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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, ImageDraw
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
from navsim.visualization.private import view_points
"""
ckpt -> pkl + valid score
"""
logger = logging.getLogger(__name__)
CONFIG_PATH = "../../navsim/planning/script/config/pdm_scoring"
CONFIG_NAME = "run_pdm_score_ddp"
# your path to these files
vocab = np.load(f'{os.getenv("NAVSIM_DEVKIT_ROOT")}/traj_final/test_8192_kmeans.npy')
gt_scores = pickle.load(open(f'{os.getenv("NAVSIM_TRAJPDM_ROOT")}/vocab_score_full_8192_navtest/navtest.pkl', 'rb'))
subscores = pickle.load(open(f'{os.getenv("NAVSIM_EXP_ROOT")}/hydra_offset_vov_fixedpading_bs8x8_ckpt/epoch09.pkl', 'rb'))
output_dir = f'{os.getenv("NAVSIM_EXP_ROOT")}/offset_vis'
os.makedirs(output_dir, exist_ok=True)
norm = plt.Normalize(vmin=0.0, vmax=1.0)
cmap = plt.get_cmap('viridis')
def get_overlay(poses, cam2lidar_rot, cam2lidar_tran, cam_intrin, color=(255, 0, 0, 255)):
coordinates = np.zeros((3, poses.shape[0]))
coordinates[0] = poses[:, 0]
coordinates[1] = poses[:, 1]
coordinates[2] = 0.0
lidar2cam_rot = np.linalg.inv(cam2lidar_rot)
coordinates -= cam2lidar_tran.reshape(-1, 1)
coordinates = np.dot(lidar2cam_rot, coordinates)
coordinates = np.dot(cam_intrin, coordinates)
heights = coordinates[2, :]
points = view_points(coordinates[:3, :], np.eye(3), normalize=True)
points[2, :] = heights
mask = np.ones(points.shape[1], dtype=bool) # type: ignore
canvas_size = (1080, 1920)
mask = np.logical_and(mask, points[0, :] < canvas_size[1] - 1)
mask = np.logical_and(mask, points[0, :] > 0)
mask = np.logical_and(mask, points[1, :] < canvas_size[0] - 1)
mask = np.logical_and(mask, points[1, :] > 0)
points = points[:, mask]
depth = heights[mask]
points = np.int16(np.round(points[:2, :]))
depth = np.int16(np.round(depth))
overlay_img = Image.new("RGBA", (canvas_size[1], canvas_size[0]), (255, 255, 255, 0))
draw = ImageDraw.Draw(overlay_img)
# Populate canvas, use maximum color_value for each bin
depth_canvas = np.zeros(canvas_size, dtype=np.int16)
for (col, row), d in zip(points.T, depth):
depth_canvas[row, col] = d
depth_canvas = torch.from_numpy(depth_canvas)
inds = (depth_canvas > 0).nonzero()
for ind in inds:
y, x = ind
x, y = x.item(), y.item()
r = 5
draw.ellipse((x - r, y - r, x + r, y + r), fill=color)
return overlay_img
def get_distribution(scores, vocab, gt_traj):
metrics = ['imi', 'noc', 'da', 'comfort', 'progress']
# Define the figure size in inches (540 pixels / 100 dpi = 5.4 inches)
fig, axes = plt.subplots(2, 3, figsize=(16.2, 10.8)) # 3 plots in a row, 2 rows
for i, ax in enumerate(axes.flat):
metric = metrics[i]
vocab_scores = scores[metric].exp().cpu().numpy()
# scale imitation scores by 10
if metric == 'imi':
vocab_scores *= 10
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.set_xlim(-5, 65)
ax.set_ylim(-25, 25)
ax.add_collection(line_collection)
# red line in imi plot is gt traj
if metric == 'imi':
ax.plot(gt_traj[:, 0], gt_traj[:, 1], c='r', alpha=1.0)
ax.set_title(f"Metric {metric}")
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, subscores, vocab = arg['token'], arg['gt_scores'], arg['subscores'], 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_score = gt_scores[token]
subscore = subscores[token]
for k, v in subscore.items():
if k != 'trajectory':
subscore[k] = torch.from_numpy(v)
# inference
# selected_index = subscore['total'].argmax(-1)
# curr_score_noc = gt_score['noc'][selected_index]
# curr_score_da = gt_score['da'][selected_index]
# curr_score_ttc = gt_score['ttc'][selected_index]
# curr_score_ep = gt_score['progress'][selected_index]
# curr_score_pdm = gt_score['total'][selected_index]
# model_traj = vocab[selected_index]
model_traj = subscore['trajectory']
gt_traj = gt_traj.poses
# file_name = f'{token}_noc{curr_score_noc}_da{curr_score_da}_ttc{curr_score_ttc}_ep{curr_score_ep}_pdm{curr_score_pdm}'
file_name = f'{token}'
save_path = f'{output_dir}/{file_name}.png'
if os.path.exists(save_path):
continue
# 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').convert('RGBA')
img = Image.alpha_composite(img, get_overlay(model_traj, cam2lidar_rot, cam2lidar_tran, cam_intrin,
color=(255, 0, 0, 255)))
img = Image.alpha_composite(img, get_overlay(gt_traj, cam2lidar_rot, cam2lidar_tran, cam_intrin,
color=(0, 255, 0, 255)))
img = img.convert('RGB')
# distributions of vocab
# figs = get_distribution(subscore, vocab, gt_traj)
# concat
total_width = img.width
# max_height = max(img.height, figs.height)
max_heigh = img.height
new_image = Image.new('RGB', (total_width, max_height))
new_image.paste(img, (0, 0))
new_image.paste(figs, (img.width, 0))
new_image.save(save_path)
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)
data_points = []
for token in tqdm(scene_loader.tokens):
data_points.append({
'token': token,
'scene_loader': scene_loader,
'vocab': vocab,
'gt_scores': gt_scores,
'subscores': subscores
})
worker_map(worker, worker_task, data_points[cfg.start_idx:cfg.end_idx])
if __name__ == "__main__":
with torch.no_grad():
main()
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