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from ultralytics import YOLO
import gradio as gr
import cv2
import os
import random
model = YOLO('best.pt')
def show_preds_image(image_path):
image = cv2.imread(image_path)
outputs = model.predict(source=image_path, conf=0.45, save=True)
print("output:", outputs)
results = outputs[0]
print("results:", results)
# for i, det in enumerate(results.boxes.xyxy.cpu().numpy()):
# cv2.rectangle(
# image,
# (int(det[0]), int(det[1])),
# (int(det[2]), int(det[3])),
# color=(random.randint(0,255), random.randint(0,255), random.randint(0,255)),
# thickness=2,
# lineType=cv2.LINE_AA
# )
return f"runs/detect/predict/{os.path.split(image_path)[-1]}"
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="filepath", label="Output Image"),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Cats and Dogs detector",
cache_examples=False,
)
gr.TabbedInterface(
[interface_image],
tab_names=['Image inference']
).queue().launch() |