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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") | |
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 by orYx Models") as demo: | |
gr.HTML(""" | |
<style> | |
.logo { | |
position: absolute; | |
top: 10px; | |
right: 10px; | |
width: 100px; /* Adjust the width of the logo as needed */ | |
height: auto; | |
} | |
</style> | |
<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">DETR Object Detection</div> | |
<img class="logo" src="https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/oryx_logo%20(2).png" alt="Logo"> | |
<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(['https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/traffic.jpg', | |
'https://huggingface.co/spaces/orYx-models/object-detection-facebook-ResNets/blob/main/flyover.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 ### | |