1. Getting Started with FCN Pre-trained Models

This is a quick demo of using GluonCV FCN model. 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://raw.githubusercontent.com/zhanghang1989/gluon-vision-figures/master/voc_examples/1.jpg'
filename = 'example.jpg'
gluoncv.utils.download(url, filename)

load the image

with open(filename, 'rb') as f:
    img = image.imdecode(f.read())

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

normalize the image using dataset mean

transform_fn = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([.485, .456, .406], [.229, .224, .225])
])
img = transform_fn(img)
img = img.expand_dims(0).as_in_context(ctx)

Load the pre-trained model and make prediction

get pre-trained model

model = gluoncv.model_zoo.get_model('fcn_resnet101_voc', pretrained=True)

make prediction using single scale

output = model.evaluate(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, 'pascal_voc')
mask.save('output.png')

show the predicted mask

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

More Examples

Total running time of the script: ( 0 minutes 5.013 seconds)