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import io
import torch
import gradio as gr
import matplotlib
import matplotlib.pyplot as plt
from PIL import Image
from transformers import AutoFeatureExtractor, AutoModelForObjectDetection
extractor = AutoFeatureExtractor.from_pretrained("hustvl/yolos-tiny")
model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny")
matplotlib.pyplot.switch_backend('Agg')
COLORS = [
[0.000, 0.447, 0.741],
[0.850, 0.325, 0.098],
[0.929, 0.694, 0.125],
[0.494, 0.184, 0.556],
[0.466, 0.674, 0.188],
[0.301, 0.745, 0.933]
]
PRED_THRESHOLD = 0.90
def fig2img(fig):
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
img = Image.open(buf)
return img
def composite_predictions(img, processed_predictions):
keep = processed_predictions["labels"] == 1 # only interested in people
boxes = processed_predictions["boxes"][keep].tolist()
scores = processed_predictions["scores"][keep].tolist()
labels = processed_predictions["labels"][keep].tolist()
labels = [model.config.id2label[x] for x in labels]
plt.figure(figsize=(16, 10))
plt.imshow(img)
axis = plt.gca()
colors = COLORS * 100
for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
axis.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
axis.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
plt.axis("off")
img = fig2img(plt.gcf())
matplotlib.pyplot.close()
return img
def process(img):
inputs = extractor(images=img, return_tensors="pt")
outputs = model(**inputs)
img_size = torch.tensor([tuple(reversed(img.size))])
processed = extractor.post_process_object_detection(outputs, PRED_THRESHOLD, img_size)
# Composite image and prediction bounding boxes + labels prediction
return composite_predictions(img, processed[0])
demo = gr.Interface(fn=process, inputs=[gr.Image(source="webcam", streaming=True, type='pil')], outputs=["image"], live=True)
demo.launch()
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