# 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()