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app.py

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+import torch
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import ImageDraw, ImageFont, Image
+import requests
+import os
+
+# Load the DETR processor and model
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+
+# Define the inference function
+def inference(image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # Convert outputs (bounding boxes and class logits) to COCO API
+    # Only keep detections with a score > 0.9
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+
+    # Draw bounding boxes and labels on the image
+    draw = ImageDraw.Draw(image)
+    font_path = "/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf"
+    if not os.path.exists(font_path):
+        font_path = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"  # Alternate font path
+    font = ImageFont.truetype(font_path, 15)
+
+    detected_objects = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        draw.rectangle(box, outline="red", width=2)
+        label_text = f"{model.config.id2label[label.item()]} ({round(score.item() * 100, 1)}%)"
+        draw.text((box[0], box[1]), label_text, fill="red", font=font)
+        detected_objects.append(f"{label_text} at {box}")
+
+    return image, "\n".join(detected_objects)
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=inference,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), gr.Textbox()],
+    title="DETR Object Detection",
+    description=(
+        "Upload an image, and this app will detect objects using the DETR model. "
+        "The detected objects are displayed with bounding boxes and confidence scores."
+    ),
+)
+
+# Launch the app
+demo.launch()

requirements.txt

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+transformers
+diffusers
+torch
+gradio
+Pillow