File size: 1,800 Bytes
89c67a6 647f0c6 558ce13 89c67a6 c04da13 89c67a6 c04da13 89c67a6 4143042 89c67a6 c04da13 7e78087 c04da13 cbde59c 7e78087 cbde59c 89c67a6 |
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 |
import gradio as gr
from ultralytics import YOLO
import cv2
examples=[["photo/a.jpg"],["photo/b.jpg"],
["photo/c.jpg"],["photo/d.jpg"],
["photo/e.jpg"],["photo/f.jpg"],
["photo/g.jpg"],["photo/h.jpg"],
["photo/multi tomatos.jpg"]]
def detect_objects_on_image(image_path, conf_threshold, iou_threshold):
image = cv2.imread(image_path)
model = YOLO("best.pt")
results = model.predict(
source=image,
conf=conf_threshold,
iou=iou_threshold,
show_labels=True,
show_conf=True,
imgsz=640,
)
result = results[0]
output = []
for box in result.boxes:
x1, y1, x2, y2 = [
round(x) for x in box.xyxy[0].tolist()
]
class_id = box.cls[0].item()
prob = round(box.conf[0].item(), 2)
output.append([
x1, y1, x2, y2, result.names[class_id], prob
])
cv2.rectangle(
image,
(x1, y1),
(x2, y2),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
cv2.putText(image,result.names[class_id]+'_'+str(prob), (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
demo = gr.Interface(
fn=detect_objects_on_image,
inputs=[
gr.Image(type="filepath", label="Input Image"),
gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
],
outputs=[
gr.Image(type="numpy", label="Output Image"),
],
title="Yolov8 Custom Object Detection",
examples=examples,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch() |