PaddleDetection-release-2.6/deploy/pipeline/tools/lane_to_mask.py

# coding: utf8
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert poly2d to mask/bitmask."""

import os
from functools import partial
from multiprocessing import Pool
from typing import Callable, Dict, List

import matplotlib  # type: ignore
import matplotlib.pyplot as plt  # type: ignore
import numpy as np
from PIL import Image
from scalabel.common.parallel import NPROC
from scalabel.common.typing import NDArrayU8
from scalabel.label.io import group_and_sort, load
from scalabel.label.transforms import poly_to_patch
from scalabel.label.typing import Config, Frame, ImageSize, Label, Poly2D
from scalabel.label.utils import (
    check_crowd,
    check_ignored,
    get_leaf_categories, )
from tqdm import tqdm

from bdd100k.common.logger import logger
from bdd100k.common.typing import BDD100KConfig
from bdd100k.common.utils import get_bdd100k_instance_id, load_bdd100k_config
from bdd100k.label.label import drivables, labels, lane_categories
from bdd100k.label.to_coco import parse_args
from bdd100k.label.to_scalabel import bdd100k_to_scalabel

IGNORE_LABEL = 255
STUFF_NUM = 30
LANE_DIRECTION_MAP = {"parallel": 0, "vertical": 1}
LANE_STYLE_MAP = {"solid": 0, "dashed": 1}


def frame_to_mask(
        out_path: str,
        shape: ImageSize,
        colors: List[NDArrayU8],
        poly2ds: List[List[Poly2D]],
        with_instances: bool=True,
        back_color: int=0,
        closed: bool=True, ) -> None:
    """Converting a frame of poly2ds to mask/bitmask."""
    assert len(colors) == len(poly2ds)
    height, width = shape.height, shape.width

    assert back_color >= 0
    if with_instances:
        img: NDArrayU8 = (
            np.ones(
                [height, width, 4], dtype=np.uint8) * back_color  # type: ignore
        )
    else:
        img = (
            np.ones(
                [height, width, 1], dtype=np.uint8) * back_color  # type: ignore
        )

    if len(colors) == 0:
        pil_img = Image.fromarray(img.squeeze())
        pil_img.save(out_path)

    matplotlib.use("Agg")
    fig = plt.figure(facecolor="0")
    fig.set_size_inches((width / fig.get_dpi()), height / fig.get_dpi())
    ax = fig.add_axes([0, 0, 1, 1])
    ax.axis("off")
    ax.set_xlim(0, width)
    ax.set_ylim(0, height)
    ax.set_facecolor((0, 0, 0, 0))
    ax.invert_yaxis()

    for i, poly2d in enumerate(poly2ds):
        for poly in poly2d:
            ax.add_patch(
                poly_to_patch(
                    poly.vertices,
                    poly.types,
                    # (0, 0, 0) for the background
                    color=(
                        ((i + 1) >> 8) / 255.0,
                        ((i + 1) % 255) / 255.0,
                        0.0, ),
                    closed=closed, ))

    fig.canvas.draw()
    out: NDArrayU8 = np.frombuffer(fig.canvas.tostring_rgb(), np.uint8)
    out = out.reshape((height, width, -1)).astype(np.int32)
    out = (out[..., 0] << 8) + out[..., 1]
    plt.close()

    for i, color in enumerate(colors):
        # 0 is for the background
        img[out == i + 1] = color

    img[img == 255] = 0

    pil_img = Image.fromarray(img.squeeze())
    pil_img.save(out_path)


def set_instance_color(label: Label, category_id: int,
                       ann_id: int) -> NDArrayU8:
    """Set the color for an instance given its attributes and ID."""
    attributes = label.attributes
    if attributes is None:
        truncated, occluded, crowd, ignored = 0, 0, 0, 0
    else:
        truncated = int(attributes.get("truncated", False))
        occluded = int(attributes.get("occluded", False))
        crowd = int(check_crowd(label))
        ignored = int(check_ignored(label))
    color: NDArrayU8 = np.array(
        [
            category_id & 255,
            (truncated << 3) + (occluded << 2) + (crowd << 1) + ignored,
            ann_id >> 8,
            ann_id & 255,
        ],
        dtype=np.uint8, )
    return color


def set_lane_color(label: Label, category_id: int) -> NDArrayU8:
    """Set the color for the lane given its attributes and category."""
    attributes = label.attributes
    if attributes is None:
        lane_direction, lane_style = 0, 0
    else:
        lane_direction = LANE_DIRECTION_MAP[str(
            attributes.get("laneDirection", "parallel"))]
        lane_style = LANE_STYLE_MAP[str(attributes.get("laneStyle", "solid"))]

    #value = category_id + (lane_direction << 5) + (lane_style << 4)
    value = category_id
    if lane_style == 0 and (category_id == 3 or category_id == 2):
        value = 1
    if lane_style == 0:
        value = 2
    else:
        value = 3

    color: NDArrayU8 = np.array([value], dtype=np.uint8)
    return color


def frames_to_masks(
        nproc: int,
        out_paths: List[str],
        shapes: List[ImageSize],
        colors_list: List[List[NDArrayU8]],
        poly2ds_list: List[List[List[Poly2D]]],
        with_instances: bool=True,
        back_color: int=0,
        closed: bool=True, ) -> None:
    """Execute the mask conversion in parallel."""
    with Pool(nproc) as pool:
        pool.starmap(
            partial(
                frame_to_mask,
                with_instances=with_instances,
                back_color=back_color,
                closed=closed, ),
            tqdm(
                zip(out_paths, shapes, colors_list, poly2ds_list),
                total=len(out_paths), ), )


def seg_to_masks(
        frames: List[Frame],
        out_base: str,
        config: Config,
        nproc: int=NPROC,
        mode: str="sem_seg",
        back_color: int=IGNORE_LABEL,
        closed: bool=True, ) -> None:
    """Converting segmentation poly2d to 1-channel masks."""
    os.makedirs(out_base, exist_ok=True)
    img_shape = config.imageSize

    out_paths: List[str] = []
    shapes: List[ImageSize] = []
    colors_list: List[List[NDArrayU8]] = []
    poly2ds_list: List[List[List[Poly2D]]] = []

    categories = dict(
        sem_seg=labels, drivable=drivables, lane_mark=lane_categories)[mode]
    cat_name2id = {
        cat.name: cat.trainId
        for cat in categories if cat.trainId != IGNORE_LABEL
    }

    logger.info("Preparing annotations for Semseg to Bitmasks")

    for image_anns in tqdm(frames):
        # Mask in .png format
        image_name = image_anns.name.replace(".jpg", ".png")
        image_name = os.path.split(image_name)[-1]
        out_path = os.path.join(out_base, image_name)
        out_paths.append(out_path)

        if img_shape is None:
            if image_anns.size is not None:
                img_shape = image_anns.size
            else:
                raise ValueError("Image shape not defined!")
        shapes.append(img_shape)

        colors: List[NDArrayU8] = []
        poly2ds: List[List[Poly2D]] = []
        colors_list.append(colors)
        poly2ds_list.append(poly2ds)

        if image_anns.labels is None:
            continue

        for label in image_anns.labels:
            if label.category not in cat_name2id:
                continue
            if label.poly2d is None:
                continue

            category_id = cat_name2id[label.category]
            if mode in ["sem_seg", "drivable"]:
                color: NDArrayU8 = np.array([category_id], dtype=np.uint8)
            else:
                color = set_lane_color(label, category_id)

            colors.append(color)
            poly2ds.append(label.poly2d)

    logger.info("Start Conversion for Seg to Masks")
    frames_to_masks(
        nproc,
        out_paths,
        shapes,
        colors_list,
        poly2ds_list,
        with_instances=False,
        back_color=back_color,
        closed=closed, )


ToMasksFunc = Callable[[List[Frame], str, Config, int], None]
semseg_to_masks: ToMasksFunc = partial(
    seg_to_masks, mode="sem_seg", back_color=IGNORE_LABEL, closed=True)
drivable_to_masks: ToMasksFunc = partial(
    seg_to_masks,
    mode="drivable",
    back_color=len(drivables) - 1,
    closed=True, )
lanemark_to_masks: ToMasksFunc = partial(
    seg_to_masks, mode="lane_mark", back_color=IGNORE_LABEL, closed=False)


def insseg_to_bitmasks(frames: List[Frame],
                       out_base: str,
                       config: Config,
                       nproc: int=NPROC) -> None:
    """Converting instance segmentation poly2d to bitmasks."""
    os.makedirs(out_base, exist_ok=True)
    img_shape = config.imageSize

    out_paths: List[str] = []
    shapes: List[ImageSize] = []
    colors_list: List[List[NDArrayU8]] = []
    poly2ds_list: List[List[List[Poly2D]]] = []

    categories = get_leaf_categories(config.categories)
    cat_name2id = {cat.name: i + 1 for i, cat in enumerate(categories)}

    logger.info("Preparing annotations for InsSeg to Bitmasks")

    for image_anns in tqdm(frames):
        ann_id = 0

        # Bitmask in .png format
        image_name = image_anns.name.replace(".jpg", ".png")
        image_name = os.path.split(image_name)[-1]
        out_path = os.path.join(out_base, image_name)
        out_paths.append(out_path)

        if img_shape is None:
            if image_anns.size is not None:
                img_shape = image_anns.size
            else:
                raise ValueError("Image shape not defined!")
        shapes.append(img_shape)

        colors: List[NDArrayU8] = []
        poly2ds: List[List[Poly2D]] = []
        colors_list.append(colors)
        poly2ds_list.append(poly2ds)

        labels_ = image_anns.labels
        if labels_ is None or len(labels_) == 0:
            continue

        # Scores higher, rendering later
        if labels_[0].score is not None:
            labels_ = sorted(labels_, key=lambda label: float(label.score))

        for label in labels_:
            if label.poly2d is None:
                continue
            if label.category not in cat_name2id:
                continue

            ann_id += 1
            category_id = cat_name2id[label.category]
            color = set_instance_color(label, category_id, ann_id)
            colors.append(color)
            poly2ds.append(label.poly2d)

    logger.info("Start conversion for InsSeg to Bitmasks")
    frames_to_masks(nproc, out_paths, shapes, colors_list, poly2ds_list)


def panseg_to_bitmasks(frames: List[Frame],
                       out_base: str,
                       config: Config,
                       nproc: int=NPROC) -> None:
    """Converting panoptic segmentation poly2d to bitmasks."""
    os.makedirs(out_base, exist_ok=True)
    img_shape = config.imageSize

    out_paths: List[str] = []
    shapes: List[ImageSize] = []
    colors_list: List[List[NDArrayU8]] = []
    poly2ds_list: List[List[List[Poly2D]]] = []
    cat_name2id = {cat.name: cat.id for cat in labels}

    logger.info("Preparing annotations for InsSeg to Bitmasks")

    for image_anns in tqdm(frames):
        cur_ann_id = STUFF_NUM

        # Bitmask in .png format
        image_name = image_anns.name.replace(".jpg", ".png")
        image_name = os.path.split(image_name)[-1]
        out_path = os.path.join(out_base, image_name)
        out_paths.append(out_path)

        if img_shape is None:
            if image_anns.size is not None:
                img_shape = image_anns.size
            else:
                raise ValueError("Image shape not defined!")
        shapes.append(img_shape)

        colors: List[NDArrayU8] = []
        poly2ds: List[List[Poly2D]] = []
        colors_list.append(colors)
        poly2ds_list.append(poly2ds)

        labels_ = image_anns.labels
        if labels_ is None or len(labels_) == 0:
            continue

        # Scores higher, rendering later
        if labels_[0].score is not None:
            labels_ = sorted(labels_, key=lambda label: float(label.score))

        for label in labels_:
            if label.poly2d is None:
                continue
            if label.category not in cat_name2id:
                continue

            category_id = cat_name2id[label.category]
            if category_id == 0:
                continue
            if category_id <= STUFF_NUM:
                ann_id = category_id
            else:
                cur_ann_id += 1
                ann_id = cur_ann_id

            color = set_instance_color(label, category_id, ann_id)
            colors.append(color)
            poly2ds.append(label.poly2d)

    logger.info("Start conversion for PanSeg to Bitmasks")
    frames_to_masks(nproc, out_paths, shapes, colors_list, poly2ds_list)


def segtrack_to_bitmasks(frames: List[Frame],
                         out_base: str,
                         config: Config,
                         nproc: int=NPROC) -> None:
    """Converting segmentation tracking poly2d to bitmasks."""
    frames_list = group_and_sort(frames)
    img_shape = config.imageSize

    out_paths: List[str] = []
    shapes: List[ImageSize] = []
    colors_list: List[List[NDArrayU8]] = []
    poly2ds_list: List[List[List[Poly2D]]] = []

    categories = get_leaf_categories(config.categories)
    cat_name2id = {cat.name: i + 1 for i, cat in enumerate(categories)}

    logger.info("Preparing annotations for SegTrack to Bitmasks")

    for video_anns in tqdm(frames_list):
        global_instance_id: int = 1
        instance_id_maps: Dict[str, int] = {}

        video_name = video_anns[0].videoName
        out_dir = os.path.join(out_base, video_name)
        if not os.path.isdir(out_dir):
            os.makedirs(out_dir)

        for image_anns in video_anns:
            # Bitmask in .png format
            image_name = image_anns.name.replace(".jpg", ".png")
            image_name = os.path.split(image_name)[-1]
            out_path = os.path.join(out_dir, image_name)
            out_paths.append(out_path)

            if img_shape is None:
                if image_anns.size is not None:
                    img_shape = image_anns.size
                else:
                    raise ValueError("Image shape not defined!")
            shapes.append(img_shape)

            colors: List[NDArrayU8] = []
            poly2ds: List[List[Poly2D]] = []
            colors_list.append(colors)
            poly2ds_list.append(poly2ds)

            labels_ = image_anns.labels
            if labels_ is None or len(labels_) == 0:
                continue

            # Scores higher, rendering later
            if labels_[0].score is not None:
                labels_ = sorted(labels_, key=lambda label: float(label.score))

            for label in labels_:
                if label.poly2d is None:
                    continue
                if label.category not in cat_name2id:
                    continue

                instance_id, global_instance_id = get_bdd100k_instance_id(
                    instance_id_maps, global_instance_id, label.id)
                category_id = cat_name2id[label.category]
                color = set_instance_color(label, category_id, instance_id)
                colors.append(color)
                poly2ds.append(label.poly2d)

    logger.info("Start Conversion for SegTrack to Bitmasks")
    frames_to_masks(nproc, out_paths, shapes, colors_list, poly2ds_list)


def main() -> None:
    """Main function."""
    args = parse_args()
    args.mode = "lane_mark"

    os.environ["QT_QPA_PLATFORM"] = "offscreen"  # matplotlib offscreen render

    convert_funcs: Dict[str, ToMasksFunc] = dict(
        sem_seg=semseg_to_masks,
        drivable=drivable_to_masks,
        lane_mark=lanemark_to_masks,
        pan_seg=panseg_to_bitmasks,
        ins_seg=insseg_to_bitmasks,
        seg_track=segtrack_to_bitmasks, )

    dataset = load(args.input, args.nproc)
    if args.config is not None:
        bdd100k_config = load_bdd100k_config(args.config)
    elif dataset.config is not None:
        bdd100k_config = BDD100KConfig(config=dataset.config)
    else:
        bdd100k_config = load_bdd100k_config(args.mode)

    if args.mode in ["ins_seg", "seg_track"]:
        frames = bdd100k_to_scalabel(dataset.frames, bdd100k_config)
    else:
        frames = dataset.frames

    convert_funcs[args.mode](frames, args.output, bdd100k_config.scalabel,
                             args.nproc)

    logger.info("Finished!")


if __name__ == "__main__":
    main()