From af51d0ecb51ad4d6c8ed086855bd3c411ebc4aa0 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:29:51 +0800
Subject: [PATCH] Fixed stuff
---
code/utils.py | 89 +++++++++++++++++++++++++++++++++-----------
1 files changed, 66 insertions(+), 23 deletions(-)
diff --git a/code/utils.py b/code/utils.py
index 645ae19..b75d51f 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)
@@ -16,14 +50,13 @@
p = pitch * np.pi / 180
y = -(yaw * np.pi / 180)
r = roll * np.pi / 180
-
if tdx != None and tdy != None:
face_x = tdx - 0.50 * size
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
@@ -50,25 +83,35 @@
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 draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size = 100):
-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
+ pitch = pitch * np.pi / 180
+ yaw = -(yaw * np.pi / 180)
+ roll = roll * np.pi / 180
-def mse_loss(input, target):
- return torch.sum(torch.abs(input.data - target.data) ** 2)
+ if tdx != None and tdy != None:
+ tdx = tdx
+ tdy = tdy
+ else:
+ height, width = img.shape[:2]
+ tdx = width / 2
+ tdy = height / 2
+
+ # X-Axis pointing to right. drawn in red
+ x1 = size * (cos(yaw) * cos(roll)) + tdx
+ y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
+
+ # Y-Axis | drawn in green
+ # v
+ x2 = size * (-cos(yaw) * sin(roll)) + tdx
+ y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
+
+ # Z-Axis (out of the screen) drawn in blue
+ x3 = size * (sin(yaw)) + tdx
+ y3 = size * (-cos(yaw) * sin(pitch)) + tdy
+
+ cv2.line(img, (int(tdx), int(tdy)), (int(x1),int(y1)),(0,0,255),3)
+ cv2.line(img, (int(tdx), int(tdy)), (int(x2),int(y2)),(0,255,0),3)
+ cv2.line(img, (int(tdx), int(tdy)), (int(x3),int(y3)),(255,0,0),2)
+
+ return img
--
Gitblit v1.8.0