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import torch
from transformers import pipeline
from PIL import Image
import matplotlib.pyplot as plt
import io
detector50 = pipeline(model="TuningAI/DETR-BASE_Marine")
import gradio as gr
fdic = {
"style" : "italic",
"size" : 10,
"color" : "red",
"weight" : "bold"
}
labels_ = { "LABEL_0":"None" , "LABEL_1": "Boat" ,"LABEL_2": "Car" ,"LABEL_3" : "Dock" , "LABEL_4" : "Jetski" ,"LABEL_5" : "Lift"}
def get_figure(in_pil_img, in_results):
plt.figure(figsize=(16, 10))
plt.imshow(in_pil_img)
ax = plt.gca()
for prediction in in_results:
selected_color ="#008000"
x, y = prediction['box']['xmin'], prediction['box']['ymin'],
w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
ax.text(x, y, f"{labels_[prediction['label']]}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
plt.axis("off")
return plt.gcf()
def infer(in_pil_img):
results = detector50(in_pil_img)
figure = get_figure(in_pil_img, results)
buf = io.BytesIO()
figure.savefig(buf, bbox_inches='tight')
buf.seek(0)
output_pil_img = Image.open(buf)
return output_pil_img
with gr.Blocks(title="DETR Object Detection") as demo:
with gr.Row():
input_image = gr.Image(label="Input image", type="pil")
output_image = gr.Image(label="Output image with predicted instances", type="pil")
send_btn = gr.Button("start")
send_btn.click(fn=infer, inputs=input_image, outputs=[output_image])
demo.launch(debug=True) |