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from transformers import pipeline
import gradio as gr
import io
import matplotlib.pyplot as plt
from PIL import Image
from random import choice
COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
"#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
"#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
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 = choice(COLORS)
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 - 3, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict={
"family" : "Arial",
"size" : 20,
"color" : selected_color,
"weight" : "bold",
})
plt.axis("off")
return plt.gcf()
def classify(in_pil_img):
detector = pipeline("object-detection", "facebook/detr-resnet-50")
results = detector(in_pil_img, { "threshold": 0.9 })
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
demo = gr.Interface(classify,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="Object Detection",
examples=["https://iili.io/JgN38oQ.jpg", "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg"]
)
demo.launch()
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