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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("/home/pc-1/Documents/Object detection/weights")
model_tiny = AutoModelForObjectDetection.from_pretrained("/home/pc-1/Documents/Object detection/weights")
import gradio as gr
COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
"#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
"#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
fdic = {
"family" : "DejaVu Serif",
"style" : "normal",
"size" : 18,
"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_int = [i.item() for i in torch.round(box).to(torch.int32)]
x, y, w, h = box_int[0], box_int[1], box_int[2]-box_int[0], box_int[3]-box_int[1]
#x, y, w, h = torch.round(box[0]).item(), torch.round(box[1]).item(), torch.round(box[2]-box[0]).item(), torch.round(box[3]-box[1]).item()
ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3, alpha=0.8))
ax.text(x, y, f"{model_tiny.config.id2label[label.item()]}: {round(score.item()*100, 2)}%", fontdict=fdic, alpha=0.8)
plt.axis("off")
return plt.gcf()
def infer(in_pil_img, in_model="yolos-tiny", in_threshold=0.9):
target_sizes = torch.tensor([in_pil_img.size[::-1]])
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
results = image_processor_tiny.post_process_object_detection(outputs, threshold=in_threshold, target_sizes=target_sizes)[0]
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", 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. Set a 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 (yolos-tiny) or 20 seconds (yolos-small).</h4>""")
send_btn = gr.Button("Infer")
send_btn.click(fn=infer, inputs=[input_image, model, threshold], outputs=[output_image])
gr.HTML("""<br/>""")
gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
gr.HTML("""<ul>""")
gr.HTML("""<li><a href="https://huggingface.co/docs/transformers/model_doc/yolos" target="_blank">Hugging Face Transformers - YOLOS</a>""")
gr.HTML("""</ul>""")
#demo.queue()
demo.launch(debug=True)
### EOF ###
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