def GetPoseandCostsS(cfg, dlc_cfg, sess, inputs, outputs, cap, nframes,
c_engine):
""" Non batch wise pose estimation for video cap."""
strwidth = int(np.ceil(np.log10(nframes))) # width for strings
if cfg["cropping"]:
cap.set_bbox(cfg["x1"], cfg["x2"], cfg["y1"], cfg["y2"])
PredicteData = {
} # np.zeros((nframes, 3 * len(dlc_cfg['all_joints_names'])))
pbar = tqdm(total=nframes)
counter = 0
step = max(10, int(nframes / 100))
while cap.video.isOpened():
if counter % step == 0:
pbar.update(step)
frame = cap.read_frame(crop=cfg["cropping"])
if frame is not None:
frame = img_as_ubyte(frame)
PredicteData["frame" + str(counter).zfill(
strwidth)] = predict.get_detectionswithcosts(
frame,
dlc_cfg,
sess,
inputs,
outputs,
outall=False,
nms_radius=dlc_cfg["nmsradius"],
det_min_score=dlc_cfg["minconfidence"],
c_engine=c_engine,
)
elif counter >= nframes:
break
counter += 1
pbar.close()
PredicteData["metadata"] = {
"nms radius":
dlc_cfg["nmsradius"],
"minimal confidence":
dlc_cfg["minconfidence"],
"PAFgraph":
dlc_cfg["partaffinityfield_graph"],
"PAFinds":
dlc_cfg.get("paf_best",
np.arange(len(dlc_cfg["partaffinityfield_graph"]))),
"all_joints": [[i] for i in range(len(dlc_cfg["all_joints"]))],
"all_joints_names": [
dlc_cfg["all_joints_names"][i]
for i in range(len(dlc_cfg["all_joints"]))
],
"nframes":
nframes,
}
return PredicteData, nframes
def get_ma_pose(image, config, session, inputs, outputs):
"""
Gets scoremap, local reference and pose from DeepLabCut using given image
Pose is most probable points for each joint, and not really used later
Scoremap and local reference is essential to extract skeletons
:param image: frame which would be analyzed
:param config, session, inputs, outputs: DeepLabCut configuration and TensorFlow variables from load_deeplabcut()
:return: tuple of scoremap, local reference and pose
"""
scmap, locref, paf, pose = predict_multianimal.get_detectionswithcosts(image, config, session, inputs, outputs,
outall=True,
nms_radius=config['nmsradius'],
det_min_score=config['minconfidence'],
c_engine=False)
return pose
def GetPoseandCostsS(cfg, dlc_cfg, sess, inputs, outputs, cap, nframes,
c_engine):
""" Non batch wise pose estimation for video cap."""
strwidth = int(np.ceil(np.log10(nframes))) # width for strings
if cfg["cropping"]:
print(
"Cropping based on the x1 = %s x2 = %s y1 = %s y2 = %s. You can adjust the cropping coordinates in the config.yaml file."
% (cfg["x1"], cfg["x2"], cfg["y1"], cfg["y2"]))
nx = cfg["x2"] - cfg["x1"]
ny = cfg["y2"] - cfg["y1"]
if nx > 0 and ny > 0:
pass
else:
raise Exception("Please check the order of cropping parameter!")
if (cfg["x1"] >= 0 and cfg["x2"] < int(cap.get(3) + 1)
and cfg["y1"] >= 0 and cfg["y2"] < int(cap.get(4) + 1)):
pass # good cropping box
else:
raise Exception("Please check the boundary of cropping!")
PredicteData = {
} # np.zeros((nframes, 3 * len(dlc_cfg['all_joints_names'])))
pbar = tqdm(total=nframes)
counter = 0
step = max(10, int(nframes / 100))
while cap.isOpened():
if counter % step == 0:
pbar.update(step)
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if cfg["cropping"]:
frame = img_as_ubyte(frame[cfg["y1"]:cfg["y2"],
cfg["x1"]:cfg["x2"]])
else:
frame = img_as_ubyte(frame)
PredicteData["frame" + str(counter).zfill(
strwidth)] = predict.get_detectionswithcosts(
frame,
dlc_cfg,
sess,
inputs,
outputs,
outall=False,
nms_radius=dlc_cfg.nmsradius,
det_min_score=dlc_cfg.minconfidence,
c_engine=c_engine,
)
else:
nframes = counter
break
counter += 1
pbar.close()
PredicteData["metadata"] = {
"nms radius":
dlc_cfg.nmsradius,
"minimal confidence":
dlc_cfg.minconfidence,
"PAFgraph":
dlc_cfg.partaffinityfield_graph,
"all_joints": [[i] for i in range(len(dlc_cfg.all_joints))],
"all_joints_names":
[dlc_cfg.all_joints_names[i] for i in range(len(dlc_cfg.all_joints))],
"nframes":
nframes,
}
# print(PredicteData)
return PredicteData, nframes
