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from transformers import pipeline
from PIL import Image, ImageDraw
import gradio as gr
import torch
import timm # Required for DETR model
# Load the model
device = 0 if torch.cuda.is_available() else -1
print(f"Using device: {'cuda' if device == 0 else 'cpu'}")
model_pipeline = pipeline(
"object-detection",
model="opria123/detr-resnet-50-dc5-hardhat-finetuned",
device=device,
threshold=0.2 # Lower threshold to catch more results
)
# Function to classify and draw results
def classify_image(image):
print("Received image for classification.")
results = model_pipeline(image)
print(f"Model results:\n{results}")
image = image.convert("RGB")
draw = ImageDraw.Draw(image)
for item in results:
box = item["box"]
label = item["label"]
score = item["score"]
print(f"Drawing box for: {label} ({score:.2f}) at {box}")
draw.rectangle(
[(box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])],
outline="red",
width=3
)
draw.text((box["xmin"] + 5, box["ymin"] - 10), f"{label} ({score:.2f})", fill="red")
return image
# Gradio interface
interface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="Hard Hat Detection",
description="Upload an image to detect hard hats using a fine-tuned DETR model."
)
interface.launch()
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