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import torch
from transformers import AutoImageProcessor, AutoModelForObjectDetection
#from transformers import pipeline
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import io
from random import choice
image_processor_tiny = AutoImageProcessor.from_pretrained("hustvl/yolos-tiny")
model_tiny = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny")
image_processor_small = AutoImageProcessor.from_pretrained("hustvl/yolos-small")
model_small = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-small")
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)
ax = plt.gca()
for score, label, box in zip(in_results["scores"], in_results["labels"], in_results["boxes"]):
selected_color = choice(COLORS)
#box = [round(i, 2) for i in box.tolist()]
x, y, w, h = int(box[0]), int(box[1]), int(box[2]-box[0]), int(box[3]-box[1])
print(x, y, w, h)
ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
ax.text(x, y, f"{model_tiny.config.id2label[label.item()]}: {round(score.item()*100, 1)}%", fontdict=fdic)
#print(
# f"Detected {model_tiny.config.id2label[label.item()]} with confidence "
# f"{round(score.item(), 3)} at location {box}"
#)
plt.axis("off")
return plt.gcf()
def infer(in_model, in_threshold, in_pil_img):
inputs = image_processor_tiny(images=in_pil_img, return_tensors="pt")
outputs = model_tiny(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
target_sizes = torch.tensor([in_pil_img.size[::-1]])
results = image_processor_tiny.post_process_object_detection(outputs, threshold=in_threshold, target_sizes=target_sizes)[
0
]
print(results)
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="YOLOS 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;">YOLOS Object Detection</div>""")
gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""")
model = gr.Radio(["yolos-tiny", "yolos-small"], value="yolos-tiny")
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. Set threshold value (default to 0.9)</h4>""")
threshold = gr.Slider(0, 1.0, value=0.9, label='threshold')
gr.HTML("""<br/>""")
gr.HTML("""<h4 style="color:navy;">4. 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, threshold, input_image], outputs=[output_image])
#demo.queue()
demo.launch(debug=True)
### EOF ###
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