# and the ground truth label map synchronously. (*Note that "nearest"
# mode upsample are applied to the label maps to avoid messing up the boundaries.*)
# We first randomly scale the input image from 0.5 to 2.0 times, then rotate
# the image from -10 to 10 degrees, and crop the image with padding if needed.
# Finally a random Gaussian blurring is applied.
#
# Random pick one example for visualization:
from random import randint
idx = randint(0, len(trainset))
img, mask = trainset[idx]
from gluoncv.utils.viz import get_color_pallete, DeNormalize
# get color pallete for visualize mask
mask = get_color_pallete(mask.asnumpy(), dataset='pascal_voc')
mask.save('mask.png')
# denormalize the image
img = DeNormalize([.485, .456, .406], [.229, .224, .225])(img)
img = np.transpose((img.asnumpy() * 255).astype(np.uint8), (1, 2, 0))
##############################################################################
# Plot the image and mask
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
# subplot 1 for img
fig = plt.figure()
fig.add_subplot(1, 2, 1)
plt.imshow(img)
# subplot 2 for the mask
mmask = mpimg.imread('mask.png')
fig.add_subplot(1, 2, 2)
plt.imshow(mmask)
##############################################################################
# Plot an Example of generated images:
#
# Random pick one example for visualization:
import random
from datetime import datetime
random.seed(datetime.now())
idx = random.randint(0, len(trainset))
img, mask = trainset[idx]
from gluoncv.utils.viz import get_color_pallete, DeNormalize
# get color pallete for visualize mask
mask = get_color_pallete(mask.asnumpy(), dataset='coco')
mask.save('mask.png')
# denormalize the image
img = DeNormalize([.485, .456, .406], [.229, .224, .225])(img)
img = np.transpose((img.asnumpy()*255).astype(np.uint8), (1, 2, 0))
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
# subplot 1 for img
fig = plt.figure()
fig.add_subplot(1,2,1)
plt.imshow(img)
# subplot 2 for the mask
mmask = mpimg.imread('mask.png')
fig.add_subplot(1,2,2)
plt.imshow(mmask)
# display
plt.show()