def main(args):
""" Classify dataset """
# Parse config and file data
dataset_config, yatsm_config = parse_config_file(args['config_file'])
# Get some attributes about the dataset
dates, sensors, images = csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format']
)
nrow, _, _, _ = get_image_attribute(images[0])
# Read in the saved classification result
try:
_ = open(args['algo'])
except:
logger.error('Could not open pickled classifier')
sys.exit(1)
classifier = joblib.load(args['algo'])
# Split into lines and classify
job_lines = calculate_lines(args['job_number'] - 1, args['total_jobs'],
nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
start_time = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
filename = get_output_name(dataset_config, job_line)
if not os.path.exists(filename):
logger.warning('No model result found for line {l} '
'(file {f})'.format(l=job_line, f=filename))
pass
if args['resume'] and try_resume(filename):
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Classifying line {l}'.format(l=job_line))
classify_line(filename, classifier)
logger.debug('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time) / 60.0, 2))
)
def main(dataset_config, yatsm_config,
check=False, resume=False,
do_not_run=False,
read_cache=False, write_cache=False,
validate_cache=False):
""" Read in dataset and YATSM for a complete line
Args:
dataset_config (dict): dict of dataset configuration options
yatsm_config (dict): dict of YATSM algorithm options
check (bool, optional): check to make sure images are readible
resume (bool, optional): do not overwrite existing results, instead
continue from first non-existing result file
do_not_run (bool, optional): Don't run YATSM
read_cache (bool, optional): try to read from cache directory
(default: False)
write_cache (bool, optional): try to to write to cache directory
(default: False)
validate_cache (bool, optional): ensure data from cache file come from
images specified in configuration (default: False)
"""
# Read in dataset
dates, sensors, images = csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format']
)
image_IDs = get_image_IDs(images)
# Check for existence of files and remove missing
if check:
to_delete = []
for i, img in enumerate(images):
if not os.path.isfile(img):
logger.warning('Could not find file {f} -- removing'.
format(f=img))
to_delete.append(i)
if len(to_delete) == 0:
logger.debug('Checked and found all input images')
else:
logger.warning('Removing {n} images'.format(n=len(to_delete)))
dates = np.delete(dates, np.array(to_delete))
images = np.delete(images, np.array(to_delete))
# Get attributes of one of the images
nrow, ncol, nband, dtype = get_image_attribute(images[0])
# Calculate the lines this job ID works on
job_lines = calculate_lines(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Calculate X feature input
X = patsy.dmatrix(yatsm_config['design_matrix'],
{'x': dates, 'sensor': sensors})
# Start running YATSM
start_time_all = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
if resume:
try:
z = np.load(get_output_name(dataset_config, job_line))
except:
pass
else:
del z
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Running line {l}'.format(l=job_line))
start_time = time.time()
try:
run_line(job_line, X, images, image_IDs,
dataset_config, yatsm_config,
nrow, ncol, nband, dtype,
do_not_run=do_not_run,
read_cache=read_cache, write_cache=write_cache,
validate_cache=validate_cache)
except Exception as e:
logger.error('Could not process line {l}'.format(l=job_line))
logger.error(type(e))
logger.error(str(e))
logger.debug('Took {s}s to run'.format(
s=round(time.time() - start_time, 2)))
logger.info('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time_all) / 60.0, 2)
))
def main(args):
# Parse and validate configuration file
dataset_config, yatsm_config = config_parser.parse_config_file(
args['config_file'])
if not os.path.isdir(dataset_config['cache_line_dir']):
os.makedirs(dataset_config['cache_line_dir'])
dates, images = utils.csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format']
)
image_IDs = utils.get_image_IDs(images)
nrow, ncol, nband, dtype = reader.get_image_attribute(images[0])
# Determine lines to work on
job_lines = utils.calculate_lines(args['job_number'],
args['total_jobs'],
nrow,
interlaced=args['interlace'])
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Determine file reader
if dataset_config['use_bip_reader']:
logger.debug('Reading in data from disk using BIP reader')
image_reader = reader.read_row_BIP
image_reader_kwargs = {'size': (ncol, nband),
'dtype': dtype}
else:
logger.debug('Reading in data from disk using GDAL')
image_reader = reader.read_row_GDAL
image_reader_kwargs = {}
# Attempt to update cache files
previous_cache = None
if args['update_pattern']:
previous_cache = fnmatch.filter(
os.listdir(dataset_config['cache_line_dir']),
args['update_pattern'])
if not previous_cache:
logger.warning('Could not find cache files to update with pattern'
'{p}'.format(p=args['update_pattern']))
else:
logger.debug('Found {n} previously cached files to update'.format(
n=len(previous_cache)))
for job_line in job_lines:
cache_filename = cache.get_line_cache_name(
dataset_config, len(images), job_line, nband)
logger.debug('Caching line {l} to {f}'.format(
l=job_line, f=cache_filename))
start_time = time.time()
# Find matching cache file
update = False
if previous_cache:
pattern = cache.get_line_cache_pattern(
job_line, nband, regex=False)
potential = fnmatch.filter(previous_cache, pattern)
if not potential:
logger.info('Found zero previous cache files for '
'line {l}'.format(l=job_line))
elif len(potential) > 1:
logger.info('Found more than one previous cache file for '
'line {l}. Keeping first'.format(l=job_line))
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
else:
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
logger.info('Updating from cache file {f}'.format(f=update))
if update:
cache.update_cache_file(
images, image_IDs,
update, cache_filename,
job_line, image_reader, image_reader_kwargs
)
else:
if dataset_config['use_bip_reader']:
# Use BIP reader
logger.debug('Reading in data from disk using BIP reader')
Y = reader.read_row_BIP(images, job_line, (ncol, nband), dtype)
else:
# Read in data just using GDAL
logger.debug('Reading in data from disk using GDAL')
Y = reader.read_row_GDAL(images, job_line)
cache.write_cache_file(cache_filename, Y, image_IDs)
logger.debug('Took {s}s to cache the data'.format(
s=round(time.time() - start_time, 2)))
def main(dataset_config,
yatsm_config,
check=False,
resume=False,
do_not_run=False,
read_cache=False,
write_cache=False,
validate_cache=False):
""" Read in dataset and YATSM for a complete line
Args:
dataset_config (dict): dict of dataset configuration options
yatsm_config (dict): dict of YATSM algorithm options
check (bool, optional): check to make sure images are readible
resume (bool, optional): do not overwrite existing results, instead
continue from first non-existing result file
do_not_run (bool, optional): Don't run YATSM
read_cache (bool, optional): try to read from cache directory
(default: False)
write_cache (bool, optional): try to to write to cache directory
(default: False)
validate_cache (bool, optional): ensure data from cache file come from
images specified in configuration (default: False)
"""
# Read in dataset
dates, sensors, images = csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format'])
image_IDs = get_image_IDs(images)
# Check for existence of files and remove missing
if check:
to_delete = []
for i, img in enumerate(images):
if not os.path.isfile(img):
logger.warning(
'Could not find file {f} -- removing'.format(f=img))
to_delete.append(i)
if len(to_delete) == 0:
logger.debug('Checked and found all input images')
else:
logger.warning('Removing {n} images'.format(n=len(to_delete)))
dates = np.delete(dates, np.array(to_delete))
images = np.delete(images, np.array(to_delete))
# Get attributes of one of the images
nrow, ncol, nband, dtype = get_image_attribute(images[0])
# Calculate the lines this job ID works on
job_lines = calculate_lines(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Calculate X feature input
X = patsy.dmatrix(yatsm_config['design_matrix'], {
'x': dates,
'sensor': sensors
})
# Start running YATSM
start_time_all = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
if resume:
try:
z = np.load(get_output_name(dataset_config, job_line))
except:
pass
else:
del z
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Running line {l}'.format(l=job_line))
start_time = time.time()
try:
run_line(job_line,
X,
images,
image_IDs,
dataset_config,
yatsm_config,
nrow,
ncol,
nband,
dtype,
do_not_run=do_not_run,
read_cache=read_cache,
write_cache=write_cache,
validate_cache=validate_cache)
except Exception as e:
logger.error('Could not process line {l}'.format(l=job_line))
logger.error(type(e))
logger.error(str(e))
logger.debug(
'Took {s}s to run'.format(s=round(time.time() - start_time, 2)))
logger.info('Completed {n} lines in {m} minutes'.format(
n=len(job_lines), m=round((time.time() - start_time_all) / 60.0, 2)))
def main(args):
# Parse and validate configuration file
dataset_config, yatsm_config = config_parser.parse_config_file(
args['config_file'])
if not os.path.isdir(dataset_config['cache_line_dir']):
os.makedirs(dataset_config['cache_line_dir'])
dates, images = utils.csvfile_to_dataset(
dataset_config['input_file'],
date_format=dataset_config['date_format'])
image_IDs = utils.get_image_IDs(images)
nrow, ncol, nband, dtype = reader.get_image_attribute(images[0])
# Determine lines to work on
job_lines = utils.calculate_lines(args['job_number'],
args['total_jobs'],
nrow,
interlaced=args['interlace'])
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Determine file reader
if dataset_config['use_bip_reader']:
logger.debug('Reading in data from disk using BIP reader')
image_reader = reader.read_row_BIP
image_reader_kwargs = {'size': (ncol, nband), 'dtype': dtype}
else:
logger.debug('Reading in data from disk using GDAL')
image_reader = reader.read_row_GDAL
image_reader_kwargs = {}
# Attempt to update cache files
previous_cache = None
if args['update_pattern']:
previous_cache = fnmatch.filter(
os.listdir(dataset_config['cache_line_dir']),
args['update_pattern'])
if not previous_cache:
logger.warning('Could not find cache files to update with pattern'
'{p}'.format(p=args['update_pattern']))
else:
logger.debug('Found {n} previously cached files to update'.format(
n=len(previous_cache)))
for job_line in job_lines:
cache_filename = cache.get_line_cache_name(dataset_config, len(images),
job_line, nband)
logger.debug('Caching line {l} to {f}'.format(l=job_line,
f=cache_filename))
start_time = time.time()
# Find matching cache file
update = False
if previous_cache:
pattern = cache.get_line_cache_pattern(job_line,
nband,
regex=False)
potential = fnmatch.filter(previous_cache, pattern)
if not potential:
logger.info('Found zero previous cache files for '
'line {l}'.format(l=job_line))
elif len(potential) > 1:
logger.info('Found more than one previous cache file for '
'line {l}. Keeping first'.format(l=job_line))
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
else:
update = os.path.join(dataset_config['cache_line_dir'],
potential[0])
logger.info('Updating from cache file {f}'.format(f=update))
if update:
cache.update_cache_file(images, image_IDs, update, cache_filename,
job_line, image_reader,
image_reader_kwargs)
else:
if dataset_config['use_bip_reader']:
# Use BIP reader
logger.debug('Reading in data from disk using BIP reader')
Y = reader.read_row_BIP(images, job_line, (ncol, nband), dtype)
else:
# Read in data just using GDAL
logger.debug('Reading in data from disk using GDAL')
Y = reader.read_row_GDAL(images, job_line)
cache.write_cache_file(cache_filename, Y, image_IDs)
logger.debug('Took {s}s to cache the data'.format(
s=round(time.time() - start_time, 2)))