If you use `utils.viz.plot_keypoints`, you need to prepare dummy data.
This is a simple example.
from matplotlib import pyplot as plt
import mxnet as mx
from gluoncv import model_zoo, data, utils
url =
'https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/segmentation/mhpv1_examples/1.jpg'
filename = 'sample.jpg'
utils.download(url, filename)
x, img = data.transforms.presets.ssd.load_test(filename, short=512)
#your predicted keypoints
Predicted_Keypoints=mx.nd.array(
[[144.49815, 188.80566],
[155.40056, 175.33923],
[133.59575, 182.07245],
[177.2053 , 182.07245],
[122.69336, 188.80566],
[220.81493, 249.4046 ],
[106.33977, 276.33746],
[258.97333, 336.9364 ],
[ 79.08377, 350.40283],
[269.87573, 444.66785],
[ 62.73018, 404.26855],
[226.2661 , 437.93463],
[160.85173, 444.66785],
[237.1685 , 505.26678],
[171.75414, 505.26678],
[280.77808, 505.26678],
[253.52211, 471.6007 ]])
Predicted_Keypoints = Predicted_Keypoints.expand_dims(0)
#dummy data
class_IDs = mx.nd.zeros(shape=(1,100,1))
confidence = mx.nd.ones(shape=(1,17,1))
bounding_boxs = mx.nd.zeros(shape=(1,100,4))
scores = mx.nd.zeros(shape=(1,100,1))
ax = utils.viz.plot_keypoints(img, Predicted_Keypoints, confidence,
class_IDs, bounding_boxs, scores,
box_thresh=0.5, keypoint_thresh=0.2)
plt.show()
Hope this helps.
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