File size: 1,486 Bytes
5c31914 e58a972 5c31914 e58a972 5c31914 e58a972 5c31914 8dc8970 5c31914 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import gradio as gr
import torch
from PIL import ImageDraw
from transformers import AutoModelForObjectDetection, AutoImageProcessor
processor = AutoImageProcessor.from_pretrained("tanukinet/hanko")
model = AutoModelForObjectDetection.from_pretrained("tanukinet/hanko", ignore_mismatched_sizes=True,)
def object_detection(image):
image = image.copy()
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.8)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
print(
f"Detected {model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}"
)
draw = ImageDraw.Draw(image)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
x, y, x2, y2 = tuple(box)
draw.rectangle((x, y, x2, y2), outline="red", width=1)
draw.text((x, y), model.config.id2label[label.item()], fill="white")
return image
demo = gr.Interface(
object_detection,
gr.Image(type="pil"),
"image",
examples=[
"sample1.png",
"sample2.png",
],
)
if __name__ == "__main__":
demo.launch()
|