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import gradio as gr
from PIL import Image, ImageDraw
from transformers import pipeline
def plot_results(image, results, threshold=0.7):
image = Image.fromarray(np.uint8(image))
draw = ImageDraw.Draw(image)
for result in results:
score = result["score"]
label = result["label"]
box = list(result["box"].values())
if score > threshold:
x, y, x2, y2 = tuple(box)
draw.rectangle((x, y, x2, y2), outline="red", width=1)
draw.text((x, y), label, fill="white")
draw.text(
(x + 0.5, y - 0.5),
text=str(score),
fill="green" if score > 0.7 else "red",
)
return image
def predict(image):
# make the object detection pipeline
obj_detector = pipeline(
"object-detection", model="anindya64/detr-resnet-50-dc5-hardhat-finetuned"
)
results = obj_detector(train_dataset[0]["image"])
return plot_results(image)
title = "Do you have your helmet on?"
description = """
DETR model finetuned on "anindya64/hardhat" for hard hats detection.
"""
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="filepath", label="Input Image"),
outputs="image",
title=title,
description=description
)
demo.launch()
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