mwmathis commited on
Commit
8f06817
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1 Parent(s): 63056c5

Update detection_utils.py

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  1. detection_utils.py +1 -28
detection_utils.py CHANGED
@@ -4,7 +4,6 @@ import gradio as gr
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  from matplotlib import cm
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  import torch
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  import torchvision
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- from dlclive import DLCLive, Processor
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  import matplotlib
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  from PIL import Image, ImageColor, ImageFont, ImageDraw
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  import numpy as np
@@ -87,30 +86,4 @@ def crop_animal_detections(img_in,
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  # add to list
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  list_np_animal_crops.append(crop_np)
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- return list_np_animal_crops
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-
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- ##########################################
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- def predict_dlc(list_np_crops,
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- kpts_likelihood_th,
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- DLCmodel,
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- dlc_proc):
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-
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- # run dlc thru list of crops
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- dlc_live = DLCLive(DLCmodel, processor=dlc_proc)
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- dlc_live.init_inference(list_np_crops[0])
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-
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- list_kpts_per_crop = []
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- all_kypts = []
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- np_aux = np.empty((1,3)) # can I avoid hardcoding here?
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- for crop in list_np_crops:
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- # scale crop here?
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- keypts_xyp = dlc_live.get_pose(crop) # third column is llk!
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- # set kpts below threhsold to nan
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-
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- #pdb.set_trace()
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- keypts_xyp[keypts_xyp[:,-1] < kpts_likelihood_th,:] = np_aux.fill(np.nan)
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- # add kpts of this crop to list
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- list_kpts_per_crop.append(keypts_xyp)
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- all_kypts.append(keypts_xyp)
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-
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- return list_kpts_per_crop
 
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  from matplotlib import cm
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  import torch
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  import torchvision
 
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  import matplotlib
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  from PIL import Image, ImageColor, ImageFont, ImageDraw
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  import numpy as np
 
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  # add to list
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  list_np_animal_crops.append(crop_np)
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+ return list_np_animal_crops