def get_flydra_frame_number_for_ufmf_frame_number(ufmf_frame):
camn = 32
h5_filename = '/home/caveman/DATA/20120924_HCS_odor_horizon/data/h5_files/DATA20121002_184808.h5'
ufmf_fnames = ['/home/caveman/DATA/20120924_HCS_odor_horizon/data/ufmfs/small_20121002_184626_Basler_21111538.ufmf']
white_background=False
max_n_frames = None
start = None
stop = None
rgb8_if_color=False
movie_cam_ids=['Basler_21111538']
camn2cam_id = None
with openFileSafe( h5_filename, mode='r' ) as h5:
if camn2cam_id is None:
camn2cam_id, cam_id2camns = result_utils.get_caminfo_dicts(h5)
parsed = result_utils.read_textlog_header(h5)
flydra_version = parsed.get('flydra_version',None)
if flydra_version is not None and flydra_version >= '0.4.45':
# camnode.py saved timestamps into .ufmf file given by
# time.time() (camn_receive_timestamp). Compare with
# mainbrain's data2d_distorted column
# 'cam_received_timestamp'.
old_camera_timestamp_source = False
timestamp_name = 'cam_received_timestamp'
else:
# camnode.py saved timestamps into .ufmf file given by
# camera driver. Compare with mainbrain's data2d_distorted
# column 'timestamp'.
old_camera_timestamp_source = True
timestamp_name = 'timestamp'
h5_data = h5.root.data2d_distorted[:]
h5_indices_for_camn = np.where(h5_data['camn']==camn)[0]
flydra_frames_for_camn = h5_data['frame'][h5_indices_for_camn]
unique_flydra_frames_for_camn, unique_h5_indices_for_camn_idx = np.unique(flydra_frames_for_camn, return_index=True)
unique_h5_indices_for_camn = h5_indices_for_camn[unique_h5_indices_for_camn_idx]
unique_flydra_timestamps = h5_data[timestamp_name][unique_h5_indices_for_camn]
maxn = 50
n = 0
for unique_flydra_index, unique_flydra_timestamp in enumerate(unique_flydra_timestamps):
ufmf_frame_number = np.argmin( np.abs(movie.get_all_timestamps() - unique_flydra_timestamp ) )
flydra_frame_number = unique_flydra_frames_for_camn[unique_flydra_index]
print ufmf_frame_number - flydra_frame_number - h5_data['frame'][0], np.abs(movie.get_all_timestamps()[ufmf_frame_number] - unique_flydra_timestamp)
n += 1
if n > maxn:
break
def iterate_frames(h5_filename,
ufmf_fnames, # or fmfs
white_background=False,
max_n_frames = None,
start = None,
stop = None,
rgb8_if_color=False,
movie_cam_ids=None,
camn2cam_id = None,
):
"""yield frame-by-frame data"""
h5_filename = '/home/caveman/DATA/20120924_HCS_odor_horizon/data/h5_files/DATA20121002_184808.h5'
ufmf_fnames = ['/home/caveman/DATA/20120924_HCS_odor_horizon/data/ufmfs/small_20121002_184626_Basler_21111538.ufmf']
white_background=False
max_n_frames = None
start = None
stop = None
rgb8_if_color=False
movie_cam_ids=['Basler_21111538']
camn2cam_id = None
# First pass over .ufmf files: get intersection of timestamps
first_ufmf_ts = -np.inf
last_ufmf_ts = np.inf
ufmfs = {}
cam_ids = []
for movie_idx,ufmf_fname in enumerate(ufmf_fnames):
if movie_cam_ids is not None:
cam_id = movie_cam_ids[movie_idx]
else:
cam_id = get_cam_id_from_ufmf_fname(ufmf_fname)
cam_ids.append( cam_id )
kwargs = {}
if ufmf_fname.lower().endswith('.fmf'):
ufmf = fmf_mod.FlyMovie(ufmf_fname)
else:
ufmf = ufmf_mod.FlyMovieEmulator(ufmf_fname,
white_background=white_background,
**kwargs)
tss = ufmf.get_all_timestamps()
ufmfs[ufmf_fname] = (ufmf, cam_id, tss)
min_ts = np.min(tss)
max_ts = np.max(tss)
if min_ts > first_ufmf_ts:
first_ufmf_ts = min_ts
if max_ts < last_ufmf_ts:
last_ufmf_ts = max_ts
#assert first_ufmf_ts < last_ufmf_ts, ".ufmf files don't all overlap in time"
#ufmf_fnames.sort()
#cam_ids.sort()
with openFileSafe( h5_filename, mode='r' ) as h5:
if camn2cam_id is None:
camn2cam_id, cam_id2camns = result_utils.get_caminfo_dicts(h5)
parsed = result_utils.read_textlog_header(h5)
flydra_version = parsed.get('flydra_version',None)
if flydra_version is not None and flydra_version >= '0.4.45':
# camnode.py saved timestamps into .ufmf file given by
# time.time() (camn_receive_timestamp). Compare with
# mainbrain's data2d_distorted column
# 'cam_received_timestamp'.
old_camera_timestamp_source = False
timestamp_name = 'cam_received_timestamp'
else:
# camnode.py saved timestamps into .ufmf file given by
# camera driver. Compare with mainbrain's data2d_distorted
# column 'timestamp'.
old_camera_timestamp_source = True
timestamp_name = 'timestamp'
h5_data = h5.root.data2d_distorted[:]
if 1:
# narrow search to local region of .h5
#cond = ((first_ufmf_ts <= h5_data[timestamp_name]) &
# (h5_data[timestamp_name] <= last_ufmf_ts))
#narrow_h5_data = h5_data[cond]
narrow_h5_data = h5_data
narrow_camns = h5_data['camn']
narrow_timestamps = h5_data[timestamp_name]
# Find the camn for each .ufmf file
cam_id2camn = {}
for cam_id in cam_ids:
cam_id_camn_already_found = False
for ufmf_fname in ufmfs.keys():
(ufmf, test_cam_id, tss) = ufmfs[ufmf_fname]
if cam_id != test_cam_id:
continue
assert not cam_id_camn_already_found
cam_id_camn_already_found = True
umin=np.min(tss)
umax=np.max(tss)
cond = (umin<=narrow_timestamps) & (narrow_timestamps<=umax)
ucamns = narrow_camns[cond]
ucamns = np.unique(ucamns)
camns = []
for camn in ucamns:
if camn2cam_id[camn]==cam_id:
camns.append(camn)
assert len(camns)<2, "can't handle multiple camns per cam_id"
if len(camns):
cam_id2camn[cam_id] = camns[0]
ff = utils.FastFinder(narrow_h5_data['frame'])
unique_frames = list(np.unique(narrow_h5_data['frame']))
unique_frames.sort()
unique_frames = np.array( unique_frames )
if start is not None:
unique_frames = unique_frames[ unique_frames >= start ]
if stop is not None:
unique_frames = unique_frames[ unique_frames <= stop ]
if max_n_frames is not None:
unique_frames = unique_frames[:max_n_frames]
for frame_enum,frame in enumerate(unique_frames):
narrow_idxs = ff.get_idxs_of_equal(frame)
# trim data under consideration to just this frame
this_h5_data = narrow_h5_data[narrow_idxs]
this_camns = this_h5_data['camn']
this_tss = this_h5_data[timestamp_name]
# a couple more checks
if np.any( this_tss < first_ufmf_ts):
continue
if np.any( this_tss >= last_ufmf_ts):
break
per_frame_dict = {}
for ufmf_fname in ufmf_fnames:
ufmf, cam_id, tss = ufmfs[ufmf_fname]
if cam_id not in cam_id2camn:
continue
camn = cam_id2camn[cam_id]
this_camn_cond = this_camns == camn
this_cam_h5_data = this_h5_data[this_camn_cond]
this_camn_tss = this_cam_h5_data[timestamp_name]
if not len(this_camn_tss):
# no h5 data for this cam_id at this frame
continue
this_camn_ts=np.unique1d(this_camn_tss)
assert len(this_camn_ts)==1
this_camn_ts = this_camn_ts[0]
# optimistic: get next frame. it's probably the one we want
try:
image,image_ts,more = ufmf.get_next_frame(_return_more=True)
except ufmf_mod.NoMoreFramesException:
image_ts = None
if this_camn_ts != image_ts:
# It was not the frame we wanted. Find it.
ufmf_frame_idxs = np.nonzero(tss == this_camn_ts)[0]
if (len(ufmf_frame_idxs)==0 and
old_camera_timestamp_source):
warnings.warn(
'low-precision timestamp comparison in '
'use due to outdated .ufmf timestamp '
'saving.')
# 2.5 msec precision required
ufmf_frame_idxs = np.nonzero(
abs( tss - this_camn_ts ) < 0.0025)[0]
assert len(ufmf_frame_idxs)==1
ufmf_frame_no = ufmf_frame_idxs[0]
image,image_ts,more = ufmf.get_frame(ufmf_frame_no,
_return_more=True)
del ufmf_frame_no, ufmf_frame_idxs
coding = ufmf.get_format()
if imops.is_coding_color(coding):
if rgb8_if_color:
image = imops.to_rgb8(coding,image)
else:
warnings.warn('color image not converted to color')
per_frame_dict[ufmf_fname] = {
'image':image,
'cam_id':cam_id,
'camn':camn,
'timestamp':this_cam_h5_data['timestamp'][0],
'cam_received_timestamp':
this_cam_h5_data['cam_received_timestamp'][0],
'ufmf_frame_timestamp':this_cam_h5_data[timestamp_name][0],
}
per_frame_dict[ufmf_fname].update(more)
per_frame_dict['tracker_data']=this_h5_data
yield (per_frame_dict,frame)