def create_video_with_all_detections(
config, videos, DLCscorername, displayedbodyparts="all", destfolder=None
):
"""
Create a video labeled with all the detections stored in a '*_full.pickle' file.
Parameters
----------
config : str
Absolute path to the config.yaml file
videos : list of str
A list of strings containing the full paths to videos for analysis or a path to the directory,
where all the videos with same extension are stored.
DLCscorername: str
Name of network. E.g. 'DLC_resnet50_project_userMar23shuffle1_50000
displayedbodyparts: list of strings, optional
This selects the body parts that are plotted in the video. Either ``all``, then all body parts
from config.yaml are used orr a list of strings that are a subset of the full list.
E.g. ['hand','Joystick'] for the demo Reaching-Mackenzie-2018-08-30/config.yaml to select only these two body parts.
destfolder: string, optional
Specifies the destination folder that was used for storing analysis data (default is the path of the video).
"""
from deeplabcut.pose_estimation_tensorflow.lib.inferenceutils import (
convertdetectiondict2listoflist,
)
import pickle, re
cfg = auxiliaryfunctions.read_config(config)
for video in videos:
videofolder = os.path.splitext(video)[0]
if destfolder is None:
outputname = "{}_full.mp4".format(videofolder + DLCscorername)
full_pickle = os.path.join(videofolder + DLCscorername + "_full.pickle")
else:
auxiliaryfunctions.attempttomakefolder(destfolder)
outputname = os.path.join(
destfolder, str(Path(video).stem) + DLCscorername + "_full.mp4"
)
full_pickle = os.path.join(
destfolder, str(Path(video).stem) + DLCscorername + "_full.pickle"
)
if not (os.path.isfile(outputname)):
print("Creating labeled video for ", str(Path(video).stem))
with open(full_pickle, "rb") as file:
data = pickle.load(file)
header = data.pop("metadata")
all_jointnames = header["all_joints_names"]
if displayedbodyparts == "all":
numjoints = len(all_jointnames)
bpts = range(numjoints)
else: # select only "displayedbodyparts"
bpts = []
for bptindex, bp in enumerate(all_jointnames):
if bp in displayedbodyparts:
bpts.append(bptindex)
numjoints = len(bpts)
frame_names = list(data)
frames = [int(re.findall(r"\d+", name)[0]) for name in frame_names]
colorclass = plt.cm.ScalarMappable(cmap=cfg["colormap"])
C = colorclass.to_rgba(np.linspace(0, 1, numjoints))
colors = (C[:, :3] * 255).astype(np.uint8)
pcutoff = cfg["pcutoff"]
dotsize = cfg["dotsize"]
clip = vp(fname=video, sname=outputname, codec="mp4v")
ny, nx = clip.height(), clip.width()
for n in trange(clip.nframes):
frame = clip.load_frame()
try:
ind = frames.index(n)
dets = convertdetectiondict2listoflist(data[frame_names[ind]], bpts)
for i, det in enumerate(dets):
color = colors[i]
for x, y, p, _ in det:
if p > pcutoff:
rr, cc = circle(y, x, dotsize, shape=(ny, nx))
frame[rr, cc] = color
except ValueError: # No data stored for that particular frame
print(n, "no data")
pass
try:
clip.save_frame(frame)
except:
print(n, "frame writing error.")
pass
clip.close()
else:
print("Detections already plotted, ", outputname)
header = data.pop("metadata")
all_jointnames = header["all_joints_names"]
numjoints = len(all_jointnames)
bpts = range(numjoints)
frame_names = list(data)
frames = [int(re.findall(r"\d+", name)[0]) for name in frame_names]
#n = 0
#ind = frames.index(n)
df = pd.DataFrame(index=frames, columns=col)
for n in tqdm(frames):
#for n in range(2):
dets = convertdetectiondict2listoflist(data[frame_names[n]], bpts)
# print(n)
for m in range(11):
for p in range(3):
# print(n, m - 11, p)
if m < 7:
try:
df.iloc[n, 3 * m + p] = dets[m][0][p]
df.iloc[n, (3 * m + p) + 33] = dets[m][1][p]
# print('succes')
except:
# print('fail')
pass
else:
try:
df.iloc[n, 3 * m + p] = dets[m][0][p]