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| import gluoncv | |
| import mxnet as mx | |
| from gluoncv.utils.viz import get_color_pallete | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from gluoncv.data.transforms.presets.segmentation import test_transform | |
| # using cpu | |
| ctx = mx.cpu(0) | |
| model = gluoncv.model_zoo.get_model("psp_resnet101_ade", pretrained=True) | |
| def segmentation(image): | |
| img = Image.fromarray(image) | |
| img = mx.ndarray.array(img) | |
| img = test_transform(img, ctx) | |
| output = model.predict(img) | |
| predict = mx.nd.squeeze(mx.nd.argmax(output, 1)).asnumpy() | |
| mask = get_color_pallete(predict, "ade20k") | |
| return mask | |
| image_in = gr.Image() | |
| image_out = gr.components.Image() | |
| description = "MXNet Image Segmentation Model" | |
| examples=['cat.jpeg'] | |
| Iface = gr.Interface( | |
| fn=segmentation, | |
| inputs=image_in, | |
| outputs=image_out, | |
| title="Semantic Segmentation - MXNet", | |
| examples=examples | |
| ).launch() | |