Upload 2 files

app.py.txt

@@ -0,0 +1,62 @@
+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 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")
+    input_image = gr.Image(label="Input image", type="pil")
+    output_image = gr.Image(label="Output image")
+    send_btn = gr.Button("Infer")
+    send_btn.click(fn=infer, inputs=[model, input_image], outputs=output_image)
+
+demo.launch()