def visualize_example(example):
img = example["image"].numpy()
cms = example["predicted_confidence_maps"].numpy()
pts_gt = example["instances"].numpy()[0]
pts_pr = example["predicted_points"].numpy()
scale = 1.0
if img.shape[0] < 512:
scale = 2.0
if img.shape[0] < 256:
scale = 4.0
fig = plot_img(img, dpi=72 * scale, scale=scale)
plot_confmaps(cms, output_scale=cms.shape[0] / img.shape[0])
plot_peaks(pts_gt, pts_pr, paired=True)
return fig
def visualize_pafs_example(example):
img = example["image"].numpy()
pafs = example["predicted_part_affinity_fields"].numpy()
scale = 1.0
if img.shape[0] < 512:
scale = 2.0
if img.shape[0] < 256:
scale = 4.0
fig = plot_img(img, dpi=72 * scale, scale=scale)
plot_pafs(
pafs,
output_scale=pafs.shape[0] / img.shape[0],
stride=1,
scale=8.0,
width=1.0,
)
return fig