Update detection_utils.py

detection_utils.py

@@ -4,7 +4,6 @@ import gradio as gr
 from matplotlib import cm
 import torch
 import torchvision
-from dlclive import DLCLive, Processor
 import matplotlib
 from PIL import Image, ImageColor, ImageFont, ImageDraw 
 import numpy as np
@@ -87,30 +86,4 @@ def crop_animal_detections(img_in,
                 # add to list
                 list_np_animal_crops.append(crop_np)
 
-    return list_np_animal_crops
-
-##########################################
-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
+    return list_np_animal_crops