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import torch
from transformers import pipeline
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from random import choice
import io
detector50 = pipeline(model="facebook/detr-resnet-50")
detecttor101 = 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_pill_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']['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):
result = 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(['sample/แมว.jpg', 'sample/แมว1.jpg', 'sample/แมว2.jpg', 'sample/หมา1.jpg', 'sample/หมา2.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) |