File size: 2,005 Bytes
aeeb601 afc670e b6d54f1 b0e1d53 fee70a2 aeeb601 9ce1868 aeeb601 c134122 b6d54f1 be96225 b6d54f1 be96225 6dedfce c134122 b6d54f1 605ba81 b6d54f1 3982a18 b6d54f1 3982a18 b6d54f1 9ce1868 b6d54f1 e857e4e c134122 b6d54f1 e857e4e c134122 bfa44c0 6cce185 |
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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import gradio as gr
import matplotlib.pyplot as plt
from PIL import Image
from ultralyticsplus import YOLO, render_result
import cv2
import numpy as np
model = YOLO('best (1).pt')
def response(image):
print(image)
results = model(image)
for i, r in enumerate(results):
# Plot results image
im_bgr = r.plot()
im_rgb = im_bgr[..., ::-1] # Convert BGR to RGB
# im_rgb = Image.fromarray(im_rgb)
return im_rgb
def yoloV8_func(image: gr.Image = None,
image_size: gr.Slider = 640,
conf_threshold: gr.Slider = 0.4,
iou_threshold: gr.Slider = 0.50):
model = YOLO('best (1).pt')
results = model.predict(image,
conf=conf_threshold,
iou=iou_threshold,
imgsz=image_size)
box = results[0].boxes
render = render_result(model=model, image=image, result=results[0], rect_th = 4, text_th = 4)
return render
inputs = [
gr.Image( label="Input Image"),
gr.Slider(minimum=320, maximum=1280, value=640,
step=32, label="Image Size"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.25,
step=0.05, label="Confidence Threshold"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.45,
step=0.05, label="IOU Threshold"),
]
outputs = gr.Image( label="Output Image")
title = "YOLOv8 Custom Object Detection by Uyen Nguyen"
# examples = [['one.jpg', 900, 0.5, 0.8],
# ['two.jpg', 1152, 0.05, 0.05],
# ['three.jpg', 1024, 0.25, 0.25],
# ['four.jpg', 832, 0.3, 0.3]]
# yolo_app = gr.Interface(
# fn=yoloV8_func,
# inputs=inputs,
# outputs=outputs,
# title=title,
# # examples=examples,
# # cache_examples=True,
# )
# Launch the Gradio interface in debug mode with queue enabled
# yolo_app.launch()
iface = gr.Interface(fn=yoloV8_func, inputs=inputs, outputs=outputs)
iface.launch()
|