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import os
import gradio as gr
import cv2
from ultralytics import YOLO
image_directory = '/home/user/app/some_flat_plans'
jpg_files = [file for file in os.listdir(image_directory) if file.lower().endswith('.jpg')]
path = [os.path.join(image_directory, filename) for filename in jpg_files]
model = YOLO('/home/user/app/best.pt')
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image"),
]
def show_preds_image(image_path):
image = cv2.imread(image_path)
outputs = model.predict(source=image_path)
results = outputs[0].cpu().numpy()
for i, det in enumerate(results.boxes.xyxy):
cv2.rectangle(
image,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Floor Plan Detector",
examples=path,
cache_examples=False,
)
gr.TabbedInterface(
[interface_image],
tab_names=['Image Inference'],
).queue().launch(debug=True) |