change_intensity

utils/change_intensity.py

@@ -0,0 +1,136 @@
+# https://github.com/Yuseung-Na/pcd2bin
+
+import numpy as np
+import os
+import argparse
+import pypcd
+import csv
+from tqdm import tqdm
+import open3d as o3d
+
+
+def load_labels(label_path):
+    """ Load semantic and instance labels in SemanticKitti format.
+    """
+    label = np.fromfile(label_path, dtype=np.uint32)
+    label = label.reshape((-1))
+
+    sem_label = label & 0xFFFF  # semantic label in lower half
+    inst_label = label >> 16  # instance id in upper half
+
+    # sanity check
+    assert ((sem_label + (inst_label << 16) == label).all())
+
+    return sem_label, inst_label
+
+
+def main():
+    # Add parser
+    parser = argparse.ArgumentParser(description="Convert .pcd to .bin")
+    parser.add_argument(
+        "--pcd_path", '-p',
+        help=".pcd file path.",
+        type=str,
+        default="/home/user/lidar_pcd"
+    )
+    parser.add_argument(
+        "--save_path", '-s',
+        help="save file path.",
+        type=str,
+        default="/home/user/lidar_bin"
+    )
+
+    args = parser.parse_args()
+
+    # Find all pcd files
+    pcd_files = []
+    for (path, dir, files) in os.walk(args.pcd_path):
+        for filename in files:
+            ext = os.path.splitext(filename)[-1]
+            if ext == '.pcd':
+                pcd_files.append(path + "/" + filename)
+
+    # Sort pcd files by file name
+    pcd_files.sort()
+    print("Finish to load point clouds!")
+
+    # Make save_path directory
+    try:
+        if not (os.path.isdir(args.save_path)):
+            os.makedirs(os.path.join(args.save_path))
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            print("Failed to create directory!!!!!")
+            raise
+
+    # Generate csv meta file
+    csv_file_path = os.path.join(args.save_path, "meta.csv")
+    csv_file = open(csv_file_path, "w")
+    meta_file = csv.writer(
+        csv_file, delimiter=",", quotechar="|", quoting=csv.QUOTE_MINIMAL
+    )
+    # Write csv meta file header
+    meta_file.writerow(
+        [
+            "pcd file name",
+            "bin file name",
+        ]
+    )
+    print("Finish to generate csv meta file")
+
+    # Converting Process
+    print("Converting Start!")
+    for pcd_file in tqdm(pcd_files):
+        # Get pcd file
+        pc = pypcd.PointCloud.from_path(pcd_file)
+        seq = pcd_file.split('/')[-1].split('.')[0]
+
+        # Generate bin file name
+        bin_file_name = "{}.pcd".format(seq)
+        bin_file_path = os.path.join(args.save_path, bin_file_name)
+
+        # Get data from pcd (x, y, z, intensity, ring, time)
+        np_x = (np.array(pc.pc_data['x'], dtype=np.float32)).astype(np.float32)
+        np_y = (np.array(pc.pc_data['y'], dtype=np.float32)).astype(np.float32)
+        np_z = (np.array(pc.pc_data['z'], dtype=np.float32)).astype(np.float32)
+        np_i = pc.pc_data['intensity'].astype(dtype=np.uint32) & 0xFFFF
+        # np_r = (np.array(pc.pc_data['ring'], dtype=np.float32)).astype(np.float32)
+        # np_t = (np.array(pc.pc_data['time'], dtype=np.float32)).astype(np.float32)
+
+        flag = (np_i == 252)
+
+        for i in [253, 254, 255, 256, 257, 258, 259]:
+            flag = np.logical_or(flag, np_i == i)
+
+        np_i = np.where(flag, 0xFFFFFFFF, 0).astype(np.float32)
+
+        # Stack all data    
+        data = np.transpose(np.vstack((np_x, np_y, np_z, np_i)))   
+        data = data.astype(np.float32)
+        data_bytes = data.tobytes()
+
+        with open(bin_file_path, 'w') as f:
+            f.write('# .PCD v0.7 - Point Cloud Data file format\n')
+            f.write('VERSION 0.7\n')
+            f.write('FIELDS x y z intensity\n')
+            f.write('SIZE 4 4 4 4\n')
+            f.write('TYPE F F F F\n')
+            f.write('COUNT 1 1 1 1\n')
+            f.write('WIDTH %d\n' % data.shape[0])
+            f.write('HEIGHT 1\n')
+            f.write('POINTS %d\n' % data.shape[0])
+            f.write('DATA binary\n')
+
+            f.write(data_bytes)
+
+        # Write csv meta file
+        meta_file.writerow(
+            [os.path.split(pcd_file)[-1], bin_file_name]
+        )
+
+    # delete the meta.csv file
+    os.remove(csv_file_path)
+
+
+if __name__ == "__main__":
+    main()

utils/pcd2bin.py

@@ -0,0 +1,146 @@
+# https://github.com/Yuseung-Na/pcd2bin
+
+import numpy as np
+import os
+import argparse
+import pypcd
+import csv
+from tqdm import tqdm
+
+
+def load_labels(label_path):
+    """ Load semantic and instance labels in SemanticKitti format.
+    """
+    label = np.fromfile(label_path, dtype=np.uint32)
+    label = label.reshape((-1))
+
+    sem_label = label & 0xFFFF  # semantic label in lower half
+    inst_label = label >> 16  # instance id in upper half
+
+    # sanity check
+    assert ((sem_label + (inst_label << 16) == label).all())
+
+    return sem_label, inst_label
+
+
+def main():
+    # Add parser
+    parser = argparse.ArgumentParser(description="Convert .pcd to .bin")
+    parser.add_argument(
+        "--pcd_path", '-p',
+        help=".pcd file path.",
+        type=str,
+        default="/home/user/lidar_pcd"
+    )
+    parser.add_argument(
+        "--bin_path", '-b',
+        help=".bin file path.",
+        type=str,
+        default="/home/user/lidar_bin"
+    )
+    parser.add_argument(
+        "--original_label_path",
+        type=str,
+        default="/home/user/lidar_bin"
+    )
+    parser.add_argument(
+        "--new_label_path",
+        type=str,
+        default="/home/user/lidar_bin"
+    )
+    parser.add_argument(
+        "--idx_path", '-i',
+        type=str,
+        default="/home/user/lidar_bin"
+    )
+
+    args = parser.parse_args()
+
+    # Find all pcd files
+    pcd_files = []
+    for (path, dir, files) in os.walk(args.pcd_path):
+        for filename in files:
+            ext = os.path.splitext(filename)[-1]
+            if ext == '.pcd':
+                pcd_files.append(path + "/" + filename)
+
+    # Sort pcd files by file name
+    pcd_files.sort()
+    print("Finish to load point clouds!")
+
+    # Make bin_path directory
+    try:
+        if not (os.path.isdir(args.bin_path)):
+            os.makedirs(os.path.join(args.bin_path))
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            print("Failed to create directory!!!!!")
+            raise
+
+    # Generate csv meta file
+    csv_file_path = os.path.join(args.bin_path, "meta.csv")
+    csv_file = open(csv_file_path, "w")
+    meta_file = csv.writer(
+        csv_file, delimiter=",", quotechar="|", quoting=csv.QUOTE_MINIMAL
+    )
+    # Write csv meta file header
+    meta_file.writerow(
+        [
+            "pcd file name",
+            "bin file name",
+        ]
+    )
+    print("Finish to generate csv meta file")
+
+    # Converting Process
+    print("Converting Start!")
+    for pcd_file in tqdm(pcd_files):
+        # Get pcd file
+        pc = pypcd.PointCloud.from_path(pcd_file)
+        seq = pcd_file.split('/')[-1].split('.')[0]
+        label_path = args.original_label_path + seq + '.label'
+        sem_label, inst_label = load_labels(label_path)
+        idx_path = args.idx_path + seq + '.txt'
+        idx = list(np.loadtxt(idx_path,dtype=np.int32))
+        sem_label = sem_label[idx]
+        inst_label = inst_label[idx]
+        new_label_path = os.path.join(args.new_label_path, seq + '.label')
+        new_label = sem_label + (inst_label << 16)
+        new_label.tofile(new_label_path)
+
+        # Generate bin file name
+        seq = int(seq)
+        bin_file_name = "{:06d}.bin".format(seq)
+        bin_file_path = os.path.join(args.bin_path, bin_file_name)
+
+        # Get data from pcd (x, y, z, intensity, ring, time)
+        np_x = (np.array(pc.pc_data['x'], dtype=np.float32)).astype(np.float32)
+        np_y = (np.array(pc.pc_data['y'], dtype=np.float32)).astype(np.float32)
+        np_z = (np.array(pc.pc_data['z'], dtype=np.float32)).astype(np.float32)
+        np_i = (np.array(pc.pc_data['intensity'],
+                dtype=np.float32)).astype(np.float32)
+        # np_r = (np.array(pc.pc_data['ring'], dtype=np.float32)).astype(np.float32)
+        # np_t = (np.array(pc.pc_data['time'], dtype=np.float32)).astype(np.float32)
+
+        # Stack all data
+        points_32 = np.transpose(np.vstack((np_x, np_y, np_z, np_i)))
+
+        # delete invalid points, e.g. depth == 0
+        index = np.linalg.norm(points_32[:, 0:3], 2, axis=1) > 0.
+        points_32 = points_32[index, :]
+
+        # Save bin file
+        points_32.tofile(bin_file_path)
+
+        # Write csv meta file
+        meta_file.writerow(
+            [os.path.split(pcd_file)[-1], bin_file_name]
+        )
+
+
+    # delete the meta.csv file
+    os.remove(csv_file_path)
+
+
+if __name__ == "__main__":
+    main()