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import gradio as gr
##############################
def gradio_inputs_for_MD_DLC(md_models_list, # list(MD_models_dict.keys())
dlc_models_list, # list(DLC_models_dict.keys())
):
# Input image
gr_image_input = gr.inputs.Image(type="pil", label="Input Image")
# Models
gr_mega_model_input = gr.inputs.Dropdown(choices=md_models_list,
default='md_v5a', # default option
type='value', # Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected.
label='Select MegaDetector model')
gr_dlc_model_input = gr.inputs.Dropdown(choices=dlc_models_list, # choices
default='full_cat', # default option
type='value', # Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected.
label='Select DeepLabCut model')
# Other inputs
gr_dlc_only_checkbox = gr.inputs.Checkbox(False,
label='Run DLClive only, directly on input image?')
gr_str_labels_checkbox = gr.inputs.Checkbox(True,
label='Show bodypart labels?')
gr_slider_conf_bboxes = gr.inputs.Slider(0,1,.02,0.8,
label='Set confidence threshold for animal detections')
gr_slider_conf_keypoints = gr.inputs.Slider(0,1,.05,0,
label='Set confidence threshold for keypoints')
# Data viz
gr_keypt_color = gr.ColorPicker(value ="#ff0000", label="choose color for keypoint label")
gr_labels_font_style = gr.inputs.Dropdown(choices=['amiko', 'animals', 'nature', 'painter', 'zen'],
default='amiko',
type='value',
label='Select keypoint label font')
gr_slider_font_size = gr.inputs.Slider(5,30,1,8,
label='Set font size')
gr_slider_marker_size = gr.inputs.Slider(1,20,1,5,
label='Set marker size')
# list of inputs
return [gr_image_input,
gr_mega_model_input,
gr_dlc_model_input,
gr_dlc_only_checkbox,
gr_str_labels_checkbox,
gr_slider_conf_bboxes,
gr_slider_conf_keypoints,
gr_labels_font_style,
gr_slider_font_size,
gr_keypt_color,
gr_slider_marker_size]
####################################################
def gradio_outputs_for_MD_DLC():
# User interface: outputs
gr_image_output = gr.outputs.Image(type="pil", label="Output Image")
gr_file_download = gr.File(label="Download JSON file")
return [gr_image_output,
gr_file_download]
##############################################
# User interace: description
def gradio_description_and_examples():
title = "DeepLabCut Model Zoo SuperAnimals"
description = " <a href='https://github.com/microsoft/CameraTraps'>MegaDetector v5a</a> + <a href='https://github.com/DeepLabCut/DeepLabCut'>DeepLabCut</a>. \
We host SuperAnimal models from the <a href='http://www.mackenziemathislab.org/dlc-modelzoo'>DeepLabCut ModelZoo Project</a>\, and two <a href='https://github.com/microsoft/CameraTraps/blob/main/megadetector.md'>Mega Detector Models</a>. This repo is based on https://huggingface.co/spaces/DeepLabCut/MegaDetector_DeepLabCut"
examples = [['examples/dog.jpeg', 'md_v5a', 'full_dog', False, True, 0.5, 0.00, 'amiko',9, 'red', 3]]
return [title,description,examples] |