def GetPoseFfmf(cfg,dlc_cfg, sess, inputs, outputs,cap,nframes,batchsize,start=0,stop=1):
''' Batchwise prediction of pose '''
ny,nx=int(cap.framesize[0]),int(cap.framesize[1])
print("annotating fmf file")
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.framesize[1]+1) and cfg['y1']>=0 and cfg['y2']<int(cap.framesize[0]+1):
pass #good cropping box
else:
raise Exception('Please check the boundary of cropping!')
nframes=int(stop*nframes)
PredicteData = np.zeros((nframes, 3 * len(dlc_cfg['all_joints_names'])))
batch_ind = 0 # keeps track of which image within a batch should be written to
batch_num = 0 # keeps track of which batch you are at
frames = np.empty((batchsize, ny, nx, 3), dtype='ubyte') # this keeps all frames in a batch
pbar=tqdm(total=nframes)
counter=0
step=max(10,int(nframes/100))
while(counter<nframes):
if counter%step==0:
pbar.update(step)
frame = cap.get_frame(counter)
if frame.ndim != 3:
frame = skimage.color.gray2rgb(frame)
frame=cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if cfg['cropping']:
frames[batch_ind] = img_as_ubyte(frame[cfg['y1']:cfg['y2'],cfg['x1']:cfg['x2']])
else:
frames[batch_ind] = img_as_ubyte(frame)
if batch_ind==batchsize-1:
pose = predict.getposeNP(frames,dlc_cfg, sess, inputs, outputs)
PredicteData[batch_num*batchsize:(batch_num+1)*batchsize, :] = pose
batch_ind = 0
batch_num += 1
else:
batch_ind+=1
counter+=1
nframes = counter
print("Detected frames: ", nframes)
if batch_ind>0:
pose = predict.getposeNP(frames, dlc_cfg, sess, inputs, outputs) #process the whole batch (some frames might be from previous batch!)
PredicteData[batch_num*batchsize:batch_num*batchsize+batch_ind, :] = pose[:batch_ind,:]
pbar.close()
return PredicteData,nframes
def GetPoseF(cfg, dlc_cfg, sess, inputs, outputs, cap, nframes, batchsize):
''' Batchwise prediction of pose '''
PredictedData = np.zeros(
(nframes,
dlc_cfg['num_outputs'] * 3 * len(dlc_cfg['all_joints_names'])))
batch_ind = 0 # keeps track of which image within a batch should be written to
batch_num = 0 # keeps track of which batch you are at
ny, nx = int(cap.get(4)), int(cap.get(3))
if cfg['cropping']:
ny, nx = checkcropping(cfg, cap)
frames = np.empty((batchsize, ny, nx, 3),
dtype='ubyte') # this keeps all frames in a batch
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']:
frames[batch_ind] = img_as_ubyte(frame[cfg['y1']:cfg['y2'],
cfg['x1']:cfg['x2']])
else:
frames[batch_ind] = img_as_ubyte(frame)
if batch_ind == batchsize - 1:
pose = predict.getposeNP(frames, dlc_cfg, sess, inputs,
outputs)
PredictedData[batch_num * batchsize:(batch_num + 1) *
batchsize, :] = pose
batch_ind = 0
batch_num += 1
else:
batch_ind += 1
else:
nframes = counter
print("Detected frames: ", nframes)
if batch_ind > 0:
pose = predict.getposeNP(
frames, dlc_cfg, sess, inputs, outputs
) #process the whole batch (some frames might be from previous batch!)
PredictedData[batch_num * batchsize:batch_num * batchsize +
batch_ind, :] = pose[:batch_ind, :]
break
counter += 1
pbar.close()
return PredictedData, nframes
def predict_im(self, im):
imfull = np.tile(im[:, :, None], (1, 1, 3))
pose = getposeNP(imfull[None, :, :, :],
self.dlc_cfg,
self.sess,
self.inputs,
self.outputs,
outall=False)
return pose.reshape(-1, 3)
def GetPosesofFrames(cfg,dlc_cfg, sess, inputs, outputs,directory,framelist,nframes,batchsize):
''' Batchwise prediction of pose for framelist in directory'''
from skimage import io
print("Starting to extract posture")
im=io.imread(os.path.join(directory,framelist[0]),mode='RGB')
ny,nx,nc=np.shape(im)
print("Overall # of frames: ", nframes," found with (before cropping) frame dimensions: ", nx,ny)
PredicteData = np.zeros((nframes, 3 * len(dlc_cfg['all_joints_names'])))
batch_ind = 0 # keeps track of which image within a batch should be written to
batch_num = 0 # keeps track of which batch you are at
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,ny=cfg['x2']-cfg['x1'],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(np.shape(im)[1]) and cfg['y1']>=0 and cfg['y2']<int(np.shape(im)[0]):
pass #good cropping box
else:
raise Exception('Please check the boundary of cropping!')
pbar=tqdm(total=nframes)
counter=0
step=max(10,int(nframes/100))
if batchsize==1:
for counter,framename in enumerate(framelist):
frame=io.imread(os.path.join(directory,framename),mode='RGB')
if counter%step==0:
pbar.update(step)
if cfg['cropping']:
frame= img_as_ubyte(frame[cfg['y1']:cfg['y2'],cfg['x1']:cfg['x2'],:])
else:
frame = img_as_ubyte(frame)
pose = predict.getpose(frame, dlc_cfg, sess, inputs, outputs)
PredicteData[counter, :] = pose.flatten()
else:
frames = np.empty((batchsize, ny, nx, 3), dtype='ubyte') # this keeps all the frames of a batch
for counter,framename in enumerate(framelist):
frame=io.imread(os.path.join(directory,framename),mode='RGB')
if counter%step==0:
pbar.update(step)
if cfg['cropping']:
frames[batch_ind] = img_as_ubyte(frame[cfg['y1']:cfg['y2'],cfg['x1']:cfg['x2'],:])
else:
frames[batch_ind] = img_as_ubyte(frame)
if batch_ind==batchsize-1:
pose = predict.getposeNP(frames,dlc_cfg, sess, inputs, outputs)
PredicteData[batch_num*batchsize:(batch_num+1)*batchsize, :] = pose
batch_ind = 0
batch_num += 1
else:
batch_ind+=1
if batch_ind>0: #take care of the last frames (the batch that might have been processed)
pose = predict.getposeNP(frames, dlc_cfg, sess, inputs, outputs) #process the whole batch (some frames might be from previous batch!)
PredicteData[batch_num*batchsize:batch_num*batchsize+batch_ind, :] = pose[:batch_ind,:]
pbar.close()
return PredicteData,nframes,nx,ny
def GetPoseF(cfg, dlc_cfg, sess, inputs, outputs, cap, nframes, batchsize):
''' Batchwise prediction of pose '''
PredicteData = np.zeros((nframes, 3 * len(dlc_cfg['all_joints_names'])))
batch_ind = 0 # keeps track of which image within a batch should be written to
batch_num = 0 # keeps track of which batch you are at
ny, nx = int(cap.get(4)), int(cap.get(3))
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!')
frames = np.empty((batchsize, ny, nx, 3),
dtype='ubyte') # this keeps all frames in a batch
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']:
frames[batch_ind] = img_as_ubyte(frame[cfg['y1']:cfg['y2'],
cfg['x1']:cfg['x2']])
else:
frames[batch_ind] = img_as_ubyte(frame)
if batch_ind == batchsize - 1:
pose, scmap = predict.getposeNP(frames, dlc_cfg, sess, inputs,
outputs)
PredicteData[batch_num * batchsize:(batch_num + 1) *
batchsize, :] = pose
scmap_total.append(scmap)
batch_ind = 0
batch_num += 1
else:
batch_ind += 1
else:
nframes = counter
print("Detected frames: ", nframes)
if batch_ind > 0:
pose, scmap = predict.getposeNP(
frames, dlc_cfg, sess, inputs, outputs
) #process the whole batch (some frames might be from previous batch!)
PredicteData[batch_num * batchsize:batch_num * batchsize +
batch_ind, :] = pose[:batch_ind, :]
scmap_total.append(scmap)
break
counter += 1
pbar.close()
return PredicteData, nframes, scmap_total
