| | |
|---|
| | | import math |
|---|
| | | from math import cos, sin |
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| | | |
|---|
| | | def softmax_temperature(tensor, temperature): |
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| | | result = torch.exp(tensor / temperature) |
|---|
| | | result = torch.div(result, torch.sum(result, 1).unsqueeze(1).expand_as(result)) |
|---|
| | | return result |
|---|
| | | |
|---|
| | | def get_pose_params_from_mat(mat_path): |
|---|
| | | # This functions gets the pose parameters from the .mat |
|---|
| | | # Annotations that come with the 300W_LP dataset. |
|---|
| | | # Annotations that come with the Pose_300W_LP dataset. |
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| | | mat = sio.loadmat(mat_path) |
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| | | # [pitch yaw roll tdx tdy tdz scale_factor] |
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| | | pre_pose_params = mat['Pose_Para'][0] |
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| | |
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| | | p = pitch * np.pi / 180 |
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| | | y = -(yaw * np.pi / 180) |
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| | | r = roll * np.pi / 180 |
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| | | |
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| | | if tdx != None and tdy != None: |
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| | | face_x = tdx - 0.50 * size |
|---|
| | | face_y = tdy - 0.50 * size |
|---|
