File size: 1,413 Bytes
8a8c8c6 a647f1e 8a8c8c6 a647f1e 8a8c8c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
from ultralytics import YOLO
from PIL import Image
import gradio as gr
from huggingface_hub import snapshot_download
import os
#model_path = "/Users/markk/Downloads/best_int8_openvino_model"
#model_path1 = "C:\Users\aungh\Downloads\6319250G\best_int8_openvino_model"
# Define the path to the YOLOv8 mo
model_path = "best_int8_openvino_model"
#Organizations model path location
MODEL_REPO_ID = "aunghlaing/testmodel"
#load model
def load_model(repo_id):
download_dir = snapshot_download(repo_id)
print(download_dir)
path = os.path.join(download_dir, "best_int8_openvino_model")
print(path)
detection_model = YOLO(path, task='detect')
return detection_model
detection_model = load_model(MODEL_REPO_ID)
#Student ID
student_info = "Student Id: 6319250G, Name: AUNG HTUN"
#prdeict
def predict(pilimg):
source = pilimg
result = detection_model.predict(source, conf=0.5, iou=0.5)
img_bgr = result[0].plot()
out_pilimg = Image.fromarray(img_bgr[..., ::-1]) # RGB-order PIL image
return out_pilimg
#UI interface
gr.Markdown("# Two Object Detection (Shark/Mask)")
gr.Markdown(student_info)
gr.Interface(fn=predict,
inputs=gr.Image(type="pil",label="Input"),
outputs=gr.Image(type="pil",label="Output"),
title="Two Object Detection (Shark/Mask)",
description=student_info,
).launch(share=True) |