def AnalyzeMultiAnimalVideo(
video,
DLCscorer,
trainFraction,
cfg,
dlc_cfg,
sess,
inputs,
outputs,
pdindex,
save_as_csv,
destfolder=None,
c_engine=False,
robust_nframes=False,
):
""" Helper function for analyzing a video with multiple individuals """
print("Starting to analyze % ", video)
vname = Path(video).stem
videofolder = str(Path(video).parents[0])
if destfolder is None:
destfolder = videofolder
auxiliaryfunctions.attempttomakefolder(destfolder)
dataname = os.path.join(destfolder, vname + DLCscorer + ".h5")
if os.path.isfile(dataname.split(".h5")[0] + "_full.pickle"):
print("Video already analyzed!", dataname)
else:
print("Loading ", video)
vid = VideoWriter(video)
if robust_nframes:
nframes = vid.get_n_frames(robust=True)
duration = vid.calc_duration(robust=True)
fps = nframes / duration
else:
nframes = len(vid)
duration = vid.calc_duration(robust=False)
fps = vid.fps
nx, ny = vid.dimensions
print(
"Duration of video [s]: ",
round(duration, 2),
", recorded with ",
round(fps, 2),
"fps!",
)
print(
"Overall # of frames: ",
nframes,
" found with (before cropping) frame dimensions: ",
nx,
ny,
)
start = time.time()
print("Starting to extract posture")
if int(dlc_cfg["batch_size"]) > 1:
PredicteData, nframes = GetPoseandCostsF(
cfg,
dlc_cfg,
sess,
inputs,
outputs,
vid,
nframes,
int(dlc_cfg["batch_size"]),
c_engine=c_engine,
)
else:
PredicteData, nframes = GetPoseandCostsS(cfg,
dlc_cfg,
sess,
inputs,
outputs,
vid,
nframes,
c_engine=c_engine)
stop = time.time()
if cfg["cropping"] == True:
coords = [cfg["x1"], cfg["x2"], cfg["y1"], cfg["y2"]]
else:
coords = [0, nx, 0, ny]
dictionary = {
"start": start,
"stop": stop,
"run_duration": stop - start,
"Scorer": DLCscorer,
"DLC-model-config file": dlc_cfg,
"fps": fps,
"batch_size": dlc_cfg["batch_size"],
"frame_dimensions": (ny, nx),
"nframes": nframes,
"iteration (active-learning)": cfg["iteration"],
"training set fraction": trainFraction,
"cropping": cfg["cropping"],
"cropping_parameters": coords,
}
metadata = {"data": dictionary}
print("Saving results in %s..." % (destfolder))
auxfun_multianimal.SaveFullMultiAnimalData(PredicteData, metadata,
dataname)
def extract_bpt_feature_from_video(
video,
DLCscorer,
trainFraction,
cfg,
dlc_cfg,
sess,
inputs,
outputs,
extra_dict,
destfolder=None,
robust_nframes=False,
):
print("Starting to analyze % ", video)
vname = Path(video).stem
videofolder = str(Path(video).parents[0])
if destfolder is None:
destfolder = videofolder
auxiliaryfunctions.attempttomakefolder(destfolder)
dataname = os.path.join(destfolder, vname + DLCscorer + ".h5")
assemble_filename = dataname.split(".h5")[0] + "_assemblies.pickle"
feature_dict = shelve.open(
dataname.split(".h5")[0] + "_bpt_features.pickle",
protocol=pickle.DEFAULT_PROTOCOL,
)
with open(assemble_filename, "rb") as f:
assemblies = pickle.load(f)
print("Loading ", video)
vid = VideoWriter(video)
if robust_nframes:
nframes = vid.get_n_frames(robust=True)
duration = vid.calc_duration(robust=True)
fps = nframes / duration
else:
nframes = len(vid)
duration = vid.calc_duration(robust=False)
fps = vid.fps
nx, ny = vid.dimensions
print(
"Duration of video [s]: ",
round(duration, 2),
", recorded with ",
round(fps, 2),
"fps!",
)
print(
"Overall # of frames: ",
nframes,
" found with (before cropping) frame dimensions: ",
nx,
ny,
)
start = time.time()
print("Starting to extract posture")
if int(dlc_cfg["batch_size"]) > 1:
# for multi animal, seems only this is used
PredicteData, nframes = GetPoseandCostsF_from_assemblies(
cfg,
dlc_cfg,
sess,
inputs,
outputs,
vid,
nframes,
int(dlc_cfg["batch_size"]),
assemblies,
feature_dict,
extra_dict,
)
else:
raise NotImplementedError(
"Not implemented yet, please raise an GitHub issue if you need this."
)