import numpy as np
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import torch
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from torch.utils.serialization import load_lua
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import os
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import scipy.io as sio
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import cv2
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import math
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from math import cos, sin
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def softmax_temperature(tensor, temperature):
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result = torch.exp(tensor / temperature)
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result = torch.div(result, torch.sum(result, 1).unsqueeze(1).expand_as(result))
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return result
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def get_pose_params_from_mat(mat_path):
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# This functions gets the pose parameters from the .mat
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# 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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# Get [pitch, yaw, roll, tdx, tdy]
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pose_params = pre_pose_params[:5]
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return pose_params
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def get_ypr_from_mat(mat_path):
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# Get yaw, pitch, roll from .mat annotation.
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# They are in radians
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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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# Get [pitch, yaw, roll]
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pose_params = pre_pose_params[:3]
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return pose_params
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def get_pt2d_from_mat(mat_path):
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# Get 2D landmarks
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mat = sio.loadmat(mat_path)
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pt2d = mat['pt2d']
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return pt2d
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def mse_loss(input, target):
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return torch.sum(torch.abs(input.data - target.data) ** 2)
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def plot_pose_cube(img, yaw, pitch, roll, tdx=None, tdy=None, size=150.):
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# Input is a cv2 image
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# pose_params: (pitch, yaw, roll, tdx, tdy)
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# Where (tdx, tdy) is the translation of the face.
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# For pose we have [pitch yaw roll tdx tdy tdz scale_factor]
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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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if tdx != None and tdy != None:
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face_x = tdx - 0.50 * size
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face_y = tdy - 0.50 * size
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else:
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height, width = img.shape[:2]
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face_x = width / 2 - 0.5 * size
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face_y = height / 2 - 0.5 * size
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x1 = size * (cos(y) * cos(r)) + face_x
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y1 = size * (cos(p) * sin(r) + cos(r) * sin(p) * sin(y)) + face_y
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x2 = size * (-cos(y) * sin(r)) + face_x
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y2 = size * (cos(p) * cos(r) - sin(p) * sin(y) * sin(r)) + face_y
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x3 = size * (sin(y)) + face_x
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y3 = size * (-cos(y) * sin(p)) + face_y
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# Draw base in red
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cv2.line(img, (int(face_x), int(face_y)), (int(x1),int(y1)),(0,0,255),3)
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cv2.line(img, (int(face_x), int(face_y)), (int(x2),int(y2)),(0,0,255),3)
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cv2.line(img, (int(x2), int(y2)), (int(x2+x1-face_x),int(y2+y1-face_y)),(0,0,255),3)
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cv2.line(img, (int(x1), int(y1)), (int(x1+x2-face_x),int(y1+y2-face_y)),(0,0,255),3)
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# Draw pillars in blue
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cv2.line(img, (int(face_x), int(face_y)), (int(x3),int(y3)),(255,0,0),2)
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cv2.line(img, (int(x1), int(y1)), (int(x1+x3-face_x),int(y1+y3-face_y)),(255,0,0),2)
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cv2.line(img, (int(x2), int(y2)), (int(x2+x3-face_x),int(y2+y3-face_y)),(255,0,0),2)
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cv2.line(img, (int(x2+x1-face_x),int(y2+y1-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(255,0,0),2)
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# Draw top in green
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cv2.line(img, (int(x3+x1-face_x),int(y3+y1-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(0,255,0),2)
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cv2.line(img, (int(x2+x3-face_x),int(y2+y3-face_y)), (int(x3+x1+x2-2*face_x),int(y3+y2+y1-2*face_y)),(0,255,0),2)
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cv2.line(img, (int(x3), int(y3)), (int(x3+x1-face_x),int(y3+y1-face_y)),(0,255,0),2)
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cv2.line(img, (int(x3), int(y3)), (int(x3+x2-face_x),int(y3+y2-face_y)),(0,255,0),2)
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return img
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def draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size = 100):
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pitch = pitch * np.pi / 180
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yaw = -(yaw * np.pi / 180)
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roll = roll * np.pi / 180
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if tdx != None and tdy != None:
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tdx = tdx
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tdy = tdy
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else:
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height, width = img.shape[:2]
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tdx = width / 2
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tdy = height / 2
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# X-Axis pointing to right. drawn in red
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x1 = size * (cos(yaw) * cos(roll)) + tdx
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y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
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# Y-Axis | drawn in green
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# v
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x2 = size * (-cos(yaw) * sin(roll)) + tdx
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y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
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# Z-Axis (out of the screen) drawn in blue
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x3 = size * (sin(yaw)) + tdx
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y3 = size * (-cos(yaw) * sin(pitch)) + tdy
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cv2.line(img, (int(tdx), int(tdy)), (int(x1),int(y1)),(0, 255, 255),3)
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cv2.line(img, (int(tdx), int(tdy)), (int(x2),int(y2)),(0,0,255),3)
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cv2.line(img, (int(tdx), int(tdy)), (int(x3),int(y3)),(0,255,0),3)
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return img
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