สร้าง app.py

app.py

@@ -1,10 +1,108 @@
-# Use a pipeline as a high-level helper
+import torch
 from transformers import pipeline
 
-pipe = pipeline("object-detection", model="facebook/detr-resnet-50")
+from PIL import Image
 
-# Load model directly
-from transformers import AutoImageProcessor, AutoModelForObjectDetection
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
 
-processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
-model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+from random import choice
+import io
+
+detector50 = pipeline(model="facebook/detr-resnet-50")
+
+detector101 = pipeline(model="facebook/detr-resnet-101")
+
+
+import gradio as gr
+
+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):
+    plt.figure(figsize=(16, 10))
+    plt.imshow(in_pil_img)
+    #pyplot.gcf()
+    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, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict=fdic)
+
+    plt.axis("off")
+
+    return plt.gcf()
+
+
+def infer(model, in_pil_img):
+
+    results = None
+    if model == "detr-resnet-101":
+        results = detector101(in_pil_img)
+    else:
+        results = detector50(in_pil_img)
+
+    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
+
+
+with gr.Blocks(title="DETR Object Detection - ClassCat",
+                    css=".gradio-container {background:lightyellow;}"
+               ) as demo:
+    #sample_index = gr.State([])
+
+    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">DETR Object Detection</div>""")
+
+    gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""")
+
+    model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50", label="Model name")
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""")
+    gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""")
+
+    with gr.Row():
+        input_image = gr.Image(label="Input image", type="pil")
+        output_image = gr.Image(label="Output image with predicted instances", type="pil")
+
+    gr.Examples(['samples/cats.jpg', 'samples/detectron2.png', 'samples/cat.jpg', 'samples/hotdog.jpg'], inputs=input_image)
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">3. Then, click "Infer" button to predict object instances. It will take about 10 seconds (on cpu)</h4>""")
+
+    send_btn = gr.Button("Infer")
+    send_btn.click(fn=infer, inputs=[model, input_image], outputs=[output_image])
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
+    gr.HTML("""<ul>""")
+    gr.HTML("""<li><a href="https://colab.research.google.com/github/facebookresearch/detr/blob/colab/notebooks/detr_attention.ipynb" target="_blank">Hands-on tutorial for DETR</a>""")
+    gr.HTML("""</ul>""")
+
+
+#demo.queue()
+demo.launch(debug=True)
+
+
+### EOF ###