From 868222967bf310e6c5bc1d6b3af0e9e49d2992c2 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期二, 08 八月 2017 10:30:30 +0800
Subject: [PATCH] Before experiments
---
code/utils.py | 58 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 29 insertions(+), 29 deletions(-)
diff --git a/code/utils.py b/code/utils.py
index 1e37045..09a47a8 100644
--- a/code/utils.py
+++ b/code/utils.py
@@ -7,6 +7,35 @@
import math
from math import cos, sin
+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.
+ mat = sio.loadmat(mat_path)
+ # [pitch yaw roll tdx tdy tdz scale_factor]
+ pre_pose_params = mat['Pose_Para'][0]
+ # Get [pitch, yaw, roll, tdx, tdy]
+ pose_params = pre_pose_params[:5]
+ return pose_params
+
+def get_ypr_from_mat(mat_path):
+ # Get yaw, pitch, roll from .mat annotation.
+ # They are in radians
+ mat = sio.loadmat(mat_path)
+ # [pitch yaw roll tdx tdy tdz scale_factor]
+ pre_pose_params = mat['Pose_Para'][0]
+ # Get [pitch, yaw, roll]
+ pose_params = pre_pose_params[:3]
+ return pose_params
+
+def get_pt2d_from_mat(mat_path):
+ # Get 2D landmarks
+ mat = sio.loadmat(mat_path)
+ pt2d = mat['pt2d']
+ return pt2d
+
+def mse_loss(input, target):
+ return torch.sum(torch.abs(input.data - target.data) ** 2)
+
def plot_pose_cube(img, yaw, pitch, roll, tdx=None, tdy=None, size=150.):
# Input is a cv2 image
# pose_params: (pitch, yaw, roll, tdx, tdy)
@@ -49,32 +78,3 @@
cv2.line(img, (int(x3), int(y3)), (int(x3+x2-face_x),int(y3+y2-face_y)),(0,255,0),2)
return img
-
-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.
- mat = sio.loadmat(mat_path)
- # [pitch yaw roll tdx tdy tdz scale_factor]
- pre_pose_params = mat['Pose_Para'][0]
- # Get [pitch, yaw, roll, tdx, tdy]
- pose_params = pre_pose_params[:5]
- return pose_params
-
-def get_ypr_from_mat(mat_path):
- # Get yaw, pitch, roll from .mat annotation.
- # They are in radians
- mat = sio.loadmat(mat_path)
- # [pitch yaw roll tdx tdy tdz scale_factor]
- pre_pose_params = mat['Pose_Para'][0]
- # Get [pitch, yaw, roll]
- pose_params = pre_pose_params[:3]
- return pose_params
-
-def get_pt2d_from_mat(mat_path):
- # Get 2D landmarks
- mat = sio.loadmat(mat_path)
- pt2d = mat['pt2d']
- return pt2d
-
-def mse_loss(input, target):
- return torch.sum(torch.abs(input.data - target.data) ** 2)
--
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