annolid
Annolid User Guide
Install Annolid
Install Detectron2 locally
Install Detectron2 on Google Colab
Optional: Install older version of Pytorch for YOLACT
Extract desired number of frames from a video based on optical flow
Display optical flow while extracting frames with
–show_flow=True
Save all the frames as images
Select frames randomly by reservoir sampling
Extract all the key frames from a video used by the compression methods
Track animals and Auto labeling
Output CSV format
Config keypoint connection rules, events, and instances
Threshold based object segmenation
Convert WMV format to mp4 format using ffmpeg
Save the extracted frames to a user selected output directory
How to track multiple objects in the video?
How to convert coco annonation format to YOLOV5 format?
How to train a custom YOLOV5 model?
How to track objects in a video with the trained model?
How to convert labelme labeled dataset to COCO format?
How to train a YOLACT model with a custom dataset?
How to evaluate a video based on a trained model?
Convert the tracking results csv file to Glitter2 csv format
Convert the keypoint annotations to labelme format
annolid
annolid.gui.widgets package
View page source
annolid.gui.widgets package
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Submodules
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annolid.gui.widgets.convert_yolo_dialog module
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annolid.gui.widgets.covert_coco_dialog module
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annolid.gui.widgets.extract_frame_dialog module
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annolid.gui.widgets.play_video module
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annolid.gui.widgets.track_dialog module
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annolid.gui.widgets.train_model_dialog module
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Module contents
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