def detectStarsAndMeteorsDirectory(dir_path, config):
""" Extract stars and detect meteors on all FF files in the given folder.
Arguments:
dir_path: [str] Path to the directory with FF files.
config: [Config obj]
Return:
calstars_name: [str] Name of the CALSTARS file.
ftpdetectinfo_name: [str] Name of the FTPdetectinfo file.
ff_detected: [list] A list of FF files with detections.
"""
# Get paths to every FF bin file in a directory
ff_dir = dir_path
ff_dir = os.path.abspath(ff_dir)
ff_list = [ff_name for ff_name in sorted(os.listdir(ff_dir)) if validFFName(ff_name)]
# Check if there are any file in the directory
if not len(ff_list):
print("No files for processing found!")
return None, None, None, None
print('Starting detection...')
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors, cores=-1, log=log, backup_dir=ff_dir)
# Start the detection
detector.startPool()
# Give detector jobs
for ff_name in ff_list:
while True:
# Add a job as long as there are available workers to receive it
if detector.available_workers.value() > 0:
print('Adding for detection:', ff_name)
detector.addJob([ff_dir, ff_name, config], wait_time=0)
break
else:
time.sleep(0.1)
log.info('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
detector.closePool()
log.info('Detection finished!')
log.info('Collecting results...')
# Get the detection results from the queue
detection_results = detector.getResults()
# Save detection to disk
calstars_name, ftpdetectinfo_name, ff_detected = saveDetections(detection_results, ff_dir, config)
return calstars_name, ftpdetectinfo_name, ff_detected, detector
def runCapture(config,
duration=None,
video_file=None,
nodetect=False,
detect_end=False,
upload_manager=None):
""" Run capture and compression for the given time.given
Arguments:
config: [config object] Configuration read from the .config file
Keyword arguments:
duration: [float] Time in seconds to capture. None by default.
video_file: [str] Path to the video file, if it was given as the video source. None by default.
nodetect: [bool] If True, detection will not be performed. False by defualt.
detect_end: [bool] If True, detection will be performed at the end of the night, when capture
finishes. False by default.
upload_manager: [UploadManager object] A handle to the UploadManager, which handles uploading files to
the central server. None by default.
"""
global STOP_CAPTURE
# Create a directory for captured files
night_data_dir_name = str(
config.stationID) + '_' + datetime.datetime.utcnow().strftime(
'%Y%m%d_%H%M%S_%f')
# Full path to the data directory
night_data_dir = os.path.join(os.path.abspath(config.data_dir),
config.captured_dir, night_data_dir_name)
# Make a directory for the night
mkdirP(night_data_dir)
log.info('Data directory: ' + night_data_dir)
# Load the default flat field image if it is available
flat_struct = None
if config.use_flat:
# Check if the flat exists
if os.path.exists(os.path.join(os.getcwd(), config.flat_file)):
flat_struct = Image.loadFlat(os.getcwd(), config.flat_file)
log.info('Loaded flat field image: ' +
os.path.join(os.getcwd(), config.flat_file))
# Get the platepar file
platepar, platepar_path, platepar_fmt = getPlatepar(config)
log.info('Initializing frame buffers...')
### For some reason, the RPi 3 does not like memory chunks which size is the multipier of its L2
### cache size (512 kB). When such a memory chunk is provided, the compression becomes 10x slower
### then usual. We are applying a dirty fix here where we just add an extra image row and column
### if such a memory chunk will be created. The compression is performed, and the image is cropped
### back to its original dimensions.
array_pad = 0
# Check if the image dimensions are divisible by RPi3 L2 cache size and add padding
if (256 * config.width * config.height) % (512 * 1024) == 0:
array_pad = 1
# Init arrays for parallel compression on 2 cores
sharedArrayBase = multiprocessing.Array(
ctypes.c_uint8,
256 * (config.width + array_pad) * (config.height + array_pad))
sharedArray = np.ctypeslib.as_array(sharedArrayBase.get_obj())
sharedArray = sharedArray.reshape(256, (config.height + array_pad),
(config.width + array_pad))
startTime = multiprocessing.Value('d', 0.0)
sharedArrayBase2 = multiprocessing.Array(
ctypes.c_uint8,
256 * (config.width + array_pad) * (config.height + array_pad))
sharedArray2 = np.ctypeslib.as_array(sharedArrayBase2.get_obj())
sharedArray2 = sharedArray2.reshape(256, (config.height + array_pad),
(config.width + array_pad))
startTime2 = multiprocessing.Value('d', 0.0)
log.info('Initializing frame buffers done!')
# Check if the detection should be performed or not
if nodetect:
detector = None
else:
if detect_end:
# Delay detection until the end of the night
delay_detection = duration
else:
# Delay the detection for 2 minutes after capture start
delay_detection = 120
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors,
cores=1,
log=log,
delay_start=delay_detection)
detector.startPool()
# Initialize buffered capture
bc = BufferedCapture(sharedArray,
startTime,
sharedArray2,
startTime2,
config,
video_file=video_file)
# Initialize the live image viewer
live_view = LiveViewer(window_name='Maxpixel')
# Initialize compression
compressor = Compressor(night_data_dir,
sharedArray,
startTime,
sharedArray2,
startTime2,
config,
detector=detector,
live_view=live_view,
flat_struct=flat_struct)
# Start buffered capture
bc.startCapture()
# Start the compression
compressor.start()
# Capture until Ctrl+C is pressed
wait(duration)
# If capture was manually stopped, end capture
if STOP_CAPTURE:
log.info('Ending capture...')
# Stop the capture
log.debug('Stopping capture...')
bc.stopCapture()
log.debug('Capture stopped')
dropped_frames = bc.dropped_frames
log.info('Total number of dropped frames: ' + str(dropped_frames))
# Stop the compressor
log.debug('Stopping compression...')
detector, live_view = compressor.stop()
log.debug('Compression stopped')
# Stop the live viewer
log.debug('Stopping live viewer...')
live_view.stop()
del live_view
log.debug('Live view stopped')
# Init data lists
star_list = []
meteor_list = []
ff_detected = []
# If detection should be performed
if not nodetect:
log.info('Finishing up the detection, ' +
str(detector.input_queue.qsize()) + ' files to process...')
# Reset the Ctrl+C to KeyboardInterrupt
resetSIGINT()
try:
# If there are some more files to process, process them on more cores
if detector.input_queue.qsize() > 0:
# Let the detector use all cores, but leave 1 free
available_cores = multiprocessing.cpu_count() - 1
if available_cores > 1:
log.info('Running the detection on {:d} cores...'.format(
available_cores))
# Start the detector
detector.updateCoreNumber(cores=available_cores)
log.info('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
detector.closePool()
log.info('Detection finished!')
except KeyboardInterrupt:
log.info('Ctrl + C pressed, exiting...')
if upload_manager is not None:
# Stop the upload manager
if upload_manager.is_alive():
log.debug('Closing upload manager...')
upload_manager.stop()
del upload_manager
# Terminate the detector
if detector is not None:
del detector
sys.exit()
# Set the Ctrl+C back to 'soft' program kill
setSIGINT()
### SAVE DETECTIONS TO FILE
log.info('Collecting results...')
# Get the detection results from the queue
detection_results = detector.getResults()
# Remove all 'None' results, which were errors
detection_results = [
res for res in detection_results if res is not None
]
# Count the number of detected meteors
meteors_num = 0
for _, _, meteor_data in detection_results:
for meteor in meteor_data:
meteors_num += 1
log.info('TOTAL: ' + str(meteors_num) + ' detected meteors.')
# Save the detections to a file
for ff_name, star_data, meteor_data in detection_results:
x2, y2, background, intensity = star_data
# Skip if no stars were found
if not x2:
continue
# Construct the table of the star parameters
star_data = zip(x2, y2, background, intensity)
# Add star info to the star list
star_list.append([ff_name, star_data])
# Handle the detected meteors
meteor_No = 1
for meteor in meteor_data:
rho, theta, centroids = meteor
# Append to the results list
meteor_list.append([ff_name, meteor_No, rho, theta, centroids])
meteor_No += 1
# Add the FF file to the archive list if a meteor was detected on it
if meteor_data:
ff_detected.append(ff_name)
# Generate the name for the CALSTARS file
calstars_name = 'CALSTARS_' + "{:s}".format(str(config.stationID)) + '_' \
+ os.path.basename(night_data_dir) + '.txt'
# Write detected stars to the CALSTARS file
CALSTARS.writeCALSTARS(star_list, night_data_dir, calstars_name, config.stationID, config.height, \
config.width)
# Generate FTPdetectinfo file name
ftpdetectinfo_name = 'FTPdetectinfo_' + os.path.basename(
night_data_dir) + '.txt'
# Write FTPdetectinfo file
FTPdetectinfo.writeFTPdetectinfo(meteor_list, night_data_dir, ftpdetectinfo_name, night_data_dir, \
config.stationID, config.fps)
# Get the platepar file
platepar, platepar_path, platepar_fmt = getPlatepar(config)
# Run calibration check and auto astrometry refinement
if platepar is not None:
# Read in the CALSTARS file
calstars_list = CALSTARS.readCALSTARS(night_data_dir,
calstars_name)
# Run astrometry check and refinement
platepar, fit_status = autoCheckFit(config, platepar,
calstars_list)
# If the fit was sucessful, apply the astrometry to detected meteors
if fit_status:
log.info('Astrometric calibration SUCCESSFUL!')
# Save the refined platepar to the night directory and as default
platepar.write(os.path.join(night_data_dir,
config.platepar_name),
fmt=platepar_fmt)
platepar.write(platepar_path, fmt=platepar_fmt)
else:
log.info(
'Astrometric calibration FAILED!, Using old platepar for calibration...'
)
# Calculate astrometry for meteor detections
applyAstrometryFTPdetectinfo(night_data_dir, ftpdetectinfo_name,
platepar_path)
log.info('Plotting field sums...')
# Plot field sums to a graph
plotFieldsums(night_data_dir, config)
# Archive all fieldsums to one archive
archiveFieldsums(night_data_dir)
# List for any extra files which will be copied to the night archive directory. Full paths have to be
# given
extra_files = []
log.info('Making a flat...')
# Make a new flat field
flat_img = makeFlat(night_data_dir, config)
# If making flat was sucessfull, save it
if flat_img is not None:
# Save the flat in the root directory, to keep the operational flat updated
scipy.misc.imsave(config.flat_file, flat_img)
flat_path = os.path.join(os.getcwd(), config.flat_file)
log.info('Flat saved to: ' + flat_path)
# Copy the flat to the night's directory as well
extra_files.append(flat_path)
else:
log.info('Making flat image FAILED!')
### Add extra files to archive
# Add the platepar to the archive if it exists
if os.path.exists(platepar_path):
extra_files.append(platepar_path)
# Add the config file to the archive too
extra_files.append(os.path.join(os.getcwd(), '.config'))
### ###
night_archive_dir = os.path.join(os.path.abspath(config.data_dir),
config.archived_dir, night_data_dir_name)
log.info('Archiving detections to ' + night_archive_dir)
# Archive the detections
archive_name = archiveDetections(night_data_dir, night_archive_dir, ff_detected, config, \
extra_files=extra_files)
# Put the archive up for upload
if upload_manager is not None:
log.info('Adding file on upload list: ' + archive_name)
upload_manager.addFiles([archive_name])
# If capture was manually stopped, end program
if STOP_CAPTURE:
log.info('Ending program')
# Stop the upload manager
if upload_manager is not None:
if upload_manager.is_alive():
upload_manager.stop()
log.info('Closing upload manager...')
sys.exit()
def detectStarsAndMeteorsDirectory(dir_path, config):
""" Extract stars and detect meteors on all FF files in the given folder.
Arguments:
dir_path: [str] Path to the directory with FF files.
config: [Config obj]
Return:
calstars_name: [str] Name of the CALSTARS file.
ftpdetectinfo_name: [str] Name of the FTPdetectinfo file.
ff_detected: [list] A list of FF files with detections.
"""
# Get paths to every FF bin file in a directory
ff_dir = dir_path
ff_dir = os.path.abspath(ff_dir)
ff_list = [ff_name for ff_name in sorted(os.listdir(ff_dir)) if validFFName(ff_name)]
# Check if there are any file in the directory
if not len(ff_list):
print("No files for processing found!")
return None, None, None, None
print('Starting detection...')
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors, cores=-1, log=log, backup_dir=ff_dir)
# Start the detection
detector.startPool()
# Give detector jobs
for ff_name in ff_list:
while True:
# Add a job as long as there are available workers to receive it
if detector.available_workers.value() > 0:
print('Adding for detection:', ff_name)
detector.addJob([ff_dir, ff_name, config], wait_time=0)
break
else:
time.sleep(0.1)
log.info('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
detector.closePool()
log.info('Detection finished!')
log.info('Collecting results...')
# Get the detection results from the queue
detection_results = detector.getResults()
# Save detection to disk
calstars_name, ftpdetectinfo_name, ff_detected = saveDetections(detection_results, ff_dir, config)
return calstars_name, ftpdetectinfo_name, ff_detected, detector
# Try loading a flat field image
flat_struct = None
if config.use_flat:
# Check if there is flat in the data directory
if os.path.exists(os.path.join(ff_dir, config.flat_file)):
flat_struct = Image.loadFlat(ff_dir, config.flat_file)
# Try loading the default flat
elif os.path.exists(config.flat_file):
flat_struct = Image.loadFlat(os.getcwd(), config.flat_file)
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors, cores=-1, log=log)
# Give detector jobs
for ff_name in ff_list:
detector.addJob([ff_dir, ff_name, config, flat_struct])
# Start the detection
detector.startPool()
log.info('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
detector.closePool()
def runCapture(config, duration=None, video_file=None, nodetect=False, detect_end=False, \
upload_manager=None, resume_capture=False):
""" Run capture and compression for the given time.given
Arguments:
config: [config object] Configuration read from the .config file.
Keyword arguments:
duration: [float] Time in seconds to capture. None by default.
video_file: [str] Path to the video file, if it was given as the video source. None by default.
nodetect: [bool] If True, detection will not be performed. False by defualt.
detect_end: [bool] If True, detection will be performed at the end of the night, when capture
finishes. False by default.
upload_manager: [UploadManager object] A handle to the UploadManager, which handles uploading files to
the central server. None by default.
resume_capture: [bool] Resume capture in the last data directory in CapturedFiles.
Return:
night_archive_dir: [str] Path to the archive folder of the processed night.
"""
global STOP_CAPTURE
# Check if resuming capture to the last capture directory
night_data_dir_name = None
if resume_capture:
log.info("Resuming capture in the last capture directory...")
# Find the latest capture directory
capturedfiles_path = os.path.join(os.path.abspath(config.data_dir),
config.captured_dir)
most_recent_dir_time = 0
for dir_name in sorted(os.listdir(capturedfiles_path)):
dir_path_check = os.path.join(capturedfiles_path, dir_name)
# Check it's a directory
if os.path.isdir(dir_path_check):
# Check if it starts with the correct station code
if dir_name.startswith(str(config.stationID)):
dir_mod_time = os.path.getmtime(dir_path_check)
# Check that it is the most recent directory
if (night_data_dir_name is None) or (dir_mod_time >
most_recent_dir_time):
night_data_dir_name = dir_name
night_data_dir = dir_path_check
most_recent_dir_time = dir_mod_time
if night_data_dir_name is None:
log.info(
"Previous capture directory could not be found! Creating a new one..."
)
else:
log.info("Previous capture directory found: {:s}".format(
night_data_dir))
# Resume run is finished now, reset resume flag
cml_args.resume = False
# Make a name for the capture data directory
if night_data_dir_name is None:
# Create a directory for captured files
night_data_dir_name = str(config.stationID) + '_' \
+ datetime.datetime.utcnow().strftime('%Y%m%d_%H%M%S_%f')
# Full path to the data directory
night_data_dir = os.path.join(os.path.abspath(config.data_dir), config.captured_dir, \
night_data_dir_name)
# Wait before the capture starts if a time has been given
if (not resume_capture) and (video_file is None):
log.info("Waiting {:d} seconds before capture start...".format(
int(config.capture_wait_seconds)))
time.sleep(config.capture_wait_seconds)
# Add a note about Patreon supporters
print("################################################################")
print("Thanks to our Patreon supporters in the 'Dinosaur Killer' class:")
print("- Myron Valenta")
print("https://www.patreon.com/globalmeteornetwork")
print("\n\n\n" \
+ " .:' .:' .:' .:' \n"\
+ " _.::' _.::' _.::' _.::' \n"\
+ " (_.' (_.' (_.' (_.' \n"\
+ " __ \n"\
+ " / _) \n"\
+ "_\\/_ _/\\/\\/\\_/ / _\\/_ \n"\
+ "/o\\ _| / //o\\ \n"\
+ " | _| ( | ( | | \n"\
+ "_|____ /__.-'|_|--|_| ______|__\n")
print("################################################################")
# Make a directory for the night
mkdirP(night_data_dir)
log.info('Data directory: ' + night_data_dir)
# Copy the used config file to the capture directory
if os.path.isfile(config.config_file_name):
try:
shutil.copy2(config.config_file_name,
os.path.join(night_data_dir, ".config"))
except:
log.error("Cannot copy the config file to the capture directory!")
# Get the platepar file
platepar, platepar_path, platepar_fmt = getPlatepar(config, night_data_dir)
# If the platepar is not none, set the FOV from it
if platepar is not None:
config.fov_w = platepar.fov_h
config.fov_h = platepar.fov_v
log.info('Initializing frame buffers...')
### For some reason, the RPi 3 does not like memory chunks which size is the multipier of its L2
### cache size (512 kB). When such a memory chunk is provided, the compression becomes 10x slower
### then usual. We are applying a dirty fix here where we just add an extra image row and column
### if such a memory chunk will be created. The compression is performed, and the image is cropped
### back to its original dimensions.
array_pad = 0
# Check if the image dimensions are divisible by RPi3 L2 cache size and add padding
if (256 * config.width * config.height) % (512 * 1024) == 0:
array_pad = 1
# Init arrays for parallel compression on 2 cores
sharedArrayBase = multiprocessing.Array(
ctypes.c_uint8,
256 * (config.width + array_pad) * (config.height + array_pad))
sharedArray = np.ctypeslib.as_array(sharedArrayBase.get_obj())
sharedArray = sharedArray.reshape(256, (config.height + array_pad),
(config.width + array_pad))
startTime = multiprocessing.Value('d', 0.0)
sharedArrayBase2 = multiprocessing.Array(
ctypes.c_uint8,
256 * (config.width + array_pad) * (config.height + array_pad))
sharedArray2 = np.ctypeslib.as_array(sharedArrayBase2.get_obj())
sharedArray2 = sharedArray2.reshape(256, (config.height + array_pad),
(config.width + array_pad))
startTime2 = multiprocessing.Value('d', 0.0)
log.info('Initializing frame buffers done!')
# Check if the detection should be performed or not
if nodetect:
detector = None
else:
if detect_end:
# Delay detection until the end of the night
delay_detection = duration
else:
# Delay the detection for 2 minutes after capture start (helps stability)
delay_detection = 120
# Add an additional postprocessing delay
delay_detection += config.postprocess_delay
# Set a flag file to indicate that previous files are being loaded (if any)
capture_resume_file_path = os.path.join(
config.data_dir, config.capture_resume_flag_file)
with open(capture_resume_file_path, 'w') as f:
pass
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors, cores=1, log=log, delay_start=delay_detection, \
backup_dir=night_data_dir)
detector.startPool()
# If the capture is being resumed into the directory, load all previously saved FF files
if resume_capture:
# Load all preocessed FF files
for i, ff_name in enumerate(sorted(os.listdir(night_data_dir))):
# Every 50 files loaded, update the flag file
if i % 50 == 0:
with open(capture_resume_file_path, 'a') as f:
f.write("{:d}\n".format(i))
# Check if the file is a valid FF files
ff_path = os.path.join(night_data_dir, ff_name)
if os.path.isfile(ff_path) and (str(
config.stationID) in ff_name) and validFFName(ff_name):
# Add the FF file to the detector
detector.addJob([night_data_dir, ff_name, config],
wait_time=0.005)
log.info(
"Added existing FF files for detection: {:s}".format(
ff_name))
# Remove the flag file
if os.path.isfile(capture_resume_file_path):
try:
os.remove(capture_resume_file_path)
except:
log.error("There was an error during removing the capture resume flag file: " \
+ capture_resume_file_path)
# Initialize buffered capture
bc = BufferedCapture(sharedArray,
startTime,
sharedArray2,
startTime2,
config,
video_file=video_file)
# Initialize the live image viewer
if config.live_maxpixel_enable:
# Enable showing the live JPG
config.live_jpg = True
live_jpg_path = os.path.join(config.data_dir, 'live.jpg')
live_view = LiveViewer(live_jpg_path,
image=True,
slideshow=False,
banner_text="Live")
live_view.start()
else:
live_view = None
# Initialize compression
compressor = Compressor(night_data_dir,
sharedArray,
startTime,
sharedArray2,
startTime2,
config,
detector=detector)
# Start buffered capture
bc.startCapture()
# Init and start the compression
compressor.start()
# Capture until Ctrl+C is pressed
wait(duration, compressor)
# If capture was manually stopped, end capture
if STOP_CAPTURE:
log.info('Ending capture...')
# Stop the capture
log.debug('Stopping capture...')
bc.stopCapture()
log.debug('Capture stopped')
dropped_frames = bc.dropped_frames
log.info('Total number of late or dropped frames: ' + str(dropped_frames))
# Stop the compressor
log.debug('Stopping compression...')
detector = compressor.stop()
# Free shared memory after the compressor is done
try:
log.debug('Freeing frame buffers...')
del sharedArrayBase
del sharedArray
del sharedArrayBase2
del sharedArray2
except Exception as e:
log.debug('Freeing frame buffers failed with error:' + repr(e))
log.debug(repr(traceback.format_exception(*sys.exc_info())))
log.debug('Compression stopped')
if live_view is not None:
# Stop the live viewer
log.debug('Stopping live viewer...')
live_view.stop()
live_view.join()
del live_view
live_view = None
log.debug('Live view stopped')
# If detection should be performed
if not nodetect:
try:
log.info('Finishing up the detection, ' + str(detector.input_queue.qsize()) \
+ ' files to process...')
except:
print(
'Finishing up the detection... error when getting input queue size!'
)
# Reset the Ctrl+C to KeyboardInterrupt
resetSIGINT()
try:
# If there are some more files to process, process them on more cores
if detector.input_queue.qsize() > 0:
# Let the detector use all cores, but leave 2 free
available_cores = multiprocessing.cpu_count() - 2
if available_cores > 1:
log.info('Running the detection on {:d} cores...'.format(
available_cores))
# Start the detector
detector.updateCoreNumber(cores=available_cores)
log.info('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
detector.closePool()
log.info('Detection finished!')
except KeyboardInterrupt:
log.info('Ctrl + C pressed, exiting...')
if upload_manager is not None:
# Stop the upload manager
if upload_manager.is_alive():
log.debug('Closing upload manager...')
upload_manager.stop()
del upload_manager
# Terminate the detector
if detector is not None:
del detector
sys.exit()
# Set the Ctrl+C back to 'soft' program kill
setSIGINT()
### SAVE DETECTIONS TO FILE
log.info('Collecting results...')
# Get the detection results from the queue
detection_results = detector.getResults()
else:
detection_results = []
# Save detection to disk and archive detection
night_archive_dir, archive_name, _ = processNight(night_data_dir, config, \
detection_results=detection_results, nodetect=nodetect)
# Put the archive up for upload
if upload_manager is not None:
log.info('Adding file to upload list: ' + archive_name)
upload_manager.addFiles([archive_name])
log.info('File added...')
# Delay the upload, if the delay is given
upload_manager.delayNextUpload(delay=60 * config.upload_delay)
# Delete detector backup files
if detector is not None:
detector.deleteBackupFiles()
# If the capture was run for a limited time, run the upload right away
if (duration is not None) and (upload_manager is not None):
log.info('Uploading data before exiting...')
upload_manager.uploadData()
# Run the external script
runExternalScript(night_data_dir, night_archive_dir, config)
# If capture was manually stopped, end program
if STOP_CAPTURE:
log.info('Ending program')
# Stop the upload manager
if upload_manager is not None:
if upload_manager.is_alive():
upload_manager.stop()
log.info('Closing upload manager...')
sys.exit()
return night_archive_dir
def extractStarsAndSave(config, ff_dir):
""" Extract stars in the given folder and save the CALSTARS file.
Arguments:
config: [config object] configuration object (loaded from the .config file)
ff_dir: [str] Path to directory where FF files are.
Return:
star_list: [list] A list of [ff_name, star_data] entries, where star_data contains a list of
(column, row, amplitude, intensity, fwhm) values for every star.
"""
time_start = time.time()
# Load mask, dark, flat
mask, dark, flat_struct = loadImageCalibration(ff_dir, config)
extraction_list = []
# Go through all files in the directory and add them to the detection list
for ff_name in sorted(os.listdir(ff_dir)):
# Check if the given file is a valid FF file
if not FFfile.validFFName(ff_name):
continue
extraction_list.append(ff_name)
# Run the QueuedPool for detection
workpool = QueuedPool(extractStars, cores=-1, backup_dir=ff_dir)
# Add jobs for the pool
for ff_name in extraction_list:
print('Adding for extraction:', ff_name)
workpool.addJob([
ff_dir, ff_name, config, None, None, None, None, flat_struct, dark,
mask
])
print('Starting pool...')
# Start the detection
workpool.startPool()
print('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
workpool.closePool()
# Get extraction results
star_list = []
for result in workpool.getResults():
ff_name, x2, y2, amplitude, intensity, fwhm_data = result
# Skip if no stars were found
if not x2:
continue
# Construct the table of the star parameters
star_data = list(zip(x2, y2, amplitude, intensity, fwhm_data))
# Add star info to the star list
star_list.append([ff_name, star_data])
dir_name = os.path.basename(os.path.abspath(ff_dir))
if dir_name.startswith(config.stationID):
prefix = dir_name
else:
prefix = "{:s}_{:s}".format(config.stationID, dir_name)
# Generate the name for the CALSTARS file
calstars_name = 'CALSTARS_' + prefix + '.txt'
# Write detected stars to the CALSTARS file
CALSTARS.writeCALSTARS(star_list, ff_dir, calstars_name, config.stationID,
config.height, config.width)
# Delete QueudPool backed up files
workpool.deleteBackupFiles()
print('Total time taken: {:.2f} s'.format(time.time() - time_start))
return star_list
def runCapture(config, duration=None, video_file=None, nodetect=False, detect_end=False, upload_manager=None):
""" Run capture and compression for the given time.given
Arguments:
config: [config object] Configuration read from the .config file
Keyword arguments:
duration: [float] Time in seconds to capture. None by default.
video_file: [str] Path to the video file, if it was given as the video source. None by default.
nodetect: [bool] If True, detection will not be performed. False by defualt.
detect_end: [bool] If True, detection will be performed at the end of the night, when capture
finishes. False by default.
upload_manager: [UploadManager object] A handle to the UploadManager, which handles uploading files to
the central server. None by default.
Return:
night_archive_dir: [str] Path to the archive folder of the processed night.
"""
global STOP_CAPTURE
# Create a directory for captured files
night_data_dir_name = str(config.stationID) + '_' + datetime.datetime.utcnow().strftime('%Y%m%d_%H%M%S_%f')
# Full path to the data directory
night_data_dir = os.path.join(os.path.abspath(config.data_dir), config.captured_dir, night_data_dir_name)
# Make a directory for the night
mkdirP(night_data_dir)
log.info('Data directory: ' + night_data_dir)
# Get the platepar file
platepar, platepar_path, platepar_fmt = getPlatepar(config, night_data_dir)
log.info('Initializing frame buffers...')
### For some reason, the RPi 3 does not like memory chunks which size is the multipier of its L2
### cache size (512 kB). When such a memory chunk is provided, the compression becomes 10x slower
### then usual. We are applying a dirty fix here where we just add an extra image row and column
### if such a memory chunk will be created. The compression is performed, and the image is cropped
### back to its original dimensions.
array_pad = 0
# Check if the image dimensions are divisible by RPi3 L2 cache size and add padding
if (256*config.width*config.height)%(512*1024) == 0:
array_pad = 1
# Init arrays for parallel compression on 2 cores
sharedArrayBase = multiprocessing.Array(ctypes.c_uint8, 256*(config.width + array_pad)*(config.height + array_pad))
sharedArray = np.ctypeslib.as_array(sharedArrayBase.get_obj())
sharedArray = sharedArray.reshape(256, (config.height + array_pad), (config.width + array_pad))
startTime = multiprocessing.Value('d', 0.0)
sharedArrayBase2 = multiprocessing.Array(ctypes.c_uint8, 256*(config.width + array_pad)*(config.height + array_pad))
sharedArray2 = np.ctypeslib.as_array(sharedArrayBase2.get_obj())
sharedArray2 = sharedArray2.reshape(256, (config.height + array_pad), (config.width + array_pad))
startTime2 = multiprocessing.Value('d', 0.0)
log.info('Initializing frame buffers done!')
# Check if the detection should be performed or not
if nodetect:
detector = None
else:
if detect_end:
# Delay detection until the end of the night
delay_detection = duration
else:
# Delay the detection for 2 minutes after capture start
delay_detection = 120
# Initialize the detector
detector = QueuedPool(detectStarsAndMeteors, cores=1, log=log, delay_start=delay_detection, \
backup_dir=night_data_dir)
detector.startPool()
# Initialize buffered capture
bc = BufferedCapture(sharedArray, startTime, sharedArray2, startTime2, config, video_file=video_file)
# Initialize the live image viewer
if config.live_maxpixel_enable:
live_view = LiveViewer(night_data_dir, slideshow=False, banner_text="Live")
live_view.start()
else:
live_view = None
# Initialize compression
compressor = Compressor(night_data_dir, sharedArray, startTime, sharedArray2, startTime2, config,
detector=detector)
# Start buffered capture
bc.startCapture()
# Init and start the compression
compressor.start()
# Capture until Ctrl+C is pressed
wait(duration, compressor)
# If capture was manually stopped, end capture
if STOP_CAPTURE:
log.info('Ending capture...')
# Stop the capture
log.debug('Stopping capture...')
bc.stopCapture()
log.debug('Capture stopped')
dropped_frames = bc.dropped_frames
log.info('Total number of late or dropped frames: ' + str(dropped_frames))
# Stop the compressor
log.debug('Stopping compression...')
detector = compressor.stop()
# Free shared memory after the compressor is done
try:
log.debug('Freeing frame buffers...')
del sharedArrayBase
del sharedArray
del sharedArrayBase2
del sharedArray2
except Exception as e:
log.debug('Freeing frame buffers failed with error:' + repr(e))
log.debug(repr(traceback.format_exception(*sys.exc_info())))
log.debug('Compression stopped')
if live_view is not None:
# Stop the live viewer
log.debug('Stopping live viewer...')
live_view.stop()
live_view.join()
del live_view
live_view = None
log.debug('Live view stopped')
# If detection should be performed
if not nodetect:
try:
log.info('Finishing up the detection, ' + str(detector.input_queue.qsize()) \
+ ' files to process...')
except:
print('Finishing up the detection... error when getting input queue size!')
# Reset the Ctrl+C to KeyboardInterrupt
resetSIGINT()
try:
# If there are some more files to process, process them on more cores
if detector.input_queue.qsize() > 0:
# Let the detector use all cores, but leave 2 free
available_cores = multiprocessing.cpu_count() - 2
if available_cores > 1:
log.info('Running the detection on {:d} cores...'.format(available_cores))
# Start the detector
detector.updateCoreNumber(cores=available_cores)
log.info('Waiting for the detection to finish...')
# Wait for the detector to finish and close it
detector.closePool()
log.info('Detection finished!')
except KeyboardInterrupt:
log.info('Ctrl + C pressed, exiting...')
if upload_manager is not None:
# Stop the upload manager
if upload_manager.is_alive():
log.debug('Closing upload manager...')
upload_manager.stop()
del upload_manager
# Terminate the detector
if detector is not None:
del detector
sys.exit()
# Set the Ctrl+C back to 'soft' program kill
setSIGINT()
### SAVE DETECTIONS TO FILE
log.info('Collecting results...')
# Get the detection results from the queue
detection_results = detector.getResults()
else:
detection_results = []
# Save detection to disk and archive detection
night_archive_dir, archive_name, _ = processNight(night_data_dir, config, \
detection_results=detection_results, nodetect=nodetect)
# Put the archive up for upload
if upload_manager is not None:
log.info('Adding file to upload list: ' + archive_name)
upload_manager.addFiles([archive_name])
log.info('File added...')
# Delete detector backup files
if detector is not None:
detector.deleteBackupFiles()
# If the capture was run for a limited time, run the upload right away
if (duration is not None) and (upload_manager is not None):
log.info('Uploading data before exiting...')
upload_manager.uploadData()
# Run the external script
runExternalScript(night_data_dir, night_archive_dir, config)
# If capture was manually stopped, end program
if STOP_CAPTURE:
log.info('Ending program')
# Stop the upload manager
if upload_manager is not None:
if upload_manager.is_alive():
upload_manager.stop()
log.info('Closing upload manager...')
sys.exit()
return night_archive_dir
extraction_list = []
# Go through all files in the directory and add them to the detection list
for ff_name in sorted(ff_list):
# Check if the given file is a valid FF file
if not FFfile.validFFName(ff_name):
continue
#print(ff_name)
extraction_list.append(ff_name)
star_list = []
# Run the QueuedPool for detection
workpool = QueuedPool(extractStars, cores=-1, backup_dir=ff_dir)
# Add jobs for the pool
for ff_name in extraction_list:
print('Adding for extraction:', ff_name)
workpool.addJob([
ff_dir, ff_name, config, None, None, None, None, flat_struct, dark,
mask
])
print('Starting pool...')
# Start the detection
workpool.startPool()
print('Waiting for the detection to finish...')
# Go through all files in the directory and add them to the detection list
for ff_name in sorted(ff_list):
# Check if the given file is a valid FF file
if not FFfile.validFFName(ff_name):
continue
#print(ff_name)
extraction_list.append(ff_name)
star_list = []
# Run the QueuedPool for detection
workpool = QueuedPool(extractStars, cores=-1, backup_dir=ff_dir)
# Add jobs for the pool
for ff_name in extraction_list:
print('Adding for extraction:', ff_name)
workpool.addJob([ff_dir, ff_name, config, flat_struct])
print('Starting pool...')
# Start the detection
workpool.startPool()
print('Waiting for the detection to finish...')