app.py

import gradio as gr
import torch
# from sahi.prediction import ObjectPrediction
# from sahi.utils.cv import visualize_object_predictions, read_image
import os 
import requests
import json

from PIL import Image
from huggingface_hub import hf_hub_download

from ultralyticsplus import YOLO, render_result

# from ultralyticsplus import render_result
# import requests
# import cv2

image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],['test_images/2ce19ce0191acb44920b.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/2daab6ea3310e14eb801.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/4a137deefb14294a7005 (1).jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/7e77c596436c9132c87d.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/170f914014bac6e49fab.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/3355ec3269c8bb96e2d9.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/546306a88052520c0b43.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/33148464019ed3c08a8f.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/a17a992a1cd0ce8e97c1.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/b5db5e42d8b80ae653a9 (1).jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],['test_images/b8ee1f5299a84bf612b9.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/b272fec7783daa63f32c.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],['test_images/bb202b3eaec47c9a25d5.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/bf1e22b0a44a76142f5b.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/ea5473c5f53f27617e2e.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],
             ['test_images/ee106392e56837366e79.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/f88d2214a4ee76b02fff.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45]]

# Load YOLO model
model = YOLO('linhcuem/cham_diem_yolov8')
# model = YOLO('linhcuem/cham_diem_yolov8_ver20')

###################################################
def yolov8_img_inference(
    image,
    model_path= None,
    image_size= 640,
    conf_threshold= 0.25,
    iou_threshold = 0.45,
):
    # model = YOLO(model_path)
    model.conf = conf_threshold
    model.iou = iou_threshold
    # model.overrides['conf'] = conf_threshold
    # model.overrides['iou'] = iou_threshold
    # model.overrides['agnostic_nms'] = False
    # model.overrides['max_det'] = 1000
    # image = read_image
    results = model.predict(image)
    render = render_result(model=model, image=image, result=results[0])
    # get the model names list
    names = model.names
    # get the 'obj' class id
    # obj_id = list(names)[list(names.values()).index('lo_ytv')]
    # ('hop_dln','hop_jn','hop_vtg','hop_ytv','lo_kids', 'lo_ytv','loc_dln','loc_jn','loc_kids','loc_ytv')]
    # obj_id = list(names)[list(names.values()).index([0])]
    # count 'car' objects in the results
    # count_result = results[0].boxes.cls[0].item()
    #count_result = results[0]boxes.cls[0].tolist()

    object_counts = {x: 0 for x in names}
    for r in results:
        for c in r.boxes.cls:
            c = int(c)
            if c in names:
                object_counts[c] += 1
            elif c not in names:
                object_counts[c] = 1

    present_objects = object_counts.copy()

    for i in object_counts:
        if object_counts[i] < 1:
            present_objects.pop(i)
    

    return render, {names[k]: v for k, v in present_objects.items()}
    # results = model.predict(image, imgsz=image_size, return_outputs=True)
    # results = model.predict(image)
    # object_prediction_list = []
    # for _, image_results in enumerate(results):
    #     if len(image_results)!=0:
    #         image_predictions_in_xyxy_format = image_results['det']
    #         for pred in image_predictions_in_xyxy_format:
    #             x1, y1, x2, y2 = (
    #                 int(pred[0]),
    #                 int(pred[1]),
    #                 int(pred[2]),
    #                 int(pred[3]),
    #             )
    #             bbox = [x1, y1, x2, y2]
    #             score = pred[4]
    #             category_name = model.model.names[int(pred[5])]
    #             category_id = pred[5]
    #             object_prediction = ObjectPrediction(
    #                 bbox=bbox,
    #                 category_id=int(category_id),
    #                 score=score,
    #                 category_name=category_name,
    #             )
    #             object_prediction_list.append(object_prediction)

    # image = read_image(image)
    # output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
    # return output_image['image']
    # render = render_result(model=model, image=image, result=results[0])
 

        

inputs_image = [
    # gr.inputs.Image(type="filepath", label="Input Image"),
    gr.Image(type="pil"),
    gr.Dropdown(["linhcuem/linhcuem/cham_diem_yolov8"], 
                        default="linhcuem/cham_diem_yolov8", label="Model"),
    gr.Slider(maximum=1280, step=32, value = 640 label="Image Size"),
    gr.Slider(maximum=1.0 , step=0.05, value = 0.25, label="Confidence Threshold"),
    gr.Slider(maximum=1.0, step=0.05, value = 0.45, label="IOU Threshold"),
    
    
]

# outputs_image =gr.outputs.Image(type="filepath", label="Output Image")
# count_obj = gr.Textbox(show_label=False)

title = "Tất cả do anh Đạt"

interface_image = gr.Interface(
    fn=yolov8_img_inference,
    inputs=inputs_image,
    outputs=[gr.Image(type="pil"),gr.Textbox(show_label=False)],
    title=title,
    examples=image_path,
    cache_examples=True,
    theme='huggingface'
)

# gr.TabbedInterface(
#     [interface_image],
#     tab_names=['Image inference']
# ).queue().launch()

interface_image.launch(debug=True, enable_queue=True)