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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-live'>DeepLabCut-live</a>. \ | |
| We host 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" | |
| # article = "<p style='text-align: center'>This app makes predictions using a YOLOv5x6 model that was trained to detect animals, humans, and vehicles in camera trap images; find out more about the project on <a href='https://github.com/microsoft/CameraTraps'>GitHub</a>. This app was built by Henry Lydecker but really depends on code and models developed by <a href='http://ecologize.org/'>Ecologize</a> and <a href='http://aka.ms/aiforearth'>Microsoft AI for Earth</a>. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset and developed by Ultralytics, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>" | |
| examples = [['examples/dog.jpeg', 'md_v5a', 'full_dog', False, True, 0.5, 0.00, 'amiko',9, 'yellow', 3]] | |
| return [title,description,examples] |