def test_bbb_convert_frame_add_cols(frame_data_all):
"""Frame with additional columns is correctly converted to np array."""
frame = frame_data_all
expected_keys = ('frameId', 'timedelta', 'timestamp', 'decodedId', 'detectionsUnion')
# one col, single value for whole columns
arr = convert_frame_to_numpy(frame, keys=expected_keys, add_cols={'camId': 2})
assert 'camId' in arr.dtype.names
assert np.all(arr['camId'] == 2)
# two cols, single value
arr = convert_frame_to_numpy(frame, keys=expected_keys, add_cols={'camId': 2, 'second': 3})
assert 'camId' in arr.dtype.names
assert 'second' in arr.dtype.names
assert np.all(arr['camId'] == 2)
assert np.all(arr['second'] == 3)
# list for whole column
arr = convert_frame_to_numpy(frame, keys=expected_keys, add_cols={'camId': range(0, 4)})
assert 'camId' in arr.dtype.names
assert np.all(arr['camId'] == np.array(range(0, 4)))
# existing column
with pytest.raises(AssertionError):
arr = convert_frame_to_numpy(frame, keys=expected_keys, add_cols={'frameId': 9})
def run(gt_file,
videos,
images,
visualize_debug,
output,
fix_utc_2014,
nb_bits=12):
"""
Converts bb_binary ground truth Cap'n Proto files to hdf5 files and
extracts the corresponding rois from videos or images.
"""
def get_filenames(f):
if f is None:
return []
else:
return [line.rstrip('\n') for line in f.readlines()]
gen_factory = FrameGeneratorFactory(get_filenames(videos),
get_filenames(images))
if os.path.exists(output):
os.remove(output)
distribution = DistributionCollection([('bits', Bernoulli(), nb_bits)])
dset = DistributionHDF5Dataset(output, distribution)
camIdxs = []
periods = []
for fname in gt_file:
fc = load_frame_container(fname)
camIdx, start_dt, end_dt = parse_video_fname(fname)
if fix_utc_2014 and start_dt.year == 2014:
start_dt -= timedelta(hours=2)
gt_frames = []
gen = gen_factory.get_generator(camIdx, start_dt)
for frame, (video_frame, video_filename) in zip(fc.frames, gen):
gt = {}
np_frame = convert_frame_to_numpy(frame)
rois, mask, positions = extract_gt_rois(np_frame, video_frame,
start_dt)
for name in np_frame.dtype.names:
gt[name] = np_frame[name][mask]
bits = [int_id_to_binary(id)[::-1] for id in gt["decodedId"]]
gt["bits"] = 2 * np.array(bits, dtype=np.float) - 1
gt["tags"] = 2 * (rois / 255.).astype(np.float16) - 1
gt['filename'] = os.path.basename(video_filename)
gt['camIdx'] = camIdx
gt_frames.append(gt)
print('.', end='', flush=True)
print()
gt_period = GTPeriod(camIdx, start_dt, end_dt, fname, gt_frames)
periods.append(
[int(gt_period.start.timestamp()),
int(gt_period.end.timestamp())])
camIdxs.append(gt_period.camIdx)
append_gt_to_hdf5(gt_period, dset)
dset.attrs['periods'] = np.array(periods)
dset.attrs['camIdxs'] = np.array(camIdxs)
visualize_detection_tiles(dset, os.path.splitext(output)[0])
dset.close()
def get_detections_on_timestamp(self, timestamp):
dset = ds.DetectionSet()
frame = self.frames[timestamp.frame]
data = convert_frame_to_numpy(frame)
#print 'timestamp: ' + str(frame.timestamp)
for detection_data in data:
dset.add_detection(
ds.Detection(
detection_data['idx'],
timestamp,
np.array([
detection_data['ypos'], detection_data['xpos']
]), # rotated, otherwise will be portrait orientation
detection_data['localizerSaliency'],
detection_data['decodedId']
[::-1], # reversed, we want least significant bit last
detection_data['xRotation'],
detection_data['yRotation'],
detection_data['zRotation']))
return dset
def test_bbb_convert_frame_det(frame_data_all):
"""Frame and detections are correctly converted to np array."""
frame = frame_data_all
expected_keys = ['frameId', 'timedelta', 'timestamp']
expected_keys.extend(get_detection_keys(frame.detectionsUnion.which()))
arr = convert_frame_to_numpy(frame, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
def test_bbb_convert_frame_det(frame_data_all):
"""Frame and detections are correctly converted to np array."""
frame = frame_data_all
expected_keys = ['frameId', 'timedelta', 'timestamp']
expected_keys.extend(get_detection_keys(frame.detectionsUnion.which()))
arr = convert_frame_to_numpy(frame, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
def test_bbb_convert_frame_default(frame_data_all):
"""Convert frame to np arrays without explicitly requesting certain keys."""
frame = frame_data_all
frame_keys = set(['frameId', 'dataSourceIdx', 'frameIdx', 'timestamp', 'timedelta'])
detection_keys = set(get_detection_keys(frame.detectionsUnion.which()))
expected_keys = frame_keys | detection_keys | set(['camId'])
# extract frame without explicitly asking for keys
arr = convert_frame_to_numpy(frame, add_cols={'camId': 0})
bbb_check_frame_data(frame, arr, expected_keys)
def test_bbb_frame_container_errors(frame_data_all):
"""Test minimal keys when building `FrameContainer`"""
frame = frame_data_all
expected_keys = ('timestamp', 'xpos', 'ypos', 'detectionsUnion')
arr = convert_frame_to_numpy(frame, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
detections = pd.DataFrame(arr)
with pytest.raises(AssertionError) as error_information:
build_frame_container_from_df(detections, frame.detectionsUnion.which(), 0)
assert 'decodedId' in str(error_information.value)
def test_bbb_convert_only_frame(frame_data_all):
"""Frame is correctly converted to np array and detections are ignored."""
frame = frame_data_all
expected_keys = ('frameId', 'timedelta', 'timestamp', 'detectionsUnion')
arr = _convert_frame_to_numpy(frame, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
# now compare behaviour of helper and parent function
arr_frame = convert_frame_to_numpy(frame, keys=expected_keys)
assert np.all(arr == arr_frame)
def test_bbb_convert_detections(frame_data_all):
"""Detections are correctly converted to np array and frame is ignored."""
frame = frame_data_all
expected_keys = get_detection_keys(frame.detectionsUnion.which())
detections = _get_detections(frame)
arr = _convert_detections_to_numpy(detections, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
# now compare behaviour of helper and parent function
arr_frame = convert_frame_to_numpy(frame, keys=expected_keys)
assert np.all(arr == arr_frame)
def test_bbb_convert_only_frame(frame_data_all):
"""Frame is correctly converted to np array and detections are ignored."""
frame = frame_data_all
expected_keys = ('frameId', 'timedelta', 'timestamp', 'detectionsUnion')
arr = _convert_frame_to_numpy(frame, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
# now compare behaviour of helper and parent function
arr_frame = convert_frame_to_numpy(frame, keys=expected_keys)
assert np.all(arr == arr_frame)
def test_bbb_convert_detections(frame_data_all):
"""Detections are correctly converted to np array and frame is ignored."""
frame = frame_data_all
expected_keys = get_detection_keys(frame.detectionsUnion.which())
detections = _get_detections(frame)
arr = _convert_detections_to_numpy(detections, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
# now compare behaviour of helper and parent function
arr_frame = convert_frame_to_numpy(frame, keys=expected_keys)
assert np.all(arr == arr_frame)
def test_bbb_convert_frame_default(frame_data_all):
"""Convert frame to np arrays without explicitly requesting certain keys."""
frame = frame_data_all
frame_keys = set(
['frameId', 'dataSourceIdx', 'frameIdx', 'timestamp', 'timedelta'])
detection_keys = set(get_detection_keys(frame.detectionsUnion.which()))
expected_keys = frame_keys | detection_keys | set(['camId'])
# extract frame without explicitly asking for keys
arr = convert_frame_to_numpy(frame, add_cols={'camId': 0})
bbb_check_frame_data(frame, arr, expected_keys)
def test_bbb_frame_container_errors(frame_data_all):
"""Test minimal keys when building `FrameContainer`"""
frame = frame_data_all
expected_keys = ('timestamp', 'xpos', 'ypos', 'detectionsUnion')
arr = convert_frame_to_numpy(frame, keys=expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
detections = pd.DataFrame(arr)
with pytest.raises(AssertionError) as error_information:
build_frame_container_from_df(detections,
frame.detectionsUnion.which(), 0)
assert 'decodedId' in str(error_information.value)
def test_bbb_convert_frame_add_cols(frame_data_all):
"""Frame with additional columns is correctly converted to np array."""
frame = frame_data_all
expected_keys = ('frameId', 'timedelta', 'timestamp', 'decodedId',
'detectionsUnion')
# one col, single value for whole columns
arr = convert_frame_to_numpy(frame,
keys=expected_keys,
add_cols={'camId': 2})
assert 'camId' in arr.dtype.names
assert np.all(arr['camId'] == 2)
# two cols, single value
arr = convert_frame_to_numpy(frame,
keys=expected_keys,
add_cols={
'camId': 2,
'second': 3
})
assert 'camId' in arr.dtype.names
assert 'second' in arr.dtype.names
assert np.all(arr['camId'] == 2)
assert np.all(arr['second'] == 3)
# list for whole column
arr = convert_frame_to_numpy(frame,
keys=expected_keys,
add_cols={'camId': range(0, 4)})
assert 'camId' in arr.dtype.names
assert np.all(arr['camId'] == np.array(range(0, 4)))
# existing column
with pytest.raises(AssertionError):
arr = convert_frame_to_numpy(frame,
keys=expected_keys,
add_cols={'frameId': 9})
def run(bb_gt_files, video_dir, image_dir, visualize_debug, force, output):
"""
Converts bb_binary ground truth Cap'n Proto files to hdf5 files and
extracts the corresponding rois from videos or images.
"""
gen_factory = FrameGeneratorFactory(video_dir, image_dir)
if force and os.path.exists(output):
os.remove(output)
dset = HDF5Dataset(output)
camIdxs = []
periods = []
for fname in bb_gt_files:
fc = load_frame_container(fname)
camIdx, start_dt, end_dt = parse_video_fname(fname)
basename = os.path.basename(fname)
gt_frames = []
print(basename)
gen = gen_factory.get_generator(camIdx, start_dt)
first = True
for frame, (video_frame, video_filename) in zip(fc.frames, gen):
gt = {}
np_frame = convert_frame_to_numpy(frame)
rois, mask, positions = extract_gt_rois(np_frame, video_frame,
start_dt)
for name in np_frame.dtype.names:
gt[name] = np_frame[name][mask]
gt["bits"] = np.array(
[int_id_to_binary(id)[::-1] for id in gt["decodedId"]])
gt["tags"] = rois
gt['filename'] = os.path.basename(video_filename)
gt_frames.append(gt)
if first and visualize_debug:
visualize_detections(gt, positions, video_frame)
first = False
print('.', end='')
gt_period = GTPeriod(camIdx, start_dt, end_dt, fname, gt_frames)
periods.append(
[int(gt_period.start.timestamp()),
int(gt_period.end.timestamp())])
camIdxs.append(gt_period.camIdx)
append_gt_to_hdf5(gt_period, dset)
dset.attrs['periods'] = np.array(periods)
dset.attrs['camIdxs'] = np.array(camIdxs)
dset.close()
def get_detections_on_timestamp( self, timestamp ): dset = ds.DetectionSet() frame = self.frames[ timestamp.frame ] data = convert_frame_to_numpy( frame ) for detection_data in data: dset.add_detection( ds.Detection( detection_data[ 'idx' ], timestamp, np.array( [ detection_data[ 'ypos' ], detection_data[ 'xpos' ] ] ), # rotated, otherwise will be portrait orientation detection_data[ 'localizerSaliency' ], detection_data[ 'decodedId' ][::-1] # reversed, we want least significant bit last ) ) return dset
def run(bb_gt_files, video_dir, image_dir, visualize_debug, force, output):
"""
Converts bb_binary ground truth Cap'n Proto files to hdf5 files and
extracts the corresponding rois from videos or images.
"""
gen_factory = FrameGeneratorFactory(video_dir, image_dir)
if force and os.path.exists(output):
os.remove(output)
dset = HDF5Dataset(output)
camIdxs = []
periods = []
for fname in bb_gt_files:
fc = load_frame_container(fname)
camIdx, start_dt, end_dt = parse_video_fname(fname)
basename = os.path.basename(fname)
gt_frames = []
print(basename)
gen = gen_factory.get_generator(camIdx, start_dt)
first = True
for frame, (video_frame, video_filename) in zip(fc.frames, gen):
gt = {}
np_frame = convert_frame_to_numpy(frame)
rois, mask, positions = extract_gt_rois(np_frame, video_frame, start_dt)
for name in np_frame.dtype.names:
gt[name] = np_frame[name][mask]
gt["bits"] = np.array([int_id_to_binary(id)[::-1] for id in gt["decodedId"]])
gt["tags"] = rois
gt['filename'] = os.path.basename(video_filename)
gt_frames.append(gt)
if first and visualize_debug:
visualize_detections(gt, positions, video_frame)
first = False
print('.', end='')
gt_period = GTPeriod(camIdx, start_dt, end_dt, fname, gt_frames)
periods.append([int(gt_period.start.timestamp()), int(gt_period.end.timestamp())])
camIdxs.append(gt_period.camIdx)
append_gt_to_hdf5(gt_period, dset)
dset.attrs['periods'] = np.array(periods)
dset.attrs['camIdxs'] = np.array(camIdxs)
dset.close()
def main(): # loading data if not os.path.exists( config.DATA_FOLDER ): print 'Error: folder not found' return dset_store = ds.DetectionSetStore() repo = Repository.load( config.DATA_FOLDER ) start_time = datetime( config.DATE[ 0 ], config.DATE[ 1 ], config.DATE[ 2 ], config.TIME[ 0 ], config.TIME[ 1 ], tzinfo=pytz.utc ) end_time = datetime( config.DATE[ 0 ], config.DATE[ 1 ], config.DATE[ 2 ], config.TIME[ 0 ], config.TIME[ 1 ]+1, tzinfo=pytz.utc ) fnames = repo.iter_fnames( begin=start_time, end=end_time ) for fname in fnames: frame_container = load_frame_container( fname ) cam = frame_container.camId dset_store.source = frame_container.dataSources[ 0 ].filename previous_timestamp = None frame_index = config.FRAME_START for frame in list( frame_container.frames )[ config.FRAME_START : config.FRAME_END + 1 ]: timestamp = ds.TimeStamp( frame_index, cam ) timestamp.connect_with_previous( previous_timestamp ) previous_timestamp = timestamp dset = ds.DetectionSet() dset_store.store[ timestamp ] = dset data = convert_frame_to_numpy( frame ) for detection_data in data: dset.add_detection( ds.Detection( detection_data[ 'idx' ], timestamp, np.array( [ detection_data[ 'ypos' ], detection_data[ 'xpos' ] ] ), # rotated, otherwise will be portrait orientation detection_data[ 'localizerSaliency' ], detection_data[ 'decodedId' ][::-1] # reversed, we want least significant bit last ) ) dset.build_kd_tree() frame_index += 1 # break because we only load the first fname break # loading truth if not os.path.isfile( config.PATHS_FILE ): print 'Error: file not found' return with open( config.PATHS_FILE, 'rb' ) as paths_file: input = pickle.load( paths_file ) if input[ 'source' ] != dset_store.source: print 'Error: data sources do not match' return paths_input = input[ 'paths' ] # match for tag_id in paths_input.keys(): for path_id in paths_input[ tag_id ].keys(): for frame,detection_data in paths_input[ tag_id ][ path_id ].items(): old_detection_id, pos_x, pos_y, readability = detection_data timestamp = dset_store.get_timestamp( frame ) new_detection_id = None distance = None if timestamp is not None and readability < 3: dset = dset_store.get( timestamp ) distances, indices = dset.kd_tree.query( [ pos_x, pos_y ], k=1 ) distance = distances[ 0 ][ 0 ] index = indices[ 0 ][ 0 ] if distance <= MATCH_DISTANCE_LIMIT: new_detection_id = index # use this if you're matching to the same output for test purposes: #if new_detection_id != old_detection_id: # print 'mismatch old: ' + str(old_detection_id) + ', new: ' + str(new_detection_id) paths_input[ tag_id ][ path_id ][ frame ] = ( new_detection_id, pos_x, pos_y, readability ) # saving truth with open( config.PATHS_FILE, 'wb' ) as paths_file: pickle.dump( input, paths_file ) print 'done'
def load_data( self ): if not os.path.exists( config.DATA_FOLDER ): print 'Error: folder not found' return self.block_inputs( True ) self.dset_store = ds.DetectionSetStore() self.path_manager = None self.paths_load_progress.setValue( 0 ) self.paths_load_label.setText( '' ) try: repo = Repository( config.DATA_FOLDER ) start_time = datetime( config.DATE[ 0 ], config.DATE[ 1 ], config.DATE[ 2 ], config.TIME[ 0 ], config.TIME[ 1 ], tzinfo=pytz.utc ) fnames = repo.iter_fnames( begin=start_time ) for fname in fnames: frame_container = load_frame_container( fname ) cam = frame_container.camId #frame_container.fromTimestamp # already available #frame_container.toTimestamp # already available self.dset_store.source = frame_container.dataSources[ 0 ].filename previous_timestamp = None self.data_load_progress.setMaximum( config.FRAME_END + 1 - config.FRAME_START ) self.app.processEvents() frame_index = config.FRAME_START for frame in list( frame_container.frames )[ config.FRAME_START : config.FRAME_END + 1 ]: #timestamp = frame.timestamp # not included yet #frame.id # not included yet timestamp = ds.TimeStamp( frame_index, cam ) timestamp.connect_with_previous( previous_timestamp ) previous_timestamp = timestamp dset = ds.DetectionSet() self.dset_store.store[ timestamp ] = dset data = convert_frame_to_numpy( frame ) for detection_data in data: dset.add_detection( ds.Detection( detection_data[ 'idx' ], timestamp, np.array( [ detection_data[ 'ypos' ], detection_data[ 'xpos' ] ] ), # rotated, otherwise will be portrait orientation detection_data[ 'localizerSaliency' ], detection_data[ 'decodedId' ][::-1] # reversed, we want least significant bit last ) ) frame_index += 1 self.data_load_progress.setValue( frame_index - config.FRAME_START ) self.app.processEvents() self.data_load_label.setText( str( len( self.dset_store.store ) ) + ' frames loaded' ) self.app.processEvents() # break because we only load the first fname break except: pass self.block_inputs( False )
def load_data(self):
if not os.path.exists(config.DATA_FOLDER):
print 'Error: folder not found'
return
self.block_inputs(True)
self.dset_store = ds.DetectionSetStore()
self.path_manager = None
self.paths_load_progress.setValue(0)
self.paths_load_label.setText('')
try:
repo = Repository(config.DATA_FOLDER)
start_time = datetime(config.DATE[0],
config.DATE[1],
config.DATE[2],
config.TIME[0],
config.TIME[1],
tzinfo=pytz.utc)
fnames = repo.iter_fnames(begin=start_time)
for fname in fnames:
frame_container = load_frame_container(fname)
cam = frame_container.camId
#frame_container.fromTimestamp # already available
#frame_container.toTimestamp # already available
self.dset_store.source = frame_container.dataSources[
0].filename
previous_timestamp = None
self.data_load_progress.setMaximum(config.FRAME_END + 1 -
config.FRAME_START)
self.app.processEvents()
frame_index = config.FRAME_START
for frame in list(frame_container.frames
)[config.FRAME_START:config.FRAME_END + 1]:
#timestamp = frame.timestamp # not included yet
#frame.id # not included yet
timestamp = ds.TimeStamp(frame_index, cam)
timestamp.connect_with_previous(previous_timestamp)
previous_timestamp = timestamp
dset = ds.DetectionSet()
self.dset_store.store[timestamp] = dset
data = convert_frame_to_numpy(frame)
for detection_data in data:
dset.add_detection(
ds.Detection(
detection_data['idx'],
timestamp,
np.array(
[
detection_data['ypos'],
detection_data['xpos']
]
), # rotated, otherwise will be portrait orientation
detection_data['localizerSaliency'],
detection_data['decodedId']
[::
-1] # reversed, we want least significant bit last
))
frame_index += 1
self.data_load_progress.setValue(frame_index -
config.FRAME_START)
self.app.processEvents()
self.data_load_label.setText(
str(len(self.dset_store.store)) + ' frames loaded')
self.app.processEvents()
# break because we only load the first fname
break
except:
pass
self.block_inputs(False)
def test_bbb_fc_from_df(frame_data_all):
"""Data in DataFrame correctly converted to `FrameContainer`."""
frame = frame_data_all
union_type = frame.detectionsUnion.which()
expected_keys = ['timestamp', ]
expected_keys.extend(get_detection_keys(union_type))
arr = convert_frame_to_numpy(frame, expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
detections = make_df_from_np(arr, union_type)
expected_detections = detections.copy()
n_detections = len(getattr(frame.detectionsUnion, union_type))
offset = 0
# test minimal set
dfr = detections.copy()
fco, offset = build_frame_container_from_df(dfr, union_type, offset)
assert offset == 1
assert fco.id == 0
assert fco.camId == 0
assert fco.fromTimestamp == expected_detections.timestamp[0]
assert fco.toTimestamp == expected_detections.timestamp[3]
assert len(fco.frames) == 1
frame0 = fco.frames[0]
assert frame0.id == offset - 1
assert frame0.frameIdx == 0
assert frame0.timestamp == expected_detections.timestamp[0]
assert len(getattr(frame0.detectionsUnion, union_type)) == 4
arr = convert_frame_to_numpy(frame0, expected_keys)
converted_detections = make_df_from_np(arr, union_type)
assert_frame_equal(expected_detections, converted_detections)
# test without readability
dfr = detections.copy()
if union_type == 'detectionsTruth':
dfr.drop('readability', axis=1, inplace=True)
fco, offset = build_frame_container_from_df(dfr, union_type, offset, frame_offset=offset)
assert offset == 2 # test offset and id to test for fixed assignments
assert fco.id == 1
assert fco.camId == 1
assert len(fco.frames) == 1
frame0 = fco.frames[0]
assert frame0.id == offset - 1 # test if offset is considered
assert frame0.frameIdx == 0
if union_type == 'detectionsTruth':
for i in range(0, n_detections): # test for default readability value
assert str(frame0.detectionsUnion.detectionsTruth[i].readability) == 'unknown'
# test with datetime instead of unixtimestamp
dfr = detections.copy()
dfr.timestamp = dfr.timestamp.apply(to_datetime)
fco, offset = build_frame_container_from_df(dfr, union_type, offset, frame_offset=offset)
assert offset == 3
assert fco.fromTimestamp == expected_detections.timestamp[0]
assert len(fco.frames) == 1
# test with additional column for frames
dfr = detections.copy()
dfr['dataSourceIdx'] = 99
fco, offset = build_frame_container_from_df(dfr, union_type, offset, frame_offset=offset)
assert offset == 4
assert len(fco.frames) == 1
frame0 = fco.frames[0]
assert frame0.dataSourceIdx == dfr.dataSourceIdx[0]
# test with additional column for detections
dfr = detections.copy()
dfr['xposHive'] = range(0, n_detections)
fco, offset = build_frame_container_from_df(dfr, union_type, offset, frame_offset=offset)
assert offset == 5
assert len(fco.frames) == 1
converted_detections = getattr(fco.frames[0].detectionsUnion, union_type)
for i in range(0, n_detections):
assert converted_detections[i].xposHive == dfr.xposHive[i]
# test with camId column
dfr = detections.copy()
dfr['camId'] = [offset] * dfr.shape[0]
dfr.loc[0, 'camId'] = offset - 1
fco, offset = build_frame_container_from_df(dfr, union_type, offset, frame_offset=offset)
assert offset == 6
assert len(fco.frames) == 1
converted_detections = getattr(fco.frames[0].detectionsUnion, union_type)
assert len(converted_detections) == n_detections - 1 # one detection removed!
def test_bbb_fc_from_df(frame_data_all):
"""Data in DataFrame correctly converted to `FrameContainer`."""
frame = frame_data_all
union_type = frame.detectionsUnion.which()
expected_keys = [
'timestamp',
]
expected_keys.extend(get_detection_keys(union_type))
arr = convert_frame_to_numpy(frame, expected_keys)
bbb_check_frame_data(frame, arr, expected_keys)
detections = make_df_from_np(arr, union_type)
expected_detections = detections.copy()
n_detections = len(getattr(frame.detectionsUnion, union_type))
offset = 0
# test minimal set
dfr = detections.copy()
fco, offset = build_frame_container_from_df(dfr, union_type, offset)
assert offset == 1
assert fco.id == 0
assert fco.camId == 0
assert fco.fromTimestamp == expected_detections.timestamp[0]
assert fco.toTimestamp == expected_detections.timestamp[3]
assert len(fco.frames) == 1
frame0 = fco.frames[0]
assert frame0.id == offset - 1
assert frame0.frameIdx == 0
assert frame0.timestamp == expected_detections.timestamp[0]
assert len(getattr(frame0.detectionsUnion, union_type)) == 4
arr = convert_frame_to_numpy(frame0, expected_keys)
converted_detections = make_df_from_np(arr, union_type)
assert_frame_equal(expected_detections, converted_detections)
# test without readability
dfr = detections.copy()
if union_type == 'detectionsTruth':
dfr.drop('readability', axis=1, inplace=True)
fco, offset = build_frame_container_from_df(dfr,
union_type,
offset,
frame_offset=offset)
assert offset == 2 # test offset and id to test for fixed assignments
assert fco.id == 1
assert fco.camId == 1
assert len(fco.frames) == 1
frame0 = fco.frames[0]
assert frame0.id == offset - 1 # test if offset is considered
assert frame0.frameIdx == 0
if union_type == 'detectionsTruth':
for i in range(0, n_detections): # test for default readability value
assert str(frame0.detectionsUnion.detectionsTruth[i].readability
) == 'unknown'
# test with datetime instead of unixtimestamp
dfr = detections.copy()
dfr.timestamp = dfr.timestamp.apply(to_datetime)
fco, offset = build_frame_container_from_df(dfr,
union_type,
offset,
frame_offset=offset)
assert offset == 3
assert fco.fromTimestamp == expected_detections.timestamp[0]
assert len(fco.frames) == 1
# test with additional column for frames
dfr = detections.copy()
dfr['dataSourceIdx'] = 99
fco, offset = build_frame_container_from_df(dfr,
union_type,
offset,
frame_offset=offset)
assert offset == 4
assert len(fco.frames) == 1
frame0 = fco.frames[0]
assert frame0.dataSourceIdx == dfr.dataSourceIdx[0]
# test with additional column for detections
dfr = detections.copy()
dfr['xposHive'] = range(0, n_detections)
fco, offset = build_frame_container_from_df(dfr,
union_type,
offset,
frame_offset=offset)
assert offset == 5
assert len(fco.frames) == 1
converted_detections = getattr(fco.frames[0].detectionsUnion, union_type)
for i in range(0, n_detections):
assert converted_detections[i].xposHive == dfr.xposHive[i]
# test with camId column
dfr = detections.copy()
dfr['camId'] = [offset] * dfr.shape[0]
dfr.loc[0, 'camId'] = offset - 1
fco, offset = build_frame_container_from_df(dfr,
union_type,
offset,
frame_offset=offset)
assert offset == 6
assert len(fco.frames) == 1
converted_detections = getattr(fco.frames[0].detectionsUnion, union_type)
assert len(
converted_detections) == n_detections - 1 # one detection removed!