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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] |