if recursive:
|
||
self.segment_all(path, force, ecotaxa_export)
|
||
else:
|
||
self.segment_list(path, force, ecotaxa_export)
|
||
else:
|
||
self.segment_all(self.__img_path)
|
||
|
||
elif last_message["action"] == "stop":
|
||
logger.info("The segmentation has been interrupted.")
|
||
|
||
# Publish the status "Interrupted" to via MQTT to Node-RED
|
||
self.segmenter_client.client.publish(
|
||
"status/segmenter", '{"status":"Interrupted"}'
|
||
)
|
||
|
||
elif last_message["action"] == "update_config":
|
||
logger.error(
|
||
"We can't update the configuration while we are segmenting."
|
||
)
|
||
|
||
# Publish the status "Interrupted" to via MQTT to Node-RED
|
||
self.segmenter_client.client.publish(
|
||
"status/segmenter", '{"status":"Busy"}'
|
||
)
|
||
|
||
elif last_message["action"] != "":
|
||
logger.warning(
|
||
f"We did not understand the received request {action} - {last_message}"
|
||
)
|
||
|
||
################################################################################
|
||
# While loop for capturing commands from Node-RED
|
||
################################################################################
|
||
@logger.catch
|
||
def run(self):
|
||
"""This is the function that needs to be started to create a thread"""
|
||
logger.info(
|
||
f"The segmenter control thread has been started in process {os.getpid()}"
|
||
)
|
||
|
||
# MQTT Service connection
|
||
self.segmenter_client = planktoscope.mqtt.MQTT_Client(
|
||
topic="segmenter/#", name="segmenter_client"
|
||
)
|
||
|
||
# Publish the status "Ready" to via MQTT to Node-RED
|
||
self.segmenter_client.client.publish("status/segmenter", '{"status":"Ready"}')
|
||
|
||
logger.info("Setting up the streaming server thread")
|
||
address = ("", 8001)
|
||
fps = 0.5
|
||
refresh_delay = 3 # was 1/fps
|
||
handler = functools.partial(
|
||
planktoscope.segmenter.streamer.StreamingHandler, refresh_delay
|
||
)
|
||
server = planktoscope.segmenter.streamer.StreamingServer(address, handler)
|
||
self.streaming_thread = threading.Thread(
|
||
target=server.serve_forever, daemon=True
|
||
)
|
||
# start streaming only when needed
|
||
self.streaming_thread.start()
|
||
|
||
logger.success("Segmenter is READY!")
|
||
|
||
# This is the loop
|
||
while not self.stop_event.is_set():
|
||
self.treat_message()
|
||
time.sleep(0.5)
|
||
|
||
logger.info("Shutting down the segmenter process")
|
||
planktoscope.segmenter.streamer.sender.close()
|
||
self.segmenter_client.client.publish("status/segmenter", '{"status":"Dead"}')
|
||
self.segmenter_client.shutdown()
|
||
logger.success("Segmenter process shut down! See you!")
|
||
|
||
|
||
# This is called if this script is launched directly
|
||
if __name__ == "__main__":
|
||
# TODO This should be a test suite for this library
|
||
segmenter_thread = SegmenterProcess(
|
||
None, "/home/rbazile/Documents/pro/PlanktonPlanet/Planktonscope/Segmenter/data/"
|
||
)
|
||
segmenter_thread.segment_path(
|
||
"/home/rbazile/Documents/pro/PlanktonPlanet/Planktonscope/Segmenter/data/test"
|
||
)
|