Table Of Contents
Table Of Contents

3. Test with DeepLabV3 Pre-trained Models

This is a quick demo of using GluonCV DeepLabV3 model on ADE20K dataset. Please follow the installation guide to install MXNet and GluonCV if not yet.

import mxnet as mx
from mxnet import image
from mxnet.gluon.data.vision import transforms
import gluoncv
# using cpu
ctx = mx.cpu(0)

Prepare the image

download the example image

url = 'https://github.com/zhanghang1989/image-data/blob/master/encoding/' + \
    'segmentation/ade20k/ADE_val_00001755.jpg?raw=true'
filename = 'ade20k_example.jpg'
gluoncv.utils.download(url, filename, True)

load the image

img = image.imread(filename)

from matplotlib import pyplot as plt
plt.imshow(img.asnumpy())
plt.show()

normalize the image using dataset mean

from gluoncv.data.transforms.presets.segmentation import test_transform
img = test_transform(img, ctx)

Load the pre-trained model and make prediction

get pre-trained model

model = gluoncv.model_zoo.get_model('deeplab_resnet101_ade', pretrained=True)

make prediction using single scale

output = model.predict(img)
predict = mx.nd.squeeze(mx.nd.argmax(output, 1)).asnumpy()

Add color pallete for visualization

from gluoncv.utils.viz import get_color_pallete
import matplotlib.image as mpimg
mask = get_color_pallete(predict, 'ade20k')
mask.save('output.png')

show the predicted mask

mmask = mpimg.imread('output.png')
plt.imshow(mmask)
plt.show()

Total running time of the script: ( 0 minutes 0.000 seconds)

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