app.py

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


import gradio as gr

import torch
import torchvision
from dlclive import DLCLive, Processor

from PIL import Image, ImageColor, ImageFont, ImageDraw 
# check git lfs pull!!
import numpy as np
import math

# import json
import os
import yaml

import pdb

#########################################
# Input params
FONTS = {'amiko': "font/Amiko-Regular.ttf",
        'nature': "font/LoveNature.otf", 
        'painter':"font/PainterDecorator.otf",
        'animals': "font/UncialAnimals.ttf", 
        'zen': "font/ZEN.TTF"}

Megadet_Models = {'md_v5a': "megadet_model/md_v5a.0.0.pt",
                  'md_v5b': "megadet_model/md_v5b.0.0.pt"}

DLC_models = {'full_cat': "model/DLC_Cat_resnet_50_iteration-0_shuffle-0",
              'full_dog': "model/DLC_Dog_resnet_50_iteration-0_shuffle-0",
              'primate_face': "model/DLC_FacialLandmarks_resnet_50_iteration-1_shuffle-1",
              'full_human': "model/DLC_human_dancing_resnet_101_iteration-0_shuffle-1",
              'full_macaque': 'model/DLC_monkey_resnet_50_iteration-0_shuffle-1'}                  
           
#########################################
#  Draw keypoints on image
def draw_keypoints_on_image(image,
                            keypoints,
                            map_label_id_to_str,
                            flag_show_str_labels,
                            use_normalized_coordinates=True,                           
                            font_style='amiko',
                            font_size=8,
                            keypt_color="#ff0000",
                            marker_size='2'):
    """Draws keypoints on an image.
    Modified from:
        https://www.programcreek.com/python/?code=fjchange%2Fobject_centric_VAD%2Fobject_centric_VAD-master%2Fobject_detection%2Futils%2Fvisualization_utils.py
    Args:
    image: a PIL.Image object.
    keypoints: a numpy array with shape [num_keypoints, 2].
    map_label_id_to_str: dict with keys=label number and values= label string
    flag_show_str_labels: boolean to select whether or not to show string labels
    color: color to draw the keypoints with. Default is red.
    radius: keypoint radius. Default value is 2.
    use_normalized_coordinates: if True (default), treat keypoint values as
        relative to the image.  Otherwise treat them as absolute.
    """
    # get a drawing context
    draw = ImageDraw.Draw(image)  

    im_width, im_height = image.size
    keypoints_x = [k[0] for k in keypoints]
    keypoints_y = [k[1] for k in keypoints]

    # adjust keypoints coords if required
    if use_normalized_coordinates:
        keypoints_x = tuple([im_width * x for x in keypoints_x])
        keypoints_y = tuple([im_height * y for y in keypoints_y])

    # draw ellipses around keypoints and add string labels
    for i, (keypoint_x, keypoint_y) in enumerate(zip(keypoints_x, keypoints_y)):
        draw.ellipse([(keypoint_x - marker_size, keypoint_y - marker_size),
                      (keypoint_x + marker_size, keypoint_y + marker_size)],
                       outline=keypt_color, 
                       fill=keypt_color)

        # add string labels around keypoints
        if flag_show_str_labels:
            font = ImageFont.truetype(FONTS[font_style], 
                                     font_size) 
            draw.text((keypoint_x + marker_size, keypoint_y + marker_size),#(0.5*im_width, 0.5*im_height), #-------
                      map_label_id_to_str[i],
                      ImageColor.getcolor(keypt_color, "RGB"), # rgb
                      font=font)
                    
############################################
# Predict detections with MegaDetector v5a model
def predict_md(im, 
               mega_model_input,
               size=640):
               
    # resize image
    g = (size / max(im.size))  # multipl factor to make max size of the image equal to input size
    im = im.resize((int(x * g) for x in im.size), 
                    Image.ANTIALIAS)  # resize
    MD_model = torch.hub.load('ultralytics/yolov5', 'custom', Megadet_Models[mega_model_input])
    
    ## detect objects
    results = MD_model(im)  # inference # vars(results).keys()= dict_keys(['imgs', 'pred', 'names', 'files', 'times', 'xyxy', 'xywh', 'xyxyn', 'xywhn', 'n', 't', 's'])
    results.render()  # updates results.imgs with boxes and labels

    return results  

##########################################
def crop_animal_detections(yolo_results, 
                           likelihood_th):

    ## Extract animal crops
    #print(yolo_results)
    list_labels_as_str = [i for i in yolo_results.names.values()]  # ['animal', 'person', 'vehicle'] 
    list_np_animal_crops = []
    # for every image
    #pdb.set_trace()
    for img, det_array in zip(yolo_results.ims,
                              yolo_results.xyxy):

        # for every detection
        for j in range(det_array.shape[0]):

            # compute coords around bbox rounded to the nearest integer (for pasting later)
            xmin_rd = int(math.floor(det_array[j,0])) # int() should suffice?
            ymin_rd = int(math.floor(det_array[j,1]))

            xmax_rd = int(math.ceil(det_array[j,2]))
            ymax_rd = int(math.ceil(det_array[j,3]))

            pred_llk = det_array[j,4] 
            pred_label = det_array[j,5]

            # keep animal crops above threshold
            #pdb.set_trace()
            if (pred_label == list_labels_as_str.index('animal')) and \
                (pred_llk >= likelihood_th):
                area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd)

                crop = Image.fromarray(img).crop(area)
                crop_np = np.asarray(crop)

                # add to list
                list_np_animal_crops.append(crop_np)

    return list_np_animal_crops

##########################################
def predict_dlc(list_np_crops,
                kpts_likelihood_th,
                DLCmodel,
                dlc_proc):

    # run dlc thru list of crops
    dlc_live = DLCLive(DLCmodel, processor=dlc_proc)
    dlc_live.init_inference(list_np_crops[0])

    list_kpts_per_crop = []
    np_aux = np.empty((1,3)) # can I avoid hardcoding here?
    for crop in list_np_crops:
        # scale crop here?
        keypts_xyp = dlc_live.get_pose(crop) # third column is llk!
        # set kpts below threhsold to nan
        print(keypts_xyp)
        #pdb.set_trace()
        keypts_xyp[keypts_xyp[:,-1] < kpts_likelihood_th,:] = np_aux.fill(np.nan)
        # add kpts of this crop to list 
        list_kpts_per_crop.append(keypts_xyp)
    #return confidence here
    return list_kpts_per_crop


#####################################################
def predict_pipeline(img_input,
                     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,
                     ):

    ############################################################
    ## Get DLC model and labels as strings   
    # TODO: make a dict as for megadetector
    # pdb.set_trace()
    path_to_DLCmodel = DLC_models[dlc_model_input_str]
    pose_cfg_path = os.path.join(path_to_DLCmodel,'pose_cfg.yaml')        
                     
    # extract map label ids to strings
    # pose_cfg_dict['all_joints'] is a list of one-element lists,
    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_names'])])

    ############################################################                                               
    # ### Run Megadetector
    md_results = predict_md(img_input, 
                            mega_model_input,
                            size=640) #Image.fromarray(results.imgs[0])
    pdb.set_trace()
    ################################################################
    # Obtain animal crops for bboxes with confidence above th

    list_crops = crop_animal_detections(md_results,
                                        bbox_likelihood_th)

    ##############################################################
    # Run DLC
    dlc_proc = Processor()

    # 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
        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)
        return img_input

    else:
        # Compute kpts for each crop
        list_kpts_per_crop = predict_dlc(list_crops,
                                         kpts_likelihood_th,
                                         path_to_DLCmodel,
                                         dlc_proc)

        # Produce final image
        img_background = Image.fromarray(md_results.imgs[0]) # img_input or Image.fromarray(md_results.imgs[0])? 
        # Image.fromarray(md_results.imgs[0]) --> (640, 479)
        # img_input.size ---> (259, 194)
        # pdb.set_trace()

        # resize image to match  megadetector output
        # g = (640 / max(img_background.size))  # gain
        # img_background = img_background.resize((int(x * g) for x in img_background.size), Image.ANTIALIAS)  # resize
        for ic, (np_crop, kpts_crop) in enumerate(zip(list_crops,
                                                      list_kpts_per_crop)):

            ## Draw keypts on crop
            img_crop = Image.fromarray(np_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)

            ## Paste crop in original image
            # https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.paste
            img_background.paste(img_crop,
                                 box = tuple([int(t) for t in md_results.xyxy[0][ic,:2]]))
                                                                
        return img_background

#############################################
# %% 
# User interface: inputs

# Input image
gr_image_input = gr.inputs.Image(type="pil", label="Input Image")


# Models
gr_dlc_model_input = gr.inputs.Dropdown(choices=list(DLC_models.keys()), # 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')                                                                     
gr_mega_model_input = gr.inputs.Dropdown(choices=list(Megadet_Models.keys()),
                                         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')
# 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,.05,0.8,
                                        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(label="choose color for keypoint label") 

gr_labels_font_style = gr.inputs.Dropdown(choices=['amiko', 'nature', 'painter', 'animals', '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(0.5,5,0.2,2,
                                        label='Set marker size')   

# list of inputs
inputs = [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,
             ]                                                                                
####################################################
# %% 
# User interface: outputs
gr_image_output = gr.outputs.Image(type="pil", label="Output Image")
outputs = [gr_image_output]

##############################################
# User interace: description
gr_title = "MegaDetector v5 + DeepLabCut-Live!"
gr_description = "Contributed by Sofia Minano, Neslihan Wittek, Nirel Kadzo, VicShaoChih Chiang -- DLC AI Residents 2022..\
                  This App detects and estimate the pose of animals in camera trap images using <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'>MegaDetector Models</a>. Please carefully check their licensing information if you use this project. The App additionally builds upon on work from <a href='https://huggingface.co/spaces/hlydecker/MegaDetector_v5'>hlydecker/MegaDetector_v5</a> \
                  <a href='https://huggingface.co/spaces/sofmi/MegaDetector_DLClive'>sofmi/MegaDetector_DLClive</a> \
                  <a href='https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels'>Neslihan/megadetector_dlcmodels</a>\."

# 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 = [['example/monkey_full.jpg', 'md_v5a','full_macaque', False, True, 0.5, 0.3, 'amiko', 5, 'blue', 3],
            ['example/dog.jpeg', 'md_v5a', 'full_dog', False, True, 0.5, 0.05, 'amiko', 5, 'yellow', 3],
            ['example/cat.jpg', 'md_v5a', 'full_cat', False, True, 0.5, 0.05, 'amiko', 5, 'purple', 3]]

################################################
# %% Define and launch gradio interface
demo = gr.Interface(predict_pipeline, 
                    inputs=inputs,
                    outputs=outputs, 
                    title=gr_title, 
                    description=gr_description,
                    examples=examples,
                    theme="huggingface",
                    #live=True
                    )

demo.launch(enable_queue=True, share=True)