128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
import numpy as np
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import tensorflow as tf
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def euler_to_rotation_matrix(x, y, z):
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"""
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:param x: Tensor of shape (B, 1) - x axis rotation
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:param y: Tensor of shape (B, 1) - y axis rotation
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:param z: Tensor of shape (B, 1) - z axis rotation
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:return: Rotation matrix for the given euler anglers, in the order rotation(x).rotation(y).rotation(z)
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"""
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B = tf.shape(z)[0]
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# Euler angles should be between -pi and pi, clip so the pose network is coerced to this range
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z = tf.clip_by_value(z, -np.pi, np.pi)
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y = tf.clip_by_value(y, -np.pi, np.pi)
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x = tf.clip_by_value(x, -np.pi, np.pi)
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# Expand to B x 1 x 1
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z = tf.expand_dims(tf.expand_dims(z, -1), -1)
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y = tf.expand_dims(tf.expand_dims(y, -1), -1)
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x = tf.expand_dims(tf.expand_dims(x, -1), -1)
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zeros = tf.zeros([B, 1, 1])
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ones = tf.ones([B, 1, 1])
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cosx = tf.cos(x)
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sinx = tf.sin(x)
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rotx_1 = tf.concat([ones, zeros, zeros], axis=3)
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rotx_2 = tf.concat([zeros, cosx, -sinx], axis=3)
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rotx_3 = tf.concat([zeros, sinx, cosx], axis=3)
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xmat = tf.concat([rotx_1, rotx_2, rotx_3], axis=2)
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cosz = tf.cos(z)
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sinz = tf.sin(z)
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rotz_1 = tf.concat([cosz, -sinz, zeros], axis=3)
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rotz_2 = tf.concat([sinz, cosz, zeros], axis=3)
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rotz_3 = tf.concat([zeros, zeros, ones], axis=3)
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zmat = tf.concat([rotz_1, rotz_2, rotz_3], axis=2)
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cosy = tf.cos(y)
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siny = tf.sin(y)
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roty_1 = tf.concat([cosy, zeros, siny], axis=3)
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roty_2 = tf.concat([zeros, ones, zeros], axis=3)
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roty_3 = tf.concat([-siny, zeros, cosy], axis=3)
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ymat = tf.concat([roty_1, roty_2, roty_3], axis=2)
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rotMat = tf.matmul(tf.matmul(xmat, ymat), zmat)
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return rotMat
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def pose_vec2mat(vec):
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"""Converts 6DoF parameters to transformation matrix
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Args:
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vec: 6DoF parameters in the order of tx, ty, tz, rx, ry, rz -- [B, 6]
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Returns:
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A transformation matrix -- [B, 4, 4]
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"""
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batch_size, _ = vec.get_shape().as_list()
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translation = tf.slice(vec, [0, 0], [-1, 3])
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translation = tf.expand_dims(translation, -1)
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rx = tf.slice(vec, [0, 3], [-1, 1])
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ry = tf.slice(vec, [0, 4], [-1, 1])
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rz = tf.slice(vec, [0, 5], [-1, 1])
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rot_mat = euler_to_rotation_matrix(rx, ry, rz)
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rot_mat = tf.squeeze(rot_mat, axis=[1])
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filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])
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filler = tf.tile(filler, [batch_size, 1, 1])
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transform_mat = tf.concat([rot_mat, translation], axis=2)
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transform_mat = tf.concat([transform_mat, filler], axis=1)
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return transform_mat
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def image_coordinate(batch, height, width):
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"""
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Construct a tensor for the given height/width with elements the homogenous coordinates for the pixel
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:param batch: Number of images in a batch
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:param height: Height of image
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:param width: Width of image
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:return: Tensor of shape (B, height, width, 3), homogenous coordinates for an image.
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Coordinates are in order [x, y, 1]
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"""
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x_coords = tf.range(width)
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y_coords = tf.range(height)
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x_mesh, y_mesh = tf.meshgrid(x_coords, y_coords)
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ones_mesh = tf.cast(tf.ones([height, width]), tf.int32)
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stacked = tf.stack([x_mesh, y_mesh, ones_mesh], axis=2)
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return tf.repeat(tf.expand_dims(stacked, axis=0), batch, axis=0)
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def projective_inverse_warp(target_img, source_img, depth, pose, intrinsics, coordinates):
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"""
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Calculate the reprojected image from the source to the target, based on the given depth, pose and intrinsics
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SFM Learner inverse warp step
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ps ~ K.T(t->s).Dt(pt).K^-1.pt
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Idea is to map the pixel coordinates of the target image to 3d space (Dt(pt).K^-1.pt), then map these onto
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the source image in pixel coordinates (K.T(t->s).{3d coord}), then using the projected coordinates we sample
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the pixels in the source image (ps) to reconstruct the target image.
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:param target_img: Tensor (batch, height, width, 3)
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:param source_img: Tensor, same shape as target_img
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:param depth: Tensor, (batch, height, width, 1)
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:param pose: (batch, 6)
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:param intrinsics: (batch, 3, 3)
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:param coordinates: (batch, height, width, 3) - coordinates for the image. Pass this in so it doesn't need to be
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calculated on every warp step
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:return: The source image reprojected to the target
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"""
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# Convert pose vector (output of pose net) to pose matrix (4x4)
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# Convert intrinsics matrix (3x3) to (4x4) so it can be multiplied by the pose net
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# intrinsics_4x4 =
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# Calculate inverse of the 4x4 intrinsics matrix
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tf.linalg.inv()
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# Create grid of homogenous coordinates
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#
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pass
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