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import deeplabcut
from tkinter import W
import gradio as gr
import numpy as np
from dlclive import DLCLive, Processor
##########################################
def predict_dlc(list_np_crops,
kpts_likelihood_th,
dlc_model_folder,
dlc_proc):
# run dlc thru list of crops
dlc_live = DLCLive(dlc_model_folder, processor=dlc_proc)
dlc_live.init_inference(list_np_crops[0])
list_kpts_per_crop = []
all_kypts = []
np_aux = np.empty((1,3)) # can I avoid hardcoding here?
for crop in list_np_crops:
# scale crop here?
keypts_xyp = dlc_live.get_pose(crop) # third column is llk!
# set kpts below threhsold to nan
#pdb.set_trace()
keypts_xyp[keypts_xyp[:,-1] < kpts_likelihood_th,:] = np_aux.fill(np.nan)
# add kpts of this crop to list
list_kpts_per_crop.append(keypts_xyp)
all_kypts.append(keypts_xyp)
return list_kpts_per_crop |