- add image import script for Pechgraben images
This commit is contained in:
Arno Kaimbacher
parent
dc58b7235f
commit
d33e9d2b55
9 changed files with 503 additions and 112 deletions
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@ -10,6 +10,12 @@
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<gml:identifier codeSpace=\"uniqueID\">{procedure_identifier}</gml:identifier>
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<sml:identification>
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<sml:IdentifierList>
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<sml:identifier>
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<sml:Term definition=\"urn:ogc:def:identifier:OGC:1.0:longName\">
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<sml:label>longName</sml:label>
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<sml:value>{procedure_name}</sml:value>
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</sml:Term>
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</sml:identifier>
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<sml:identifier>
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<sml:Term definition=\"urn:ogc:def:identifier:OGC:1.0:shortName\">
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<sml:label>shortName</sml:label>
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@ -28,6 +34,20 @@
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</sml:capability>
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</sml:CapabilityList>
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</sml:capabilities>
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<sml:capabilities name=\"metadata\">
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<sml:CapabilityList> <!-- status indicates, whether sensor is insitu (true) or remote (false) -->
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<sml:capability name=\"insitu\">
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<swe:Boolean definition=\"insitu\">
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<swe:value>true</swe:value>
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</swe:Boolean>
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</sml:capability> <!-- status indicates, whether sensor is mobile (true) or fixed/stationary (false) -->
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<sml:capability name=\"mobile\">
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<swe:Boolean definition=\"mobile\">
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<swe:value>false</swe:value>
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</swe:Boolean>
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</sml:capability>
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</sml:CapabilityList>
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</sml:capabilities>
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<sml:featuresOfInterest>
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<sml:FeatureList definition=\"http://www.opengis.net/def/featureOfInterest/identifier\">
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<swe:label>featuresOfInterest</swe:label>
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@ -48,10 +68,10 @@
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</sml:featuresOfInterest>
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<sml:outputs>
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<sml:OutputList>
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<sml:output name=\"Image\">
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<sml:output name=\"HumanVisualPerception\">
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<swe:DataRecord>
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<swe:field name=\"manuel_observation\">
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<swe:Text definition=\"manuel_observation\"/>
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<swe:field name=\"HumanVisualPerception\">
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<swe:Text definition=\"HumanVisualPerception\"/>
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</swe:field>
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</swe:DataRecord>
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</sml:output>
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@ -61,19 +81,19 @@
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<swe:Vector referenceFrame=\"urn:ogc:def:crs:EPSG::4326\">
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<swe:coordinate name=\"easting\">
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<swe:Quantity axisID=\"x\">
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<swe:uom code=\"degree\" />
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<swe:uom code=\"degree\"/>
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<swe:value>{cord_x}</swe:value>
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</swe:Quantity>
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</swe:coordinate>
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<swe:coordinate name=\"northing\">
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<swe:Quantity axisID=\"y\">
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<swe:uom code=\"degree\" />
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<swe:uom code=\"degree\"/>
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<swe:value>{cord_y}</swe:value>
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</swe:Quantity>
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</swe:coordinate>
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<swe:coordinate name=\"altitude\">
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<swe:Quantity axisID=\"z\">
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<swe:uom code=\"m\" />
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<swe:uom code=\"m\"/>
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<swe:value>{height}</swe:value>
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</swe:Quantity>
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</swe:coordinate>
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@ -111,8 +111,8 @@
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<swes:observableProperty>http://www.opengis.net/def/property/humanVisualPerception</swes:observableProperty>
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<swes:metadata>
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<sos:SosInsertionMetadata>
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<sos:observationType>http://www.opengis.net/def/observationType/OGCOM/2.0/OM_CategoryObservation</sos:observationType>
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<sos:featureOfInterestType>http://www.opengis.net/def/samplingFeatureType/OGCOM/2.0/SF_SamplingPoint</sos:featureOfInterestType>
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<sos:observationType>http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_CategoryObservation</sos:observationType>
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<sos:featureOfInterestType>http://www.opengis.net/def/samplingFeatureType/OGC-OM/2.0/SF_SamplingPoint</sos:featureOfInterestType>
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</sos:SosInsertionMetadata>
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</swes:metadata>
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</swes:InsertSensor>
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File diff suppressed because one or more lines are too long
165
pechgraben_images/import_image_observations.py
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165
pechgraben_images/import_image_observations.py
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@ -0,0 +1,165 @@
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'''
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Sqlalchemy version: 1.2.15
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Python version: 3.7
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'''
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import os
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import uuid
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from datetime import datetime
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from sqlalchemy.orm import session
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from sqlalchemy import asc, desc
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from exif import Image
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from db.models import (
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create_pg_session, Observation,
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Dataset, Procedure, Phenomenon, Platform, Format)
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def main():
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''' main method '''
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pg_session: session = create_pg_session()
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platform_sta_identifier = "pechgraben_images"
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sensor = "camera2"
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pg_query = pg_session.query(Dataset) \
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.join(Procedure) \
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.join(Phenomenon) \
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.filter(Procedure.sta_identifier == sensor.lower())
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visual_perception_dataset: Dataset = pg_query.filter(
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Phenomenon.sta_identifier == "HumanVisualPerception").first()
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if not visual_perception_dataset:
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print("Sensor " + sensor + " ist noch nicht angelegt!")
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exit()
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if not visual_perception_dataset.is_published:
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visual_perception_dataset.is_published = 1
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visual_perception_dataset.is_hidden = 0
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visual_perception_dataset.dataset_type = "timeseries"
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visual_perception_dataset.observation_type = "simple"
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visual_perception_dataset.value_type = "text"
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pg_session.commit()
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platform_exists: bool = pg_session.query(Platform.id).filter_by(
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sta_identifier=platform_sta_identifier).scalar() is not None
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if platform_exists:
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sensor_platform = pg_session.query(Platform.id) \
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.filter(Platform.sta_identifier == platform_sta_identifier) \
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.first()
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visual_perception_dataset.fk_platform_id = sensor_platform.id
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format_exists: bool = pg_session.query(Format.id).filter_by(
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definition="http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_TextObservation"
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).scalar() is not None
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if format_exists:
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sensor_format = pg_session.query(Format.id) \
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.filter(Format.definition == "http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_TextObservation") \
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.first()
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visual_perception_dataset.fk_format_id = sensor_format.id
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# import all the images for the given sensor names
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import_images(visual_perception_dataset, pg_session)
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# save first and last values of all the observations
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first_observation: Observation = pg_session.query(Observation) \
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.filter(Observation.fk_dataset_id == visual_perception_dataset.id) \
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.order_by(asc('sampling_time_start')) \
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.first()
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if first_observation is not None:
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visual_perception_dataset.first_time = first_observation.sampling_time_start
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# visual_perception_dataset.first_value = first_observation.value_quantity
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visual_perception_dataset.fk_first_observation_id = first_observation.id
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last_observation: Observation = pg_session.query(Observation) \
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.filter(Observation.fk_dataset_id == visual_perception_dataset.id) \
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.order_by(desc('sampling_time_start')) \
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.first()
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if last_observation is not None:
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visual_perception_dataset.last_time = last_observation.sampling_time_start
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# visual_perception_dataset.last_value = last_observation.value_quantity
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visual_perception_dataset.fk_last_observation_id = last_observation.id
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pg_session.commit()
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pg_session.close()
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def import_images(dataset: Dataset, pg_session):
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''' main method '''
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folder_path = 'C:/Users/kaiarn/Documents/Fotos'
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# img_filename = '_DSC9548.JPG'
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# img_path = f'{folder_path}/{img_filename}'
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# Get the list of image files in the directory that exifread supports
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directory = os.listdir(folder_path)
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for file_name in directory:
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if file_name.endswith(('jpg', 'JPG', 'png', 'PNG', 'tiff', 'TIFF')):
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file_path = os.path.join(folder_path, file_name)
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# print(file_path)
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img_file = open(file_path, 'rb')
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img: Image = Image(img_file)
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if img.has_exif:
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info = f" has the EXIF {img.exif_version}"
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else:
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info = "does not contain any EXIF information"
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# print(f"Image {img_file.name}: {info}")
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# Original datetime that image was taken (photographed)
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# print(f'DateTime (Original): {img.get("datetime_original")}')
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datetime_original = img.get("datetime_original")
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# Grab the date
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date_obj = datetime.strptime(
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datetime_original, '%Y:%m:%d %H:%M:%S')
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# print(date_obj)
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create_observation(dataset, date_obj, file_name)
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pg_session.commit()
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def create_observation(dataset: Dataset, datetime_original, file_name):
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"""
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This function creates a new observation in the people structure
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based on the passed-in observation data
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:param observation: person to create in people structure
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:return: 201 on success, observation on person exists
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"""
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# deserialize to python object
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new_observation: Observation = Observation()
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# new_observation.id = max_id
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new_observation.sta_identifier = str(uuid.uuid4())
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new_observation.result_time = datetime_original
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new_observation.sampling_time_start = new_observation.result_time
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new_observation.sampling_time_end = new_observation.result_time
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new_observation.value_type = "text"
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new_observation.value_text = "https://geomon.geologie.ac.at/images/" + file_name
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new_observation.fk_dataset_id = dataset.id
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# Add the person to the database
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dataset.observations.append(new_observation)
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# db_session.commit()
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if __name__ == "__main__":
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# load_dotenv(find_dotenv())
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# print('sensors: {}'.format(os.environ.get(
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# 'GLASFASER_GSCHLIEFGRABEN_SENSORS', [])))
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main()
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# print(img.list_all())
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# print(img.has_exif)
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# # Make of device which captured image: NIKON CORPORATION
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# print(f'Make: {img.get("make")}')
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# # Model of device: NIKON D7000
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# print(f'Model: {img.get("model")}')
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# # Software involved in uploading and digitizing image: Ver.1.04
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# print(f'Software: {img.get("software")}')
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# # Name of photographer who took the image: not defined
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# print(f'Artist: {img.get("artist")}')
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# # Original datetime that image was taken (photographed)
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# print(f'DateTime (Original): {img.get("datetime_original")}')
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# # Details of flash function
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# print(f'Flash Details: {img.get("flash")}')
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# print(f"Coordinates - Image")
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# print("---------------------")
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# print(f"Latitude: {img.copyright} {img.get('gps_latitude_ref')}")
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# print(f"Longitude: {img.get('gps_longitude')} {img.get('gps_longitude_ref')}\n")
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@ -1,78 +0,0 @@
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'''
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Sqlalchemy version: 1.2.15
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Python version: 3.7
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'''
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import os
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from datetime import datetime
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from exif import Image
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def main():
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''' main method '''
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folder_path = 'C:/Users/kaiarn/Documents/Fotos'
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# img_filename = '_DSC9548.JPG'
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# img_path = f'{folder_path}/{img_filename}'
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# Get the list of image files in the directory that exifread supports
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directory = os.listdir(folder_path)
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for files in directory:
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if files.endswith(('jpg', 'JPG', 'png', 'PNG', 'tiff', 'TIFF')):
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file_path = os.path.join(folder_path, files)
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# print(file_path)
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img_file = open(file_path, 'rb')
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img: Image = Image(img_file)
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if img.has_exif:
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info = f" has the EXIF {img.exif_version}"
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else:
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info = "does not contain any EXIF information"
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print(f"Image {img_file.name}: {info}")
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# Original datetime that image was taken (photographed)
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# print(f'DateTime (Original): {img.get("datetime_original")}')
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datetime_original = img.get("datetime_original")
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# print(datetime_original)
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# Grab the date
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date_obj = datetime.strptime(
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datetime_original, '%Y:%m:%d %H:%M:%S')
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print(date_obj)
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# print(f"Longitude: {img.get('gps_longitude')} {img.get('gps_longitude_ref')}\n")
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# with open(img_path, 'rb') as img_file:
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# img = Image(img_file)
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# if img.has_exif:
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# info = f" has the EXIF {img.exif_version}"
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# else:
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# info = "does not contain any EXIF information"
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# print(f"Image {img_file.name}: {info}")
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# print(img.list_all())
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# print(img.has_exif)
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# # Make of device which captured image: NIKON CORPORATION
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# print(f'Make: {img.get("make")}')
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# # Model of device: NIKON D7000
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# print(f'Model: {img.get("model")}')
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# # Software involved in uploading and digitizing image: Ver.1.04
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# print(f'Software: {img.get("software")}')
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# # Name of photographer who took the image: not defined
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# print(f'Artist: {img.get("artist")}')
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# # Original datetime that image was taken (photographed)
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# print(f'DateTime (Original): {img.get("datetime_original")}')
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# # Details of flash function
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# print(f'Flash Details: {img.get("flash")}')
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# print(f"Coordinates - Image")
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# print("---------------------")
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# print(f"Latitude: {img.copyright} {img.get('gps_latitude_ref')}")
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# print(f"Longitude: {img.get('gps_longitude')} {img.get('gps_longitude_ref')}\n")
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if __name__ == "__main__":
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# load_dotenv(find_dotenv())
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# print('sensors: {}'.format(os.environ.get(
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# 'GLASFASER_GSCHLIEFGRABEN_SENSORS', [])))
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main()
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