navsim/agents/expansion/vis_vocab_tl.py

import io
import logging
import os
import pickle
import uuid
from pathlib import Path

import hydra
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from hydra.utils import instantiate
from matplotlib.collections import LineCollection
from nuplan.planning.utils.multithreading.worker_utils import worker_map
from omegaconf import DictConfig
from tqdm import tqdm

from navsim.common.dataclasses import AgentInput, Scene
from navsim.common.dataclasses import SensorConfig
from navsim.common.dataloader import SceneLoader
from navsim.planning.script.builders.worker_pool_builder import build_worker

logger = logging.getLogger(__name__)
CONFIG_PATH = "../../planning/script/config/pdm_scoring"
CONFIG_NAME = "run_pdm_score_ddp"
norm = plt.Normalize(vmin=0.0, vmax=1.0)
cmap = plt.get_cmap('viridis')

def get_distribution(scores, vocab, gt_traj):
    # metrics = ['gt', 'noc', 'da', 'tl', 'progress', 'lk', 'dr']
    metrics = ['gt', 'noc', 'tl', 'progress', 'lk', 'dr']
    fig, axes = plt.subplots(2, 3, figsize=(16.2, 10.8))

    for i, ax in enumerate(axes.flat):
        metric = metrics[i]
        ax.set_xlim(-5, 65)
        ax.set_ylim(-25, 25)
        ax.set_title(f"Metric {metric}")
        if metric == 'gt':
            ax.plot(gt_traj[:, 0], gt_traj[:, 1], c='r', alpha=1.0)
            continue
        vocab_scores = scores[metric]
        line_collection = LineCollection(vocab[..., :2],
                                         colors=[cmap(norm(score)) for score in vocab_scores],
                                         alpha=[1.0 if score > 0.1 else 0.001 for score in vocab_scores])
        ax.add_collection(line_collection)

    fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), cax=fig.add_axes([0.92, 0.15, 0.02, 0.7]))
    plt.tight_layout(rect=[0, 0, 0.9, 1])
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    image = Image.open(buf)

    return image


def worker_task(args):
    node_id = int(os.environ.get("NODE_RANK", 0))
    thread_id = str(uuid.uuid4())
    logger.info(f"Starting worker in thread_id={thread_id}, node_id={node_id}")

    for arg in tqdm(args, desc="Running visualization"):
        token, gt_scores, vocab = arg['token'], arg['gt_scores'], arg['vocab']
        scene_loader = arg['scene_loader']
        agent_input = AgentInput.from_scene_dict_list(
            scene_loader.scene_frames_dicts[token],
            scene_loader._sensor_blobs_path,
            scene_loader._scene_filter.num_history_frames,
            scene_loader._sensor_config
        )
        gt_traj = Scene.from_scene_dict_list(
            scene_loader.scene_frames_dicts[token],
            scene_loader._sensor_blobs_path,
            scene_loader._scene_filter.num_history_frames,
            10,
            scene_loader._sensor_config
        ).get_future_trajectory(int(4 / 0.5))

        gt_traj = gt_traj.poses

        # inf traj + gt traj
        cam = agent_input.cameras[-1].cam_f0
        img, cam2lidar_rot, cam2lidar_tran, cam_intrin = cam.image, cam.sensor2lidar_rotation, cam.sensor2lidar_translation, cam.intrinsics

        img = Image.fromarray(img.astype('uint8'), 'RGB')

        # distributions of vocab
        figs = get_distribution(gt_scores, vocab, gt_traj)

        # concat
        total_width = img.width + figs.width
        max_height = max(img.height, figs.height)
        new_image = Image.new('RGB', (total_width, max_height))
        new_image.paste(img, (0, 0))
        new_image.paste(figs, (img.width, 0))

        output_dir = args[0]['result_dir']
        new_image.save(f'{output_dir}/{token}/{token}.png')

    return []


@hydra.main(config_path=CONFIG_PATH, config_name=CONFIG_NAME)
def main(cfg: DictConfig) -> None:
    data_path = Path(cfg.navsim_log_path)
    sensor_blobs_path = Path(cfg.sensor_blobs_path)
    scene_filter = instantiate(cfg.scene_filter)
    scene_loader = SceneLoader(
        data_path=data_path,
        scene_filter=scene_filter,
        sensor_blobs_path=sensor_blobs_path,
        sensor_config=SensorConfig(
            cam_f0=True,
            cam_l0=True,
            cam_l1=True,
            cam_l2=True,
            cam_r0=True,
            cam_r1=True,
            cam_r2=True,
            cam_b0=True,
            lidar_pc=False,
        )
    )
    worker = build_worker(cfg)
    result_dir = f'{os.getenv("NAVSIM_TRAJPDM_ROOT")}/vocab_expanded_{cfg.vocab_size}_{cfg.scene_filter_name}'
    vocab = np.load(f'{os.getenv("NAVSIM_DEVKIT_ROOT")}/traj_final/test_{cfg.vocab_size}_kmeans.npy')

    data_points = []
    valid_tokens = os.listdir(result_dir)
    valid_tokens = set(valid_tokens) & set(scene_loader.tokens)
    for token in tqdm(valid_tokens):
        gt_scores = pickle.load(open(f'{result_dir}/{token}/tmp.pkl', 'rb'))
        data_points.append({
            'token': token,
            'scene_loader': scene_loader,
            'result_dir': result_dir,
            'vocab': vocab,
            'gt_scores': gt_scores,
        })

    worker_map(worker, worker_task, data_points)


if __name__ == "__main__":
    main()