app.py

import gradio as gr
import torch
from io import BytesIO
import os

import cv2
import gradio as gr
import numpy as np
import requests
from PIL import Image

try:

    import super_gradients
except ImportError:
    os.system("pip install -e .")

    import super_gradients
from super_gradients.common.object_names import Models
from super_gradients.training import models
from super_gradients.training.utils.visualization.detection import draw_bbox


# Initialize your pose estimation model
yolo_nas_pose = models.get("best.pt",
                           num_classes=1,
                           checkpoint_path="./best.pt")

def process_and_predict(url=None,
                        image=None,
                        confidence=0.5,
                        iou=0.5):
    # If a URL is provided, use it directly for prediction
    if url is not None and url.strip() != "":
        response = requests.get(url)
        image = Image.open(BytesIO(response.content))
        image = np.array(image)
        result = yolo_nas_pose.predict(image, conf=confidence,iou=iou)
    # If a file is uploaded, read it, convert it to a numpy array and use it for prediction
    elif image is not None:
        result = yolo_nas_pose.predict(image, conf=confidence,iou=iou)
    else:
        return None  # If no input is provided, return None

    # Extract prediction data
    image_prediction = result._images_prediction_lst[0]

    pose_data = image_prediction.prediction

    # Visualize the prediction
    output_image = PoseVisualization.draw_poses(
        image=image_prediction.image,
        poses=pose_data.poses,
        boxes=pose_data.bboxes_xyxy,
        scores=pose_data.scores,
        is_crowd=None,
        edge_links=pose_data.edge_links,
        edge_colors=pose_data.edge_colors,
        keypoint_colors=pose_data.keypoint_colors,
        joint_thickness=2,
        box_thickness=2,
        keypoint_radius=5
    )

    blank_image = np.zeros_like(image_prediction.image)

    skeleton_image = PoseVisualization.draw_poses(
    image=blank_image,
    poses=pose_data.poses,
    boxes=pose_data.bboxes_xyxy,
    scores=pose_data.scores,
    is_crowd=None,
    edge_links=pose_data.edge_links,
    edge_colors=pose_data.edge_colors,
    keypoint_colors=pose_data.keypoint_colors,
    joint_thickness=2,
    box_thickness=2,
    keypoint_radius=5
)

    return output_image, skeleton_image

def greet(name):
    return "Hello " + name + "!!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")

from urllib.request import urlretrieve

# get image examples from github
urlretrieve("https://github.com/SamDaaLamb/ValorantTracker/blob/main/clip2_-1450-_jpg.jpg?raw=true", "clip2_-1450-_jpg.jpg") # make sure to use "copy image address when copying image from Github"
urlretrieve("https://github.com/SamDaaLamb/ValorantTracker/blob/main/clip2_-539-_jpg.jpg?raw=true", "clip2_-539-_jpg.jpg")
examples = [ # need to manually delete cache everytime new examples are added
    ["clip2_-1450-_jpg.jpg"], 
    ["clip2_-539-_jpg.jpg"]]


# define app features and run
title = "SpecLab Demo"
description = "<p style='text-align: center'>Gradio demo for an ASPP model architecture trained on the SpecLab dataset. To use it, simply add your image, or click one of the examples to load them. Since this demo is run on CPU only, please allow additional time for processing. </p>"
article = "<p style='text-align: center'><a href='https://github.com/Nano1337/SpecLab'>Github Repo</a></p>"
css = "#0 {object-fit: contain;} #1 {object-fit: contain;}"
demo = gr.Interface(fn=speclab, 
                    title=title, 
                    description=description,
                    article=article,
                    inputs=gr.Image(elem_id=0, show_label=False), 
                    outputs=gr.Image(elem_id=1, show_label=False),
                    css=css, 
                    examples=examples, 
                    cache_examples=True,
                    allow_flagging='never')
demo.launch()