add scannet raw data process

.isort.cfg

@@ -3,6 +3,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmdet,mmdet3d
-known_third_party = cv2,mmcv,numba,numpy,nuscenes,pycocotools,pyquaternion,pytest,scipy,shapely,six,skimage,torch,torchvision
+known_third_party = cv2,load_scannet_data,mmcv,numba,numpy,nuscenes,plyfile,pycocotools,pyquaternion,pytest,scannet_utils,scipy,shapely,six,skimage,torch,torchvision
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

data/scannet/README.md

+### Prepare ScanNet Data
+
+1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Move/link the `scans` folder such that under `scans` there should be folders with names such as `scene0001_01`.
+
+2. Extract point clouds and annotations (semantic seg, instance seg etc.) by running `python batch_load_scannet_data.py`, which will create a folder named `scannet_train_detection_data` here.

data/scannet/batch_load_scannet_data.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+""" Batch mode in loading Scannet scenes with vertices and ground truth labels
+for semantic and instance segmentations
+
+Usage example: python ./batch_load_scannet_data.py
+"""
+import datetime
+import os
+
+import numpy as np
+from load_scannet_data import export
+
+SCANNET_DIR = 'scans'
+TRAIN_SCAN_NAMES = [
+    line.rstrip() for line in open('meta_data/scannet_train.txt')
+]
+LABEL_MAP_FILE = 'meta_data/scannetv2-labels.combined.tsv'
+DONOTCARE_CLASS_IDS = np.array([])
+OBJ_CLASS_IDS = np.array(
+    [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39])
+MAX_NUM_POINT = 50000
+OUTPUT_FOLDER = './scannet_train_detection_data'
+
+
+def export_one_scan(scan_name, output_filename_prefix):
+    mesh_file = os.path.join(SCANNET_DIR, scan_name,
+                             scan_name + '_vh_clean_2.ply')
+    agg_file = os.path.join(SCANNET_DIR, scan_name,
+                            scan_name + '.aggregation.json')
+    seg_file = os.path.join(SCANNET_DIR, scan_name,
+                            scan_name + '_vh_clean_2.0.010000.segs.json')
+    meta_file = os.path.join(
+        SCANNET_DIR, scan_name, scan_name +
+        '.txt')  # includes axisAlignment info for the train set scans.
+    mesh_vertices, semantic_labels, instance_labels, instance_bboxes, \
+        instance2semantic = export(mesh_file, agg_file, seg_file,
+                                   meta_file, LABEL_MAP_FILE, None)
+
+    mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS))
+    mesh_vertices = mesh_vertices[mask, :]
+    semantic_labels = semantic_labels[mask]
+    instance_labels = instance_labels[mask]
+
+    num_instances = len(np.unique(instance_labels))
+    print('Num of instances: ', num_instances)
+
+    bbox_mask = np.in1d(instance_bboxes[:, -1], OBJ_CLASS_IDS)
+    instance_bboxes = instance_bboxes[bbox_mask, :]
+    print('Num of care instances: ', instance_bboxes.shape[0])
+
+    N = mesh_vertices.shape[0]
+    if N > MAX_NUM_POINT:
+        choices = np.random.choice(N, MAX_NUM_POINT, replace=False)
+        mesh_vertices = mesh_vertices[choices, :]
+        semantic_labels = semantic_labels[choices]
+        instance_labels = instance_labels[choices]
+
+    np.save(output_filename_prefix + '_vert.npy', mesh_vertices)
+    np.save(output_filename_prefix + '_sem_label.npy', semantic_labels)
+    np.save(output_filename_prefix + '_ins_label.npy', instance_labels)
+    np.save(output_filename_prefix + '_bbox.npy', instance_bboxes)
+
+
+def batch_export():
+    if not os.path.exists(OUTPUT_FOLDER):
+        print('Creating new data folder: {}'.format(OUTPUT_FOLDER))
+        os.mkdir(OUTPUT_FOLDER)
+
+    for scan_name in TRAIN_SCAN_NAMES:
+        print('-' * 20 + 'begin')
+        print(datetime.datetime.now())
+        print(scan_name)
+        output_filename_prefix = os.path.join(OUTPUT_FOLDER, scan_name)
+        if os.path.isfile(output_filename_prefix + '_vert.npy'):
+            print('File already exists. skipping.')
+            print('-' * 20 + 'done')
+            continue
+        try:
+            export_one_scan(scan_name, output_filename_prefix)
+        except Exception:
+            print('Failed export scan: %s' % (scan_name))
+        print('-' * 20 + 'done')
+
+
+if __name__ == '__main__':
+    batch_export()

data/scannet/load_scannet_data.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+""" Load Scannet scenes with vertices and ground truth labels
+for semantic and instance segmentations
+"""
+import argparse
+import inspect
+import json
+import os
+
+import numpy as np
+import scannet_utils
+
+currentdir = os.path.dirname(
+    os.path.abspath(inspect.getfile(inspect.currentframe())))
+
+
+def read_aggregation(filename):
+    assert os.path.isfile(filename)
+    object_id_to_segs = {}
+    label_to_segs = {}
+    with open(filename) as f:
+        data = json.load(f)
+        num_objects = len(data['segGroups'])
+        for i in range(num_objects):
+            object_id = data['segGroups'][i][
+                'objectId'] + 1  # instance ids should be 1-indexed
+            label = data['segGroups'][i]['label']
+            segs = data['segGroups'][i]['segments']
+            object_id_to_segs[object_id] = segs
+            if label in label_to_segs:
+                label_to_segs[label].extend(segs)
+            else:
+                label_to_segs[label] = segs
+    return object_id_to_segs, label_to_segs
+
+
+def read_segmentation(filename):
+    assert os.path.isfile(filename)
+    seg_to_verts = {}
+    with open(filename) as f:
+        data = json.load(f)
+        num_verts = len(data['segIndices'])
+        for i in range(num_verts):
+            seg_id = data['segIndices'][i]
+            if seg_id in seg_to_verts:
+                seg_to_verts[seg_id].append(i)
+            else:
+                seg_to_verts[seg_id] = [i]
+    return seg_to_verts, num_verts
+
+
+def export(mesh_file,
+           agg_file,
+           seg_file,
+           meta_file,
+           label_map_file,
+           output_file=None):
+    """ points are XYZ RGB (RGB in 0-255),
+    semantic label as nyu40 ids,
+    instance label as 1-#instance,
+    box as (cx,cy,cz,dx,dy,dz,semantic_label)
+    """
+    label_map = scannet_utils.read_label_mapping(
+        label_map_file, label_from='raw_category', label_to='nyu40id')
+    mesh_vertices = scannet_utils.read_mesh_vertices_rgb(mesh_file)
+
+    # Load scene axis alignment matrix
+    lines = open(meta_file).readlines()
+    for line in lines:
+        if 'axisAlignment' in line:
+            axis_align_matrix = [
+                float(x)
+                for x in line.rstrip().strip('axisAlignment = ').split(' ')
+            ]
+            break
+    axis_align_matrix = np.array(axis_align_matrix).reshape((4, 4))
+    pts = np.ones((mesh_vertices.shape[0], 4))
+    pts[:, 0:3] = mesh_vertices[:, 0:3]
+    pts = np.dot(pts, axis_align_matrix.transpose())  # Nx4
+    mesh_vertices[:, 0:3] = pts[:, 0:3]
+
+    # Load semantic and instance labels
+    object_id_to_segs, label_to_segs = read_aggregation(agg_file)
+    seg_to_verts, num_verts = read_segmentation(seg_file)
+    label_ids = np.zeros(shape=(num_verts), dtype=np.uint32)  # 0: unannotated
+    object_id_to_label_id = {}
+    for label, segs in label_to_segs.items():
+        label_id = label_map[label]
+        for seg in segs:
+            verts = seg_to_verts[seg]
+            label_ids[verts] = label_id
+    instance_ids = np.zeros(
+        shape=(num_verts), dtype=np.uint32)  # 0: unannotated
+    num_instances = len(np.unique(list(object_id_to_segs.keys())))
+    for object_id, segs in object_id_to_segs.items():
+        for seg in segs:
+            verts = seg_to_verts[seg]
+            instance_ids[verts] = object_id
+            if object_id not in object_id_to_label_id:
+                object_id_to_label_id[object_id] = label_ids[verts][0]
+    instance_bboxes = np.zeros((num_instances, 7))
+    for obj_id in object_id_to_segs:
+        label_id = object_id_to_label_id[obj_id]
+        obj_pc = mesh_vertices[instance_ids == obj_id, 0:3]
+        if len(obj_pc) == 0:
+            continue
+        # Compute axis aligned box
+        # An axis aligned bounding box is parameterized by
+        # (cx,cy,cz) and (dx,dy,dz) and label id
+        # where (cx,cy,cz) is the center point of the box,
+        # dx is the x-axis length of the box.
+        xmin = np.min(obj_pc[:, 0])
+        ymin = np.min(obj_pc[:, 1])
+        zmin = np.min(obj_pc[:, 2])
+        xmax = np.max(obj_pc[:, 0])
+        ymax = np.max(obj_pc[:, 1])
+        zmax = np.max(obj_pc[:, 2])
+        bbox = np.array([(xmin + xmax) / 2, (ymin + ymax) / 2,
+                         (zmin + zmax) / 2, xmax - xmin, ymax - ymin,
+                         zmax - zmin, label_id])
+        # NOTE: this assumes obj_id is in 1,2,3,.,,,.NUM_INSTANCES
+        instance_bboxes[obj_id - 1, :] = bbox
+
+    if output_file is not None:
+        np.save(output_file + '_vert.npy', mesh_vertices)
+        np.save(output_file + '_sem_label.npy', label_ids)
+        np.save(output_file + '_ins_label.npy', instance_ids)
+        np.save(output_file + '_bbox.npy', instance_bboxes)
+
+    return mesh_vertices, label_ids, instance_ids,\
+        instance_bboxes, object_id_to_label_id
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--scan_path',
+        required=True,
+        help='path to scannet scene (e.g., data/ScanNet/v2/scene0000_00')
+    parser.add_argument('--output_file', required=True, help='output file')
+    parser.add_argument(
+        '--label_map_file',
+        required=True,
+        help='path to scannetv2-labels.combined.tsv')
+    opt = parser.parse_args()
+
+    scan_name = os.path.split(opt.scan_path)[-1]
+    mesh_file = os.path.join(opt.scan_path, scan_name + '_vh_clean_2.ply')
+    agg_file = os.path.join(opt.scan_path, scan_name + '.aggregation.json')
+    seg_file = os.path.join(opt.scan_path,
+                            scan_name + '_vh_clean_2.0.010000.segs.json')
+    meta_file = os.path.join(
+        opt.scan_path, scan_name +
+        '.txt')  # includes axisAlignment info for the train set scans.
+    export(mesh_file, agg_file, seg_file, meta_file, opt.label_map_file,
+           opt.output_file)
+
+
+if __name__ == '__main__':
+    main()

data/scannet/meta_data/scannetv2_test.txt

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data/scannet/meta_data/scannetv2_train.txt

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data/scannet/scannet_utils.py

+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+''' Ref: https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts '''
+import csv
+import os
+
+import numpy as np
+from plyfile import PlyData
+
+
+def represents_int(s):
+    ''' if string s represents an int. '''
+    try:
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+
+def read_label_mapping(filename,
+                       label_from='raw_category',
+                       label_to='nyu40id'):
+    assert os.path.isfile(filename)
+    mapping = dict()
+    with open(filename) as csvfile:
+        reader = csv.DictReader(csvfile, delimiter='\t')
+        for row in reader:
+            mapping[row[label_from]] = int(row[label_to])
+    if represents_int(list(mapping.keys())[0]):
+        mapping = {int(k): v for k, v in mapping.items()}
+    return mapping
+
+
+def read_mesh_vertices(filename):
+    """ read XYZ for each vertex.
+    """
+    assert os.path.isfile(filename)
+    with open(filename, 'rb') as f:
+        plydata = PlyData.read(f)
+        num_verts = plydata['vertex'].count
+        vertices = np.zeros(shape=[num_verts, 3], dtype=np.float32)
+        vertices[:, 0] = plydata['vertex'].data['x']
+        vertices[:, 1] = plydata['vertex'].data['y']
+        vertices[:, 2] = plydata['vertex'].data['z']
+    return vertices
+
+
+def read_mesh_vertices_rgb(filename):
+    """ read XYZ RGB for each vertex.
+    Note: RGB values are in 0-255
+    """
+    assert os.path.isfile(filename)
+    with open(filename, 'rb') as f:
+        plydata = PlyData.read(f)
+        num_verts = plydata['vertex'].count
+        vertices = np.zeros(shape=[num_verts, 6], dtype=np.float32)
+        vertices[:, 0] = plydata['vertex'].data['x']
+        vertices[:, 1] = plydata['vertex'].data['y']
+        vertices[:, 2] = plydata['vertex'].data['z']
+        vertices[:, 3] = plydata['vertex'].data['red']
+        vertices[:, 4] = plydata['vertex'].data['green']
+        vertices[:, 5] = plydata['vertex'].data['blue']
+    return vertices