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import gradio as gr
import torch
from PIL import Image, ImageDraw, ImageFont
from transformers import DetrImageProcessor, DetrForObjectDetection
from pathlib import Path
# Load DETR model and processor from Hugging Face
model_name = "facebook/detr-resnet-50"
processor = DetrImageProcessor.from_pretrained(model_name)
model = DetrForObjectDetection.from_pretrained(model_name)
# Load font
font_path = Path("assets/fonts/arial.ttf")
if not font_path.exists():
# If the font file does not exist, use the default PIL font
print(f"Font file {font_path} not found. Using default font.")
font = ImageFont.load_default()
else:
font = ImageFont.truetype(str(font_path), size=100)
print(f"CUDA is available: {torch.cuda.is_available()}")
# Main function: takes an image and returns it with boxes and labels
def detect_objects(image):
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# Convert model output to usable detection results
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(
outputs, threshold=0.9, target_sizes=target_sizes
)[0]
# Draw bounding boxes and labels on a copy of the image
image_with_boxes = image.copy()
draw = ImageDraw.Draw(image_with_boxes)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(x, 2) for x in box.tolist()]
draw.rectangle(box, outline="red", width=3)
# Prepare label text
label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}"
# Measure text size
text_bbox = draw.textbbox((0, 0), label_text, font=font)
text_width = text_bbox[2] - text_bbox[0]
text_height = text_bbox[3] - text_bbox[1]
# Set background rectangle for text
text_background = [
box[0], box[1] - text_height,
box[0] + text_width, box[1]
]
draw.rectangle(text_background, fill="black") # Background
draw.text((box[0], box[1] - text_height), label_text, fill="white", font=font)
return image_with_boxes
with gr.Blocks() as app:
with gr.Row():
gr.Markdown("## Object Detection App\nUpload an image to detect objects using Facebook's DETR model.")
with gr.Row():
input_image = gr.Image(type="pil", label="Input Image")
output_image = gr.Image(label="Detected Objects")
with gr.Row():
button = gr.Button("Detect Objects")
button.click(fn=detect_objects, inputs=input_image, outputs=output_image)
if __name__ == "__main__":
app.launch()
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