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| import io | |
| from random import choice | |
| from PIL import Image | |
| import gradio as gr | |
| from transformers import pipeline | |
| import matplotlib.pyplot as plt | |
| # Initialize the models | |
| detector50 = pipeline(model="facebook/detr-resnet-50") | |
| detector101 = pipeline(model="facebook/detr-resnet-101") | |
| # Define colors and font dictionary for bounding boxes and labels | |
| COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff", | |
| "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf", | |
| "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"] | |
| fdic = { | |
| "family": "Impact", | |
| "style": "italic", | |
| "size": 15, | |
| "color": "yellow", | |
| "weight": "bold" | |
| } | |
| def get_figure(in_pil_img, in_results): | |
| # Create a figure to display the image and annotations | |
| plt.figure(figsize=(16, 10)) | |
| plt.imshow(in_pil_img) | |
| ax = plt.gca() | |
| # Add bounding boxes and labels to the image | |
| for prediction in in_results: | |
| selected_color = choice(COLORS) | |
| x, y = prediction['box']['xmin'], prediction['box']['ymin'] | |
| w, h = prediction['box']['xmax'] - x, prediction['box']['ymax'] - y | |
| ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3)) | |
| ax.text(x, y, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic) | |
| plt.axis("off") | |
| plt.tight_layout() | |
| # Convert the figure to a PIL Image and return | |
| buf = io.BytesIO() | |
| plt.savefig(buf, format='png', bbox_inches='tight') | |
| buf.seek(0) | |
| return Image.open(buf) | |
| def infer(model, in_pil_img): | |
| # Perform inference using the specified model and input image | |
| results = detector101(in_pil_img) if model == "detr-resnet-101" else detector50(in_pil_img) | |
| return get_figure(in_pil_img, results) | |
| # Define Gradio interface with local image examples | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## DETR Object Detection") | |
| model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name") | |
| # Use local image files instead of URLs | |
| examples = gr.Examples( | |
| examples=[ | |
| ["image1.jpg"], | |
| ["image2.jpg"] | |
| ], | |
| inputs=[gr.Image(type="pil")], | |
| label="Try these example images" | |
| ) | |
| input_image = gr.Image(label="Input image", type="pil") | |
| output_image = gr.Image(label="Output image") | |
| send_btn = gr.Button("Infer") | |
| # Trigger inference on button click | |
| send_btn.click(fn=infer, inputs=[model, input_image], outputs=output_image) | |
| demo.launch() | |