Missing most of my video for DeepLabCut after video Analysis step

Image Analysis
#1

I ran a 6 minute long video through the DeepLabCut program, Colab_TrainNetwork_VideoAnalysis.ipynb. Every step worked fine, until I got to the end and saw that the labeled video was only 1 second long, the first second of the original video. I noticed that during the analysis step, the number of frames went down from 23512 to 58, and I don’t know why this happened.

Below is the following code that was generated. Below that is my config file for any additional information that might be needed. I have never ran into this issue before, please let me know if there are any fixes for this.

Config:
{‘all_joints’: [[0], [1], [2], [3], [4], [5], [6], [7], [8]],
‘all_joints_names’: [‘nose’,
‘leftear’,
‘rightear’,
‘spine1’,
‘spine2’,
‘spine3’,
‘tailbase’,
‘tailmid’,
‘tailend’],
‘batch_size’: 1,
‘crop_pad’: 0,
‘dataset’: 'training-datasets/iteration-0/UnaugmentedDataSet_Cohort 5 ’
'Baseline Mouse INov22/Cohort 5 Baseline Mouse ’
‘I_Nicole95shuffle1.mat’,
‘dataset_type’: ‘imgaug’,
‘deterministic’: False,
‘fg_fraction’: 0.25,
‘global_scale’: 0.8,
‘init_weights’: ‘/usr/local/lib/python3.6/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt’,
‘intermediate_supervision’: False,
‘intermediate_supervision_layer’: 12,
‘location_refinement’: True,
‘locref_huber_loss’: True,
‘locref_loss_weight’: 1.0,
‘locref_stdev’: 7.2801,
‘log_dir’: ‘log’,
‘mean_pixel’: [123.68, 116.779, 103.939],
‘mirror’: False,
‘net_type’: ‘resnet_50’,
‘num_joints’: 9,
‘optimizer’: ‘sgd’,
‘pairwise_huber_loss’: True,
‘pairwise_predict’: False,
‘partaffinityfield_predict’: False,
‘regularize’: False,
‘scoremap_dir’: ‘test’,
‘shuffle’: True,
‘snapshot_prefix’: '/content/drive/My Drive/Cohort 5 Baseline Mouse ’
'I-Nicole-2020-11-22/dlc-models/iteration-0/Cohort 5 ’
‘Baseline Mouse INov22-trainset95shuffle1/test/snapshot’,
‘stride’: 8.0,
‘weigh_negatives’: False,
‘weigh_only_present_joints’: False,
‘weigh_part_predictions’: False,
‘weight_decay’: 0.0001}
Using snapshot-13500 for model /content/drive/My Drive/Cohort 5 Baseline Mouse I-Nicole-2020-11-22/dlc-models/iteration-0/Cohort 5 Baseline Mouse INov22-trainset95shuffle1
Initializing ResNet
Analyzing all the videos in the directory…
Starting to analyze % /content/drive/My Drive/Cohort 5 Baseline Mouse I-Nicole-2020-11-22/videos/GOPR1483.MP4
/content/drive/My Drive/Cohort 5 Baseline Mouse I-Nicole-2020-11-22/videos already exists!
Loading /content/drive/My Drive/Cohort 5 Baseline Mouse I-Nicole-2020-11-22/videos/GOPR1483.MP4
0%| | 0/23512 [00:00<?, ?it/s]Duration of video [s]: 392.26 , recorded with 59.94 fps!
Overall # of frames: 23512 found with (before cropping) frame dimensions: 1920 1080
Starting to extract posture
Detected frames: 58
1%| | 235/23512 [00:18<30:05, 12.90it/s]Saving results in /content/drive/My Drive/Cohort 5 Baseline Mouse I-Nicole-2020-11-22/videos…
The videos are analyzed. Now your research can truly start!
You can create labeled videos with ‘create_labeled_video’
If the tracking is not satisfactory for some videos, consider expanding the training set. You can use the function ‘extract_outlier_frames’ to extract a few representative outlier frames.

DLC_resnet50_Cohort 5 Baseline Mouse INov22shuffle1_13500

# Project path (change when moving around)

project_path: /content/drive/My Drive/Cohort 5 Baseline Mouse I-Nicole-2020-11-22

# Annotation data set configuration (and individual video cropping parameters)

video_sets:
/content/drive/My Drive/Cohort 5 Baseline Mouse I-Nicole-2020-11-22/videos/GOPR1483.MP4:
crop: 0, 1920, 0, 1080
bodyparts:

  • nose
  • leftear
  • rightear
  • spine1
  • spine2
  • spine3
  • tailbase
  • tailmid
  • tailend

start: 0
stop: 1
numframes2pick: 20

# Plotting configuration

skeleton:

    • leftear
    • spine1
    • spine3
    • tailbase
    • rightear
    • tailend
    • nose
    • rightear
    • leftear
    • tailbase
    • rightear
    • spine3
    • nose
    • spine1
    • leftear
    • rightear
    • tailbase
    • tailmid
    • leftear
    • spine3
    • spine1
    • spine2
    • rightear
    • tailbase
    • spine2
    • spine3
    • leftear
    • spine2
    • rightear
    • spine1
    • nose
    • leftear
    • rightear
    • tailmid
    • leftear
    • tailend
    • leftear
    • tailmid
    • tailmid
    • tailend

    • rightear

    • spine2
      skeleton_color: cyan
      pcutoff: 0.6
      dotsize: 12
      alphavalue: 0.7
      colormap: plasma

      Training,Evaluation and Analysis configuration

TrainingFraction:

  • 0.95
    iteration: 0
    default_net_type: resnet_50
    default_augmenter: default
    snapshotindex: -1
    batch_size: 8

    Cropping Parameters (for analysis and outlier frame detection)

cropping: false
croppedtraining: false
#if cropping is true for analysis, then set the values here:
x1: 0
x2: 640
y1: 277
y2: 624

# Refinement configuration (parameters from annotation dataset configuration also relevant in this stage)

corner2move2:

  • 50
  • 50
    move2corner: true
#2

Hi, I responded on github, thanks!