diff --git a/README.md b/README.md index fd60467..fbe6cae 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,8 @@ # Background The OPS-SAT SmartCam is an image acquisition and classification app for the European Space Agency's [OPS-SAT](https://www.esa.int/Enabling_Support/Operations/OPS-SAT_your_flying_laboratory) spacecraft. An acquired image can go through a pipeline of multiple image classification models that are applied in a sequence. +The app features geospatial awareness with the ability to acquire images when the spacecraft is located above pre-defined areas of interests that are described as polygons and/or multi-polygons in a GeoJSON file. + 1. [Neural Networks](https://github.com/georgeslabreche/opssat-smartcam#neural-networks) 2. [Contribute](https://github.com/georgeslabreche/opssat-smartcam#contribute) 3. [How does it work?](https://github.com/georgeslabreche/opssat-smartcam#how-does-it-work) @@ -10,7 +12,7 @@ The OPS-SAT SmartCam is an image acquisition and classification app for the Euro 5. [Image Metadata](https://github.com/georgeslabreche/opssat-smartcam#image-metadata) ## 1. Neural Networks -The app can apply any .tflite neural network image classification model file trained with TensorFlow. The default model labels the images acquired by the spacecraft's camera as either "earth", "edge", or "bad". The SmartCam's image classification program [uses the TensorFlow Lite C API for model inference](https://github.com/georgeslabreche/tensorflow-opssat-smartcam). Tensorflow Lite inference is thus available to any experimenter without being restricted to image classification. +The app can apply any .tflite neural network image classification model file trained with TensorFlow. The default model's labels are "earth", "edge", and "bad". The SmartCam's image classification program [uses the TensorFlow Lite C API for model inference](https://github.com/georgeslabreche/tensorflow-opssat-smartcam). Tensorflow Lite inference is thus available to any experimenter without being restricted to image classification. ## 2. Contribute Ways to contribute: @@ -25,22 +27,22 @@ The SmartCam's app configuration is set in the config.ini file. The gist of the 1. Acquires ims_rgb (raw) and png image files using the spacecraft's HD camera. 2. Creates a thumbnail jpeg image. -3. Creates a neural network input jpeg image. -4. Labels the image using the entry point neural network model file specified by *entry_point_model* in config.ini. -5. If the applied label is part of the model's *labels_keep* in config.ini then label the image further with the next model in model pipeline. +3. Creates an input jpeg image for the image classifier. +4. Labels the image using the entry point model file specified by *entry_point_model* in config.ini. +5. If the applied label is part of the model's *labels_keep* in config.ini then label the image further with the next model in image classification pipeline. 6. Repeat step 5 until either the applied label is not part of the current model's configured *labels_keep* or until the last model of the pipeline has been applied. -7. The labeled images are moved into the experiment and the filestore's toGround folders depending on the keep images and downlink configurations set in config.ini. +7. The labeled image is moved into the experiment and the filestore's toGround folders depending on the keep images and downlink configurations set in config.ini. +8. Repeat steps 1 through 7 until the image acquisition loop as gone through the number of iterations set by *gen_number* in config.ini. ### 3.2. Building an image classification pipeline 1. Each model consists of a .tflite and a labels.txt file located in a model folder under `/home/exp1000/models`, e.g: `/home/exp1000/models/default` and `/home/exp1000/models/cloud_detection`. 2. Create a config.ini section for each model. Prefix the section name with `model_`, e.g. `[model_default]` and `[model_cloud_detection]`. 3. Each model's config section will specify which label to keep via the *labels_keep* property. For instance, if the default model can label an image as either "earth", "edge", or "bad", but we only want to keep images classified with the first two labels, then `labels_keep = ["earth", "edge"]`. 4. If another image classification needs to follow up after an image was previously classified with a certain label, then the follow up model name can be appended following a colon. E.g. `["earth:cloud_detection", "edge"]`. -5. The entry point model that will be the first image classification applid in the model pipeline is specified in the config.ini's *entry_point_model* property, e.g. `entry_point_model = default`. +5. The entry point model that will be the first model applied in the image classification pipeline is specified in the config.ini's *entry_point_model* property, e.g. `entry_point_model = default`. ## 4. Configuration -Consult the app's config.ini file for the default configuration values. - +This section describes the app's configuration parameters in the `config.ini` file. ### 4.1. General - *downlink_log_if_no_images* - indicate whether or not the log file(s) should be downlinked even if no images are labeled for downlink (yes/no). - *entry_point_model* - the first image classification model to apply in the model pipeline. @@ -53,11 +55,26 @@ Consult the app's config.ini file for the default configuration values. - *quota_toGround* - experiment's toGround folder size limit (KB). Image acquisition is skipped if this limit is exceeded. ### 4.2. Image Acquisition -- *gen_interval* - default image acquisition period (in seconds). -- *gen_interval_throttle* - image acquisition period used when a label of interest has been applied to the previously acquired image (in seconds). -- *gen_number* - number of image acquisition interations. -- *gen_exposure* - camera exposure value. -- *gen_gains* - rgb gains. +There are two types of image acquisition that can beet set: Polling or Area-of-Interest (AOI): +- Polling: acquire images in a loop that begins at the experiment start time. +- AOI: acquire images whenever the spacecraft is above an area of interest, during daytime, as defined by polygon shapes in a GeoJSON file. + +#### 4.2.1. AOI GeoJSON Files +- The default AOI GeoJSON files defines multipolygon representations of all continents except Antarctica. +- Use [geojson.io](https://geojson.io) to define custom AOI polygons for the app to consume. +- Use [mapshaper](https://mapshaper.org/) to simplify GeoJSON files onbtained from third-party providers in order to keep the file sizes small. +- Coordinates with high precision floating point numbers do not contribute much and are best avoided in favour of reduced GeoJSON file size. + +#### 4.2.2. Camera Settings +- *cam_exposure* - exposure value (in milliseconds). +- *cam_gains* - rgb gains (e.g. [8, 8, 8]). + +#### 4.2.3. Acquisition Type +- *gen_type* - can be either `aoi` or `poll` for "area-of-interest" or "polling", respectively. +- *gen_interval* - wait time between image acquisition loop iterations (in seconds). +- *gen_interval_throttle* - wait time between image acquisition loop iterations when a label of interest has been applied to the previously acquired image (in seconds). +- *gen_number* - number of image acquisitions. +- *gen_geojson* - path of the GeoJSON file with polygons defining areas of interest for image acquisition. ### 4.3. Images - *raw_keep* - flag if the raw image file should be kept. diff --git a/home/exp1000/acquire_and_label_images.py b/home/exp1000/acquire_and_label_images.py index 6c77938..339729c 100644 --- a/home/exp1000/acquire_and_label_images.py +++ b/home/exp1000/acquire_and_label_images.py @@ -13,8 +13,8 @@ import re import csv import ephem -from ephem import degree from pathlib import Path +from shapely import geometry __author__ = 'Georges Labreche, Georges.Labreche@esa.int' @@ -57,6 +57,9 @@ # Image filename prefix. IMG_FILENAME_PREFIX = "img_msec_" +# Image generation type: AOI. +GEN_TYPE_AOI = 'aoi' + # The logger. logger = None @@ -70,6 +73,9 @@ def __init__(self): # Init the conf config section properties. self.init_conf_props() + # Init the camera section properties. + self.init_camera_props() + # Init the gen config section properties. self.init_gen_props() @@ -105,7 +111,7 @@ def init_conf_props(self): # TLE file path. self.tle_path = self.config.get('conf', 'tle_path') - # Size quote for the experiment's toGround folder. + # Size quota for the experiment's toGround folder. self.quota_toGround = self.config.getint('conf', 'quota_toGround') @@ -152,31 +158,41 @@ def init_compression_fapec_props(self): return True + def init_camera_props(self): + """Fetch camera parameters.""" + + self.cam_exposure = self.config.getint('camera', 'cam_exposure') + if self.cam_exposure <= 1: + self.cam_exposure = 2 + + self.cam_gains = json.loads(self.config.get('camera', 'cam_gains')) + if self.cam_gains[0] >= 255: + self.cam_gains[0] = 255 + + if self.cam_gains[1] >= 255: + self.cam_gains[1] = 255 + + if self.cam_gains[2] >= 255: + self.cam_gains[2] = 255 + def init_gen_props(self): """Fetch image acquisition parameters.""" - self.gen_interval = self.config.getint('gen', 'gen_interval') + # Image generation type: polling or area of interest (AOI). + self.gen_type = self.config.get('gen', 'gen_type') + + self.gen_interval = self.config.getfloat('gen', 'gen_interval') - self.gen_interval_throttle = self.config.getint('gen', 'gen_interval_throttle') + self.gen_interval_throttle = self.config.getfloat('gen', 'gen_interval_throttle') + # Number of images to acquire. self.gen_number = self.config.getint('gen', 'gen_number') if self.gen_number <= 0: - self. gen_number = 1 - - self.gen_exposure = self.config.getint('gen', 'gen_exposure') - if self.gen_exposure <= 1: - self.gen_exposure = 2 + self.gen_number = 1 - self.gen_gains = json.loads(self.config.get('gen', 'gen_gains')) - if self.gen_gains[0] >= 255: - self.gen_gains[0] = 255 - - if self.gen_gains[1] >= 255: - self.gen_gains[1] = 255 - - if self.gen_gains[2] >= 255: - self.gen_gains[2] = 255 + # The GeoJSON AOI file that will be used in case of "aoi" image generation type. + self.gen_geojson = self.config.get('gen', 'gen_geojson') def init_img_props(self): @@ -268,6 +284,63 @@ def __init__(self, tle_path, gains, exposure): self.metadata_list = [] + def get_groundtrack_coordinates(self): + """Get coordinates of the geographic point beneath the satellite.""" + + try: + # Get current timestamp in milliseconds. + d = datetime.datetime.utcnow() + + # Ephem datetime object representation of the current timestamp. + d_ephem = ephem.Date(d) + + # Compute based on reference TLE and the current timestamp. + self.tle.compute(d_ephem) + + # Return ground track coordinates of the point beneath the spacecraft. + return { + 'lat': self.tle.sublat, + 'lng': self.tle.sublong, + 'dt': d + } + + except: + # Something went wrong. + logger.exception("Failed to fetch groundtrack coordinates.") + + # Return none. + return None + + + def is_daytime(self, ephem_lat, ephem_lng, dt): + """Check if it's daytime at the given location for the given time.""" + + try: + # Create an Observer object. + observer = ephem.Observer() + + # Set the observer to the given location and time. + observer.lat = ephem_lat + observer.long = ephem_lng + observer.date = ephem.Date(dt) + + # Create a Sun object. + sun = ephem.Sun() + + # Compute the sun's position with respect to the observer. + sun.compute(observer) + + # If the sun is above the observer then it's daytime. If not, then it's nighttime. + return sun.alt > 0 + + except: + # Something went wrong. + logger.exception("Failed to fetch whether it is daytime or not at the given location.") + + # Return false (nighttime) in case of error. + return False + + def collect_metadata(self, filename_png, label, confidence, keep): """Collect metadata for the image acquired at the given timestamp.""" @@ -297,7 +370,7 @@ def collect_metadata(self, filename_png, label, confidence, keep): # Image acquisition datetime. d = datetime.datetime.utcfromtimestamp(timestamp / 1000.0) - # Image acquisition epheme datetime object. + # Image acquisition ephem datetime object. d_ephem = ephem.Date(d) # Compute based on reference TLE and acquisition datetime. @@ -317,8 +390,8 @@ def collect_metadata(self, filename_png, label, confidence, keep): 'acq_dt': str(d_ephem), # Image acquisition datetime. 'ref_dt': str(self.tle._epoch), # Reference epoch. 'tle_age': d_ephem - self.tle._epoch, # TLE age (days). - 'lat': self.tle.sublat / degree, # Latitude (deg). - 'lng': self.tle.sublong / degree, # Longitude (deg). + 'lat': self.tle.sublat / ephem.degree,# Latitude (deg). + 'lng': self.tle.sublong / ephem.degree,# Longitude (deg). 'h': self.tle.elevation, # Geocentric height above sea level (m). 'tle_ref_line1': self.tle_line1, # Reference TLE line 1. 'tle_ref_line2': self.tle_line2 # Rererence TLE line 2. @@ -382,12 +455,68 @@ def write_metadata(self, csv_filename): logger.info("No metadata data was collected.") +class GeoJsonUtils: + + def __init__(self, geojson_filename): + """Initialize the GeoJSON Utils class.""" + + try: + + # load GeoJSON file containing polygons. + with open(geojson_filename) as f: + self.geojson = json.load(f) + + except: + logger.exception("Failed to load GeoJSON file: " + geojson_filename) + + + def is_point_in_polygon(self, lat, lng): + """Check if given point coordinates is located inside the polygon defined in the GeoJSON file.""" + + # Default to False if GeoJSON file was not loaded. + if self.geojson is None: + return False + + # Define a point based on the given longitude and latitude. + point = geometry.Point(lng, lat) + + # Check each feature to see if it contains the point. + for feature in self.geojson['features']: + + # Features representing a continent can either by a Polygon or a MultiPolygon. + shape = geometry.shape(feature['geometry']) + + # If the shape is a Polygon the check if it contains the given point. + if isinstance(shape, geometry.Polygon): + + # Check if polygon contains point. + if polygon.contains(point): + + # The point is inside one of the shapes defined in the GeoJSON file. + return True + + # If the shape is a MultiPolygon then iterate through each Polygon. + elif isinstance(shape, geometry.MultiPolygon): + + # For each Polygon of the MultiPolygon. + for polygon in shape: + + # Check if polygon contains point. + if polygon.contains(point): + + # The point is inside one of the shapes defined in the GeoJSON file. + return True + + # The given point is not in any target shapes. + return False + + class Fapec: bin_path = FAPEC_BIN_PATH def __init__(self, chunk, threads, dtype, band, losses, meaningful_bits, lev): - """Initialize the Fapec compression class.""" + """Initialize the FAPEC compression class.""" self.chunk = chunk self.threads = threads @@ -486,7 +615,7 @@ def get_image_keep_status_and_next_model(self, applied_label, labels_keep): # Multiple follw up models were defined. # Log a warning that this is not ssupported. # TODO: Support this case of "model branching". Look into approaching this with a node graph. - logging.warning("Branching to multiple follow up models is currently unsupported. Selecting the first next model listed.") + logger.warning("Branching to multiple follow up models is currently unsupported. Selecting the first next model listed.") # Only follow up with the first follow up model that is listed. next_model = label_split[1] @@ -642,6 +771,7 @@ def log_housekeeping_data(self): df_output = subprocess.check_output(['df', '-h']).decode('utf-8') logger.info('Disk usage:\n' + df_output) + class HDCamera: def __init__(self, gains, exposure): @@ -649,7 +779,7 @@ def __init__(self, gains, exposure): self.exposure = exposure def acquire_image(self): - """Acquire and image with the on-board camera.""" + """Acquire an image with the on-board camera.""" # Build the image acquisition execution command string. cmd_image_acquisition = 'ims100_testapp -R {R} -G {G} -B {B} -c /dev/ttyACM0 -m /dev/sda -v 0 -n 1 -p -e {E} >> {L} 2>&1'.format(\ @@ -832,68 +962,117 @@ def run_experiment(): # The config parser. cfg = AppConfig() + # Instanciate classes. utils = Utils() - camera = HDCamera(cfg.gen_gains, cfg.gen_exposure) + geojson_utils = GeoJsonUtils(cfg.gen_geojson) + camera = HDCamera(cfg.cam_gains, cfg.cam_exposure) img_editor = ImageEditor() img_classifier = ImageClassifier() - img_metadata = ImageMetaData(cfg.tle_path, cfg.gen_gains, cfg.gen_exposure) - - # Flag and counter to keep track. - done = False - counter = 0 - - # Check if the experiment's toGround folder is below a configured quota before proceeding with the experiment. - try: - toGround_size = int(subprocess.check_output(['du', '-s', TOGROUND_PATH]).decode('utf-8').split()[0]) - done = True if toGround_size >= cfg.quota_toGround else False - - if done: - logger.info("Exiting: the experiment's toGround folder is greater than the configured quota: {TG} KB > {Q} KB.".format(\ - TG=toGround_size,\ - Q=cfg.quota_toGround)) - - except: - logger.exception("Exiting: failed to check disk space use of the experiment's toGround folder.") - done = False + img_metadata = ImageMetaData(cfg.tle_path, cfg.cam_gains, cfg.cam_exposure) # Instanciate a compressor object if a compression algorithm was specified and configured in the config.ini. raw_compressor = None - # Proceed with instanciate the compressor object in case the is not marked to stop. - if not done: + # Raw image file compression will only be applied if we enable compressed raw downlinking. + if cfg.downlink_compressed_raws and cfg.raw_compression_type == 'fapec' and cfg.init_compression_fapec_props() is True: - # Raw image file compression will only be applied if we enable compressed raw downlinking. - if cfg.downlink_compressed_raws and cfg.raw_compression_type == 'fapec' and cfg.init_compression_fapec_props() is True: + # Instanciate compression object that will be used to compress the raw image files. + raw_compressor = Fapec(cfg.compression_fapec_chunk,\ + cfg.compression_fapec_threads,\ + cfg.compression_fapec_dtype,\ + cfg.compression_fapec_band,\ + cfg.compression_fapec_losses,\ + cfg.compression_fapec_meaningful_bits,\ + cfg.compression_fapec_lev) - # Instanciate compression object that will be used to compress the raw image files. - raw_compressor = Fapec(cfg.compression_fapec_chunk,\ - cfg.compression_fapec_threads,\ - cfg.compression_fapec_dtype,\ - cfg.compression_fapec_band,\ - cfg.compression_fapec_losses,\ - cfg.compression_fapec_meaningful_bits,\ - cfg.compression_fapec_lev) + logger.info("Raw image file compression enabled: " + cfg.raw_compression_type + ".") - logger.info("Raw image file compression enabled: " + cfg.raw_compression_type + ".") + else: + # No compression will be applied to the raw image files. + logger.info("Raw image file compression disabled.") - else: - # No compression will be applied to the raw image files. - logger.info("Raw image file compression disabled.") # Default immage acquisition interval. Can be throttled when an acquired image is labeled to keep. image_acquisition_period = cfg.gen_interval + # Image acquisition loop flag and counter to keep track. + done = False + counter = 0 + # Image acquisition loop. while not done: + # Flag indicating whether or not we should skip the image acquisition and labeling process + # We want to skip in case some criteria is not met or in case we encounter an error. + success = True + + # Init keep image flag indicating if we kepp the image. + keep_image = False + + # Cleanup any files that may have been left over from a previous run that may have terminated ungracefully. + # Skip image acquisition in case of file deletion failure. + if utils.cleanup() < 0: + success = False + + # Check if the experiment's toGround folder is below a configured quota before proceeding with image acquisition. try: + toGround_size = int(subprocess.check_output(['du', '-s', TOGROUND_PATH]).decode('utf-8').split()[0]) + done = True if toGround_size >= cfg.quota_toGround else False + + if done: + # Exit the image acquisition loop in case toGround disk size is too large. + logger.info("Exiting: the experiment's toGround folder is greater than the configured quota: {TG} KB > {Q} KB.".format(\ + TG=toGround_size,\ + Q=cfg.quota_toGround)) + + # Break out the image acquisition loop. + break + + except: + # Exit the image acquisition loop in case of exception. + logger.exception("Exiting: failed to check disk space use of the experiment's toGround folder.") + break + + try: + # If the image acquisition type is AOI then only acquire an image if the spacecraft is located above an area of interest. + # Areas of interests are defined as geophraphic shapes represented by polygons listed in the GeoJSON file. + if cfg.gen_type == GEN_TYPE_AOI: + + try: + # Get the coordinates of the spacecraft's current groundtrack position. + coords = img_metadata.get_groundtrack_coordinates() + + # Proceed if groundtrack coordinates successfully fetched. + if coords is not None: - # Flag indicating if we should skip the image acquisition and labeling process in case of an encountered error. - success = True + # Find out if it is daytime at the point directly below the spacecraft (i.e. at the point coordinate of the spacecraft's groundtrack position). + is_daytime = img_metadata.is_daytime(coords['lat'], coords['lng'], coords['dt']) + + # If the groundtrack coordinates are above a point on Earth's surface where it is daylight then proceed in checking if we are above an area of interest. + if is_daytime: - # Cleanup any files that may have been left over from a previous run that may have terminated ungracefully. - if utils.cleanup() < 0: - success = False + # Check if the spacecraft is above an area of interest. + # Continue with the image acquisition if it is by setting the success flag to True. + success = geojson_utils.is_point_in_polygon(coords['lat'] / ephem.degree, coords['lng'] / ephem.degree) + + else: + # It's nightime so skip image acquisition. + success = False + + else: + # Skip this image acquisition loop if ground track coordinates not fetched. + logger.error("Skipping image acquisition: failed to fetch groundtrack coordinates.") + success = False + + except: + # Skip this image acquisition loop if an unexpected exception occurred. + logger.exception("Failed to acquire image based on geographic area of interest.") + success = False + + # If we are skipping image acquisition then reset the image acquisition period to the default value. + # Do this in case the period was throttled in the previous iteration of the image acquisition loop. + if not success: + image_acquisition_period = cfg.gen_interval # If experiment's root directory is clean, i.e. no images left over from a previous image acquisition, then acquire a new image. if success: @@ -1030,7 +1209,7 @@ def run_experiment(): utils.move_images_for_keeping(cfg.raw_keep, cfg.png_keep, applied_label) # An image of interest has been acquired: throttle image acquisition frequency. - image_acquisition_period = cfg.gen_interval_throttle + image_acquisition_period = cfg.gen_interval_throttle except: # In case of exception just log the stack trace and proceed to the next image acquisition iteration. @@ -1039,12 +1218,25 @@ def run_experiment(): # Error handling here to not risk an unlikely infinite loop. try: - # Increment image acquisition labeling counter. - counter = counter + 1 + # Flag indicating if AOI image was acquired. Use this flag to determine if the loop counter gets incremented or not. + # If image aquisition is set to AOI but an image is not acquired then don't increment the counter for this iteration. + # This is because in AOI mode the maximum counter value is the total number of images we want to acquire rather than + # the maximum number of labelled images (as is the case for the Looping mode for image aquisition). + if cfg.gen_type == GEN_TYPE_AOI: + if keep_image: + counter = counter + 1 + + else: # Increment image acquisition labeling counter for the polling mode. + counter = counter + 1 + # Wait the configured sleep time before proceeding to the next image acquisition and labeling. if counter < cfg.gen_number: - logger.info("Wait {T} seconds...".format(T=image_acquisition_period)) + + # Don't span the log in case of a long run for image acquisition type "aoi". + if cfg.gen_type != GEN_TYPE_AOI: + logger.info("Wait {T} seconds...".format(T=image_acquisition_period)) + time.sleep(image_acquisition_period) else: logger.info("Image acquisition loop completed.") @@ -1060,6 +1252,7 @@ def run_experiment(): # Log the exception and exit the loop. logger.exception("An unlikely failure occured while waiting for the next image acquisition.") done = True + # We have exited the image acquisition and labeling loop. # This means that we have finished labeling the acquired images. diff --git a/home/exp1000/aois/continents.json b/home/exp1000/aois/continents.json new file mode 100644 index 0000000..cbb11c3 --- /dev/null +++ b/home/exp1000/aois/continents.json @@ -0,0 +1,9 @@ +{"type":"FeatureCollection", "features": [ 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+]} \ No newline at end of file diff --git a/home/exp1000/config.ini b/home/exp1000/config.ini index f211cd3..70432c1 100644 --- a/home/exp1000/config.ini +++ b/home/exp1000/config.ini @@ -9,12 +9,16 @@ collect_metadata = yes tle_path = /etc/tle quota_toGround = 100000 +[camera] +cam_exposure = 2 +cam_gains = [8,8,8] + [gen] -gen_interval = 5 -gen_interval_throttle = 1 -gen_number = 15 -gen_exposure = 2 -gen_gains = [8,8,8] +gen_type = aoi +gen_interval = 0.5 +gen_interval_throttle = 0 +gen_number = 30 +gen_geojson = /home/exp1000/aois/continents.json [img] raw_keep = no @@ -33,7 +37,7 @@ input_height = 224 input_width = 224 input_mean = 0 input_std = 255 -confidence_threshold = 0.75 +confidence_threshold = 0.70 [model_TBD] tflite_model = /home/exp1000/models/TBD/model.tflite @@ -43,7 +47,7 @@ input_height = 224 input_width = 224 input_mean = 0 input_std = 255 -confidence_threshold = 0.75 +confidence_threshold = 0.70 [compression_fapec] chunk = 512K diff --git a/sepp_package/CONTROL/control b/sepp_package/CONTROL/control old mode 100755 new mode 100644 index 6094138..db69405 --- a/sepp_package/CONTROL/control +++ b/sepp_package/CONTROL/control @@ -3,5 +3,5 @@ Priority: optional Maintainer: esoc Architecture: sepp Section: Applications -Version: 2.0 +Version: 2.1 Description: hdcam image acquisition and artificial intelligence classification diff --git a/sepp_package/CONTROL/debian-binary b/sepp_package/CONTROL/debian-binary old mode 100755 new mode 100644 diff --git a/sepp_package/CONTROL/postrm b/sepp_package/CONTROL/postrm index c981811..679fd28 100755 --- a/sepp_package/CONTROL/postrm +++ b/sepp_package/CONTROL/postrm @@ -1,2 +1,6 @@ #!/bin/sh -userdel -r -f exp1000 + +# Delete experiment user if exists. +if id -u exp1000 >/dev/null 2>&1; then + userdel -r -f exp1000 +fi \ No newline at end of file diff --git a/sepp_package/CONTROL/preinst b/sepp_package/CONTROL/preinst index a462353..4f4cb11 100755 --- a/sepp_package/CONTROL/preinst +++ b/sepp_package/CONTROL/preinst @@ -1,3 +1,8 @@ #!/bin/sh -useradd exp1000 -m -d /home/exp1000 -s /bin/sh -passwd -l exp1000 + +# Create the experiment user if does not exist already. +# User could already exist if this is an upgrade instead of a fresh installation. +if ! (id -u exp1000 >/dev/null 2>&1); then + useradd exp1000 -m -d /home/exp1000 -s /bin/sh + passwd -l exp1000 +fi diff --git a/sepp_package/CONTROL/prerm b/sepp_package/CONTROL/prerm index a61b31e..a8cec30 100755 --- a/sepp_package/CONTROL/prerm +++ b/sepp_package/CONTROL/prerm @@ -1,3 +1,9 @@ #!/bin/sh + +# Stop the experiment. /home/exp1000/stop_exp1000.sh + +# Remove symbolic links created during installation. +rm -rf /home/exp1000/bin/_solib_armhf + exit 0