From dd62d6fa4a85f18a29de009a972f5599b19ec946 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 14 九月 2017 00:51:53 +0800
Subject: [PATCH] Fixing hopenet
---
code/utils.py | 61 ++++++++++++++++++------------
1 files changed, 36 insertions(+), 25 deletions(-)
diff --git a/code/utils.py b/code/utils.py
index 645ae19..5a18d7c 100644
--- a/code/utils.py
+++ b/code/utils.py
@@ -7,6 +7,40 @@
import math
from math import cos, sin
+def softmax_temperature(tensor, temperature):
+ 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 Pose_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)
@@ -22,8 +56,8 @@
face_y = tdy - 0.50 * size
else:
height, width = img.shape[:2]
- face_x = width / 2 - 0.15 - size
- face_y = height / 2 - 0.15 - size
+ face_x = width / 2 - 0.5 * size
+ face_y = height / 2 - 0.5 * size
x1 = size * (cos(y) * cos(r)) + face_x
y1 = size * (cos(p) * sin(r) + cos(r) * sin(p) * sin(y)) + face_y
@@ -49,26 +83,3 @@
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 mse_loss(input, target):
- return torch.sum(torch.abs(input.data - target.data) ** 2)
--
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