segmenter: bugfixes
This commit is contained in:
Romain Bazile
parent
fbacf21016
commit
a35b70e0fb
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@ -264,7 +264,9 @@ class ImagerProcess(multiprocessing.Process):
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nodered_metadata = last_message["config"]
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# Definition of the few important metadata
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local_metadata = {
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"process_datetime": datetime.datetime.now().isoformat(),
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"process_datetime": datetime.datetime.now()
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.isoformat()
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.split(".")[0],
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"acq_camera_resolution": self.__resolution,
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"acq_camera_iso": self.__iso,
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"acq_camera_shutter_speed": self.__shutter_speed,
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@ -350,6 +352,7 @@ class ImagerProcess(multiprocessing.Process):
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# We only keep the date '2020-09-25T15:25:21.079769'
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self.__global_metadata["process_datetime"].split("T")[0],
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str(self.__global_metadata["sample_id"]),
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str(self.__global_metadata["acq_id"]),
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)
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if not os.path.exists(self.__export_path):
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# create the path!
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@ -374,7 +377,7 @@ class ImagerProcess(multiprocessing.Process):
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json.dumps(
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{
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"action": "move",
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"direction": "BACKWARD",
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"direction": "FORWARD",
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"volume": self.__pump_volume,
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"flowrate": 2,
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}
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@ -399,6 +402,7 @@ class ImagerProcess(multiprocessing.Process):
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filename_path = os.path.join(self.__export_path, filename)
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logger.info(f"Capturing an image to {filename_path}")
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# TODO Insert here a delay to stabilize the flow before we image
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# Capture an image with the proper filename
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self.__camera.capture(filename_path)
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@ -409,7 +413,7 @@ class ImagerProcess(multiprocessing.Process):
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# Publish the name of the image to via MQTT to Node-RED
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self.imager_client.client.publish(
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"status/imager",
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f'{{"status":"{filename} has been imaged."}}',
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f'{{"status":"{self.__img_done + 1}/{self.__img_goal} has been imaged to {filename}."}}',
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)
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# Increment the counter
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@ -174,7 +174,7 @@ class SegmenterProcess(multiprocessing.Process):
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# Define the name of each object
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object_fn = morphocut.str.Format(
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os.path.join("/home/pi/PlanktonScope/", "OBJECTS", "{name}.jpg"),
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os.path.join(self.__working_path, "objects", "{name}.jpg"),
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name=object_id,
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)
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@ -242,31 +242,51 @@ class SegmenterProcess(multiprocessing.Process):
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self.segmenter_client.client.publish(
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"status/segmenter", '{"status":"Started"}'
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)
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img_paths = [x[0] for x in os.walk(self.__img_path)]
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logger.info(f"The pipeline will be run in {len(img_paths)} directories")
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logger.debug(f"The pipeline will be run in these directories {img_paths}")
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for path in img_paths:
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logger.info(f"Loading the metadata file for {path}")
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with open(os.path.join(path, "metadata.json"), "r") as config_file:
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self.__global_metadata = json.load(config_file)
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logger.debug(f"Configuration loaded is {self.__global_metadata}")
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logger.info("Checking for the presence of metadata.json")
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if os.path.exists(os.path.join(path, "metadata.json")):
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# The file exists, let's run the pipe!
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logger.info(f"Loading the metadata file for {path}")
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with open(os.path.join(path, "metadata.json"), "r") as config_file:
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self.__global_metadata = json.load(config_file)
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logger.debug(
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f"Configuration loaded is {self.__global_metadata}"
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)
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# Define the name of the .zip file that will contain the images and the .tsv table for EcoTaxa
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self.__archive_fn = os.path.join(
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self.__ecotaxa_path,
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# filename includes project name, timestamp and sample id
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f"export_{self.__global_metadata['sample_project']}_{self.__global_metadata['process_datetime']}_{self.__global_metadata['sample_id']}.zip",
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)
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project = self.__global_metadata["sample_project"].replace(" ", "_")
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date = self.__global_metadata["process_datetime"]
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sample = self.__global_metadata["sample_id"]
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# Define the name of the .zip file that will contain the images and the .tsv table for EcoTaxa
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self.__archive_fn = os.path.join(
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self.__ecotaxa_path,
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# filename includes project name, timestamp and sample id
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f"export_{project}_{date}_{sample}.zip",
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)
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logger.info(f"Starting the pipeline in {path}")
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# Start the MorphoCut Pipeline on the found path
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self.__working_path = path
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self.__working_path = path
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try:
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self.__pipe.run()
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except Exception as e:
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logger.exception(f"There was an error in the pipeline {e}")
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logger.info(f"Pipeline has been run for {path}")
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# Create the objects path
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if not os.path.exists(os.path.join(self.__working_path, "objects")):
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# create the path!
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os.makedirs(os.path.join(self.__working_path, "objects"))
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logger.debug(f"The archive folder is {self.__archive_fn}")
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self.__create_morphocut_pipeline()
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logger.info(f"Starting the pipeline in {path}")
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# Start the MorphoCut Pipeline on the found path
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try:
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self.__pipe.run()
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except Exception as e:
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logger.exception(f"There was an error in the pipeline {e}")
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logger.info(f"Pipeline has been run for {path}")
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else:
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logger.info("Moving to the next folder, this one's empty")
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# remove directory
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# shutil.rmtree(import_path)
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@ -320,7 +340,7 @@ class SegmenterProcess(multiprocessing.Process):
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)
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# Instantiate the morphocut pipeline
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self.__create_morphocut_pipeline()
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# self.__create_morphocut_pipeline()
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# Publish the status "Ready" to via MQTT to Node-RED
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self.segmenter_client.client.publish("status/segmenter", '{"status":"Ready"}')
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