app.py

# Adapted from https://huggingface.co/spaces/hlydecker/MegaDetector_v5 
# Adapted from https://huggingface.co/spaces/sofmi/MegaDetector_DLClive/blob/main/app.py
# Adapted from https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels/blob/main/app.py 
# Adapted from  https://huggingface.co/spaces/DeepLabCut/MegaDetector_DeepLabCut

import os
import threading
import yaml
import numpy as np
from matplotlib import cm
import gradio as gr
import deeplabcut
import dlclibrary
import dlclive
# import transformers

from PIL import Image, ImageColor, ImageFont, ImageDraw

from viz_utils import save_results_as_json, draw_keypoints_on_image, draw_bbox_w_text, save_results_only_dlc, save_results_pytorch
from viz_utils import add_confidence_legend, keypoint_confidence_rows, save_annotated_image
from detection_utils import predict_md, crop_animal_detections
from dlc_utils import predict_dlc
from pytorch_utils import predict_superanimal, load_superanimal, PYTORCH_MODELS
from ui_utils import gradio_inputs_for_MD_DLC, gradio_outputs_for_MD_DLC, gradio_description_and_examples

from deeplabcut.utils import auxiliaryfunctions
from dlclibrary.dlcmodelzoo.modelzoo_download import (
    download_huggingface_model,
    MODELOPTIONS,
)
from dlclive import DLCLive, Processor


# TESTING (passes) download the SuperAnimal models:
#model = 'superanimal_topviewmouse'
#train_dir = 'DLC_models/sa-tvm'
#download_huggingface_model(model, train_dir)

# megadetector and dlc model look up
MD_models_dict = {'md_v5a': "MD_models/md_v5a.0.0.pt", # 
                  'md_v5b': "MD_models/md_v5b.0.0.pt"}

BACKENDS = ["PyTorch", "TensorFlow (legacy)"]

# TF (legacy) DLC models: model zoo name and target dir, per SuperAnimal
DLC_models_dict = {'superanimal_topviewmouse': ('superanimal_topviewmouse_dlcrnet', "DLC_models/sa-tvm"),
                   'superanimal_quadruped': ('superanimal_quadruped_dlcrnet', "DLC_models/sa-q")}



#####################################################
def finalize_outputs(img_output, download_file, kpts_per_animal, map_label_id_to_str, color_by_confidence):
    # confidence legend, annotated image for download and per-keypoint confidence table
    if color_by_confidence:
        img_output = add_confidence_legend(img_output)
    annotated_file = save_annotated_image(img_output)
    confidence_rows = keypoint_confidence_rows(kpts_per_animal, map_label_id_to_str)
    return img_output, download_file, annotated_file, confidence_rows


#####################################################
def predict_pipeline_pytorch(img_input,
                             superanimal,
                             flag_dlc_only,
                             flag_show_str_labels,
                             bbox_likelihood_th,
                             kpts_likelihood_th,
                             font_style,
                             font_size,
                             keypt_color,
                             marker_size,
                             flag_color_by_confidence,
                             ):
    # detection + pose with the SuperAnimal PyTorch models (keypoints in image coords)
    img_output, animals, bodyparts = predict_superanimal(img_input,
                                                         superanimal,
                                                         bbox_likelihood_th,
                                                         kpts_likelihood_th,
                                                         full_image=flag_dlc_only)
    map_label_id_to_str = dict(enumerate(bodyparts))

    for animal in animals:
        draw_keypoints_on_image(img_output,
                                animal['kpts'],
                                map_label_id_to_str,
                                flag_show_str_labels,
                                use_normalized_coordinates=False,
                                font_style=font_style,
                                font_size=font_size,
                                keypt_color=keypt_color,
                                marker_size=marker_size,
                                color_by_confidence=flag_color_by_confidence)
        if not flag_dlc_only:
            draw_bbox_w_text(img_output,
                             animal['bbox'],
                             font_size=font_size)

    pose_model, detector = PYTORCH_MODELS[superanimal]
    download_file = save_results_pytorch(animals, map_label_id_to_str, superanimal,
                                         pose_model, None if flag_dlc_only else detector)
    return finalize_outputs(img_output, download_file,
                            [animal['kpts'] for animal in animals], map_label_id_to_str,
                            flag_color_by_confidence)


#####################################################
def predict_pipeline(img_input,
                     backend,
                     mega_model_input,
                     dlc_model_input_str,
                     flag_dlc_only,
                     flag_show_str_labels,
                     bbox_likelihood_th,
                     kpts_likelihood_th,
                     font_style,
                     font_size,
                     keypt_color,
                     marker_size,
                     flag_color_by_confidence,
                     ):

    if backend == "PyTorch":
        return predict_pipeline_pytorch(img_input,
                                        dlc_model_input_str,
                                        flag_dlc_only,
                                        flag_show_str_labels,
                                        bbox_likelihood_th,
                                        kpts_likelihood_th,
                                        font_style,
                                        font_size,
                                        keypt_color,
                                        marker_size,
                                        flag_color_by_confidence)

    # TensorFlow (legacy): MegaDetector crops + DLCLive
    dlc_model_name, dlc_model_dir = DLC_models_dict[dlc_model_input_str]

    if not flag_dlc_only:
        ############################################################                                               
        # ### Run Megadetector
        md_results = predict_md(img_input, 
                                MD_models_dict[mega_model_input], #mega_model_input,
                                size=640) #Image.fromarray(results.imgs[0])

        ################################################################
        # Obtain animal crops (and their bboxes) with confidence above th
        list_crops, list_bboxes = crop_animal_detections(img_input,
                                                         md_results,
                                                         bbox_likelihood_th)

        ############################################################

    ## Get DLC model and label map  
    
    # If model is found: do not download (previous execution is likely within same day)
    # TODO: can we ask the user whether to reload dlc model if a directory is found?
    path_to_DLCmodel = dlc_model_dir
    if not (os.path.isdir(dlc_model_dir) and len(os.listdir(dlc_model_dir)) > 0):
        download_huggingface_model(dlc_model_name, path_to_DLCmodel)

    # extract map label ids to strings
    pose_cfg_path = os.path.join(dlc_model_dir,
                                 'pose_cfg.yaml')
    with open(pose_cfg_path, "r") as stream:
        pose_cfg_dict = yaml.safe_load(stream) 
    map_label_id_to_str = dict([(k,v) for k,v in zip([el[0] for el in pose_cfg_dict['all_joints']],  # pose_cfg_dict['all_joints'] is a list of one-element lists,
                                                     pose_cfg_dict['all_joints_names'])])


##############################################################
    # Run DLC and visualize results
    dlc_proc = Processor() #TODO: update deeplabcut.video_inference_superanimal() once merged

    # if required: ignore MD crops and run DLC on full image [mostly for testing]
    if flag_dlc_only:
        # compute kpts on input img
        list_kpts_per_crop = predict_dlc([np.asarray(img_input)],
                                         kpts_likelihood_th,
                                         path_to_DLCmodel,
                                         dlc_proc)
        # draw kpts on input img #fix!
        draw_keypoints_on_image(img_input,
                                list_kpts_per_crop[0], # a numpy array with shape [num_keypoints, 2].
                                map_label_id_to_str,
                                flag_show_str_labels,
                                use_normalized_coordinates=False,
                                font_style=font_style,
                                font_size=font_size,
                                keypt_color=keypt_color,
                                marker_size=marker_size,
                                color_by_confidence=flag_color_by_confidence)

        donw_file = save_results_only_dlc(list_kpts_per_crop[0], map_label_id_to_str,dlc_model_name)

        return finalize_outputs(img_input, donw_file,
                                [list_kpts_per_crop[0]], map_label_id_to_str,
                                flag_color_by_confidence)

    else:
        # Compute kpts for each crop
        list_kpts_per_crop = predict_dlc(list_crops,
                                         kpts_likelihood_th,
                                         path_to_DLCmodel,
                                         dlc_proc)
        
        # resize input image to match megadetector output
        img_background = img_input.resize((md_results.ims[0].shape[1],
                                           md_results.ims[0].shape[0]))
        
        # draw keypoints on each crop and paste to background img
        for np_crop, kpts_crop, bb_per_animal in zip(list_crops,
                                                     list_kpts_per_crop,
                                                     list_bboxes):

            img_crop = Image.fromarray(np_crop)

            # Draw keypts on crop
            draw_keypoints_on_image(img_crop,
                                    kpts_crop, # a numpy array with shape [num_keypoints, 2].
                                    map_label_id_to_str,
                                    flag_show_str_labels,
                                    use_normalized_coordinates=False,  # if True, then I should use md_results.xyxyn for list_kpts_crop
                                    font_style=font_style,
                                    font_size=font_size,
                                    keypt_color=keypt_color,
                                    marker_size=marker_size,
                                    color_by_confidence=flag_color_by_confidence)

            # Paste crop in original image
            img_background.paste(img_crop,
                                 box = tuple([int(t) for t in bb_per_animal[:2]]))

            # Plot bbox
            draw_bbox_w_text(img_background,
                             bb_per_animal,
                             font_size=font_size)  # TODO: add selectable color for bbox?


        # Save detection results as json
        download_file  = save_results_as_json(md_results,list_kpts_per_crop,list_bboxes,map_label_id_to_str,dlc_model_name,mega_model_input)

        return finalize_outputs(img_background, download_file,
                                list_kpts_per_crop, map_label_id_to_str,
                                flag_color_by_confidence)



#########################################################
# Define user interface and launch
[gr_title,
 gr_description,
 examples] = gradio_description_and_examples()

with gr.Blocks(title=gr_title) as demo:
    gr.Markdown(f"# {gr_title}\n{gr_description}")
    with gr.Row():
        with gr.Column():
            inputs = gradio_inputs_for_MD_DLC(BACKENDS,
                                              list(MD_models_dict.keys()),
                                              list(DLC_models_dict.keys()))
            run_button = gr.Button("Run", variant="primary")
        with gr.Column():
            outputs = gradio_outputs_for_MD_DLC()

    # the MegaDetector choice only applies to the TensorFlow (legacy) backend
    gr_backend_input, gr_mega_model_input = inputs[1], inputs[2]
    gr_backend_input.change(lambda backend: gr.update(visible=backend != "PyTorch"),
                            inputs=gr_backend_input,
                            outputs=gr_mega_model_input)

    run_button.click(predict_pipeline, inputs=inputs, outputs=outputs, api_name="predict")

    # cached on first click, so a failing download cannot block startup
    gr.Examples(examples,
                inputs=inputs,
                outputs=outputs,
                fn=predict_pipeline,
                cache_examples=True,
                cache_mode="lazy")

# download and build the default model while the app starts; a request arriving
# earlier waits on the same lock instead of downloading again
threading.Thread(target=load_superanimal, args=("superanimal_quadruped",), daemon=True).start()

demo.queue(default_concurrency_limit=1)  # PyTorch runners are not thread-safe
demo.launch(theme=gr.themes.Default())