initial commit

.gitignore

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+Weights/

Code/Inference.py

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+""" 
+inference on single image for MaskRCNN (FROM DETECTRON) + DLC 
+
+two step, pretrained MaskRCNN, then DLC
+
+"""
+import cv2 
+import torch
+
+import sys
+sys.path.append("Repositories/DeepLabCut-live")
+
+import deeplabcut as dlc
+from dlclive import DLCLive, Processor
+
+import matplotlib.pyplot as plt
+import numpy as np
+from tqdm import tqdm
+import os
+import shutil
+
+import torchvision
+from torchvision.transforms import transforms as transforms
+
+import pickle
+
+import detectron2
+
+# import some common detectron2 utilities
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog, DatasetCatalog
+
+import cv2
+
+
+
+COCO_INSTANCE_CATEGORY_NAMES = [
+    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
+    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
+    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
+    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
+    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
+    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+]
+
+
+def Process_Crop(Crop, CropSize):
+    """Crop image and pad, if too big, will scale down """
+    # import ipdb;ipdb.set_trace()
+    if Crop.shape[0] > CropSize[0] or Crop.shape[1] > CropSize[1]: #Crop is bigger, scale down
+        ScaleProportion = min(CropSize[0]/Crop.shape[0],CropSize[1]/Crop.shape[1])
+        
+        width_scaled = int(Crop.shape[1] * ScaleProportion)
+        height_scaled = int(Crop.shape[0] * ScaleProportion)
+        Crop = cv2.resize(Crop, (width_scaled,height_scaled), interpolation=cv2.INTER_LINEAR)  # resize image
+
+        # Points2D = {k:[v[0]*ScaleProportion,v[1]*ScaleProportion] for k,v in Points2D.items()}
+    else:
+        ScaleProportion = 1
+        
+    if Crop.shape[0] %2 ==0:
+        #Shape is even number
+        YPadTop = int((CropSize[1] - Crop.shape[0])/2)
+        YPadBot = int((CropSize[1] - Crop.shape[0])/2)
+    else:
+        YPadTop = int( ((CropSize[1] - Crop.shape[0])/2)-0.5)
+        YPadBot = int(((CropSize[1] - Crop.shape[0])/2)+0.5)
+    ##Padding:
+    if Crop.shape[1] %2 ==0:
+        #Shape is even number
+        XPadLeft = int((CropSize[0] - Crop.shape[1])/2)
+        XPadRight= int((CropSize[0] - Crop.shape[1])/2)
+    else:
+        XPadLeft =  int(((CropSize[0] - Crop.shape[1])/2)-0.5)
+        XPadRight= int(((CropSize[0] - Crop.shape[1])/2)+0.5)
+
+
+
+    OutImage = cv2.copyMakeBorder(Crop, YPadTop,YPadBot,XPadLeft,XPadRight,cv2.BORDER_CONSTANT,value=[0,0,0])
+    
+    return OutImage,ScaleProportion, YPadTop,XPadLeft
+
+
+
+def DLCInference(Crop,dlc_liveObj,CropSize):
+    """Inference for DLC"""
+
+    ###Scale crop if image bigger than cropsize
+    # import ipdb;ipdb.set_trace()
+    if Crop.shape[0] > CropSize[0] or Crop.shape[1] > CropSize[1]: #Image bigger than crop size, scale down
+        ScaleRatio = min([CropSize[0]/Crop.shape[0], CropSize[1]/Crop.shape[1]])
+        ScaleWidth = round(Crop.shape[1] * ScaleRatio)
+        ScaleHeight = round(Crop.shape[0]*ScaleRatio)
+        resizedCrop = cv2.resize(Crop, (ScaleWidth,ScaleHeight), interpolation=cv2.INTER_LINEAR)  # resize image
+        ScaleUpRatio = 1/ScaleRatio #ratio to scale keypoints back up to original
+        # import ipdb;ipdb.set_trace()
+    else:
+        resizedCrop = Crop
+        ScaleUpRatio = 1
+    # cv2.imwrite(filename="tempresize.jpg", img=resizedCrop)
+    # cv2.imwrite(filename="temp.jpg", img=Crop)
+    if dlc_liveObj.sess == None: #if first time, init
+        DLCPredict2D = dlc_liveObj.init_inference(resizedCrop)
+
+    DLCPredict2D= dlc_liveObj.get_pose(resizedCrop)
+    DLCPredict2D[:,0] = DLCPredict2D[:,0]*ScaleUpRatio
+    DLCPredict2D[:,1] = DLCPredict2D[:,1]*ScaleUpRatio
+
+    return DLCPredict2D
+
+
+def VisualizeAll(frame, box, DLCPredict2D,MeanConfidence,ScaleBBox, imsize):
+    """Visualize all stuff"""
+    # colourList = [(255,255,0),(255,0 ,255),(128,0,128),(203,192,255),(0, 255, 255),(255, 0 , 0 ),(63,133,205),(0,255,0),(0,0,255)]
+    colourList = [(0,255,255),(255,0 ,255),(128,0,128),(255,192,203),(255, 255, 0),(0, 0 , 255 ),(205,133,63),(0,255,0),(255,0,0)]
+
+    ##Order: Lshoulder, Rshoulder, topKeel,botKeel,Tail,Beak,Nose,Leye,Reye
+    ##Points:
+    PlotPoints = []
+    for x,point in enumerate(DLCPredict2D):
+        roundPoint = [round(point[0]+box[0]),round(point[1]+box[1])]
+        cv2.circle(frame,roundPoint,1,colourList[x], 5)
+        PlotPoints.append(roundPoint)
+
+
+    ##change box to XYWH to scale down
+    # bbox = [box[0],box[1],box[2]-box[0],box[3]-box[1]]
+    # ScaleWidth = (bbox[2]/ScaleBBox)/2
+    # ScaleHeight = (bbox[3]/ScaleBBox)/2
+    # ###based on ScaleBBox, scale back down bounding box for plotting
+    # x1 = round(bbox[0]+ScaleWidth) if round(bbox[0]+ScaleWidth)>0 else 0
+    # y1 = round(bbox[1]+ScaleHeight)if round(bbox[1]+ScaleHeight)>0 else 0
+    # x2 = round(bbox[0]+bbox[2]-ScaleWidth) if round(bbox[0]+bbox[2]-ScaleWidth) < imsize[0] else imsize[0]
+    # y2 = round(bbox[1]+bbox[3]-ScaleHeight)if round(bbox[1]+bbox[3]-ScaleHeight) < imsize[1] else imsize[1]
+    # box = [x1,y1,x2,y2]
+    cv2.rectangle(frame,(round(box[0]),round(box[1])),(round(box[2]),round(box[3])),[0,0,255],3)
+
+    #plot mean confidence
+    # import ipdb;ipdb.set_trace()
+    # font = cv2.FONT_HERSHEY_SIMPLEX
+    # cv2.putText(frame,str(round(MeanConfidence,3)),(round(box[0]),round(box[1])),font,2,[255,0,0],2)
+
+    return frame, PlotPoints
+
+
+def Inference(frame,predictor,dlc_liveObj,ScaleBBox=1,Dilate=5,DLCThreshold=0.3):
+    """Loop through video for SAM, save framewise info"""
+
+    InferFrame = frame.copy()
+    outputs = predictor(InferFrame)["instances"].to("cpu")
+    CropSize = (320,320)
+
+    # import ipdb;ipdb.set_trace()
+    imsize = [frame.shape[1],frame.shape[0]]
+
+    BirdIndex = np.where(outputs.pred_classes.numpy() == 14)[0] #14 is ID for bird
+    BirdBBox = outputs.pred_boxes[BirdIndex].tensor.numpy()
+    # import ipdb;ipdb.set_trace()
+    BirdMasks = (outputs.pred_masks>0.95).numpy()[BirdIndex]
+
+    for x in range(BirdBBox.shape[0]):
+        # import ipdb;ipdb.set_trace()
+
+        bbox = list(BirdBBox[x])
+        Mask = BirdMasks[x]>0
+        Mask = np.array(Mask,dtype=np.uint8)
+        # show_anns(frame, Mask)
+
+        if Dilate > 0:
+            DilateKernel = np.ones((Dilate,Dilate),np.uint8)
+            Mask = cv2.dilate(Mask,DilateKernel,iterations = 3) 
+
+        # import ipdb;ipdb.set_trace()
+        Mask = np.array(Mask,dtype=np.uint8)
+        Mask = Mask.reshape(imsize[1],imsize[0],1)
+        Crop = cv2.bitwise_and(InferFrame, InferFrame, mask=Mask)
+
+        # cv2.imwrite(filename="temp.jpg", img = Crop)
+
+        ##change box to XYWH to scale up
+        bbox = [bbox[0],bbox[1],bbox[2]-bbox[0],bbox[3]-bbox[1]]
+        ScaleWidth = ((ScaleBBox * bbox[2])/2)-(bbox[2]/2)
+        ScaleHeight = ((ScaleBBox * bbox[3])/2)-(bbox[3]/2)
+        # import ipdb;ipdb.set_trace()
+        # BirdCrop = frame[round(bbox[1]):round(bbox[3]),round(bbox[0]):round(bbox[2])] #bbox is XYWH
+
+        x1 = round(bbox[0]-ScaleWidth) if round(bbox[0]-ScaleWidth)>0 else 0
+        y1 = round(bbox[1]-ScaleHeight)if round(bbox[1]-ScaleHeight)>0 else 0
+        x2 = round(bbox[0]+bbox[2]+ScaleWidth) if round(bbox[0]+bbox[2]+ScaleWidth) < imsize[0] else imsize[0]
+        y2 = round(bbox[1]+bbox[3]+ScaleHeight)if round(bbox[1]+bbox[3]+ScaleHeight) < imsize[1] else imsize[1]
+        bbox = [x1,y1,x2,y2]
+        BirdCrop = Crop[y1:y2,x1:x2] #bbox is XYWH
+
+        DLCPredict2D= DLCInference(BirdCrop,dlc_liveObj,CropSize)
+        MeanConfidence = DLCPredict2D[:,2].mean()
+
+        if MeanConfidence > DLCThreshold: #if mean keypoint confidence is higher than this threshold, consider bird
+            bbox.append(MeanConfidence)
+            frame, PlotPoints = VisualizeAll(frame, bbox, DLCPredict2D,MeanConfidence,ScaleBBox,imsize)
+
+    if BirdBBox.shape[0] == 0:
+        DLCPredict2D= DLCInference(InferFrame,dlc_liveObj,CropSize)
+        MeanConfidence = DLCPredict2D[:,2].mean()
+        bbox = [0,0,0,0]
+        if MeanConfidence > DLCThreshold: #if mean keypoint confidence is higher than this threshold, consider bird
+            frame, PlotPoints = VisualizeAll(frame, bbox, DLCPredict2D,MeanConfidence,ScaleBBox,imsize)
+
+    return frame
+
+
+
+
+if __name__ == "__main__":
+    # VidPath = "/media/alexchan/Extreme SSD/WorkDir/Pigeon3DTrack/ManualClickTrials/Videos/Cam1_C0008.MP4_Trimmed.mp4"
+    VidPath = "/media/alexchan/Extreme SSD/SampleDatasets/PigeonsEverywhere/2022-12-01_Pilot_bottom_control.mp4"
+
+    OutDir = "/media/alexchan/Extreme SSD/WorkDir/Pigeon3DTrack/OutdoorTracking/Final2D"
+
+    ExportModelPath = "/media/alexchan/Extreme SSD/WorkDir/Pigeon3DTrack/Weights/DLC_Weights/N6000_DLC_Mask/exported-models/DLC_DLC_Segmented_resnet_50_iteration-0_shuffle-1"
+    # ExportModelPath = "/media/alexchan/Extreme SSD/WorkDir/Pigeon3DTrack/Weights/DLC_Weights/DLC_PigeonSuperModel_imgaug_efficientnet-b0_iteration-0_shuffle-2"
+
+    if not os.path.isdir(ExportModelPath):
+        #need to export model first
+        # DLC_Config = "/media/alexchan/Extreme SSD/SampleDatasets/ImageTrainingData/N5000/DLC_Seg_Aug/config.yaml"
+
+        dlc.export_model(DLC_Config)
+    else:
+        print("model already exported!")
+
+    CropSize = (320,320)
+    # WeightPath = "/home/alexchan/Documents/Pigeon3DTrack/Data/YOLO_Weights/yolov8m.pt"
+    # VidPath= "/media/alexchan/Extreme SSD/PigeonOutdoors/28032023/Cam1_C0004.MP4"
+    # import ipdb;ipdb.set_trace()
+    # YOLOModel = YOLO(YOLOPath)
+    device = "cuda"
+    # device = "cpu"
+
+    ###Detectron:
+    cfg = get_cfg()
+    # add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
+    cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml"))
+    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7  # set threshold for this model
+    # Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
+    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml")
+    predictor = DefaultPredictor(cfg)
+
+
+    ##DLC:
+    dlc_proc = Processor()
+    dlc_liveObj = DLCLive(ExportModelPath, processor=dlc_proc)
+    
+    RunInference(predictor,dlc_liveObj, device,OutDir,
+                 VidPath,CropSize,startFrame=0,
+                 TotalFrames =900, ScaleBBox=1.3,
+                 DLCThreshold = 0, Dilate=5)
+    
+
+
+    ###DLC tests
+    # DLC_Config = "/media/alexchan/Extreme SSD/SampleDatasets/ImageTrainingData/N5000/DLC/config.yaml"
+    # Video = "/media/alexchan/Extreme SSD/SampleDatasets/ImageTrainingData/N5000/DLC/videos/Video1.mp4"
+    # dlc.analyze_videos(DLC_Config,[Video])
+    # dlc.export_model(DLC_Config)
+
+    # import pandas as pd
+    # pd.read_hdf("/media/alexchan/Extreme SSD/SampleDatasets/ImageTrainingData/N5000/DLC/videos/Video1DLC_resnet50_DLC20230401-173058shuffle1_3000.h5")

Repositories/DeepLabCut-live/.gitignore

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+# DeepLabCut-live
+
+# Data related to benchmark!
+benchmarking/Data*
+benchmarking/results*
+
+*test*
+**DS_Store*
+*vscode*
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

Repositories/DeepLabCut-live/CITATION.cff

@@ -0,0 +1,55 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: >-
+  Real-time, low-latency closed-loop feedback using
+  markerless posture tracking
+message: >-
+  If you utilize our tool, please [cite Kane et al,
+  eLife
+  2020](https://elifesciences.org/articles/61909).
+  The preprint is available here:
+  https://www.biorxiv.org/content/10.1101/2020.08.04.236422v2
+type: article
+authors:
+  - given-names: Gary
+    name-particle: A
+    family-names: Kane
+    affiliation: >-
+      The Rowland Institute at Harvard, Harvard
+      University, Cambridge, United States
+  - given-names: Gonçalo
+    family-names: Lopes
+    affiliation: 'NeuroGEARS Ltd, London, United Kingdom'
+  - given-names: Jonny
+    name-particle: L
+    family-names: Saunders
+    affiliation: >-
+      Institute of Neuroscience, Department of
+      Psychology, University of Oregon, Eugene,
+      United States
+  - given-names: Alexander
+    family-names: Mathis
+    affiliation: >-
+      The Rowland Institute at Harvard, Harvard
+      University, Cambridge, United States; Center
+      for Neuroprosthetics, Center for Intelligent
+      Systems, & Brain Mind Institute, School of Life
+      Sciences, Swiss Federal Institute of Technology
+      (EPFL), Lausanne, Switzerland
+  - given-names: Mackenzie
+    name-particle: W
+    family-names: Mathis
+    affiliation: >-
+      The Rowland Institute at Harvard, Harvard
+      University, Cambridge, United States; Center
+      for Neuroprosthetics, Center for Intelligent
+      Systems, & Brain Mind Institute, School of Life
+      Sciences, Swiss Federal Institute of Technology
+      (EPFL), Lausanne, Switzerland
+    email: mackenzie.mathis@epfl.ch
+date-released: 2020-08-05
+doi: "10.7554/eLife.61909"
+license: "AGPL-3.0-or-later"
+version:  "1.0.3"

Repositories/DeepLabCut-live/LICENSE

@@ -0,0 +1,666 @@
+Copyright 2020-2022 by Mackenzie Mathis, Gary Kane, Alexander Mathis and contributors. All rights reserved.
+This software may not be used to harm any person deliberately.
+
+This project and all its files are licensed under GNU AGPLv3 or later version.
+
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
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+
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+to take away your freedom to share and change the works.  By contrast,
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+
+  When we speak of free software, we are referring to freedom, not
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+
+  Developers that use our General Public Licenses protect your rights
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+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU Affero General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
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+  "The Program" refers to any copyrightable work licensed under this
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+For more information on this, and how to apply and follow the GNU AGPL, see
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Repositories/DeepLabCut-live/MANIFEST.in

@@ -0,0 +1 @@
+include dlclive/check_install/*

Repositories/DeepLabCut-live/README.md

@@ -0,0 +1,153 @@
+# DeepLabCut-live! SDK<img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1606082050387-M8M1CFI5DFUZCBAAUI0W/ke17ZwdGBToddI8pDm48kLuMKy7Ws6mFofiFehYynfdZw-zPPgdn4jUwVcJE1ZvWQUxwkmyExglNqGp0IvTJZUJFbgE-7XRK3dMEBRBhUpzp2tFVMcEgqZM8QO7VXXQogrsLnYKC4n4YnYuHC1HMRWygQlqMNAoTF9HaycikLeg/DLClive.png?format=750w" width="350" title="DLC-live" alt="DLC LIVE!" align="right" vspace = "50">
+
+<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
+![PyPI - Python Version](https://img.shields.io/pypi/v/deeplabcut-live)
+[![Downloads](https://pepy.tech/badge/deeplabcut-live)](https://pepy.tech/project/deeplabcut-live)
+[![Downloads](https://pepy.tech/badge/deeplabcut-live/month)](https://pepy.tech/project/deeplabcut-live)
+![Python package](https://github.com/DeepLabCut/DeepLabCut-live/workflows/Python%20package/badge.svg)
+[![GitHub stars](https://img.shields.io/github/stars/DeepLabCut/DeepLabCut-live.svg?style=social&label=Star)](https://github.com/DeepLabCut/DeepLabCut-live)
+[![GitHub forks](https://img.shields.io/github/forks/DeepLabCut/DeepLabCut-live.svg?style=social&label=Fork)](https://github.com/DeepLabCut/DeepLabCut-live)
+[![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&amp;url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fdeeplabcut.json&amp;query=%24.topic_list.tags.0.topic_count&amp;colorB=brightgreen&amp;&amp;suffix=%20topics&amp;logo=data:image/png;base64,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)](https://forum.image.sc/tags/deeplabcut)
+[![Gitter](https://badges.gitter.im/DeepLabCut/community.svg)](https://gitter.im/DeepLabCut/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+[![Twitter Follow](https://img.shields.io/twitter/follow/DeepLabCut.svg?label=DeepLabCut&style=social)](https://twitter.com/DeepLabCut)
+
+This package contains a [DeepLabCut](http://www.mousemotorlab.org/deeplabcut) inference pipeline for real-time applications that has minimal (software) dependencies. Thus, it is as easy to install as possible (in particular, on atypical systems like [NVIDIA Jetson boards](https://developer.nvidia.com/buy-jetson)).
+
+**Performance:** If you would like to see estimates on how your model should perform given different video sizes, neural network type, and hardware, please see: https://deeplabcut.github.io/DLC-inferencespeed-benchmark/
+
+If you have different hardware, please consider submitting your results too! https://github.com/DeepLabCut/DLC-inferencespeed-benchmark
+
+**What this SDK provides:** This package provides a `DLCLive` class which enables pose estimation online to provide feedback. This object loads and prepares a DeepLabCut network for inference, and will return the predicted pose for single images. 
+
+To perform processing on poses (such as predicting the future pose of an animal given it's current pose, or to trigger external hardware like send TTL pulses to a laser for optogenetic stimulation), this object takes in a `Processor` object. Processor objects must contain two methods: process and save.
+
+- The `process` method takes in a pose, performs some processing, and returns processed pose.
+- The `save` method saves any valuable data created by or used by the processor
+
+For more details and examples, see documentation [here](dlclive/processor/README.md).
+
+###### 🔥🔥🔥🔥🔥 Note :: alone, this object does not record video or capture images from a camera. This must be done separately, i.e. see our [DeepLabCut-live GUI](https://github.com/gkane26/DeepLabCut-live-GUI).🔥🔥🔥
+
+### News! 
+- March 2022: DeepLabCut-Live! 1.0.2 supports poetry installation `poetry install deeplabcut-live`, thanks to PR #60.
+- March 2021: DeepLabCut-Live! [**version 1.0** is released](https://pypi.org/project/deeplabcut-live/), with support for tensorflow 1 and tensorflow 2!
+- Feb 2021: DeepLabCut-Live! was featured in **Nature Methods**: ["Real-time behavioral analysis"](https://www.nature.com/articles/s41592-021-01072-z)
+- Jan 2021: full **eLife** paper is published: ["Real-time, low-latency closed-loop feedback using markerless posture tracking"](https://elifesciences.org/articles/61909)
+- Dec 2020: we talked to **RTS Suisse Radio** about DLC-Live!: ["Capture animal movements in real time"](https://www.rts.ch/play/radio/cqfd/audio/capturer-les-mouvements-des-animaux-en-temps-reel?id=11782529)
+
+
+### Installation:
+
+Please see our instruction manual to install on a [Windows or Linux machine](docs/install_desktop.md) or on a [NVIDIA Jetson Development Board](docs/install_jetson.md). Note, this code works with tensorflow (TF) 1 or TF 2 models, but TF requires that whatever version you exported your model with, you must import with the same version (i.e., export with TF1.13, then use TF1.13 with DlC-Live; export with TF2.3, then use TF2.3 with DLC-live).
+
+- available on pypi as: `pip install deeplabcut-live`
+
+Note, you can then test your installation by running:
+
+`dlc-live-test`
+
+If installed properly, this script will i) create a temporary folder ii) download the full_dog model from the [DeepLabCut Model Zoo](http://www.mousemotorlab.org/dlc-modelzoo), iii) download a short video clip of a dog, and iv) run inference while displaying keypoints. v) remove the temporary folder.
+
+<img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1606081086014-TG9GWH63ZGGOO7K779G3/ke17ZwdGBToddI8pDm48kHiSoSToKfKUI9t99vKErWoUqsxRUqqbr1mOJYKfIPR7LoDQ9mXPOjoJoqy81S2I8N_N4V1vUb5AoIIIbLZhVYxCRW4BPu10St3TBAUQYVKcOoIGycwr1shdgJWzLuxyzjLbSRGBFFxjYMBr42yCvRK5HHsLZWtMlAHzDU294nCd/dlclivetest.png?format=1000w" width="650" title="DLC-live-test" alt="DLC LIVE TEST" align="center" vspace = "50">
+
+### Quick Start: instructions for use:
+
+1. Initialize `Processor` (if desired)
+2. Initialize the `DLCLive` object
+3. Perform pose estimation!
+
+```python
+from dlclive import DLCLive, Processor
+dlc_proc = Processor()
+dlc_live = DLCLive(<path to exported model directory>, processor=dlc_proc)
+dlc_live.init_inference(<your image>)
+dlc_live.get_pose(<your image>)
+```
+
+`DLCLive` **parameters:**
+
+  - `path` = string; full path to the exported DLC model directory
+  - `model_type` = string; the type of model to use for inference. Types include:
+      - `base` = the base DeepLabCut model
+      - `tensorrt` = apply [tensor-rt](https://developer.nvidia.com/tensorrt) optimizations to model
+      - `tflite` = use [tensorflow lite](https://www.tensorflow.org/lite) inference (in progress...)
+  - `cropping` = list of int, optional; cropping parameters in pixel number: [x1, x2, y1, y2]
+  - `dynamic` = tuple, optional; defines parameters for dynamic cropping of images
+      - `index 0` = use dynamic cropping, bool
+      - `index 1` = detection threshold, float
+      - `index 2` = margin (in pixels) around identified points, int
+  - `resize` = float, optional; factor by which to resize image (resize=0.5 downsizes both width and height of image by half). Can be used to downsize large images for faster inference
+  - `processor` = dlc pose processor object, optional
+  - `display` = bool, optional; display processed image with DeepLabCut points? Can be used to troubleshoot cropping and resizing parameters, but is very slow
+
+`DLCLive` **inputs:**
+
+  - `<path to exported model directory>` = path to the folder that has the `.pb` files that you acquire after running `deeplabcut.export_model`
+  - `<your image>` = is a numpy array of each frame
+
+
+### Benchmarking/Analyzing your exported DeepLabCut models
+
+DeepLabCut-live offers some analysis tools that allow users to peform the following operations on videos, from python or from the command line:
+
+1. Test inference speed across a range of image sizes, downsizing images by specifying the `resize` or `pixels` parameter. Using the `pixels` parameter will resize images to the desired number of `pixels`, without changing the aspect ratio. Results will be saved (along with system info) to a pickle file if you specify an output directory.
+##### python
+```python
+dlclive.benchmark_videos('/path/to/exported/model', ['/path/to/video1', '/path/to/video2'], output='/path/to/output', resize=[1.0, 0.75, '0.5'])
+```
+##### command line
+```
+dlc-live-benchmark /path/to/exported/model /path/to/video1 /path/to/video2 -o /path/to/output -r 1.0 0.75 0.5
+```
+
+2. Display keypoints to visually inspect the accuracy of exported models on different image sizes (note, this is slow and only for testing purposes):
+
+##### python
+```python
+dlclive.benchmark_videos('/path/to/exported/model', '/path/to/video', resize=0.5, display=True, pcutoff=0.5, display_radius=4, cmap='bmy')
+```
+##### command line
+```
+dlc-live-benchmark /path/to/exported/model /path/to/video -r 0.5 --display --pcutoff 0.5 --display-radius 4 --cmap bmy
+```
+
+3. Analyze and create a labeled video using the exported model and desired resize parameters. This option functions similar to `deeplabcut.benchmark_videos` and `deeplabcut.create_labeled_video` (note, this is slow and only for testing purposes).
+
+##### python
+```python
+dlclive.benchmark_videos('/path/to/exported/model', '/path/to/video', resize=[1.0, 0.75, 0.5], pcutoff=0.5, display_radius=4, cmap='bmy', save_poses=True, save_video=True)
+```
+##### command line
+```
+dlc-live-benchmark /path/to/exported/model /path/to/video -r 0.5 --pcutoff 0.5 --display-radius 4 --cmap bmy --save-poses --save-video
+```
+
+## License:
+
+This project is licensed under the GNU AGPLv3. Note that the software is provided "as is", without warranty of any kind, express or implied. If you use the code or data, we ask that you please cite us! This software is available for licensing via the EPFL Technology Transfer Office (https://tto.epfl.ch/, info.tto@epfl.ch).
+
+## Community Support, Developers, & Help:
+
+This is an actively developed package and we welcome community development and involvement.
+
+- If you want to contribute to the code, please read our guide [here](https://github.com/DeepLabCut/DeepLabCut/blob/master/CONTRIBUTING.md), which is provided at the main repository of DeepLabCut.
+
+- We are a community partner on the [![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&amp;url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fdeeplabcut.json&amp;query=%24.topic_list.tags.0.topic_count&amp;colorB=brightgreen&amp;&amp;suffix=%20topics&amp;logo=data:image/png;base64,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)](https://forum.image.sc/tags/deeplabcut). Please post help and support questions on the forum with the tag DeepLabCut. Check out their mission statement [Scientific Community Image Forum: A discussion forum for scientific image software](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000340).
+
+- If you encounter a previously unreported bug/code issue, please post here (we encourage you to search issues first): https://github.com/DeepLabCut/DeepLabCut-live/issues
+
+- For quick discussions here: [![Gitter](https://badges.gitter.im/DeepLabCut/community.svg)](https://gitter.im/DeepLabCut/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+### Reference:
+
+If you utilize our tool, please [cite Kane et al, eLife 2020](https://elifesciences.org/articles/61909). The preprint is available here: https://www.biorxiv.org/content/10.1101/2020.08.04.236422v2
+
+```
+@Article{Kane2020dlclive,
+  author    = {Kane, Gary and Lopes, Gonçalo and Sanders, Jonny and Mathis, Alexander and Mathis, Mackenzie},
+  title     = {Real-time, low-latency closed-loop feedback using markerless posture tracking},
+  journal   = {eLife},
+  year      = {2020},
+}
+```
+

Repositories/DeepLabCut-live/benchmarking/run_dlclive_benchmark.py

@@ -0,0 +1,43 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+# Script for running the official benchmark from Kane et al, 2020.
+# Please share your results at https://github.com/DeepLabCut/DLC-inferencespeed-benchmark
+
+import os, pathlib
+import glob
+
+from dlclive import benchmark_videos, download_benchmarking_data
+
+datafolder = os.path.join(
+    pathlib.Path(__file__).parent.absolute(), "Data-DLC-live-benchmark"
+)
+
+if not os.path.isdir(datafolder):  # only download if data doesn't exist!
+    # Downloading data.... this takes a while (see terminal)
+    download_benchmarking_data(datafolder)
+
+n_frames = 10000  # change to 10000 for testing on a GPU!
+pixels = [2500, 10000, 40000, 160000, 320000, 640000]
+
+dog_models = glob.glob(datafolder + "/dog/*[!avi]")
+dog_video = glob.glob(datafolder + "/dog/*.avi")[0]
+mouse_models = glob.glob(datafolder + "/mouse_lick/*[!avi]")
+mouse_video = glob.glob(datafolder + "/mouse_lick/*.avi")[0]
+
+this_dir = os.path.dirname(os.path.realpath(__file__))
+# storing results in /benchmarking/results: (for your PR)
+out_dir = os.path.normpath(this_dir + "/results")
+
+if not os.path.isdir(out_dir):
+    os.mkdir(out_dir)
+
+for m in dog_models:
+    benchmark_videos(m, dog_video, output=out_dir, n_frames=n_frames, pixels=pixels)
+
+for m in mouse_models:
+    benchmark_videos(m, mouse_video, output=out_dir, n_frames=n_frames, pixels=pixels)

Repositories/DeepLabCut-live/dlclive/__init__.py

@@ -0,0 +1,11 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+from dlclive.version import __version__, VERSION
+from dlclive.dlclive import DLCLive
+from dlclive.processor import Processor
+from dlclive.benchmark import benchmark, benchmark_videos, download_benchmarking_data

Repositories/DeepLabCut-live/dlclive/benchmark.py

@@ -0,0 +1,726 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import platform
+import os
+import time
+import sys
+import warnings
+import subprocess
+import typing
+import pickle
+import colorcet as cc
+from PIL import ImageColor
+import ruamel
+
+try:
+    from pip._internal.operations import freeze
+except ImportError:
+    from pip.operations import freeze
+
+from tqdm import tqdm
+import numpy as np
+import tensorflow as tf
+import cv2
+
+from dlclive import DLCLive
+from dlclive import VERSION
+from dlclive import __file__ as dlcfile
+
+from dlclive.utils import decode_fourcc
+
+
+def download_benchmarking_data(
+    target_dir=".",
+    url="http://deeplabcut.rowland.harvard.edu/datasets/dlclivebenchmark.tar.gz",
+):
+    """
+    Downloads a DeepLabCut-Live benchmarking Data (videos & DLC models).
+    """
+    import urllib.request
+    import tarfile
+    from tqdm import tqdm
+
+    def show_progress(count, block_size, total_size):
+        pbar.update(block_size)
+
+    def tarfilenamecutting(tarf):
+        """' auxfun to extract folder path
+        ie. /xyz-trainsetxyshufflez/
+        """
+        for memberid, member in enumerate(tarf.getmembers()):
+            if memberid == 0:
+                parent = str(member.path)
+                l = len(parent) + 1
+            if member.path.startswith(parent):
+                member.path = member.path[l:]
+                yield member
+
+    response = urllib.request.urlopen(url)
+    print(
+        "Downloading the benchmarking data from the DeepLabCut server @Harvard -> Go Crimson!!! {}....".format(
+            url
+        )
+    )
+    total_size = int(response.getheader("Content-Length"))
+    pbar = tqdm(unit="B", total=total_size, position=0)
+    filename, _ = urllib.request.urlretrieve(url, reporthook=show_progress)
+    with tarfile.open(filename, mode="r:gz") as tar:
+        tar.extractall(target_dir, members=tarfilenamecutting(tar))
+
+
+def get_system_info() -> dict:
+    """ Return summary info for system running benchmark
+    Returns
+    -------
+    dict
+        Dictionary containing the following system information:
+        * ``host_name`` (str): name of machine
+        * ``op_sys`` (str): operating system
+        * ``python`` (str): path to python (which conda/virtual environment)
+        * ``device`` (tuple): (device type (``'GPU'`` or ``'CPU'```), device information)
+        * ``freeze`` (list): list of installed packages and versions
+        * ``python_version`` (str): python version
+        * ``git_hash`` (str, None): If installed from git repository, hash of HEAD commit
+        * ``dlclive_version`` (str): dlclive version from :data:`dlclive.VERSION`
+    """
+
+    # get os
+
+    op_sys = platform.platform()
+    host_name = platform.node().replace(" ", "")
+
+    # A string giving the absolute path of the executable binary for the Python interpreter, on systems where this makes sense.
+    if platform.system() == "Windows":
+        host_python = sys.executable.split(os.path.sep)[-2]
+    else:
+        host_python = sys.executable.split(os.path.sep)[-3]
+
+    # try to get git hash if possible
+    dlc_basedir = os.path.dirname(os.path.dirname(dlcfile))
+    git_hash = None
+    try:
+        git_hash = subprocess.check_output(
+            ["git", "rev-parse", "HEAD"], cwd=dlc_basedir
+        )
+        git_hash = git_hash.decode("utf-8").rstrip("\n")
+    except subprocess.CalledProcessError:
+        # not installed from git repo, eg. pypi
+        # fine, pass quietly
+        pass
+
+    # get device info (GPU or CPU)
+    dev = None
+    if tf.test.is_gpu_available():
+        gpu_name = tf.test.gpu_device_name()
+        from tensorflow.python.client import device_lib
+
+        dev_desc = [
+            d.physical_device_desc
+            for d in device_lib.list_local_devices()
+            if d.name == gpu_name
+        ]
+        dev = [d.split(",")[1].split(":")[1].strip() for d in dev_desc]
+        dev_type = "GPU"
+    else:
+        from cpuinfo import get_cpu_info
+
+        dev = [get_cpu_info()["brand"]]
+        dev_type = "CPU"
+
+    return {
+        "host_name": host_name,
+        "op_sys": op_sys,
+        "python": host_python,
+        "device_type": dev_type,
+        "device": dev,
+        # pip freeze to get versions of all packages
+        "freeze": list(freeze.freeze()),
+        "python_version": sys.version,
+        "git_hash": git_hash,
+        "dlclive_version": VERSION,
+    }
+
+
+def benchmark(
+    model_path,
+    video_path,
+    tf_config=None,
+    resize=None,
+    pixels=None,
+    cropping=None,
+    dynamic=(False, 0.5, 10),
+    n_frames=1000,
+    print_rate=False,
+    display=False,
+    pcutoff=0.0,
+    display_radius=3,
+    cmap="bmy",
+    save_poses=False,
+    save_video=False,
+    output=None,
+) -> typing.Tuple[np.ndarray, tuple, bool, dict]:
+    """ Analyze DeepLabCut-live exported model on a video:
+    Calculate inference time,
+    display keypoints, or
+    get poses/create a labeled video
+
+    Parameters
+    ----------
+    model_path : str
+        path to exported DeepLabCut model
+    video_path : str
+        path to video file
+    tf_config : :class:`tensorflow.ConfigProto`
+        tensorflow session configuration
+    resize : int, optional
+        resize factor. Can only use one of resize or pixels. If both are provided, will use pixels. by default None
+    pixels : int, optional
+        downsize image to this number of pixels, maintaining aspect ratio. Can only use one of resize or pixels. If both are provided, will use pixels. by default None
+    cropping : list of int
+        cropping parameters in pixel number: [x1, x2, y1, y2]
+    dynamic: triple containing (state, detectiontreshold, margin)
+        If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold),
+        then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This  window is
+        expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. <detectiontreshold). The
+        current position is utilized for updating the crop window for the next frame (this is why the margin is important and should be set large
+        enough given the movement of the animal)
+    n_frames : int, optional
+        number of frames to run inference on, by default 1000
+    print_rate : bool, optional
+        flat to print inference rate frame by frame, by default False
+    display : bool, optional
+        flag to display keypoints on images. Useful for checking the accuracy of exported models.
+    pcutoff : float, optional
+        likelihood threshold to display keypoints
+    display_radius : int, optional
+        size (radius in pixels) of keypoint to display
+    cmap : str, optional
+        a string indicating the :package:`colorcet` colormap, `options here <https://colorcet.holoviz.org/>`, by default "bmy"
+    save_poses : bool, optional
+        flag to save poses to an hdf5 file. If True, operates similar to :function:`DeepLabCut.benchmark_videos`, by default False
+    save_video : bool, optional
+        flag to save a labeled video. If True, operates similar to :function:`DeepLabCut.create_labeled_video`, by default False
+    output : str, optional
+        path to directory to save pose and/or video file. If not specified, will use the directory of video_path, by default None
+
+    Returns
+    -------
+    :class:`numpy.ndarray`
+        vector of inference times
+    tuple
+        (image width, image height)
+    bool
+        tensorflow inference flag
+    dict
+        metadata for video
+
+    Example
+    -------
+    Return a vector of inference times for 10000 frames:
+    dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000)
+
+    Return a vector of inference times, resizing images to half the width and height for inference
+    dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000, resize=0.5)
+
+    Display keypoints to check the accuracy of an exported model
+    dlclive.benchmark('/my/exported/model', 'my_video.avi', display=True)
+
+    Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video`
+    dlclive.benchmark('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True)
+    """
+
+    ### load video
+
+    cap = cv2.VideoCapture(video_path)
+    ret, frame = cap.read()
+    n_frames = (
+        n_frames
+        if (n_frames > 0) and (n_frames < cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1)
+        else (cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1)
+    )
+    n_frames = int(n_frames)
+    im_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    ### get resize factor
+
+    if pixels is not None:
+        resize = np.sqrt(pixels / (im_size[0] * im_size[1]))
+    if resize is not None:
+        im_size = (int(im_size[0] * resize), int(im_size[1] * resize))
+
+    ### create video writer
+
+    if save_video:
+        colors = None
+        out_dir = (
+            output
+            if output is not None
+            else os.path.dirname(os.path.realpath(video_path))
+        )
+        out_vid_base = os.path.basename(video_path)
+        out_vid_file = os.path.normpath(
+            f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_LABELED.avi"
+        )
+        fourcc = cv2.VideoWriter_fourcc(*"DIVX")
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        vwriter = cv2.VideoWriter(out_vid_file, fourcc, fps, im_size)
+
+    ### check for pandas installation if using save_poses flag
+
+    if save_poses:
+        try:
+            import pandas as pd
+
+            use_pandas = True
+        except:
+            use_pandas = False
+            warnings.warn(
+                "Could not find installation of pandas; saving poses as a numpy array with the dimensions (n_frames, n_keypoints, [x, y, likelihood])."
+            )
+
+    ### initialize DLCLive and perform inference
+
+    inf_times = np.zeros(n_frames)
+    poses = []
+
+    live = DLCLive(
+        model_path,
+        tf_config=tf_config,
+        resize=resize,
+        cropping=cropping,
+        dynamic=dynamic,
+        display=display,
+        pcutoff=pcutoff,
+        display_radius=display_radius,
+        display_cmap=cmap,
+    )
+
+    poses.append(live.init_inference(frame))
+    TFGPUinference = True if len(live.outputs) == 1 else False
+
+    iterator = range(n_frames) if (print_rate) or (display) else tqdm(range(n_frames))
+    for i in iterator:
+
+        ret, frame = cap.read()
+
+        if not ret:
+            warnings.warn(
+                "Did not complete {:d} frames. There probably were not enough frames in the video {}.".format(
+                    n_frames, video_path
+                )
+            )
+            break
+
+        start_pose = time.time()
+        poses.append(live.get_pose(frame))
+        inf_times[i] = time.time() - start_pose
+
+        if save_video:
+
+            if colors is None:
+                all_colors = getattr(cc, cmap)
+                colors = [
+                    ImageColor.getcolor(c, "RGB")[::-1]
+                    for c in all_colors[:: int(len(all_colors) / poses[-1].shape[0])]
+                ]
+
+            this_pose = poses[-1]
+            for j in range(this_pose.shape[0]):
+                if this_pose[j, 2] > pcutoff:
+                    x = int(this_pose[j, 0])
+                    y = int(this_pose[j, 1])
+                    frame = cv2.circle(
+                        frame, (x, y), display_radius, colors[j], thickness=-1
+                    )
+
+            if resize is not None:
+                frame = cv2.resize(frame, im_size)
+            vwriter.write(frame)
+
+        if print_rate:
+            print("pose rate = {:d}".format(int(1 / inf_times[i])))
+
+    if print_rate:
+        print("mean pose rate = {:d}".format(int(np.mean(1 / inf_times))))
+
+    ### gather video and test parameterization
+
+    # dont want to fail here so gracefully failing on exception --
+    # eg. some packages of cv2 don't have CAP_PROP_CODEC_PIXEL_FORMAT
+    try:
+        fourcc = decode_fourcc(cap.get(cv2.CAP_PROP_FOURCC))
+    except:
+        fourcc = ""
+
+    try:
+        fps = round(cap.get(cv2.CAP_PROP_FPS))
+    except:
+        fps = None
+
+    try:
+        pix_fmt = decode_fourcc(cap.get(cv2.CAP_PROP_CODEC_PIXEL_FORMAT))
+    except:
+        pix_fmt = ""
+
+    try:
+        frame_count = round(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    except:
+        frame_count = None
+
+    try:
+        orig_im_size = (
+            round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+            round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
+        )
+    except:
+        orig_im_size = None
+
+    meta = {
+        "video_path": video_path,
+        "video_codec": fourcc,
+        "video_pixel_format": pix_fmt,
+        "video_fps": fps,
+        "video_total_frames": frame_count,
+        "original_frame_size": orig_im_size,
+        "dlclive_params": live.parameterization,
+    }
+
+    ### close video and tensorflow session
+
+    cap.release()
+    live.close()
+
+    if save_video:
+        vwriter.release()
+
+    if save_poses:
+
+        cfg_path = os.path.normpath(f"{model_path}/pose_cfg.yaml")
+        ruamel_file = ruamel.yaml.YAML()
+        dlc_cfg = ruamel_file.load(open(cfg_path, "r"))
+        bodyparts = dlc_cfg["all_joints_names"]
+        poses = np.array(poses)
+
+        if use_pandas:
+
+            poses = poses.reshape((poses.shape[0], poses.shape[1] * poses.shape[2]))
+            pdindex = pd.MultiIndex.from_product(
+                [bodyparts, ["x", "y", "likelihood"]], names=["bodyparts", "coords"]
+            )
+            pose_df = pd.DataFrame(poses, columns=pdindex)
+
+            out_dir = (
+                output
+                if output is not None
+                else os.path.dirname(os.path.realpath(video_path))
+            )
+            out_vid_base = os.path.basename(video_path)
+            out_dlc_file = os.path.normpath(
+                f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_POSES.h5"
+            )
+            pose_df.to_hdf(out_dlc_file, key="df_with_missing", mode="w")
+
+        else:
+
+            out_vid_base = os.path.basename(video_path)
+            out_dlc_file = os.path.normpath(
+                f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_POSES.npy"
+            )
+            np.save(out_dlc_file, poses)
+
+    return inf_times, im_size, TFGPUinference, meta
+
+
+def save_inf_times(
+    sys_info, inf_times, im_size, TFGPUinference, model=None, meta=None, output=None
+):
+    """ Save inference time data collected using :function:`benchmark` with system information to a pickle file.
+    This is primarily used through :function:`benchmark_videos`
+
+
+    Parameters
+    ----------
+    sys_info : tuple
+        system information generated by :func:`get_system_info`
+    inf_times : :class:`numpy.ndarray`
+        array of inference times generated by :func:`benchmark`
+    im_size : tuple or :class:`numpy.ndarray`
+        image size (width, height) for each benchmark run. If an array, each row corresponds to a row in inf_times
+    TFGPUinference: bool
+        flag if using tensorflow inference or numpy inference DLC model
+    model: str, optional
+        name of model
+    meta : dict, optional
+        metadata returned by :func:`benchmark`
+    output : str, optional
+        path to directory to save data. If None, uses pwd, by default None
+
+    Returns
+    -------
+    bool
+        flag indicating successful save
+    """
+
+    output = output if output is not None else os.getcwd()
+    model_type = None
+    if model is not None:
+        if "resnet" in model:
+            model_type = "resnet"
+        elif "mobilenet" in model:
+            model_type = "mobilenet"
+        else:
+            model_type = None
+
+    fn_ind = 0
+    base_name = (
+        f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle"
+    )
+    out_file = os.path.normpath(f"{output}/{base_name}")
+    while os.path.isfile(out_file):
+        fn_ind += 1
+        base_name = f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle"
+        out_file = os.path.normpath(f"{output}/{base_name}")
+
+    # summary stats (mean inference time & standard error of mean)
+    stats = zip(
+        np.mean(inf_times, 1),
+        np.std(inf_times, 1) * 1.0 / np.sqrt(np.shape(inf_times)[1]),
+    )
+
+    # for stat in stats:
+    #    print("Stats:", stat)
+
+    data = {
+        "model": model,
+        "model_type": model_type,
+        "TFGPUinference": TFGPUinference,
+        "im_size": im_size,
+        "inference_times": inf_times,
+        "stats": stats,
+    }
+
+    data.update(sys_info)
+    if meta:
+        data.update(meta)
+
+    os.makedirs(os.path.normpath(output), exist_ok=True)
+    pickle.dump(data, open(out_file, "wb"))
+
+    return True
+
+
+def benchmark_videos(
+    model_path,
+    video_path,
+    output=None,
+    n_frames=1000,
+    tf_config=None,
+    resize=None,
+    pixels=None,
+    cropping=None,
+    dynamic=(False, 0.5, 10),
+    print_rate=False,
+    display=False,
+    pcutoff=0.5,
+    display_radius=3,
+    cmap="bmy",
+    save_poses=False,
+    save_video=False,
+):
+    """Analyze videos using DeepLabCut-live exported models.
+    Analyze multiple videos and/or multiple options for the size of the video
+    by specifying a resizing factor or the number of pixels to use in the image (keeping aspect ratio constant).
+    Options to record inference times (to examine inference speed),
+    display keypoints to visually check the accuracy,
+    or save poses to an hdf5 file as in :function:`deeplabcut.benchmark_videos` and
+    create a labeled video as in :function:`deeplabcut.create_labeled_video`.
+
+    Parameters
+    ----------
+    model_path : str
+        path to exported DeepLabCut model
+    video_path : str or list
+        path to video file or list of paths to video files
+    output : str
+        path to directory to save results
+    tf_config : :class:`tensorflow.ConfigProto`
+        tensorflow session configuration
+    resize : int, optional
+        resize factor. Can only use one of resize or pixels. If both are provided, will use pixels. by default None
+    pixels : int, optional
+        downsize image to this number of pixels, maintaining aspect ratio. Can only use one of resize or pixels. If both are provided, will use pixels. by default None
+    cropping : list of int
+        cropping parameters in pixel number: [x1, x2, y1, y2]
+    dynamic: triple containing (state, detectiontreshold, margin)
+        If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold),
+        then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This  window is
+        expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. <detectiontreshold). The
+        current position is utilized for updating the crop window for the next frame (this is why the margin is important and should be set large
+        enough given the movement of the animal)
+    n_frames : int, optional
+        number of frames to run inference on, by default 1000
+    print_rate : bool, optional
+        flat to print inference rate frame by frame, by default False
+    display : bool, optional
+        flag to display keypoints on images. Useful for checking the accuracy of exported models.
+    pcutoff : float, optional
+        likelihood threshold to display keypoints
+    display_radius : int, optional
+        size (radius in pixels) of keypoint to display
+    cmap : str, optional
+        a string indicating the :package:`colorcet` colormap, `options here <https://colorcet.holoviz.org/>`, by default "bmy"
+    save_poses : bool, optional
+        flag to save poses to an hdf5 file. If True, operates similar to :function:`DeepLabCut.benchmark_videos`, by default False
+    save_video : bool, optional
+        flag to save a labeled video. If True, operates similar to :function:`DeepLabCut.create_labeled_video`, by default False
+
+    Example
+    -------
+    Return a vector of inference times for 10000 frames on one video or two videos:
+    dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', n_frames=10000)
+    dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000)
+
+    Return a vector of inference times, testing full size and resizing images to half the width and height for inference, for two videos
+    dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000, resize=[1.0, 0.5])
+
+    Display keypoints to check the accuracy of an exported model
+    dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', display=True)
+
+    Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video`
+    dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True)
+    """
+
+    # convert video_paths to list
+
+    video_path = video_path if type(video_path) is list else [video_path]
+
+    # fix resize
+
+    if pixels:
+        pixels = pixels if type(pixels) is list else [pixels]
+        resize = [None for p in pixels]
+    elif resize:
+        resize = resize if type(resize) is list else [resize]
+        pixels = [None for r in resize]
+    else:
+        resize = [None]
+        pixels = [None]
+
+    # loop over videos
+
+    for v in video_path:
+
+        # initialize full inference times
+
+        inf_times = []
+        im_size_out = []
+
+        for i in range(len(resize)):
+
+            print(f"\nRun {i+1} / {len(resize)}\n")
+
+            this_inf_times, this_im_size, TFGPUinference, meta = benchmark(
+                model_path,
+                v,
+                tf_config=tf_config,
+                resize=resize[i],
+                pixels=pixels[i],
+                cropping=cropping,
+                dynamic=dynamic,
+                n_frames=n_frames,
+                print_rate=print_rate,
+                display=display,
+                pcutoff=pcutoff,
+                display_radius=display_radius,
+                cmap=cmap,
+                save_poses=save_poses,
+                save_video=save_video,
+                output=output,
+            )
+
+            inf_times.append(this_inf_times)
+            im_size_out.append(this_im_size)
+
+        inf_times = np.array(inf_times)
+        im_size_out = np.array(im_size_out)
+
+        # save results
+
+        if output is not None:
+            sys_info = get_system_info()
+            save_inf_times(
+                sys_info,
+                inf_times,
+                im_size_out,
+                TFGPUinference,
+                model=os.path.basename(model_path),
+                meta=meta,
+                output=output,
+            )
+
+
+def main():
+    """Provides a command line interface :function:`benchmark_videos`
+    """
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("model_path", type=str)
+    parser.add_argument("video_path", type=str, nargs="+")
+    parser.add_argument("-o", "--output", type=str, default=None)
+    parser.add_argument("-n", "--n-frames", type=int, default=1000)
+    parser.add_argument("-r", "--resize", type=float, nargs="+")
+    parser.add_argument("-p", "--pixels", type=float, nargs="+")
+    parser.add_argument("-v", "--print-rate", default=False, action="store_true")
+    parser.add_argument("-d", "--display", default=False, action="store_true")
+    parser.add_argument("-l", "--pcutoff", default=0.5, type=float)
+    parser.add_argument("-s", "--display-radius", default=3, type=int)
+    parser.add_argument("-c", "--cmap", type=str, default="bmy")
+    parser.add_argument("--cropping", nargs="+", type=int, default=None)
+    parser.add_argument("--dynamic", nargs="+", type=float, default=[])
+    parser.add_argument("--save-poses", action="store_true")
+    parser.add_argument("--save-video", action="store_true")
+    args = parser.parse_args()
+
+    if (args.cropping) and (len(args.cropping) < 4):
+        raise Exception(
+            "Cropping not properly specified. Must provide 4 values: x1, x2, y1, y2"
+        )
+
+    if not args.dynamic:
+        args.dynamic = (False, 0.5, 10)
+    elif len(args.dynamic) < 3:
+        raise Exception(
+            "Dynamic cropping not properly specified. Must provide three values: 0 or 1 as boolean flag, pcutoff, and margin"
+        )
+    else:
+        args.dynamic = (bool(args.dynamic[0]), args.dynamic[1], args.dynamic[2])
+
+    benchmark_videos(
+        args.model_path,
+        args.video_path,
+        output=args.output,
+        resize=args.resize,
+        pixels=args.pixels,
+        cropping=args.cropping,
+        dynamic=args.dynamic,
+        n_frames=args.n_frames,
+        print_rate=args.print_rate,
+        display=args.display,
+        pcutoff=args.pcutoff,
+        display_radius=args.display_radius,
+        cmap=args.cmap,
+        save_poses=args.save_poses,
+        save_video=args.save_video,
+    )
+
+
+if __name__ == "__main__":
+    main()

Repositories/DeepLabCut-live/dlclive/check_install/check_install.py

@@ -0,0 +1,88 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import sys
+import shutil
+import warnings
+
+from dlclive import benchmark_videos
+import urllib.request
+import argparse
+from pathlib import Path
+from dlclibrary.dlcmodelzoo.modelzoo_download import (
+    download_huggingface_model,
+)
+
+
+def urllib_pbar(count, blockSize, totalSize):
+    percent = int(count * blockSize * 100 / totalSize)
+    outstr = f"{round(percent)}%"
+    sys.stdout.write(outstr)
+    sys.stdout.write("\b"*len(outstr))
+    sys.stdout.flush()
+
+
+def main(display:bool=None):
+    parser = argparse.ArgumentParser(
+        description="Test DLC-Live installation by downloading and evaluating a demo DLC project!")
+    parser.add_argument('--nodisplay', action='store_false', help="Run the test without displaying tracking")
+    args = parser.parse_args()
+
+    if display is None:
+        display = args.nodisplay
+
+    if not display:
+        print('Running without displaying video')
+
+    # make temporary directory in $HOME
+    print("\nCreating temporary directory...\n")
+    tmp_dir = Path().home() / 'dlc-live-tmp'
+    tmp_dir.mkdir(mode=0o775,exist_ok=True)
+
+    video_file = str(tmp_dir / 'dog_clip.avi')
+    model_dir = tmp_dir / 'DLC_Dog_resnet_50_iteration-0_shuffle-0'
+
+    # download dog test video from github:
+    print(f"Downloading Video to {video_file}")
+    url_link = "https://github.com/DeepLabCut/DeepLabCut-live/blob/master/check_install/dog_clip.avi?raw=True"
+    urllib.request.urlretrieve(url_link, video_file, reporthook=urllib_pbar)
+
+    # download exported dog model from DeepLabCut Model Zoo
+    if Path(model_dir / 'snapshot-75000.pb').exists():
+        print('Model already downloaded, using cached version')
+    else:
+        print("Downloading full_dog model from the DeepLabCut Model Zoo...")
+        download_huggingface_model("full_dog", model_dir)
+
+    # assert these things exist so we can give informative error messages
+    assert Path(video_file).exists()
+    assert Path(model_dir / 'snapshot-75000.pb').exists()
+
+    # run benchmark videos
+    print("\n Running inference...\n")
+    # model_dir = "DLC_Dog_resnet_50_iteration-0_shuffle-0"
+    # print(video_file)
+    benchmark_videos(str(model_dir), video_file, display=display, resize=0.5, pcutoff=0.25)
+
+    # deleting temporary files
+    print("\n Deleting temporary files...\n")
+    try:
+        shutil.rmtree(tmp_dir)
+    except PermissionError:
+        warnings.warn(f'Could not delete temporary directory {str(tmp_dir)} due to a permissions error, but otherwise dlc-live seems to be working fine!')
+
+    print("\nDone!\n")
+
+
+if __name__ == "__main__":
+
+
+    display = args.nodisplay
+
+
+    main(display=args.nodisplay)

Repositories/DeepLabCut-live/dlclive/display.py

@@ -0,0 +1,117 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+from tkinter import Tk, Label
+import colorcet as cc
+from PIL import Image, ImageTk, ImageDraw
+
+
+class Display(object):
+    """
+    Simple object to display frames with DLC labels.
+
+    Parameters
+    -----------
+    cmap : string
+        string indicating the Matoplotlib colormap to use.
+    pcutoff : float
+        likelihood threshold to display points
+    """
+
+    def __init__(self, cmap="bmy", radius=3, pcutoff=0.5):
+        """ Constructor method
+        """
+
+        self.cmap = cmap
+        self.colors = None
+        self.radius = radius
+        self.pcutoff = pcutoff
+        self.window = None
+
+    def set_display(self, im_size, bodyparts):
+        """ Create tkinter window to display image
+        
+        Parameters
+        ----------
+        im_size : tuple
+            (width, height) of image
+        bodyparts : int
+            number of bodyparts
+        """
+
+        self.window = Tk()
+        self.window.title("DLC Live")
+        self.lab = Label(self.window)
+        self.lab.pack()
+
+        all_colors = getattr(cc, self.cmap)
+        self.colors = all_colors[:: int(len(all_colors) / bodyparts)]
+
+    def display_frame(self, frame, pose=None):
+        """
+        Display the image with DeepLabCut labels using opencv imshow
+
+        Parameters
+        -----------
+        frame :class:`numpy.ndarray`
+            an image as a numpy array
+
+        pose :class:`numpy.ndarray`
+            the pose estimated by DeepLabCut for the image
+        """
+
+        im_size = (frame.shape[1], frame.shape[0])
+
+        if pose is not None:
+
+            if self.window is None:
+                self.set_display(im_size, pose.shape[0])
+
+            img = Image.fromarray(frame)
+            draw = ImageDraw.Draw(img)
+
+            for i in range(pose.shape[0]):
+                if pose[i, 2] > self.pcutoff:
+                    try:
+                        x0 = (
+                            pose[i, 0] - self.radius
+                            if pose[i, 0] - self.radius > 0
+                            else 0
+                        )
+                        x1 = (
+                            pose[i, 0] + self.radius
+                            if pose[i, 0] + self.radius < im_size[0]
+                            else im_size[1]
+                        )
+                        y0 = (
+                            pose[i, 1] - self.radius
+                            if pose[i, 1] - self.radius > 0
+                            else 0
+                        )
+                        y1 = (
+                            pose[i, 1] + self.radius
+                            if pose[i, 1] + self.radius < im_size[1]
+                            else im_size[0]
+                        )
+                        coords = [x0, y0, x1, y1]
+                        draw.ellipse(
+                            coords, fill=self.colors[i], outline=self.colors[i]
+                        )
+                    except Exception as e:
+                        print(e)
+
+        img_tk = ImageTk.PhotoImage(image=img, master=self.window)
+        self.lab.configure(image=img_tk)
+        self.window.update()
+
+    def destroy(self):
+        """
+        Destroys the opencv image window
+        """
+
+        self.window.destroy()

Repositories/DeepLabCut-live/dlclive/dlclive.py

@@ -0,0 +1,480 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+import os
+import ruamel.yaml
+import glob
+import warnings
+import numpy as np
+import tensorflow as tf
+import typing
+from pathlib import Path
+from typing import Optional, Tuple, List
+
+try:
+    TFVER = [int(v) for v in tf.__version__.split(".")]
+    if TFVER[1] < 14:
+        from tensorflow.contrib.tensorrt import trt_convert as trt
+    else:
+        from tensorflow.python.compiler.tensorrt import trt_convert as trt
+except Exception:
+    pass
+
+from dlclive.graph import (
+    read_graph,
+    finalize_graph,
+    get_output_nodes,
+    get_output_tensors,
+    extract_graph,
+)
+from dlclive.pose import extract_cnn_output, argmax_pose_predict, multi_pose_predict
+from dlclive.display import Display
+from dlclive import utils
+from dlclive.exceptions import DLCLiveError, DLCLiveWarning
+if typing.TYPE_CHECKING:
+    from dlclive.processor import Processor
+
+class DLCLive(object):
+    """
+    Object that loads a DLC network and performs inference on single images (e.g. images captured from a camera feed)
+
+    Parameters
+    -----------
+
+    path : string
+        Full path to exported model directory
+
+    model_type: string, optional
+        which model to use: 'base', 'tensorrt' for tensorrt optimized graph, 'lite' for tensorflow lite optimized graph
+
+    precision : string, optional
+        precision of model weights, only for model_type='tensorrt'. Can be 'FP16' (default), 'FP32', or 'INT8'
+
+    cropping : list of int
+        cropping parameters in pixel number: [x1, x2, y1, y2]
+
+    dynamic: triple containing (state, detectiontreshold, margin)
+        If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold),
+        then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This  window is
+        expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. <detectiontreshold). The
+        current position is utilized for updating the crop window for the next frame (this is why the margin is important and should be set large
+        enough given the movement of the animal).
+
+    resize : float, optional
+        Factor to resize the image.
+        For example, resize=0.5 will downsize both the height and width of the image by a factor of 2.
+
+    processor: dlc pose processor object, optional
+        User-defined processor object. Must contain two methods: process and save.
+        The 'process' method takes in a pose, performs some processing, and returns processed pose.
+        The 'save' method saves any valuable data created by or used by the processor
+        Processors can be used for two main purposes:
+        i) to run a forward predicting model that will predict the future pose from past history of poses (history can be stored in the processor object, but is not stored in this DLCLive object)
+        ii) to trigger external hardware based on pose estimation (e.g. see 'TeensyLaser' processor)
+
+    convert2rgb : bool, optional
+        boolean flag to convert frames from BGR to RGB color scheme
+
+    display : bool, optional
+        Display frames with DeepLabCut labels?
+        This is useful for testing model accuracy and cropping parameters, but it is very slow.
+
+    display_lik : float, optional
+        Likelihood threshold for display
+
+    display_raidus : int, optional
+        radius for keypoint display in pixels, default=3
+    """
+
+    PARAMETERS = (
+        "path",
+        "cfg",
+        "model_type",
+        "precision",
+        "cropping",
+        "dynamic",
+        "resize",
+        "processor",
+    )
+
+    def __init__(
+        self,
+        model_path:str,
+        model_type:str="base",
+        precision:str="FP32",
+        tf_config=None,
+        cropping:Optional[List[int]]=None,
+        dynamic:Tuple[bool, float, float]=(False, 0.5, 10),
+        resize:Optional[float]=None,
+        convert2rgb:bool=True,
+        processor:Optional['Processor']=None,
+        display:typing.Union[bool, Display]=False,
+        pcutoff:float=0.5,
+        display_radius:int=3,
+        display_cmap:str="bmy",
+    ):
+
+        self.path = model_path
+        self.cfg = None # type: typing.Optional[dict]
+        self.model_type = model_type
+        self.tf_config = tf_config
+        self.precision = precision
+        self.cropping = cropping
+        self.dynamic = dynamic
+        self.dynamic_cropping = None
+        self.resize = resize
+        self.processor = processor
+        self.convert2rgb = convert2rgb
+        if isinstance(display, Display):
+            self.display = display
+        elif display:
+            self.display = Display(pcutoff=pcutoff, radius=display_radius, cmap=display_cmap)
+        else:
+            self.display = None
+
+        self.sess = None
+        self.inputs = None
+        self.outputs = None
+        self.tflite_interpreter = None
+        self.pose = None
+        self.is_initialized = False
+
+        # checks
+
+        if self.model_type == "tflite" and self.dynamic[0]:
+            self.dynamic = (False, *self.dynamic[1:])
+            warnings.warn(
+                "Dynamic cropping is not supported for tensorflow lite inference. Dynamic cropping will not be used...",
+                DLCLiveWarning,
+            )
+
+        self.read_config()
+
+    def read_config(self):
+        """ Reads configuration yaml file
+
+        Raises
+        ------
+        FileNotFoundError
+            error thrown if pose configuration file does nott exist
+        """
+
+        cfg_path = Path(self.path).resolve() / "pose_cfg.yaml"
+        if not cfg_path.exists():
+            raise FileNotFoundError(
+                f"The pose configuration file for the exported model at {str(cfg_path)} was not found. Please check the path to the exported model directory"
+            )
+
+        ruamel_file = ruamel.yaml.YAML()
+        self.cfg = ruamel_file.load(open(str(cfg_path), "r"))
+
+    @property
+    def parameterization(self) -> dict:
+        """
+        Return
+        Returns
+        -------
+        """
+        return {param: getattr(self, param) for param in self.PARAMETERS}
+
+    def process_frame(self, frame):
+        """
+        Crops an image according to the object's cropping and dynamic properties.
+
+        Parameters
+        -----------
+        frame :class:`numpy.ndarray`
+            image as a numpy array
+
+        Returns
+        ----------
+        frame :class:`numpy.ndarray`
+            processed frame: convert type, crop, convert color
+        """
+
+        if frame.dtype != np.uint8:
+
+            frame = utils.convert_to_ubyte(frame)
+
+        if self.cropping:
+
+            frame = frame[
+                self.cropping[2] : self.cropping[3], self.cropping[0] : self.cropping[1]
+            ]
+
+        if self.dynamic[0]:
+
+            if self.pose is not None:
+
+                detected = self.pose[:, 2] > self.dynamic[1]
+
+                if np.any(detected):
+
+                    x = self.pose[detected, 0]
+                    y = self.pose[detected, 1]
+
+                    x1 = int(max([0, int(np.amin(x)) - self.dynamic[2]]))
+                    x2 = int(min([frame.shape[1], int(np.amax(x)) + self.dynamic[2]]))
+                    y1 = int(max([0, int(np.amin(y)) - self.dynamic[2]]))
+                    y2 = int(min([frame.shape[0], int(np.amax(y)) + self.dynamic[2]]))
+                    self.dynamic_cropping = [x1, x2, y1, y2]
+
+                    frame = frame[y1:y2, x1:x2]
+
+                else:
+
+                    self.dynamic_cropping = None
+
+        if self.resize != 1:
+            frame = utils.resize_frame(frame, self.resize)
+
+        if self.convert2rgb:
+            frame = utils.img_to_rgb(frame)
+
+        return frame
+
+    def init_inference(self, frame=None, **kwargs):
+        """
+        Load model and perform inference on first frame -- the first inference is usually very slow.
+
+        Parameters
+        -----------
+        frame :class:`numpy.ndarray`
+            image as a numpy array
+
+        Returns
+        --------
+        pose :class:`numpy.ndarray`
+            the pose estimated by DeepLabCut for the input image
+        """
+
+        # get model file
+
+        model_file = glob.glob(os.path.normpath(self.path + "/*.pb"))[0]
+        if not os.path.isfile(model_file):
+            raise FileNotFoundError(
+                "The model file {} does not exist.".format(model_file)
+            )
+
+        # process frame
+
+        if frame is None and (self.model_type == "tflite"):
+            raise DLCLiveError(
+                "No image was passed to initialize inference. An image must be passed to the init_inference method"
+            )
+
+        if frame is not None:
+            if frame.ndim == 2:
+                self.convert2rgb = True
+            processed_frame = self.process_frame(frame)
+
+        # load model
+
+        if self.model_type == "base":
+
+            graph_def = read_graph(model_file)
+            graph = finalize_graph(graph_def)
+            self.sess, self.inputs, self.outputs = extract_graph(
+                graph, tf_config=self.tf_config
+            )
+
+        elif self.model_type == "tflite":
+
+            ###
+            # the frame size needed to initialize the tflite model as
+            # tflite does not support saving a model with dynamic input size
+            ###
+
+            # get input and output tensor names from graph_def
+            graph_def = read_graph(model_file)
+            graph = finalize_graph(graph_def)
+            output_nodes = get_output_nodes(graph)
+            output_nodes = [on.replace("DLC/", "") for on in output_nodes]
+            
+            tf_version_2 = tf.__version__[0] == '2'
+
+            if tf_version_2:
+                converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
+                    model_file,
+                    ["Placeholder"],
+                    output_nodes,
+                    input_shapes={"Placeholder": [1, processed_frame.shape[0], processed_frame.shape[1], 3]},
+                )
+            else:
+                converter = tf.lite.TFLiteConverter.from_frozen_graph(
+                    model_file,
+                    ["Placeholder"],
+                    output_nodes,
+                    input_shapes={"Placeholder": [1, processed_frame.shape[0], processed_frame.shape[1], 3]},
+                )
+                
+            try:
+                tflite_model = converter.convert()
+            except Exception:
+                raise DLCLiveError(
+                    (
+                        "This model cannot be converted to tensorflow lite format. "
+                        "To use tensorflow lite for live inference, "
+                        "make sure to set TFGPUinference=False "
+                        "when exporting the model from DeepLabCut"
+                    )
+                )
+
+            self.tflite_interpreter = tf.lite.Interpreter(model_content=tflite_model)
+            self.tflite_interpreter.allocate_tensors()
+            self.inputs = self.tflite_interpreter.get_input_details()
+            self.outputs = self.tflite_interpreter.get_output_details()
+
+        elif self.model_type == "tensorrt":
+
+            graph_def = read_graph(model_file)
+            graph = finalize_graph(graph_def)
+            output_tensors = get_output_tensors(graph)
+            output_tensors = [ot.replace("DLC/", "") for ot in output_tensors]
+
+            if (TFVER[0] > 1) | (TFVER[0] == 1 & TFVER[1] >= 14):
+                converter = trt.TrtGraphConverter(
+                    input_graph_def=graph_def,
+                    nodes_blacklist=output_tensors,
+                    is_dynamic_op=True,
+                )
+                graph_def = converter.convert()
+            else:
+                graph_def = trt.create_inference_graph(
+                    input_graph_def=graph_def,
+                    outputs=output_tensors,
+                    max_batch_size=1,
+                    precision_mode=self.precision,
+                    is_dynamic_op=True,
+                )
+
+            graph = finalize_graph(graph_def)
+            self.sess, self.inputs, self.outputs = extract_graph(
+                graph, tf_config=self.tf_config
+            )
+
+        else:
+
+            raise DLCLiveError(
+                "model_type = {} is not supported. model_type must be 'base', 'tflite', or 'tensorrt'".format(
+                    self.model_type
+                )
+            )
+
+        # get pose of first frame (first inference is often very slow)
+
+        if frame is not None:
+            pose = self.get_pose(frame, **kwargs)
+        else:
+            pose = None
+
+        self.is_initialized = True
+
+        return pose
+
+    def get_pose(self, frame=None, **kwargs):
+        """
+        Get the pose of an image
+
+        Parameters
+        -----------
+        frame :class:`numpy.ndarray`
+            image as a numpy array
+
+        Returns
+        --------
+        pose :class:`numpy.ndarray`
+            the pose estimated by DeepLabCut for the input image
+        """
+
+        if frame is None:
+            raise DLCLiveError("No frame provided for live pose estimation")
+
+        frame = self.process_frame(frame)
+
+        if self.model_type in ["base", "tensorrt"]:
+
+            pose_output = self.sess.run(
+                self.outputs, feed_dict={self.inputs: np.expand_dims(frame, axis=0)}
+            )
+
+        elif self.model_type == "tflite":
+
+            self.tflite_interpreter.set_tensor(
+                self.inputs[0]["index"],
+                np.expand_dims(frame, axis=0).astype(np.float32),
+            )
+            self.tflite_interpreter.invoke()
+
+            if len(self.outputs) > 1:
+                pose_output = [
+                    self.tflite_interpreter.get_tensor(self.outputs[0]["index"]),
+                    self.tflite_interpreter.get_tensor(self.outputs[1]["index"]),
+                ]
+            else:
+                pose_output = self.tflite_interpreter.get_tensor(
+                    self.outputs[0]["index"]
+                )
+
+        else:
+
+            raise DLCLiveError(
+                "model_type = {} is not supported. model_type must be 'base', 'tflite', or 'tensorrt'".format(
+                    self.model_type
+                )
+            )
+
+        # check if using TFGPUinference flag
+        # if not, get pose from network output
+
+        if len(pose_output) > 1:
+            scmap, locref = extract_cnn_output(pose_output, self.cfg)
+            num_outputs = self.cfg.get("num_outputs", 1)
+            if num_outputs > 1:
+                self.pose = multi_pose_predict(
+                    scmap, locref, self.cfg["stride"], num_outputs
+                )
+            else:
+                self.pose = argmax_pose_predict(scmap, locref, self.cfg["stride"])
+        else:
+            pose = np.array(pose_output[0])
+            self.pose = pose[:, [1, 0, 2]]
+
+        # display image if display=True before correcting pose for cropping/resizing
+
+        if self.display is not None:
+            self.display.display_frame(frame, self.pose)
+
+        # if frame is cropped, convert pose coordinates to original frame coordinates
+
+        if self.resize is not None:
+            self.pose[:, :2] *= 1 / self.resize
+
+        if self.cropping is not None:
+            self.pose[:, 0] += self.cropping[0]
+            self.pose[:, 1] += self.cropping[2]
+
+        if self.dynamic_cropping is not None:
+            self.pose[:, 0] += self.dynamic_cropping[0]
+            self.pose[:, 1] += self.dynamic_cropping[2]
+
+        # process the pose
+
+        if self.processor:
+            self.pose = self.processor.process(self.pose, **kwargs)
+
+        return self.pose
+
+    def close(self):
+        """ Close tensorflow session
+        """
+
+        self.sess.close()
+        self.sess = None
+        self.is_initialized = False
+        if self.display is not None:
+            self.display.destroy()

Repositories/DeepLabCut-live/dlclive/exceptions.py

@@ -0,0 +1,18 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+class DLCLiveError(Exception):
+    """ Generic error type for incorrect use of the DLCLive class """
+
+    pass
+
+
+class DLCLiveWarning(Warning):
+    """ Generic warning for incorrect use of the DLCLive class """
+
+    pass

Repositories/DeepLabCut-live/dlclive/graph.py

@@ -0,0 +1,138 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import tensorflow as tf
+
+vers = (tf.__version__).split(".")
+if int(vers[0]) == 2 or int(vers[0]) == 1 and int(vers[1]) > 12:
+    tf = tf.compat.v1
+else:
+    tf = tf
+
+
+def read_graph(file):
+    """
+    Loads the graph from a protobuf file
+
+    Parameters
+    -----------
+    file : string
+        path to the protobuf file
+
+    Returns
+    --------
+    graph_def :class:`tensorflow.tf.compat.v1.GraphDef`
+        The graph definition of the DeepLabCut model found at the object's path
+    """
+
+    with tf.io.gfile.GFile(file, "rb") as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        return graph_def
+
+
+def finalize_graph(graph_def):
+    """
+    Finalize the graph and get inputs to model
+
+    Parameters
+    -----------
+    graph_def :class:`tensorflow.compat.v1.GraphDef`
+        The graph of the DeepLabCut model, read using the :func:`read_graph` method
+
+    Returns
+    --------
+    graph :class:`tensorflow.compat.v1.GraphDef`
+        The finalized graph of the DeepLabCut model
+    inputs :class:`tensorflow.Tensor`
+        Input tensor(s) for the model
+    """
+
+    graph = tf.Graph()
+    with graph.as_default():
+        tf.import_graph_def(graph_def, name="DLC")
+    graph.finalize()
+
+    return graph
+
+
+def get_output_nodes(graph):
+    """
+    Get the output node names from a graph
+
+    Parameters
+    -----------
+    graph :class:`tensorflow.Graph`
+        The graph of the DeepLabCut model
+
+    Returns
+    --------
+    output : list
+        the output node names as a list of strings
+    """
+
+    op_names = [str(op.name) for op in graph.get_operations()]
+    if "concat_1" in op_names[-1]:
+        output = [op_names[-1]]
+    else:
+        output = [op_names[-1], op_names[-2]]
+
+    return output
+
+
+def get_output_tensors(graph):
+    """
+    Get the names of the output tensors from a graph
+
+    Parameters
+    -----------
+    graph :class:`tensorflow.Graph`
+        The graph of the DeepLabCut model
+
+    Returns
+    --------
+    output : list
+        the output tensor names as a list of strings
+    """
+
+    output_nodes = get_output_nodes(graph)
+    output_tensor = [out + ":0" for out in output_nodes]
+    return output_tensor
+
+
+def get_input_tensor(graph):
+
+    input_tensor = str(graph.get_operations()[0].name) + ":0"
+    return input_tensor
+
+
+def extract_graph(graph, tf_config=None):
+    """
+    Initializes a tensorflow session with the specified graph and extracts the model's inputs and outputs
+
+    Parameters
+    -----------
+    graph :class:`tensorflow.Graph`
+        a tensorflow graph containing the desired model
+    tf_config :class:`tensorflow.ConfigProto`
+
+    Returns
+    --------
+    sess :class:`tensorflow.Session`
+        a tensorflow session with the specified graph definition
+    outputs :class:`tensorflow.Tensor`
+        the output tensor(s) for the model
+    """
+
+    input_tensor = get_input_tensor(graph)
+    output_tensor = get_output_tensors(graph)
+    sess = tf.Session(graph=graph, config=tf_config)
+    inputs = graph.get_tensor_by_name(input_tensor)
+    outputs = [graph.get_tensor_by_name(out) for out in output_tensor]
+
+    return sess, inputs, outputs

Repositories/DeepLabCut-live/dlclive/pose.py

@@ -0,0 +1,120 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import numpy as np
+
+
+def extract_cnn_output(outputs, cfg):
+    """
+    Extract location refinement and score map from DeepLabCut network
+
+    Parameters
+    -----------
+    outputs : list
+        List of outputs from DeepLabCut network.
+        Requires 2 entries:
+            index 0 is output from Sigmoid
+            index 1 is output from pose/locref_pred/block4/BiasAdd
+
+    cfg : dict
+        Dictionary read from the pose_cfg.yaml file for the network.
+
+    Returns
+    --------
+    scmap : ?
+        score map
+
+    locref : ?
+        location refinement
+    """
+
+    scmap = outputs[0]
+    scmap = np.squeeze(scmap)
+    locref = None
+    if cfg["location_refinement"]:
+        locref = np.squeeze(outputs[1])
+        shape = locref.shape
+        locref = np.reshape(locref, (shape[0], shape[1], -1, 2))
+        locref *= cfg["locref_stdev"]
+    if len(scmap.shape) == 2:  # for single body part!
+        scmap = np.expand_dims(scmap, axis=2)
+    return scmap, locref
+
+
+def argmax_pose_predict(scmap, offmat, stride):
+    """
+    Combines score map and offsets to the final pose
+
+    Parameters
+    -----------
+    scmap : ?
+        score map
+
+    offmat : ?
+        offsets
+
+    stride : ?
+        ?
+
+    Returns
+    --------
+    pose :class:`numpy.ndarray`
+        pose as a numpy array
+    """
+
+    num_joints = scmap.shape[2]
+    pose = []
+    for joint_idx in range(num_joints):
+        maxloc = np.unravel_index(
+            np.argmax(scmap[:, :, joint_idx]), scmap[:, :, joint_idx].shape
+        )
+        offset = np.array(offmat[maxloc][joint_idx])[::-1]
+        pos_f8 = np.array(maxloc).astype("float") * stride + 0.5 * stride + offset
+        pose.append(np.hstack((pos_f8[::-1], [scmap[maxloc][joint_idx]])))
+    return np.array(pose)
+
+
+def get_top_values(scmap, n_top=5):
+    batchsize, ny, nx, num_joints = scmap.shape
+    scmap_flat = scmap.reshape(batchsize, nx * ny, num_joints)
+    if n_top == 1:
+        scmap_top = np.argmax(scmap_flat, axis=1)[None]
+    else:
+        scmap_top = np.argpartition(scmap_flat, -n_top, axis=1)[:, -n_top:]
+        for ix in range(batchsize):
+            vals = scmap_flat[ix, scmap_top[ix], np.arange(num_joints)]
+            arg = np.argsort(-vals, axis=0)
+            scmap_top[ix] = scmap_top[ix, arg, np.arange(num_joints)]
+        scmap_top = scmap_top.swapaxes(0, 1)
+
+    Y, X = np.unravel_index(scmap_top, (ny, nx))
+    return Y, X
+
+
+def multi_pose_predict(scmap, locref, stride, num_outputs):
+    Y, X = get_top_values(scmap[None], num_outputs)
+    Y, X = Y[:, 0], X[:, 0]
+    num_joints = scmap.shape[2]
+    DZ = np.zeros((num_outputs, num_joints, 3))
+    for m in range(num_outputs):
+        for k in range(num_joints):
+            x = X[m, k]
+            y = Y[m, k]
+            DZ[m, k, :2] = locref[y, x, k, :]
+            DZ[m, k, 2] = scmap[y, x, k]
+
+    X = X.astype("float32") * stride + 0.5 * stride + DZ[:, :, 0]
+    Y = Y.astype("float32") * stride + 0.5 * stride + DZ[:, :, 1]
+    P = DZ[:, :, 2]
+
+    pose = np.empty((num_joints, num_outputs * 3), dtype="float32")
+    pose[:, 0::3] = X.T
+    pose[:, 1::3] = Y.T
+    pose[:, 2::3] = P.T
+
+    return pose

Repositories/DeepLabCut-live/dlclive/processor/README.md

@@ -0,0 +1,21 @@
+### DeepLabCut-live Processors
+
+The `Processor` class allows users to implement processing or computation steps after DeepLabCut pose estimation. For example, a `Processor` can detect certain features of a pose and turn on an LED or an optogenetics laser, a `Processor` can implement a forward-prediction model that predicts animal's pose ~10-100 ms into the future to apply feedback with zero latency, or a `Processor` can do both.
+
+The `Processor` is designed to be extremely flexible: it must only contain two methods: `Processor.process`, whose input and output is a pose as a numpy array, and `Processor.save`, which allows users to implement a method that saves any data the `Processor` acquires, such as the time that desired behavior occured or the times an LED or laser was turned on/off. The save method must be written by the user, so users can choose whether this data is saved as a text/csv, numpy, pickle, or pandas file to provide a few examples.
+
+To write your own custom `Processor`, your class must inherit the base `Processor` class (see [here](./processor.py)):
+```
+from dlclive import Processor
+class MyCustomProcessor(Processor):
+    ...
+```
+
+To implement your processing steps, overwrite the `Processor.process` method:
+```
+    def process(pose, **kwargs):
+        # my processing steps go here
+        return pose
+```
+
+For example `Processor` objects that communicate with Teensy microcontrollers to [control an optogenetics laser](../../example_processors/TeensyLaser), [turn on an LED when upond detecting a mouse licking](../../example_processors/MouseLickLED), or [turn on an LED upon detecting a dog's rearing movement](../../example_processors/DogJumpLED), see our [example_teensy](../../example_processors) directory.

Repositories/DeepLabCut-live/dlclive/processor/__init__.py

@@ -0,0 +1,9 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+from dlclive.processor.processor import Processor
+from dlclive.processor.kalmanfilter import KalmanFilterPredictor

Repositories/DeepLabCut-live/dlclive/processor/kalmanfilter.py

@@ -0,0 +1,144 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import time
+import numpy as np
+from dlclive.processor import Processor
+
+
+class KalmanFilterPredictor(Processor):
+    def __init__(
+        self,
+        adapt=True,
+        forward=0.002,
+        fps=30,
+        nderiv=2,
+        priors=[10, 10],
+        initial_var=5,
+        process_var=5,
+        dlc_var=20,
+        lik_thresh=0,
+        **kwargs,
+    ):
+
+        super().__init__(**kwargs)
+
+        self.adapt = adapt
+        self.forward = forward
+        self.dt = 1.0 / fps
+        self.nderiv = nderiv
+        self.priors = np.hstack(([1e5], priors))
+        self.initial_var = initial_var
+        self.process_var = process_var
+        self.dlc_var = dlc_var
+        self.lik_thresh = lik_thresh
+        self.is_initialized = False
+        self.last_pose_time = 0
+
+    def _get_forward_model(self, dt):
+
+        F = np.zeros((self.n_states, self.n_states))
+        for d in range(self.nderiv + 1):
+            for i in range(self.n_states - (d * self.bp * 2)):
+                F[i, i + (2 * self.bp * d)] = (dt ** d) / max(1, d)
+
+        return F
+
+    def _init_kf(self, pose):
+
+        # get number of body parts
+        self.bp = pose.shape[0]
+        self.n_states = self.bp * 2 * (self.nderiv + 1)
+
+        # initialize state matrix, set position to first pose
+        self.X = np.zeros((self.n_states, 1))
+        self.X[: (self.bp * 2)] = pose[:, :2].reshape(self.bp * 2, 1)
+
+        # initialize covariance matrix, measurement noise and process noise
+        self.P = np.eye(self.n_states) * self.initial_var
+        self.R = np.eye(self.n_states) * self.dlc_var
+        self.Q = np.eye(self.n_states) * self.process_var
+
+        self.H = np.eye(self.n_states)
+        self.K = np.zeros((self.n_states, self.n_states))
+        self.I = np.eye(self.n_states)
+
+        # initialize priors for forward prediction step only
+        B = np.repeat(self.priors, self.bp * 2)
+        self.B = B.reshape(B.size, 1)
+
+        self.is_initialized = True
+
+    def _predict(self):
+
+        F = self._get_forward_model(time.time() - self.last_pose_time)
+
+        Pd = np.diag(self.P).reshape(self.P.shape[0], 1)
+        X = (1 / ((1 / Pd) + (1 / self.B))) * (self.X / Pd)
+
+        self.Xp = np.dot(F, X)
+        self.Pp = np.dot(np.dot(F, self.P), F.T) + self.Q
+
+    def _get_residuals(self, pose):
+
+        z = np.zeros((self.n_states, 1))
+        z[: (self.bp * 2)] = pose[: self.bp, :2].reshape(self.bp * 2, 1)
+        for i in range(self.bp * 2, self.n_states):
+            z[i] = (z[i - (self.bp * 2)] - self.X[i - (self.bp * 2)]) / self.dt
+        self.y = z - np.dot(self.H, self.Xp)
+
+    def _update(self, liks):
+
+        S = np.dot(self.H, np.dot(self.Pp, self.H.T)) + self.R
+        K = np.dot(np.dot(self.Pp, self.H.T), np.linalg.inv(S))
+        self.X = self.Xp + np.dot(K, self.y)
+        self.X[liks < self.lik_thresh] = self.Xp[liks < self.lik_thresh]
+        self.P = np.dot(self.I - np.dot(K, self.H), self.Pp)
+
+    def _get_future_pose(self, dt):
+
+        Ff = self._get_forward_model(dt)
+        Xf = np.dot(Ff, self.X)
+        future_pose = Xf[: (self.bp * 2)].reshape(self.bp, 2)
+
+        return future_pose
+
+    def _get_state_likelihood(self, pose):
+
+        liks = pose[:, 2]
+        liks_xy = np.repeat(liks, 2)
+        liks_xy_deriv = np.tile(liks_xy, self.nderiv + 1)
+        liks_state = liks_xy_deriv.reshape(liks_xy_deriv.shape[0], 1)
+        return liks_state
+
+    def process(self, pose, **kwargs):
+
+        if not self.is_initialized:
+
+            self._init_kf(pose)
+            self.last_pose_time = time.time()
+            return pose
+
+        else:
+
+            self._predict()
+            self._get_residuals(pose)
+            liks = self._get_state_likelihood(pose)
+            self._update(liks)
+
+            forward_time = (
+                (time.time() - kwargs["frame_time"] + self.forward)
+                if self.adapt
+                else self.forward
+            )
+
+            future_pose = self._get_future_pose(forward_time)
+            future_pose = np.hstack((future_pose, pose[:, 2].reshape(self.bp, 1)))
+
+            self.last_pose_time = time.time()
+            return future_pose

Repositories/DeepLabCut-live/dlclive/processor/processor.py

@@ -0,0 +1,23 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+"""
+Default processor class. Processors must contain two methods:
+i) process: takes in a pose, performs operations, and returns a pose
+ii) save: saves any internal data generated by the processor (such as timestamps for commands to external hardware)
+"""
+
+
+class Processor(object):
+    def __init__(self, **kwargs):
+        pass
+
+    def process(self, pose, **kwargs):
+        return pose
+
+    def save(self, file=""):
+        return 0

Repositories/DeepLabCut-live/dlclive/utils.py

@@ -0,0 +1,218 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import numpy as np
+import warnings
+from dlclive.exceptions import DLCLiveWarning
+
+try:
+    import skimage
+
+    SK_IM = True
+except Exception:
+    SK_IM = False
+
+try:
+    import cv2
+
+    OPEN_CV = True
+except Exception:
+    from PIL import Image
+
+    OPEN_CV = False
+    warnings.warn(
+        "OpenCV is not installed. Using pillow for image processing, which is slower.",
+        DLCLiveWarning,
+    )
+
+
+def convert_to_ubyte(frame):
+    """ Converts an image to unsigned 8-bit integer numpy array. 
+        If scikit-image is installed, uses skimage.img_as_ubyte, otherwise, uses a similar custom function.
+
+    Parameters
+    ----------
+    image : :class:`numpy.ndarray`
+        an image as a numpy array
+
+    Returns
+    -------
+    :class:`numpy.ndarray`
+        image converted to uint8
+    """
+
+    if SK_IM:
+        return skimage.img_as_ubyte(frame)
+    else:
+        return _img_as_ubyte_np(frame)
+
+
+def resize_frame(frame, resize=None):
+    """ Resizes an image. Uses OpenCV if installed, otherwise, uses pillow
+
+    Parameters
+    ----------
+    image : :class:`numpy.ndarray`
+        an image as a numpy array
+    """
+
+    if (resize is not None) and (resize != 1):
+
+        if OPEN_CV:
+
+            new_x = int(frame.shape[0] * resize)
+            new_y = int(frame.shape[1] * resize)
+            return cv2.resize(frame, (new_y, new_x))
+
+        else:
+
+            img = Image.fromarray(frame)
+            img = img.resize((new_y, new_x))
+            return np.asarray(img)
+
+    else:
+
+        return frame
+
+
+def img_to_rgb(frame):
+    """ Convert an image to RGB. Uses OpenCV is installed, otherwise uses pillow.
+
+    Parameters
+    ----------
+    frame : :class:`numpy.ndarray
+        an image as a numpy array
+    """
+
+    if frame.ndim == 2:
+
+        return gray_to_rgb(frame)
+
+    elif frame.ndim == 3:
+
+        return bgr_to_rgb(frame)
+
+    else:
+
+        warnings.warn(
+            f"Image has {frame.ndim} dimensions. Must be 2 or 3 dimensions to convert to RGB",
+            DLCLiveWarning,
+        )
+        return frame
+
+
+def gray_to_rgb(frame):
+    """ Convert an image from grayscale to RGB. Uses OpenCV is installed, otherwise uses pillow.
+
+    Parameters
+    ----------
+    frame : :class:`numpy.ndarray
+        an image as a numpy array
+    """
+
+    if OPEN_CV:
+
+        return cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+
+    else:
+
+        img = Image.fromarray(frame)
+        img = img.convert("RGB")
+        return np.asarray(img)
+
+
+def bgr_to_rgb(frame):
+    """ Convert an image from BGR to RGB. Uses OpenCV is installed, otherwise uses pillow.
+
+    Parameters
+    ----------
+    frame : :class:`numpy.ndarray
+        an image as a numpy array
+    """
+
+    if OPEN_CV:
+
+        return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    else:
+
+        img = Image.fromarray(frame)
+        img = img.convert("RGB")
+        return np.asarray(img)
+
+
+def _img_as_ubyte_np(frame):
+    """ Converts an image as a numpy array to unsinged 8-bit integer.
+        As in scikit-image img_as_ubyte, converts negative pixels to 0 and converts range to [0, 255]
+
+    Parameters
+    ----------
+    image : :class:`numpy.ndarray`
+        an image as a numpy array
+
+    Returns
+    -------
+    :class:`numpy.ndarray`
+        image converted to uint8
+    """
+
+    frame = np.array(frame)
+    im_type = frame.dtype.type
+
+    # check if already ubyte
+    if np.issubdtype(im_type, np.uint8):
+
+        return frame
+
+    # if floating
+    elif np.issubdtype(im_type, np.floating):
+
+        if (np.min(frame) < -1) or (np.max(frame) > 1):
+            raise ValueError("Images of type float must be between -1 and 1.")
+
+        frame *= 255
+        frame = np.rint(frame)
+        frame = np.clip(frame, 0, 255)
+        return frame.astype(np.uint8)
+
+    # if integer
+    elif np.issubdtype(im_type, np.integer):
+
+        im_type_info = np.iinfo(im_type)
+        frame *= 255 / im_type_info.max
+        frame[frame < 0] = 0
+        return frame.astype(np.uint8)
+
+    else:
+
+        raise TypeError(
+            "image of type {} could not be converted to ubyte".format(im_type)
+        )
+
+
+def decode_fourcc(cc):
+    """
+    Convert float fourcc code from opencv to characters.
+    If decode fails, returns empty string.
+    https://stackoverflow.com/a/49138893
+    Arguments:
+        cc (float, int): fourcc code from opencv
+    Returns:
+         str: Character format of fourcc code
+
+    Examples:
+        >>> vid = cv2.VideoCapture('/some/video/path.avi')
+        >>> decode_fourcc(vid.get(cv2.CAP_PROP_FOURCC))
+        'DIVX'
+    """
+    try:
+        decoded = "".join([chr((int(cc) >> 8 * i) & 0xFF) for i in range(4)])
+    except:
+        decoded = ""
+
+    return decoded

Repositories/DeepLabCut-live/dlclive/version.py

@@ -0,0 +1,11 @@
+"""
+DeepLabCut Live Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+admin@deeplabcut.org
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+__version__ = "1.0.3"
+VERSION = __version__

Repositories/DeepLabCut-live/docs/install_desktop.md

@@ -0,0 +1,29 @@
+### Install DeepLabCut-live on a desktop (Windows/Ubuntu)
+
+We recommend that you install DeepLabCut-live in a conda environment (It is a standard python package though, and other distributions will also likely work). In this case, please install Anaconda:
+
+- [Windows](https://docs.anaconda.com/anaconda/install/windows/)
+- [Linux](https://docs.anaconda.com/anaconda/install/linux/)
+
+Create a conda environment with python 3.7 and tensorflow:
+
+```
+conda create -n dlc-live python=3.7 tensorflow-gpu==1.13.1 # if using GPU
+conda create -n dlc-live python=3.7 tensorflow==1.13.1 # if not using GPU
+```
+
+Activate the conda environment, install the DeepLabCut-live package, then test the installation:
+
+```
+conda activate dlc-live
+pip install deeplabcut-live
+dlc-live-test
+```
+
+Note, you can also just run the test:
+
+`dlc-live-test`
+
+If installed properly, this script will i) create a temporary folder ii) download the full_dog model from the [DeepLabCut Model Zoo](http://www.mousemotorlab.org/dlc-modelzoo), iii) download a short video clip of a dog, and iv) run inference while displaying keypoints. v) remove the temporary folder.
+
+Please note, you also should have curl installed on your computer (typically this is already installed on your system), but just in case, just run `sudo apt install curl`

Repositories/DeepLabCut-live/docs/install_jetson.md

@@ -0,0 +1,81 @@
+### Install DeepLabCut-live on a NVIDIA Jetson Development Kit
+
+First, please follow NVIDIA's specific instructions to setup your Jetson Development Kit (see [Jetson Development Kit User Guides](https://developer.nvidia.com/embedded/learn/getting-started-jetson)). Once you have installed the NVIDIA Jetpack on your Jetson Development Kit, make sure all system libraries are up-to-date. In a terminal, run:
+
+```
+sudo apt-get update
+sudo apt-get upgrade
+```
+
+Lastly, please test that CUDA is installed properly by running: `nvcc --version`. The output should say the version of CUDA installed on your Jetson.
+
+#### Install python, virtualenv, and tensorflow
+
+We highly recommend installing DeepLabCut-live in a virtual environment. Please run the following command to install system dependencies needed to run python, to create virtual environments, and to run tensorflow:
+
+```
+sudo apt-get update
+sudo apt-get install libhdf5-serial-dev \
+                     hdf5-tools \
+                     libhdf5-dev \
+                     zlib1g-dev \
+                     zip \
+                     libjpeg8-dev \
+                     liblapack-dev \
+                     libblas-dev \
+                     gfortran \
+                     python3-pip \
+                     python3-venv \
+                     python3-tk \
+                     curl
+```
+
+#### Create a virtual environment
+
+Next, create a virtual environment called `dlc-live`, activate the `dlc-live` environment, and update it's package manger:
+
+```
+python3 -m venv dlc-live
+source dlc-live/bin/activate
+pip install -U pip testresources setuptools
+```
+
+#### Install DeepLabCut-live dependencies
+
+First, install python dependencies to run tensorflow (from [NVIDIA instructions to install tensorflow on Jetson platforms](https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html)). _This may take ~15-30 minutes._
+
+```
+pip3 install numpy==1.16.1 \
+             future==0.17.1 \
+             mock==3.0.5 \
+             h5py==2.9.0 \
+             keras_preprocessing==1.0.5 \
+             keras_applications==1.0.8 \
+             gast==0.2.2 \
+             futures \
+             protobuf \
+             pybind11
+```
+
+Next, install tensorflow 1.x. This command will depend on the version of Jetpack you are using. If you are uncertain, please refer to [NVIDIA's instructions](https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html#install). To install tensorflow 1.x on the latest version of NVIDIA Jetpack (version 4.4 as of 8/2/2020), please the command below. _This step will also take 15-30 mins_.
+
+```
+pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 'tensorflow<2'
+```
+
+Lastly, copy the opencv-python bindings into your virtual environment:
+
+```
+cp -r /usr/lib/python3.6/dist-packages ~/dlc-live/lib/python3.6/dist-packages
+```
+
+#### Install the DeepLabCut-live package
+
+Finally, please install DeepLabCut-live from PyPi (_this will take 3-5 mins_), then test the installation:
+
+```
+pip install deeplabcut-live
+dlc-live-test
+```
+
+If installed properly, this script will i) download the full_dog model from the DeepLabCut Model Zoo, ii) download a short video clip of a dog, and iii) run inference while displaying keypoints.

Repositories/DeepLabCut-live/example_processors/DogJumpLED/__init__.py

@@ -0,0 +1,9 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+from .izzy_jump import IzzyJump, IzzyJumpKF
+from .izzy_jump import IzzyJumpOffline, IzzyJumpKFOffline

Repositories/DeepLabCut-live/example_processors/DogJumpLED/izzy_jump.py

@@ -0,0 +1,143 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import serial
+import struct
+import time
+import numpy as np
+
+from dlclive.processor import Processor, KalmanFilterPredictor
+
+
+class IzzyJump(Processor):
+    def __init__(self, com="", lik_thresh=0.5, baudrate=int(9600), **kwargs):
+
+        super().__init__()
+        self.ser = serial.Serial(com, baudrate, timeout=0)
+        self.lik_thresh = lik_thresh
+        self.led_times = []
+        self.last_light = 0
+
+    def close_serial(self):
+
+        self.ser.close()
+
+    def switch_led(self, val, frame_time):
+
+        ### check status of led ###
+
+        self.ser.write(b"R")
+
+        led_byte = b""
+        led_status = None
+        while (len(led_byte) != 0) or (led_status is None):
+            led_byte = self.ser.read()
+            if len(led_byte) > 0:
+                led_status = ord(led_byte)
+
+        if led_status != val:
+            ctime = time.time()
+            if ctime - self.last_light > 0.25:
+                self.ser.write(b"L")
+                self.last_light = ctime
+                self.led_times.append((val, frame_time, ctime))
+
+    def process(self, pose, **kwargs):
+
+        ### bodyparts
+        # 0. nose
+        # 1. L-eye
+        # 2. R-eye
+        # 3. L-ear
+        # 4. R-ear
+        # 5. Throat
+        # 6. Withers
+        # 7. Tailset
+        # 8. L-front-paw
+        # 9. R-front-paw
+        # 10. L-front-wrist
+        # 11. R-front-wrist
+        # 12. L-front-elbow
+        # 13. R-front-elbow
+        # ...
+
+        l_elbow = pose[12, 1] if pose[12, 2] > self.lik_thresh else None
+        r_elbow = pose[13, 1] if pose[13, 2] > self.lik_thresh else None
+        elbows = [l_elbow, r_elbow]
+        this_elbow = (
+            min([e for e in elbows if e is not None])
+            if any([e is not None for e in elbows])
+            else None
+        )
+
+        withers = pose[6, 1] if pose[6, 2] > self.lik_thresh else None
+
+        if kwargs["record"]:
+            if withers is not None and this_elbow is not None:
+                if this_elbow < withers:
+                    self.switch_led(True, kwargs["frame_time"])
+                else:
+                    self.switch_led(False, kwargs["frame_time"])
+
+        return pose
+
+    def save(self, filename):
+
+        ### save stim on and stim off times
+
+        if filename[-4:] != ".npy":
+            filename += ".npy"
+        arr = np.array(self.led_times, dtype=float)
+        try:
+            np.save(filename, arr)
+            save_code = True
+        except Exception:
+            save_code = False
+
+        return save_code
+
+
+class IzzyJumpKF(KalmanFilterPredictor, IzzyJump):
+    def __init__(
+        self,
+        com="",
+        lik_thresh=0.5,
+        baudrate=int(9600),
+        adapt=True,
+        forward=0.003,
+        fps=30,
+        nderiv=2,
+        priors=[1, 1],
+        initial_var=1,
+        process_var=1,
+        dlc_var=4,
+    ):
+
+        super().__init__(
+            adapt=adapt,
+            forward=forward,
+            fps=fps,
+            nderiv=nderiv,
+            priors=priors,
+            initial_var=initial_var,
+            process_var=process_var,
+            dlc_var=dlc_var,
+            com=com,
+            lik_thresh=lik_thresh,
+            baudrate=baudrate,
+        )
+
+    def process(self, pose, **kwargs):
+
+        future_pose = KalmanFilterPredictor.process(self, pose, **kwargs)
+        final_pose = IzzyJump.process(self, future_pose, **kwargs)
+        return final_pose
+
+    def save(self, filename):
+
+        return IzzyJump.save(self, filename)

Repositories/DeepLabCut-live/example_processors/DogJumpLED/izzy_jump_offline.py

@@ -0,0 +1,123 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import struct
+import time
+import numpy as np
+
+from dlclive.processor import Processor, KalmanFilterPredictor
+
+
+class IzzyJumpOffline(Processor):
+    def __init__(self, lik_thresh=0.5, **kwargs):
+
+        super().__init__()
+        self.lik_thresh = lik_thresh
+        self.led_times = []
+        self.last_light = 0
+        self.led_status = False
+
+    def switch_led(self, val, frame_time):
+
+        if self.led_status != val:
+            ctime = frame_time
+            if ctime - self.last_light > 0.25:
+                self.led_status = val
+                self.last_light = ctime
+                self.led_times.append((val, frame_time, ctime))
+
+    def process(self, pose, **kwargs):
+
+        ### bodyparts
+        # 0. nose
+        # 1. L-eye
+        # 2. R-eye
+        # 3. L-ear
+        # 4. R-ear
+        # 5. Throat
+        # 6. Withers
+        # 7. Tailset
+        # 8. L-front-paw
+        # 9. R-front-paw
+        # 10. L-front-wrist
+        # 11. R-front-wrist
+        # 12. L-front-elbow
+        # 13. R-front-elbow
+        # ...
+
+        l_elbow = pose[12, 1] if pose[12, 2] > self.lik_thresh else None
+        r_elbow = pose[13, 1] if pose[13, 2] > self.lik_thresh else None
+        elbows = [l_elbow, r_elbow]
+        this_elbow = (
+            min([e for e in elbows if e is not None])
+            if any([e is not None for e in elbows])
+            else None
+        )
+
+        withers = pose[6, 1] if pose[6, 2] > self.lik_thresh else None
+
+        if kwargs["record"]:
+            if withers is not None and this_elbow is not None:
+                if this_elbow < withers:
+                    self.switch_led(True, kwargs["frame_time"])
+                else:
+                    self.switch_led(False, kwargs["frame_time"])
+
+        return pose
+
+    def save(self, filename):
+
+        ### save stim on and stim off times
+
+        if filename[-4:] != ".npy":
+            filename += ".npy"
+        arr = np.array(self.led_times, dtype=float)
+        try:
+            np.save(filename, arr)
+            save_code = True
+        except Exception:
+            save_code = False
+
+        return save_code
+
+
+class IzzyJumpKFOffline(KalmanFilterPredictor, IzzyJumpOffline):
+    def __init__(
+        self,
+        lik_thresh=0.5,
+        adapt=True,
+        forward=0.003,
+        fps=30,
+        nderiv=2,
+        priors=[1, 1],
+        initial_var=1,
+        process_var=1,
+        dlc_var=4,
+    ):
+
+        super().__init__(
+            adapt=adapt,
+            forward=forward,
+            fps=fps,
+            nderiv=nderiv,
+            priors=priors,
+            initial_var=initial_var,
+            process_var=process_var,
+            dlc_var=dlc_var,
+            lik_thresh=lik_thresh,
+        )
+
+    def process(self, pose, **kwargs):
+
+        future_pose = KalmanFilterPredictor.process(self, pose, **kwargs)
+        final_pose = IzzyJumpOffline.process(self, future_pose, **kwargs)
+        return final_pose
+
+    def save(self, filename):
+
+        return IzzyJumpOffline.save(self, filename)

Repositories/DeepLabCut-live/example_processors/DogJumpLED/teensy_leds/teensy_leds.ino

@@ -0,0 +1,49 @@
+const int LED = 0;
+const int IR = 1;
+const int REC = 2;
+
+void blink() {
+
+  Serial.write(!digitalRead(REC));
+  Serial.flush();
+  noTone(IR);
+  while (digitalRead(REC) == 0) {}
+  
+}
+
+void setup() {
+
+  pinMode(LED, OUTPUT);
+  pinMode(IR, OUTPUT);
+  pinMode(REC, INPUT);
+  attachInterrupt(digitalPinToInterrupt(REC), blink, FALLING);
+
+  Serial.begin(9600);
+}
+
+void loop() {
+
+  unsigned int ser_avail = Serial.available();
+  
+  while (ser_avail > 0) {
+    
+    unsigned int cmd = Serial.read();
+
+    if (cmd == 'L') {
+      
+      digitalWrite(LED, !digitalRead(LED));
+    
+    } else if (cmd == 'R') {
+
+      Serial.write(digitalRead(LED));
+      Serial.flush();
+      
+    } else if (cmd == 'I') {
+
+      tone(IR, 38000);
+      
+    }
+    
+  }
+  
+}

Repositories/DeepLabCut-live/example_processors/MouseLickLED/__init__.py

@@ -0,0 +1,8 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+from .lick_led import MouseLickLED

Repositories/DeepLabCut-live/example_processors/MouseLickLED/lick_led.py

@@ -0,0 +1,85 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+import serial
+import struct
+import time
+import numpy as np
+
+from dlclive import Processor
+
+
+class MouseLickLED(Processor):
+    def __init__(self, com, lik_thresh=0.5, baudrate=int(9600)):
+
+        super().__init__()
+        self.ser = serial.Serial(com, baudrate, timeout=0)
+        self.lik_thresh = lik_thresh
+        self.lick_frame_time = []
+        self.out_time = []
+        self.in_time = []
+
+    def close_serial(self):
+
+        self.ser.close()
+
+    def switch_led(self):
+
+        ### flush input buffer ###
+
+        self.ser.reset_input_buffer()
+
+        ### turn on IR LED ###
+
+        self.out_time.append(time.time())
+        self.ser.write(b"I")
+
+        ### wait for receiver ###
+
+        while True:
+            led_byte = self.ser.read()
+            if len(led_byte) > 0:
+                break
+        self.in_time.append(time.time())
+
+    def process(self, pose, **kwargs):
+
+        ### bodyparts
+        # 0. pupil-top
+        # 1. pupil-left
+        # 2. pupil-bottom
+        # 3. pupil-right
+        # 4. lip-upper
+        # 5. lip-lower
+        # 6. tongue
+        # 7. tube
+
+        if kwargs["record"]:
+            if pose[6, 2] > self.lik_thresh:
+                self.lick_frame_time.append(kwargs["frame_time"])
+                self.switch_led()
+
+        return pose
+
+    def save(self, filename):
+
+        ### save stim on and stim off times
+
+        filename += ".npy"
+        out_time = np.array(self.out_time)
+        in_time = np.array(self.in_time)
+        frame_time = np.array(self.lick_frame_time)
+        try:
+            np.savez(
+                filename, out_time=out_time, in_time=in_time, frame_time=frame_time
+            )
+            save_code = True
+        except Exception:
+            save_code = False
+
+        return save_code

Repositories/DeepLabCut-live/example_processors/MouseLickLED/teensy_leds/teensy_leds.ino

@@ -0,0 +1,49 @@
+const int LED = 0;
+const int IR = 1;
+const int REC = 2;
+
+void blink() {
+
+  Serial.write(!digitalRead(REC));
+  Serial.flush();
+  noTone(IR);
+  while (digitalRead(REC) == 0) {}
+  
+}
+
+void setup() {
+
+  pinMode(LED, OUTPUT);
+  pinMode(IR, OUTPUT);
+  pinMode(REC, INPUT);
+  attachInterrupt(digitalPinToInterrupt(REC), blink, FALLING);
+
+  Serial.begin(9600);
+}
+
+void loop() {
+
+  unsigned int ser_avail = Serial.available();
+  
+  while (ser_avail > 0) {
+    
+    unsigned int cmd = Serial.read();
+
+    if (cmd == 'L') {
+      
+      digitalWrite(LED, !digitalRead(LED));
+    
+    } else if (cmd == 'R') {
+
+      Serial.write(digitalRead(LED));
+      Serial.flush();
+      
+    } else if (cmd == 'I') {
+
+      tone(IR, 38000);
+      
+    }
+    
+  }
+  
+}

Repositories/DeepLabCut-live/example_processors/TeensyLaser/__init__.py

@@ -0,0 +1,8 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+from .teensy_laser import *

Repositories/DeepLabCut-live/example_processors/TeensyLaser/teensy_laser.py

@@ -0,0 +1,86 @@
+"""
+DeepLabCut Toolbox (deeplabcut.org)
+© A. & M. Mathis Labs
+
+Licensed under GNU Lesser General Public License v3.0
+"""
+
+
+from dlclive.processor.processor import Processor
+import serial
+import struct
+import pickle
+import time
+
+
+class TeensyLaser(Processor):
+    def __init__(
+        self, com, baudrate=115200, pulse_freq=50, pulse_width=5, max_stim_dur=0
+    ):
+
+        super().__init__()
+        self.ser = serial.Serial(com, baudrate)
+        self.pulse_freq = pulse_freq
+        self.pulse_width = pulse_width
+        self.max_stim_dur = (
+            max_stim_dur if (max_stim_dur >= 0) and (max_stim_dur < 65356) else 0
+        )
+        self.stim_on = False
+        self.stim_on_time = []
+        self.stim_off_time = []
+
+    def close_serial(self):
+
+        self.ser.close()
+
+    def turn_stim_on(self):
+
+        # command to activate PWM signal to laser is the letter 'O' followed by three 16 bit integers -- pulse frequency, pulse width, and max stim duration
+        if not self.stim_on:
+            self.ser.write(
+                b"O"
+                + struct.pack(
+                    "HHH", self.pulse_freq, self.pulse_width, self.max_stim_dur
+                )
+            )
+            self.stim_on = True
+            self.stim_on_time.append(time.time())
+
+    def turn_stim_off(self):
+
+        # command to turn off PWM signal to laser is the letter 'X'
+        if self.stim_on:
+            self.ser.write(b"X")
+            self.stim_on = False
+            self.stim_off_time.append(time.time())
+
+    def process(self, pose, **kwargs):
+
+        # define criteria to stimulate (e.g. if first point is in a corner of the video)
+        box = [[0, 100], [0, 100]]
+        if (
+            (pose[0][0] > box[0][0])
+            and (pose[0][0] < box[0][1])
+            and (pose[0][1] > box[1][0])
+            and (pose[0][1] < box[1][1])
+        ):
+            self.turn_stim_on()
+        else:
+            self.turn_stim_off()
+
+        return pose
+
+    def save(self, file=None):
+
+        ### save stim on and stim off times
+        save_code = 0
+        if file:
+            try:
+                pickle.dump(
+                    {"stim_on": self.stim_on_time, "stim_off": self.stim_off_time},
+                    open(file, "wb"),
+                )
+                save_code = 1
+            except Exception:
+                save_code = -1
+        return save_code

Repositories/DeepLabCut-live/example_processors/TeensyLaser/teensy_laser/teensy_laser.ino

@@ -0,0 +1,77 @@
+/* 
+ * Commands: 
+ * O = opto on; command = O, frequency, width, duration
+ * X = opto off
+ * R = reboot
+ */
+
+
+const int opto_pin = 0;
+unsigned int opto_start = 0,
+  opto_duty_cycle = 0,
+  opto_freq = 0,
+  opto_width = 0,
+  opto_dur = 0;
+
+unsigned int read_int16() {
+  union u_tag { 
+    byte b[2];
+    unsigned int val;
+  } par;
+  for (int i=0; i<2; i++){
+    if ((Serial.available() > 0))
+      par.b[i] = Serial.read();
+    else
+      par.b[i] = 0;
+  }
+  return par.val;
+}
+
+void setup() {
+  Serial.begin(115200);
+  pinMode(opto_pin, OUTPUT);
+}
+
+void loop() {
+
+  unsigned int curr_time = millis();
+  
+  while (Serial.available() > 0) {
+
+    unsigned int cmd = Serial.read();
+    
+    if(cmd == 'O') {
+      
+      opto_start = curr_time;
+      opto_freq = read_int16();
+      opto_width = read_int16();
+      opto_dur = read_int16();
+      if (opto_dur == 0)
+        opto_dur = 65355;
+      opto_duty_cycle = opto_width * opto_freq * 4096 / 1000;
+      analogWriteFrequency(opto_pin, opto_freq);
+      analogWrite(opto_pin, opto_duty_cycle);
+
+      Serial.print(opto_freq);
+      Serial.print(',');
+      Serial.print(opto_width);
+      Serial.print(',');
+      Serial.print(opto_dur);
+      Serial.print('\n');
+      Serial.flush();
+      
+    } else if(cmd == 'X') {
+
+      analogWrite(opto_pin, 0);
+      
+    } else if(cmd == 'R') {
+
+      _reboot_Teensyduino_();
+      
+    }
+  }
+
+  if (curr_time > opto_start + opto_dur)
+    analogWrite(opto_pin, 0);
+
+}

Repositories/DeepLabCut-live/pyproject.toml

@@ -0,0 +1,46 @@
+[tool.poetry]
+name = "deeplabcut-live"
+version = "1.0.3"
+description = "Class to load exported DeepLabCut networks and perform pose estimation on single frames (from a camera feed)"
+authors = ["A. & M. Mathis Labs <admin@deeplabcut.org>"]
+license = "AGPL-3.0-or-later"
+readme = "README.md"
+homepage = "https://github.com/DeepLabCut/DeepLabCut-live"
+repository = "https://github.com/DeepLabCut/DeepLabCut-live"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
+    "Operating System :: OS Independent"
+]
+packages = [
+    { include = "dlclive" }
+]
+include = ["dlclive/check_install/*"]
+
+[tool.poetry.scripts]
+dlc-live-test = "dlclive.check_install.check_install:main"
+dlc-live-benchmark = "dlclive.benchmark:main"
+
+[tool.poetry.dependencies]
+python = ">=3.7.1,<3.11"
+numpy = "^1.20"
+"ruamel.yaml" = "^0.17.20"
+colorcet = "^3.0.0"
+Pillow = ">=8.0.0"
+py-cpuinfo = ">=5.0.0"
+tqdm = "^4.62.3"
+tensorflow = "^2.7.0,<=2.10"
+pandas = "^1.3"
+tables = "^3.6"
+opencv-python-headless = "^4.5"
+dlclibrary = ">=0.0.2"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

Repositories/DeepLabCut-live/reinstall.sh

@@ -0,0 +1,4 @@
+poetry shell          # activating current environment
+poetry install        # creating and installing current project
+poetry build          # creating the tarball
+poetry publish        # uploading to pypi

app.py

@@ -0,0 +1,46 @@
+import sys
+import subprocess
+# subprocess.check_call([sys.executable, '-m', 'pip', 'install','git+https://github.com/facebookresearch/detectron2.git'])
+
+import numpy as np
+import gradio as gr
+from Code import Inference
+import detectron2
+
+
+# import some common detectron2 utilities
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog, DatasetCatalog
+
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+
+sys.path.append("Repositories/")
+from dlclive import DLCLive, Processor
+
+def run_Inference(input_img):
+
+    ###Detectron:
+    cfg = get_cfg()
+    # add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
+    cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml"))
+    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7  # set threshold for this model
+    # Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
+    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml")
+    cfg.MODEL.DEVICE='cpu'
+    predictor = DefaultPredictor(cfg)
+
+    ##DLC:
+    dlc_proc = Processor()
+    dlc_liveObj = DLCLive("./Weights/DLC_DLC_Segmented_resnet_50_iteration-0_shuffle-1/", processor=dlc_proc)
+    
+    OutImg = Inference.Inference(input_img,predictor,dlc_liveObj,ScaleBBox=1,Dilate=5,DLCThreshold=0.3)
+
+    return OutImg
+
+demo = gr.Interface(run_Inference, gr.Image(), "image")
+demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+deeplabcut
+tensorflow
+opencv-python
+deeplabcut-live
+typing-extensions==4.8.0
+colorcet
+torch
+torchvision