Open Fangyh09 opened 6 years ago
I downloaded the data provided from http://visiondata.cis.upenn.edu/ordinal/lsp-mpii/lsp-mpii-ordinal.zip. However, I found the joints annotation does not match the image. Does joints ordinates are scaled to 256x256? Thanks!
data_dir = "lsp-mpii-ordinal/mpii_upis1h" img_path = os.path.join(data_dir, "images/12172_full.jpg") joints_mat = matio.loadmat(os.path.join(data_dir, "joints.mat"))['joints'] ordinal_mat = matio.loadmat(os.path.join(data_dir, "ordinal.mat"))['ord'] img = io.imread(img_path) height, width, _ = img.shape idx = 12171 joints = joints_mat[:, :, idx]
hi @Fangyh09 ,Will the network predict the volume heatmap and then output the marginal heatmap?
hi @Fangyh09 can you share the datasets http://visiondata.cis.upenn.edu/ordinal/lsp-mpii/lsp-mpii-ordinal.zip ?
I downloaded the data provided from http://visiondata.cis.upenn.edu/ordinal/lsp-mpii/lsp-mpii-ordinal.zip. However, I found the joints annotation does not match the image. Does joints ordinates are scaled to 256x256? Thanks!