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import gradio as gr | |
from ultralytics import YOLO | |
from PIL import Image | |
# Load a pretrained YOLOv8n model | |
model = YOLO("yolov8n.pt") | |
def main_function(lbd, img): | |
results = model(img) # predict on an image | |
r = results[0] | |
im_bgr = r.plot() # BGR-order numpy array | |
im_rgb = Image.fromarray(im_bgr[..., ::-1]) # RGB-order PIL image | |
new_img = im_rgb | |
# res = results[0].save(filename="output.jpg") # save the image | |
# # load image | |
# new_img = Image.open("output.jpg") | |
return new_img | |
iface = gr.Interface( | |
fn=main_function, | |
inputs=["slider", gr.Image(type="pil")], | |
outputs=gr.Image(type="pil"), | |
examples=[ | |
[0, "bus.jpg"], | |
], | |
) | |
iface.launch() | |