Create dlc_utils.py

dlc_utils.py

@@ -0,0 +1,31 @@
+import deeplabcut
+from tkinter import W
+import gradio as gr
+import numpy as np
+
+
+##########################################
+def predict_dlc(list_np_crops,
+                kpts_likelihood_th,
+                DLCmodel,
+                dlc_proc):
+    
+    # run dlc thru list of crops
+    dlc_live = DLCLive(DLCmodel, 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