126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
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import numpy as np
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def get_calib_from_file(calib_file):
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with open(calib_file) as f:
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lines = f.readlines()
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obj = lines[2].strip().split(' ')[1:]
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P2 = np.array(obj, dtype=np.float32)
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obj = lines[3].strip().split(' ')[1:]
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P3 = np.array(obj, dtype=np.float32)
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obj = lines[4].strip().split(' ')[1:]
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R0 = np.array(obj, dtype=np.float32)
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obj = lines[5].strip().split(' ')[1:]
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Tr_velo_to_cam = np.array(obj, dtype=np.float32)
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return {'P2': P2.reshape(3, 4),
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'P3': P3.reshape(3, 4),
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'R0': R0.reshape(3, 3),
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'Tr_velo2cam': Tr_velo_to_cam.reshape(3, 4)}
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class Calibration(object):
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def __init__(self, calib_file):
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if not isinstance(calib_file, dict):
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calib = get_calib_from_file(calib_file)
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else:
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calib = calib_file
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self.P2 = calib['P2'] # 3 x 4
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self.R0 = calib['R0'] # 3 x 3
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self.V2C = calib['Tr_velo2cam'] # 3 x 4
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# Camera intrinsics and extrinsics
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self.cu = self.P2[0, 2]
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self.cv = self.P2[1, 2]
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self.fu = self.P2[0, 0]
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self.fv = self.P2[1, 1]
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self.tx = self.P2[0, 3] / (-self.fu)
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self.ty = self.P2[1, 3] / (-self.fv)
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def cart_to_hom(self, pts):
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"""
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:param pts: (N, 3 or 2)
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:return pts_hom: (N, 4 or 3)
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"""
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pts_hom = np.hstack((pts, np.ones((pts.shape[0], 1), dtype=np.float32)))
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return pts_hom
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def rect_to_lidar(self, pts_rect):
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"""
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:param pts_lidar: (N, 3)
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:return pts_rect: (N, 3)
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"""
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pts_rect_hom = self.cart_to_hom(pts_rect) # (N, 4)
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R0_ext = np.hstack((self.R0, np.zeros((3, 1), dtype=np.float32))) # (3, 4)
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R0_ext = np.vstack((R0_ext, np.zeros((1, 4), dtype=np.float32))) # (4, 4)
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R0_ext[3, 3] = 1
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V2C_ext = np.vstack((self.V2C, np.zeros((1, 4), dtype=np.float32))) # (4, 4)
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V2C_ext[3, 3] = 1
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pts_lidar = np.dot(pts_rect_hom, np.linalg.inv(np.dot(R0_ext, V2C_ext).T))
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return pts_lidar[:, 0:3]
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def lidar_to_rect(self, pts_lidar):
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"""
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:param pts_lidar: (N, 3)
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:return pts_rect: (N, 3)
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"""
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pts_lidar_hom = self.cart_to_hom(pts_lidar)
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pts_rect = np.dot(pts_lidar_hom, np.dot(self.V2C.T, self.R0.T))
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# pts_rect = reduce(np.dot, (pts_lidar_hom, self.V2C.T, self.R0.T))
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return pts_rect
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def rect_to_img(self, pts_rect):
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"""
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:param pts_rect: (N, 3)
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:return pts_img: (N, 2)
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"""
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pts_rect_hom = self.cart_to_hom(pts_rect)
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pts_2d_hom = np.dot(pts_rect_hom, self.P2.T)
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pts_img = (pts_2d_hom[:, 0:2].T / pts_rect_hom[:, 2]).T # (N, 2)
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pts_rect_depth = pts_2d_hom[:, 2] - self.P2.T[3, 2] # depth in rect camera coord
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return pts_img, pts_rect_depth
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def lidar_to_img(self, pts_lidar):
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"""
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:param pts_lidar: (N, 3)
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:return pts_img: (N, 2)
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"""
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pts_rect = self.lidar_to_rect(pts_lidar)
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pts_img, pts_depth = self.rect_to_img(pts_rect)
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return pts_img, pts_depth
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def img_to_rect(self, u, v, depth_rect):
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"""
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:param u: (N)
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:param v: (N)
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:param depth_rect: (N)
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:return:
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"""
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x = ((u - self.cu) * depth_rect) / self.fu + self.tx
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y = ((v - self.cv) * depth_rect) / self.fv + self.ty
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pts_rect = np.concatenate((x.reshape(-1, 1), y.reshape(-1, 1), depth_rect.reshape(-1, 1)), axis=1)
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return pts_rect
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def corners3d_to_img_boxes(self, corners3d):
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"""
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:param corners3d: (N, 8, 3) corners in rect coordinate
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:return: boxes: (None, 4) [x1, y1, x2, y2] in rgb coordinate
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:return: boxes_corner: (None, 8) [xi, yi] in rgb coordinate
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"""
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sample_num = corners3d.shape[0]
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corners3d_hom = np.concatenate((corners3d, np.ones((sample_num, 8, 1))), axis=2) # (N, 8, 4)
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img_pts = np.matmul(corners3d_hom, self.P2.T) # (N, 8, 3)
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x, y = img_pts[:, :, 0] / img_pts[:, :, 2], img_pts[:, :, 1] / img_pts[:, :, 2]
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x1, y1 = np.min(x, axis=1), np.min(y, axis=1)
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x2, y2 = np.max(x, axis=1), np.max(y, axis=1)
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boxes = np.concatenate((x1.reshape(-1, 1), y1.reshape(-1, 1), x2.reshape(-1, 1), y2.reshape(-1, 1)), axis=1)
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boxes_corner = np.concatenate((x.reshape(-1, 8, 1), y.reshape(-1, 8, 1)), axis=2)
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return boxes, boxes_corner
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