Add object detection

app.py

@@ -1,7 +1,55 @@
+from transformers import pipeline
 import gradio as gr
+import io
+import matplotlib.pyplot as plt
+from PIL import Image
+from random import choice
 
-def greet(name):
-    return "Hello " + name + "!!"
+COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
+            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
+            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def get_figure(in_pil_img, in_results):
+  plt.figure(figsize=(16, 10))
+  plt.imshow(in_pil_img)
+  ax = plt.gca()
+
+  for prediction in in_results:
+    selected_color = choice(COLORS)
+
+    x, y = prediction['box']['xmin'], prediction['box']['ymin'],
+    w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
+
+    ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
+    ax.text(x, y - 3, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict={
+      "family" : "Arial",
+      "size" : 20,
+      "color" : selected_color,
+      "weight" : "bold",
+    })
+
+  plt.axis("off")
+
+  return plt.gcf()
+
+def classify(in_pil_img):
+    detector = pipeline("object-detection", "facebook/detr-resnet-50")
+    results = detector(in_pil_img, { "threshold": 0.9 })
+
+    figure = get_figure(in_pil_img, results)
+
+    buf = io.BytesIO()
+    figure.savefig(buf, bbox_inches='tight')
+    buf.seek(0)
+    output_pil_img = Image.open(buf)
+
+    return output_pil_img
+
+demo = gr.Interface(classify,
+  inputs=gr.Image(type="pil"),
+  outputs=gr.Image(type="pil"),
+  title="Object Detection",
+  examples=["https://iili.io/JgN38oQ.jpg", "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg"]
+)
+
+demo.launch()