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pcdet/utils/calibration_kitti.py

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