def saveTrajData(trajectories_data, masked_image_file, skeletons_file):
#save data into the skeletons file
with tables.File(skeletons_file, "a") as ske_file_id:
trajectories_data_f = ske_file_id.create_table(
'/',
'trajectories_data',
obj=trajectories_data.to_records(
index=False,
column_dtypes={t[0]: t[1]
for t in TRAJECTORIES_DATA_DTYPES}),
filters=TABLE_FILTERS)
plate_worms = ske_file_id.get_node('/plate_worms')
if 'bgnd_param' in plate_worms._v_attrs:
bgnd_param = plate_worms._v_attrs['bgnd_param']
else:
bgnd_param = bytes(json.dumps({}), 'utf-8') #default empty
trajectories_data_f._v_attrs['bgnd_param'] = bgnd_param
#read and the units information information
fps, microns_per_pixel, is_light_background = \
copy_unit_conversions(trajectories_data_f, masked_image_file)
if not '/timestamp' in ske_file_id:
read_and_save_timestamp(masked_image_file, skeletons_file)
ske_file_id.flush()
def _ini_plate_worms(traj_fid, masked_image_file):
# intialize main table
int_dtypes = [('worm_index_blob', np.int),
('worm_index_joined', np.int), ('frame_number', np.int)]
dd = [
'coord_x', 'coord_y', 'box_length', 'box_width', 'angle', 'area',
'bounding_box_xmin', 'bounding_box_xmax', 'bounding_box_ymin',
'bounding_box_ymax', 'threshold'
]
float32_dtypes = [(x, np.float32) for x in dd]
plate_worms_dtype = np.dtype(int_dtypes + float32_dtypes)
plate_worms = traj_fid.create_table('/',
"plate_worms",
plate_worms_dtype,
"Worm feature List",
filters=TABLE_FILTERS)
#find if it is a mask from fluorescence and save it in the new group
plate_worms._v_attrs['is_light_background'] = is_light_background
plate_worms._v_attrs['expected_fps'] = expected_fps
#make sure it is in a "serializable" format
plate_worms._v_attrs['bgnd_param'] = bytes(json.dumps(bgnd_param),
'utf-8')
read_and_save_timestamp(masked_image_file, trajectories_file)
return plate_worms
def fix_timestamps(masked_file, skeletons_file):
timestamp, timestamp_time = read_and_save_timestamp(masked_file, dst_file='')
if os.path.exists(skeletons_file):
read_and_save_timestamp(masked_file, dst_file=skeletons_file)
with pd.HDFStore(skeletons_file, 'r') as fid:
trajectories_data = fid['trajectories_data']
ind = trajectories_data['frame_number'].values.astype(np.int32)
trajectories_data['timestamp_raw'] = timestamp[ind]
trajectories_data['timestamp_time'] = timestamp_time[ind]
if np.all(np.isnan(timestamp)) or np.all(np.diff(trajectories_data['timestamp_raw'])>=0):
save_modified_table(skeletons_file, trajectories_data, 'trajectories_data')
else:
return (masked_file, skeletons_file)
def compressVideo(video_file,
masked_image_file,
mask_param,
expected_fps=25,
microns_per_pixel=None,
bgnd_param={},
buffer_size=-1,
save_full_interval=-1,
max_frame=1e32,
is_extract_timestamp=False,
fovsplitter_param={}):
'''
Compresses video by selecting pixels that are likely to have worms on it and making the rest of
the image zero. By creating a large amount of redundant data, any lossless compression
algorithm will dramatically increase its efficiency. The masked images are saved as hdf5 with gzip compression.
The mask is calculated over a minimum projection of an image stack. This projection preserves darker regions
(or brighter regions, in the case of fluorescent labelling)
where the worm has more probability to be located. Additionally it has the advantage of reducing
the processing load by only requiring to calculate the mask once per image stack.
video_file -- original video file
masked_image_file --
buffer_size -- size of the image stack used to calculate the minimal projection and the mask
save_full_interval -- have often a full image is saved
max_frame -- last frame saved (default a very large number, so it goes until the end of the video)
mask_param -- parameters used to calculate the mask
'''
#get the default values if there is any bad parameter
output = compress_defaults(masked_image_file,
expected_fps,
buffer_size=buffer_size,
save_full_interval=save_full_interval)
buffer_size = output['buffer_size']
save_full_interval = output['save_full_interval']
if len(bgnd_param) > 0:
is_bgnd_subtraction = True
assert bgnd_param['buff_size'] > 0 and bgnd_param['frame_gap'] > 0
else:
is_bgnd_subtraction = False
if len(fovsplitter_param) > 0:
is_fov_tosplit = True
assert all(key in fovsplitter_param
for key in ['total_n_wells', 'whichsideup', 'well_shape'])
assert fovsplitter_param['total_n_wells'] > 0
else:
is_fov_tosplit = False
# processes identifier.
base_name = masked_image_file.rpartition('.')[0].rpartition(os.sep)[-1]
# select the video reader class according to the file type.
vid = selectVideoReader(video_file)
# delete any previous if it existed
with tables.File(masked_image_file, "w") as mask_fid:
pass
#Extract metadata
if is_extract_timestamp:
# extract and store video metadata using ffprobe
#NOTE: i cannot calculate /timestamp until i am sure of the total number of frames
print_flush(base_name + ' Extracting video metadata...')
expected_frames = store_meta_data(video_file, masked_image_file)
else:
expected_frames = 1
# Initialize background subtraction if required
if is_bgnd_subtraction:
print_flush(base_name + ' Initializing background subtraction.')
bgnd_subtractor = BackgroundSubtractorVideo(video_file, **bgnd_param)
# intialize some variables
max_intensity, min_intensity = np.nan, np.nan
frame_number = 0
full_frame_number = 0
image_prev = np.zeros([])
# Initialise FOV splitting if needed
if is_bgnd_subtraction:
img_fov = bgnd_subtractor.bgnd.astype(np.uint8)
else:
ret, img_fov = vid.read()
# close and reopen the video, to restart from the beginning
vid.release()
vid = selectVideoReader(video_file)
if is_fov_tosplit:
# TODO: change class creator so it only needs the video name? by using
# Tierpsy's functions such as selectVideoReader it can then read the first image by itself
camera_serial = parse_camera_serial(masked_image_file)
fovsplitter = FOVMultiWellsSplitter(img_fov,
camera_serial=camera_serial,
px2um=microns_per_pixel,
**fovsplitter_param)
wells_mask = fovsplitter.wells_mask
else:
wells_mask = None
# initialize timers
print_flush(base_name + ' Starting video compression.')
if expected_frames == 1:
progressTime = TimeCounter('Compressing video.')
else:
#if we know the number of frames display it in the progress
progressTime = TimeCounter('Compressing video.', expected_frames)
with tables.File(masked_image_file, "r+") as mask_fid:
#initialize masks groups
attr_params = dict(expected_fps=expected_fps,
microns_per_pixel=microns_per_pixel,
is_light_background=int(
mask_param['is_light_background']))
mask_dataset, full_dataset, mean_intensity = initMasksGroups(
mask_fid, expected_frames, vid.height, vid.width, attr_params,
save_full_interval)
if is_bgnd_subtraction:
bg_dataset = createImgGroup(mask_fid,
"/bgnd",
1,
vid.height,
vid.width,
is_expandable=False)
bg_dataset[0, :, :] = img_fov
if vid.dtype != np.uint8:
# this will worm as flags to be sure that the normalization took place.
normalization_range = mask_fid.create_earray(
'/',
'normalization_range',
atom=tables.Float32Atom(),
shape=(0, 2),
expectedrows=expected_frames,
filters=TABLE_FILTERS)
while frame_number < max_frame:
ret, image = vid.read()
if ret != 0:
# increase frame number
frame_number += 1
# opencv can give an artificial rgb image. Let's get it back to
# gray scale.
if image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
if image.dtype != np.uint8:
# normalise image intensities if the data type is other
# than uint8
image, img_norm_range = normalizeImage(image)
normalization_range.append(img_norm_range)
#limit the image range to 1 to 255, 0 is a reserved value for the background
assert image.dtype == np.uint8
image = np.clip(image, 1, 255)
# Add a full frame every save_full_interval
if frame_number % save_full_interval == 1:
full_dataset.append(image[np.newaxis, :, :])
full_frame_number += 1
# buffer index
ind_buff = (frame_number - 1) % buffer_size
# initialize the buffer when the index correspond to 0
if ind_buff == 0:
Ibuff = np.zeros((buffer_size, vid.height, vid.width),
dtype=np.uint8)
# add image to the buffer
Ibuff[ind_buff, :, :] = image.copy()
mean_int = np.mean(image)
assert mean_int >= 0
mean_intensity.append(np.array([mean_int]))
else:
# sometimes the last image is all zeros, control for this case
if np.all(Ibuff[ind_buff] == 0):
frame_number -= 1
ind_buff -= 1
# close the buffer
Ibuff = Ibuff[:ind_buff + 1]
# mask buffer and save data into the hdf5 file
if (ind_buff == buffer_size - 1 or ret == 0) and Ibuff.size > 0:
if is_bgnd_subtraction:
Ibuff_b = bgnd_subtractor.apply(Ibuff, frame_number)
else:
Ibuff_b = Ibuff
#calculate the max/min in the of the buffer
img_reduce = reduceBuffer(Ibuff_b,
mask_param['is_light_background'])
mask = getROIMask(img_reduce,
wells_mask=wells_mask,
**mask_param)
Ibuff *= mask
# now apply the well_mask if is MWP
if is_fov_tosplit:
fovsplitter.apply_wells_mask(
Ibuff) # Ibuff will be modified after this
# add buffer to the hdf5 file
frame_first_buff = frame_number - Ibuff.shape[0]
mask_dataset.append(Ibuff)
if frame_number % 500 == 0:
# calculate the progress and put it in a string
progress_str = progressTime.get_str(frame_number)
print_flush(base_name + ' ' + progress_str)
# finish process
if ret == 0:
break
# now that the whole video is read, we definitely have a better estimate
# for its number of frames. so set the save_interval again
if is_bgnd_subtraction:
# bg_dataset._v_attrs['save_interval'] = len(vid)
# the above line is not accurate when using ffmpeg,
# it's just safer to do:
bg_dataset._v_attrs['save_interval'] = mask_dataset.shape[0]
# close the video
vid.release()
# save fovsplitting data
if is_fov_tosplit:
fovsplitter.write_fov_wells_to_file(masked_image_file)
if fovsplitter.is_dubious:
print(f'Check {masked_image_file} for plate alignment')
read_and_save_timestamp(masked_image_file)
print_flush(base_name + ' Compressed video done.')