def extract_image_set(image_files, out_dir, date, age, plate_params, ignore_previous=False):
"""Find wells in a set of scanner images and extract each well into a separate image
for further processing.
Parameters:
image_files: list of paths to a set of images.
out_dir: path to write out the extracted images and metadata.
date: date object referring to image scan date
age: age in days of the worms in these images
plate_params: configuration information for extracting wells from the plates.
This must be a parameter dictionary suitable to pass to extract_wells.extract_wells()
ignore_previous: if False, and stored results already exist, skip processing
"""
out_dir = pathlib.Path(out_dir)
metadata = out_dir / 'metadata.pickle'
if metadata.exists() and not ignore_previous:
return
images = []
print('extracting images for {}'.format(out_dir))
well_mask = freeimage.read(str(out_dir.parent / 'well_mask.png')) > 0
for image_file in image_files:
image = freeimage.read(image_file)
if image.dtype == numpy.uint16:
image = (image >> 8).astype(numpy.uint8)
images.append(image)
well_names, well_images, well_centroids = extract_wells.extract_wells(images, well_mask, **plate_params)
well_dir = util.get_dir(out_dir / 'well_images')
for well_name, well_image_set in zip(well_names, well_images):
for i, image in enumerate(well_image_set):
freeimage.write(image, str(well_dir/well_name)+'-{}.png'.format(i))
util.dump(metadata, date=date, age=age, well_names=well_names, well_centroids=well_centroids)
def process_image_dir(in_dir, out_dir, age_at_first_scan, name_params, plate_params, score_params, re_extract=False, re_score=False, max_workers=None):
"""Extract well images from scanned plate images and score worm movement.
Parameters:
in_dir: path to directory of scanned images.
out_dir: path to write out the extracted images and metadata.
age_at_first_scan: age in days of the first image scan in in_dir
name_params: information for finding image dates from file names: a dict with
keys 'image_glob', 'date_regex', and 'date_format', as per extract_well_images()
plate_params: configuration information for extracting wells from the plates.
This must be a parameter dictionary suitable to pass to extract_wells.extract_wells()
score_params: configuration information for scoring wells for movement.
This must be a parameter dictionary suitable to pass to score_wells.score_wells()
re_extract: if True, don't skip well-image extraction even if there are existing images.
re_score: if True, don't skip scoring well images even if there are existing scores.
re_extract implies re_score. If the former is True, scoring will be re-done
even if re_score is False.
max_workers: maximum number of image-extraction jobs to run in parallel. If None,
then use all CPUs that the machine has. For debugging, use 1.
Returns: whether any of the jobs caused an error.
"""
out_dir = util.get_dir(out_dir)
image_sets = parse_inputs(in_dir, **name_params)
dates = sorted(image_sets.keys())
make_well_mask(out_dir, image_sets[dates[0]][0], ignore_previous=re_extract) # maks mask from first image on first day -- least junk-filled
runner = BackgroundRunner(max_workers)
if re_extract:
re_score = True
for date in dates:
out_dir_for_date = out_dir / date.isoformat()
age = (date - dates[0]).days + age_at_first_scan
image_files = image_sets[date]
runner.submit(process_image_set, image_files, out_dir_for_date, date, age,
plate_params, score_params, re_extract, re_score)
results, was_error, error_indices, cancelled_indices = runner.wait()
for i in error_indices:
print("Error processing images for date {}:".format(dates[i]))
print(results[i])
if not was_error:
aggregate_scores(out_dir)
return was_error
