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import numpy as np |
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import scipy.io |
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def euler2rot(euler): |
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sin, cos = np.sin, np.cos |
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phi, theta, psi = euler[0], euler[1], euler[2] |
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R1 = np.array([[1, 0, 0], |
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[0, cos(phi), sin(phi)], |
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[0, -sin(phi), cos(phi)]]) |
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R2 = np.array([[cos(theta), 0, -sin(theta)], |
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[0, 1, 0], |
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[sin(theta), 0, cos(theta)]]) |
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R3 = np.array([[cos(psi), sin(psi), 0], |
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[-sin(psi), cos(psi), 0], |
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[0, 0, 1]]) |
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R = R1 @ R2 @ R3 |
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return R |
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import json |
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import os |
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from glob import glob |
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import sys |
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temp_folder = sys.argv[1] |
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output_folder = sys.argv[2] |
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import shutil |
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pose_template = np.load('util/pose_template.npy') |
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glob_names= sorted(glob('%s/cropped_images/*.png'%(temp_folder))) |
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for name_all in glob_names: |
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if os.path.isfile('%s/cropped_images/cameras.json'%(temp_folder)): |
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with open('%s/cropped_images/cameras.json'%(temp_folder), 'r') as file: |
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labels = json.load(file) |
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predict_pose = labels |
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name = os.path.basename(name_all)[:-4] |
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pose= np.array(predict_pose[name+'.png']['pose']).reshape(16) |
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pose_template[:16] = pose |
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np.save('%s/'%(output_folder)+name+'.npy',pose_template) |
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shutil.copy('%s/cropped_images/'%(temp_folder)+name+'.png','%s/'%(output_folder)+name+'.png') |
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