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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
from ultralyticsplus import YOLO, render_result
# from ultralyticsplus import render_result
# import requests
# import cv2
image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/2ce19ce0191acb44920b.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/2daab6ea3310e14eb801.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/4a137deefb14294a7005 (1).jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/7e77c596436c9132c87d.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/170f914014bac6e49fab.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/3355ec3269c8bb96e2d9.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/546306a88052520c0b43.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/33148464019ed3c08a8f.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/a17a992a1cd0ce8e97c1.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/b5db5e42d8b80ae653a9 (1).jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/b8ee1f5299a84bf612b9.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/b272fec7783daa63f32c.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/bb202b3eaec47c9a25d5.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/bf1e22b0a44a76142f5b.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/ea5473c5f53f27617e2e.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/ee106392e56837366e79.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/f88d2214a4ee76b02fff.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45]]
# Load YOLO model
model = YOLO('linhcuem/chamdiem_yolov8_ver10')
###################################################
def yolov8_img_inference(
image: gr.inputs.Image = None,
model_path: gr.inputs.Dropdown = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 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])
return render
# 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.inputs.Dropdown(["linhcuem/chamdiem_yolov8_ver10"],
default="linhcuem/chamdiem_yolov8_ver10", label="Model"),
gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs_image =gr.outputs.Image(type="filepath", label="Output Image")
title = "Tất cả do anh Đạt"
interface_image = gr.Interface(
fn=yolov8_img_inference,
inputs=inputs_image,
outputs=outputs_image,
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) |