### Import Argoverse 2.0 API and plotting libraries

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Imports core Argoverse 2.0 API components, plotting utilities, and data loading classes. Ensure matplotlib and numpy are installed.

```python
import argparse
from pathlib import Path
from typing import List

import matplotlib

import matplotlib.pyplot as plt
import numpy as np

from mpl_toolkits.axes_grid1 import make_axes_locatable

import av2.geometry.polyline_utils as polyline_utils
import av2.rendering.vector as vector_plotting_utils
from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
from av2.map.map_api import ArgoverseStaticMap, LaneSegment
```

--------------------------------

### Load and Use PinholeCamera

Source: https://context7.com/argoverse/av2-api/llms.txt

Loads a PinholeCamera from a feather file and demonstrates accessing its properties and projecting 3D points to image coordinates. Ensure the log directory and camera name are correct.

```python
from pathlib import Path
import numpy as np
from av2.geometry.camera.pinhole_camera import PinholeCamera, Intrinsics
from av2.geometry.se3 import SE3

# Load camera from feather file
log_dir = Path("/path/to/log")
cam_name = "ring_front_center"
camera = PinholeCamera.from_feather(log_dir, cam_name)

# Access camera properties
print(f"Camera: {camera.cam_name}")
print(f"Image size: {camera.width_px} x {camera.height_px}")
print(f"Intrinsics matrix K:\n{camera.intrinsics.K}")
print(f"Extrinsics (4x4):\n{camera.extrinsics}")

# Project 3D points in ego frame to image
points_ego = np.array([
    [10.0, 0.0, 0.0],   # Directly ahead
    [15.0, 5.0, 1.0],   # Right side
    [20.0, -3.0, 0.5],  # Left side
])

uv, points_cam, is_valid = camera.project_ego_to_img(points_ego)
print(f"Image coordinates (u, v):\n{uv}")
print(f"Valid projections: {is_valid}")
```

--------------------------------

### Create and Use Cuboid Annotations

Source: https://context7.com/argoverse/av2-api/llms.txt

Create a Cuboid object from its parameters, access its properties, and perform transformations. Load annotations from a feather file.

```python
import numpy as np
from pathlib import Path
from av2.structures.cuboid import Cuboid, CuboidList
from av2.geometry.se3 import SE3

# Create a cuboid from parameters
rotation = np.eye(3)
translation = np.array([5.0, 2.0, 1.0])
ego_SE3_object = SE3(rotation=rotation, translation=translation)

cuboid = Cuboid(
    dst_SE3_object=ego_SE3_object,
    length_m=4.5,  # x-axis extent
    width_m=2.0,   # y-axis extent
    height_m=1.5,  # z-axis extent
    category="REGULAR_VEHICLE",
    timestamp_ns=1234567890
)

# Access cuboid properties
print(f"Center: {cuboid.xyz_center_m}")
print(f"Dimensions (L,W,H): {cuboid.dims_lwh_m}")
print(f"Category: {cuboid.category}")

# Get the 8 corner vertices of the cuboid
vertices = cuboid.vertices_m  # Shape: (8, 3)
print(f"Vertices shape: {vertices.shape}")

# Check which points are inside the cuboid
query_points = np.random.randn(1000, 3) + translation
interior_points, is_interior = cuboid.compute_interior_points(query_points)
print(f"Points inside cuboid: {is_interior.sum()}")

# Transform cuboid to a different reference frame
city_SE3_ego = SE3(rotation=np.eye(3), translation=np.array([100, 200, 0]))
cuboid_city = cuboid.transform(city_SE3_ego)
print(f"Cuboid center in city: {cuboid_city.xyz_center_m}")

# Load annotations from feather file
annotations_path = Path("/path/to/log/annotations.feather")
cuboid_list = CuboidList.from_feather(annotations_path)
print(f"Loaded {len(cuboid_list)} cuboids")

# Access all centers and dimensions as arrays
centers = cuboid_list.xyz_center_m      # Shape: (N, 3)
dimensions = cuboid_list.dims_lwh_m     # Shape: (N, 3)
categories = cuboid_list.categories     # List of strings
```

--------------------------------

### Construct detection configuration

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/object_detection_evaluation.ipynb

Initializes the DetectionCfg object with the dataset directory. This configuration is used for evaluation and defaults to competition parameters. It includes logic to remove cuboids outside the region-of-interest.

```python
dataset_dir = (
    Path.home() / "data" / "datasets" / "av2" / "sensor"
)  # Path to your AV2 sensor dataset directory.
competition_cfg = DetectionCfg(
    dataset_dir=dataset_dir
)  # Defaults to competition parameters.
```

--------------------------------

### Load Sensor Data with AV2SensorDataLoader

Source: https://context7.com/argoverse/av2-api/llms.txt

Retrieves LiDAR sweeps, camera images, ego poses, and annotations from AV2 sensor logs. Requires valid paths to the dataset directories.

```python
from pathlib import Path
from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader
from av2.structures.sweep import Sweep
from av2.structures.cuboid import CuboidList

# Initialize the dataloader
data_dir = Path("/path/to/av2/sensor/val")
labels_dir = Path("/path/to/av2/sensor/val")
loader = AV2SensorDataLoader(data_dir=data_dir, labels_dir=labels_dir)

# Get all available log IDs
log_ids = loader.get_log_ids()
print(f"Found {len(log_ids)} logs")

# Get timestamps for a specific log
log_id = log_ids[0]
timestamps = loader.get_ordered_log_lidar_timestamps(log_id)
print(f"Log {log_id} has {len(timestamps)} LiDAR sweeps")

# Load a LiDAR sweep
lidar_fpath = loader.get_lidar_fpath(log_id, timestamps[0])
sweep = Sweep.from_feather(lidar_fpath)
print(f"Sweep has {len(sweep)} points, shape: {sweep.xyz.shape}")

# Get ego pose in city coordinates
city_SE3_ego = loader.get_city_SE3_ego(log_id, timestamps[0])
points_city = city_SE3_ego.transform_point_cloud(sweep.xyz)

# Load annotations at a timestamp
cuboids = loader.get_labels_at_lidar_timestamp(log_id, timestamps[0])
print(f"Found {len(cuboids)} annotations")
for cuboid in cuboids.cuboids:
    print(f"  {cuboid.category}: {cuboid.dims_lwh_m}")

# Get synchronized camera image
cam_name = "ring_front_center"
img_fpath = loader.get_closest_img_fpath(log_id, cam_name, timestamps[0])
print(f"Camera image: {img_fpath}")

# Get city name for the log
city_name = loader.get_city_name(log_id)
print(f"City: {city_name}")
```

--------------------------------

### PinholeCamera Frustum and Ray Utilities

Source: https://context7.com/argoverse/av2-api/llms.txt

Demonstrates culling points to the camera's view frustum, obtaining frustum planes, and computing pixel ray directions. The `cull_to_view_frustum` method requires image coordinates and corresponding 3D points.

```python
# Cull points to camera view frustum
is_in_frustum = camera.cull_to_view_frustum(uv, points_cam)

# Get camera frustum planes for rendering
frustum_planes = camera.frustum_planes(near_clip_dist=0.5)  # Shape: (5, 4)

# Get camera yaw in ego frame
yaw_rad = camera.egovehicle_yaw_cam_rad
print(f"Camera yaw: {np.degrees(yaw_rad):.1f} degrees")

# Scale camera intrinsics (for multi-resolution processing)
camera_half = camera.scale(0.5)
print(f"Scaled image size: {camera_half.width_px} x {camera_half.height_px}")

# Compute pixel ray directions
pixel_coords = np.array([[960, 600], [480, 300]])  # (u, v) coordinates
ray_dirs = camera.compute_pixel_ray_directions(pixel_coords)  # (N, 3)
```

--------------------------------

### Create Namespace object for arguments

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Constructs a Namespace object to hold data root and log ID, converting them to Path objects.

```python
args = Namespace(**{"dataroot": Path(dataroot), "log_id": Path(log_id)})
```

--------------------------------

### Visualize Sensor Dataset

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Visualizes ego-vehicle poses and local vector maps for sensor datasets.

```python
def argoverse2_sensor_dataset_teaser(
    args: argparse.Namespace, save_plot: bool = False
) -> None:
    """Visualize both ego-vehicle poses and the per-log local vector map.

    Crosswalks are plotted in purple. Lane segments plotted in dark gray. Ego-pose in red.
    """
    loader = AV2SensorDataLoader(data_dir=args.dataroot, labels_dir=args.dataroot)

    fig = plt.figure(1, figsize=(10, 10))
```

--------------------------------

### Write Video to Disk

Source: https://context7.com/argoverse/av2-api/llms.txt

Creates a video file from a NumPy array of RGB frames. Specify the output path, color format, frames per second, and quality settings. The `crf` parameter controls quality (lower is better quality, larger file size).

```python
import numpy as np
from pathlib import Path
from av2.rendering.video import write_video
from av2.rendering.color import ColorFormats

# Create a video from RGB frames
num_frames = 100
height, width = 480, 640
video_frames = np.random.randint(0, 255, (num_frames, height, width, 3), dtype=np.uint8)

# Write video to disk
output_path = Path("/tmp/output_video.mp4")
write_video(
    video=video_frames,
    dst=output_path,
    color_format=ColorFormats.RGB,
    fps=10,
    crf=23,  # Quality (lower = better quality, larger file)
    preset="fast",
)
print(f"Video saved to: {output_path}")
```

--------------------------------

### Render Single Log Teaser

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Renders a bird's eye view of local crosswalks and pedestrian crossings for a single log.

```python
def single_log_teaser(args: argparse.Namespace) -> None:
    """For a single log, render all local crosswalks in green, and pedestrian crossings in purple, in a bird's eye view."""
    log_map_dirpath = Path(args.dataroot) / args.log_id / "map"
    avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=False)

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot()

    for _, ls in avm.vector_lane_segments.items():
        # right_ln_bnd
        # left_ln_bnd
        vector_plotting_utils.draw_polygon_mpl(
            ax, ls.polygon_boundary, color="g", linewidth=0.5
        )
        vector_plotting_utils.plot_polygon_patch_mpl(
            ls.polygon_boundary, ax, color="g", alpha=0.2
        )

    # plot all pedestrian crossings
    for _, pc in avm.vector_pedestrian_crossings.items():
        vector_plotting_utils.draw_polygon_mpl(ax, pc.polygon, color="m", linewidth=0.5)
        vector_plotting_utils.plot_polygon_patch_mpl(
            pc.polygon, ax, color="m", alpha=0.2
        )

    plt.show()
```

```python
single_log_teaser(args)
```

--------------------------------

### Serialize Motion Forecasting Scenarios

Source: https://context7.com/argoverse/av2-api/llms.txt

Load and save motion forecasting scenarios using parquet files. Provides access to track metadata and object states.

```python
from pathlib import Path
from av2.datasets.motion_forecasting.scenario_serialization import (
    load_argoverse_scenario_parquet,
    serialize_argoverse_scenario_parquet,
)
from av2.datasets.motion_forecasting.data_schema import (
    ArgoverseScenario,
    Track,
    ObjectState,
    ObjectType,
    TrackCategory,
)

# Load a scenario from parquet file
scenario_path = Path("/path/to/scenario/scenario_uuid.parquet")
scenario = load_argoverse_scenario_parquet(scenario_path)

# Access scenario metadata
print(f"Scenario ID: {scenario.scenario_id}")
print(f"City: {scenario.city_name}")
print(f"Focal track ID: {scenario.focal_track_id}")
print(f"Number of timestamps: {len(scenario.timestamps_ns)}")
print(f"Number of tracks: {len(scenario.tracks)}")

# Iterate over tracks
for track in scenario.tracks:
    print(f"\nTrack {track.track_id}:")
    print(f"  Type: {track.object_type.value}")
    print(f"  Category: {track.category.name}")
    print(f"  States: {len(track.object_states)}")

    # Access individual states
    for state in track.object_states[:3]:
        print(f"    Timestep {state.timestep}: pos={state.position}, "
              f"heading={state.heading:.2f}, vel={state.velocity}")

# Find the focal track
focal_track = next(t for t in scenario.tracks if t.track_id == scenario.focal_track_id)
print(f"\nFocal track type: {focal_track.object_type.value}")

# Get observed vs predicted timesteps
observed_states = [s for s in focal_track.object_states if s.observed]
future_states = [s for s in focal_track.object_states if not s.observed]
print(f"Observed: {len(observed_states)}, Future: {len(future_states)}")

# Save a scenario
output_path = Path("/path/to/output/scenario.parquet")
serialize_argoverse_scenario_parquet(output_path, scenario)
```

--------------------------------

### Import necessary libraries for evaluation

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/object_detection_evaluation.ipynb

Imports the required modules for 3D object detection evaluation and configuration.

```python
from av2.evaluation.detection.eval import evaluate
from av2.evaluation.detection.utils import DetectionCfg
from pathlib import Path
from av2.utils.io import read_feather, read_all_annotations
```

--------------------------------

### Load and evaluate detections

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/object_detection_evaluation.ipynb

Loads detection data from a feather file and then evaluates it against the ground truth annotations using the competition configuration. The function returns the evaluated detections, ground truths, and computed metrics.

```python
# If you've already aggregated your detections into one file.
dts_path = Path("detections.feather")
dts = read_feather(dts_path)

dts, gts, metrics = evaluate(dts, gts, cfg=competition_cfg)  # Evaluate instances.
```

--------------------------------

### Load Detection Data with PyTorch

Source: https://context7.com/argoverse/av2-api/llms.txt

Utilizes the DetectionDataLoader for high-performance 3D object detection tasks. Supports point cloud accumulation and integration with Kornia for coordinate transformations.

```python
from pathlib import Path
from av2.torch.data_loaders.detection import DetectionDataLoader
from kornia.geometry.linalg import transform_points
from tqdm import tqdm

# Initialize the PyTorch data loader
root_dir = Path.home() / "data" / "datasets"
data_loader = DetectionDataLoader(
    root_dir=root_dir,
    dataset_name="av2",
    split_name="val",
    num_accumulated_sweeps=1,  # Accumulate multiple sweeps for denser point clouds
    memory_mapped=False
)

print(f"Dataset has {len(data_loader)} sweeps")

# Iterate over the dataset
for i, sweep in enumerate(tqdm(data_loader)):
    # Get the SE(3) transformation matrix (4x4)
    city_SE3_ego_mat4 = sweep.city_SE3_ego.matrix()

    # Get LiDAR tensor (x, y, z, intensity)
    lidar_tensor = sweep.lidar.as_tensor()
    print(f"LiDAR shape: {lidar_tensor.shape}")  # (N, 4)

    # Transform points to city coordinates using kornia
    lidar_xyz_city = transform_points(city_SE3_ego_mat4, lidar_tensor[:, :3])

    # Get synchronized ring camera images
    synchronized_images = data_loader.get_synchronized_images(i)
    print(f"Number of camera images: {len(synchronized_images)}")

    # Access cuboid annotations (if available)
    if sweep.cuboids is not None:
        # Get cuboid parameters (x, y, z, length, width, height, yaw)
        xyzlwh_t = sweep.cuboids.as_tensor()
        categories = sweep.cuboids.category
        track_uuids = sweep.cuboids.track_uuid
        print(f"Found {len(categories)} cuboids")

    if i >= 5:
        break
```

--------------------------------

### Visualize Motion Forecasting Scenario

Source: https://context7.com/argoverse/av2-api/llms.txt

Generates a video visualization for a motion forecasting scenario, combining track trajectories with HD map data. This function requires scenario data and a static map object. Ensure paths to scenario and map files are correct.

```python
from pathlib import Path
from av2.datasets.motion_forecasting import scenario_serialization
from av2.datasets.motion_forecasting.viz.scenario_visualization import visualize_scenario
from av2.map.map_api import ArgoverseStaticMap

# Load scenario and map
scenario_dir = Path("/path/to/motion_forecasting/val")
scenario_files = list(scenario_dir.rglob("*.parquet"))

for scenario_path in scenario_files[:5]:
    # Extract scenario ID and load data
    scenario_id = scenario_path.stem.split("_")[-1]
    map_path = scenario_path.parent / f"log_map_archive_{scenario_id}.json"

    scenario = scenario_serialization.load_argoverse_scenario_parquet(scenario_path)
    static_map = ArgoverseStaticMap.from_json(map_path)

    # Generate visualization video
    output_path = Path(f"/tmp/viz_{scenario_id}.mp4")
    visualize_scenario(scenario, static_map, output_path)
    print(f"Generated visualization: {output_path}")
```

--------------------------------

### Tile Multiple Camera Images

Source: https://context7.com/argoverse/av2-api/llms.txt

Combines images from multiple cameras into a single tiled visualization. The input is a dictionary where keys are camera names and values are NumPy arrays representing the images.

```python
from av2.rendering.video import tile_cameras

# Tile multiple camera images into a single frame
# Simulated camera images (landscape format)
camera_images = {
    "ring_front_left": np.random.randint(0, 255, (1550, 2048, 3), dtype=np.uint8),
    "ring_front_center": np.random.randint(0, 255, (2048, 1550, 3), dtype=np.uint8),
    "ring_front_right": np.random.randint(0, 255, (1550, 2048, 3), dtype=np.uint8),
    "ring_side_left": np.random.randint(0, 255, (1550, 2048, 3), dtype=np.uint8),
    "ring_side_right": np.random.randint(0, 255, (1550, 2048, 3), dtype=np.uint8),
    "ring_rear_left": np.random.randint(0, 255, (1550, 2048, 3), dtype=np.uint8),
    "ring_rear_right": np.random.randint(0, 255, (1550, 2048, 3), dtype=np.uint8),
}

# Create tiled visualization
tiled_image = tile_cameras(camera_images)
print(f"Tiled image shape: {tiled_image.shape}")
```

--------------------------------

### Import necessary modules

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Imports required modules for argument parsing and path manipulation.

```python
from argparse import Namespace
from pathlib import Path
```

--------------------------------

### Load LiDAR Sweep Data

Source: https://context7.com/argoverse/av2-api/llms.txt

Load a LiDAR sweep from a feather file and access its point cloud data and sensor poses. The sweep data is egomotion compensated to the reference timestamp.

```python
from pathlib import Path
from av2.structures.sweep import Sweep

# Load a sweep from feather file
sweep_path = Path("/path/to/log/sensors/lidar/315968193359876000.feather")
sweep = Sweep.from_feather(sweep_path)

# Access point cloud data
xyz = sweep.xyz              # (N, 3) float array - x, y, z in meters
intensity = sweep.intensity  # (N,) uint8 array - intensity values [0-255]
laser_number = sweep.laser_number  # (N,) uint8 array - laser beam indices [0-63]
offset_ns = sweep.offset_ns  # (N,) int array - nanosecond offsets from sweep start

print(f"Number of points: {len(sweep)}")
print(f"Point cloud shape: {xyz.shape}")
print(f"Timestamp: {sweep.timestamp_ns} ns")

# Access sensor poses in ego frame
ego_SE3_up_lidar = sweep.ego_SE3_up_lidar
ego_SE3_down_lidar = sweep.ego_SE3_down_lidar
print(f"Up lidar position: {ego_SE3_up_lidar.translation}")
```

--------------------------------

### Compute Motion Forecasting Metrics

Source: https://context7.com/argoverse/av2-api/llms.txt

Calculate evaluation metrics like ADE and FDE for forecasted trajectories. Requires ground truth and predicted trajectory arrays.

```python
import numpy as np
from av2.datasets.motion_forecasting.eval.metrics import (
    compute_ade,
    compute_fde,
    compute_is_missed_prediction,
    compute_brier_ade,
    compute_brier_fde,
)

# Ground truth trajectory (N timesteps, 2D coordinates)
gt_trajectory = np.array([
    [0.0, 0.0],
    [1.0, 0.5],
    [2.0, 1.0],
    [3.0, 1.5],
    [4.0, 2.0],
])  # Shape: (N, 2)

# K predicted trajectories
K = 6  # Number of predictions
N = 5  # Number of timesteps
forecasted_trajectories = np.random.randn(K, N, 2) + gt_trajectory  # Shape: (K, N, 2)

# Compute Average Displacement Error for each prediction
ade = compute_ade(forecasted_trajectories, gt_trajectory)
print(f"ADE for each prediction: {ade}")  # Shape: (K,)
print(f"Best ADE (minADE): {ade.min():.3f}")

# Compute Final Displacement Error for each prediction
fde = compute_fde(forecasted_trajectories, gt_trajectory)
print(f"FDE for each prediction: {fde}")  # Shape: (K,)
print(f"Best FDE (minFDE): {fde.min():.3f}")
```

--------------------------------

### Load Argoverse Static Map

Source: https://context7.com/argoverse/av2-api/llms.txt

Load the Argoverse static map data from a JSON file or a directory containing raster data. Access scenario lane segments.

```python
from pathlib import Path
import numpy as np
from av2.map.map_api import ArgoverseStaticMap

# Load map from JSON file
map_path = Path("/path/to/log/map/log_map_archive_uuid.json")
static_map = ArgoverseStaticMap.from_json(map_path)

# Or load from directory with raster data
map_dir = Path("/path/to/log/map")
static_map = ArgoverseStaticMap.from_map_dir(map_dir, build_raster=True)

# Get all lane segments
lane_segments = static_map.get_scenario_lane_segments()
print(f"Number of lane segments: {len(lane_segments)}")
```

--------------------------------

### Compute Motion Forecasting Metrics

Source: https://context7.com/argoverse/av2-api/llms.txt

Calculate prediction miss rates and probability-weighted metrics like Brier scores for forecasted trajectories.

```python
miss_threshold = 2.0  # meters
is_missed = compute_is_missed_prediction(
    forecasted_trajectories, gt_trajectory, miss_threshold_m=miss_threshold
)
miss_rate = is_missed.mean()
print(f"Miss rate (threshold={miss_threshold}m): {miss_rate:.2%}")

# Compute probability-weighted metrics (Brier score)
forecast_probabilities = np.array([0.3, 0.25, 0.2, 0.15, 0.07, 0.03])  # Sum to 1

brier_ade = compute_brier_ade(
    forecasted_trajectories, gt_trajectory, forecast_probabilities, normalize=True
)
brier_fde = compute_brier_fde(
    forecasted_trajectories, gt_trajectory, forecast_probabilities, normalize=True
)
print(f"Brier-weighted ADE: {brier_ade.min():.3f}")
print(f"Brier-weighted FDE: {brier_fde.min():.3f}")
```

--------------------------------

### Visualize Raster Layers

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Visualizes ground surface height and side-by-side plots of raster arrays.

```python
def visualize_raster_layers(args: argparse.Namespace) -> None:
    """Visualize the ground surface height/elevation map, w/ a colorbar indicating the value range.

    Also, visualize side-by-side plots of the 3 raster arrays -- ground height, drivable area, ROI.
    """
    log_map_dirpath = Path(args.dataroot) / args.log_id / "map"
    avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True)

    height_array = avm.raster_ground_height_layer.array
    ax = plt.subplot()
    plt.title("Ground surface height (@ 30 centimeter resolution).")
    img = plt.imshow(np.flipud(height_array))

    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    plt.colorbar(img, cax=cax)
    plt.show()

    plt.figure(figsize=(20, 10))
    plt.subplot(1, 3, 1)
    plt.imshow(np.flipud(height_array))
    plt.title("Ground Surface Height")

    plt.subplot(1, 3, 2)
    da_array = avm.raster_drivable_area_layer.array
    plt.imshow(np.flipud(da_array))
    plt.title("Drivable Area (rasterized \nfrom vector polygons)")

    plt.subplot(1, 3, 3)
    roi_array = avm.raster_roi_layer.array
    plt.imshow(np.flipud(roi_array))
    plt.title("Region of Interest (ROI)")
    plt.show()
```

```python
visualize_raster_layers(args)
```

--------------------------------

### Evaluate 3D Object Detection

Source: https://context7.com/argoverse/av2-api/llms.txt

Configure and run detection evaluation using pandas DataFrames for detections and ground truth data.

```python
import pandas as pd
import numpy as np
from pathlib import Path
from av2.evaluation.detection.eval import evaluate
from av2.evaluation.detection.utils import DetectionCfg

# Prepare detection DataFrame
# Columns: log_id, timestamp_ns, category, tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, score
detections = pd.DataFrame({
    "log_id": ["log_001"] * 3,
    "timestamp_ns": [1234567890] * 3,
    "category": ["REGULAR_VEHICLE", "PEDESTRIAN", "REGULAR_VEHICLE"],
    "tx_m": [10.0, 15.0, 20.0],
    "ty_m": [5.0, 8.0, 3.0],
    "tz_m": [1.0, 1.0, 1.0],
    "length_m": [4.5, 0.8, 4.2],
    "width_m": [2.0, 0.6, 1.9],
    "height_m": [1.5, 1.7, 1.6],
    "qw": [1.0, 1.0, 0.707],
    "qx": [0.0, 0.0, 0.0],
    "qy": [0.0, 0.0, 0.0],
    "qz": [0.0, 0.0, 0.707],
    "score": [0.95, 0.88, 0.75],
})

# Prepare ground truth DataFrame
# Columns: log_id, timestamp_ns, category, tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, num_interior_pts
ground_truth = pd.DataFrame({
    "log_id": ["log_001"] * 2,
    "timestamp_ns": [1234567890] * 2,
    "category": ["REGULAR_VEHICLE", "PEDESTRIAN"],
    "tx_m": [10.1, 15.2],
    "ty_m": [5.1, 7.9],
    "tz_m": [1.0, 1.0],
    "length_m": [4.5, 0.8],
    "width_m": [2.0, 0.6],
    "height_m": [1.5, 1.7],
    "qw": [1.0, 1.0],
    "qx": [0.0, 0.0],
    "qy": [0.0, 0.0],
    "qz": [0.0, 0.0],
    "num_interior_pts": [100, 50],
})

# Configure evaluation
cfg = DetectionCfg(
    categories=["REGULAR_VEHICLE", "PEDESTRIAN"],
    eval_only_roi_instances=False,  # Set True to filter by ROI
    # dataset_dir=Path("/path/to/dataset"),  # Required if eval_only_roi_instances=True
)

# Run evaluation
dts_results, gts_results, metrics = evaluate(
    dts=detections,
    gts=ground_truth,
    cfg=cfg,
    n_jobs=4,
)

# Display metrics
print("\nDetection Metrics:")
print(metrics)
# Metrics include: AP (Average Precision), ATE (Translation Error),
# ASE (Scale Error), AOE (Orientation Error), CDS (Composite Detection Score)
```

--------------------------------

### Display evaluation metrics

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/object_detection_evaluation.ipynb

Displays the computed evaluation metrics after running the detection evaluation.

```python
display(metrics)
```

--------------------------------

### Call Argoverse2 Sensor Dataset Teaser Function

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

This is a function call to initiate the Argoverse2 sensor dataset teaser. It likely triggers a series of visualizations or data processing steps.

```python
argoverse2_sensor_dataset_teaser(args)
```

--------------------------------

### Load ground truth annotations

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/object_detection_evaluation.ipynb

Reads all ground truth annotations for a specified split (e.g., 'val') from the dataset directory. The annotations are returned as a pandas DataFrame.

```python
split = "val"
gts = read_all_annotations(
    dataset_dir=dataset_dir, split=split
)  # Contains all annotations in a particular split.
display(gts)
```

--------------------------------

### Evaluate Scene Flow

Source: https://context7.com/argoverse/av2-api/llms.txt

Compute point-wise metrics for 3D motion estimation, including end-point error and accuracy thresholds.

```python
import numpy as np
from pathlib import Path
from av2.evaluation.scene_flow.eval import (
    compute_end_point_error,
    compute_accuracy_strict,
    compute_accuracy_relax,
    compute_angle_error,
    evaluate_directories,
    results_to_dict,
)

# Compute point-wise end-point error
predicted_flow = np.array([
    [0.1, 0.2, 0.05],
    [0.5, -0.1, 0.02],
    [1.0, 0.0, 0.0],
])  # Shape: (N, 3)

ground_truth_flow = np.array([
    [0.12, 0.18, 0.04],
    [0.48, -0.12, 0.03],
    [1.1, 0.05, 0.0],
])  # Shape: (N, 3)

# End-Point Error (EPE)
epe = compute_end_point_error(predicted_flow, ground_truth_flow)
print(f"End-Point Error: {epe}")  # Shape: (N,)
print(f"Mean EPE: {epe.mean():.4f} meters")

# Accuracy with strict threshold (0.05)
acc_strict = compute_accuracy_strict(predicted_flow, ground_truth_flow)
print(f"Strict Accuracy: {acc_strict.mean():.2%}")

# Accuracy with relaxed threshold (0.1)
acc_relax = compute_accuracy_relax(predicted_flow, ground_truth_flow)
print(f"Relaxed Accuracy: {acc_relax.mean():.2%}")

# Angle error in space-time
angle_err = compute_angle_error(predicted_flow, ground_truth_flow)
print(f"Angle Error: {np.degrees(angle_err).mean():.2f} degrees")

# Evaluate from directories (for challenge submissions)
annotations_dir = Path("/path/to/annotations")
predictions_dir = Path("/path/to/predictions")

# results_df = evaluate_directories(annotations_dir, predictions_dir)
# results_dict = results_to_dict(results_df)
#
# print("\nScene Flow Results:")
# for metric, value in results_dict.items():
#     print(f"  {metric}: {value:.4f}")
```

--------------------------------

### Plot Argoverse Log Data with Matplotlib

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Visualizes ego-vehicle trajectory and map elements from an Argoverse log. Ensure necessary imports and data loading functions are available.

```python
ax = fig.add_subplot(111)

log_map_dirpath = Path(args.dataroot) / args.log_id / "map"

avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=False)

# retain every pose first.
traj_ns = loader.get_subsampled_ego_trajectory(args.log_id, sample_rate_hz=1e9)
# now, sample @ 1 Hz
traj_1hz = loader.get_subsampled_ego_trajectory(args.log_id, sample_rate_hz=1.0)
med_x, med_y = np.median(traj_ns, axis=0)

# Derive plot area from trajectory (with radius defined in infinity norm).
view_radius_m = 50
xlims = [med_x - view_radius_m, med_x + view_radius_m]
ylims = [med_y - view_radius_m, med_y + view_radius_m]

crosswalk_color = PURPLE_RGB_MPL
CROSSWALK_ALPHA = 0.6
for pc in avm.get_scenario_ped_crossings():
    vector_plotting_utils.plot_polygon_patch_mpl(
        polygon_pts=pc.polygon[:, :2],
        ax=ax,
        color=crosswalk_color,
        alpha=CROSSWALK_ALPHA,
        zorder=3,
    )

plot_lane_segments(ax=ax, lane_segments=avm.get_scenario_lane_segments())

ax.plot(traj_ns[:, 0], traj_ns[:, 1], color="r", zorder=4, label="Ego-vehicle pose")
ax.scatter(
    traj_1hz[:, 0], traj_1hz[:, 1], 100, facecolors="none", edgecolors="r", zorder=4
)  # marker='o', color="r")

plt.axis("equal")
plt.xlim(*xlims)
plt.ylim(*ylims)
plt.title(f"Log {args.log_id}")
plt.axis("off")
plt.legend()
plt.tight_layout()
plt.show()
```

--------------------------------

### Transform Point Cloud

Source: https://context7.com/argoverse/av2-api/llms.txt

Transform a point cloud using a given transformation matrix. Ensure the input points are in the correct shape (N, 3).

```python
import numpy as np
from av2.geometry.se3 import SE3

# Assuming 'transform' is an SE3 object
# Access the 4x4 homogeneous transformation matrix
transform_matrix = transform.transform_matrix
print(f"Transform matrix shape: {transform_matrix.shape}")  # (4, 4)

# Transform a point cloud
points = np.random.randn(1000, 3)  # N x 3 point cloud
transformed_points = transform.transform_point_cloud(points)
# Equivalent: transformed_points = transform.transform_from(points)

# Compute the inverse transformation
inverse_transform = transform.inverse()
original_points = inverse_transform.transform_point_cloud(transformed_points)
np.testing.assert_allclose(points, original_points, atol=1e-10)

# Compose two transformations: result = T1 * T2
T1 = SE3(rotation=np.eye(3), translation=np.array([1, 0, 0]))
T2 = SE3(rotation=np.eye(3), translation=np.array([0, 1, 0]))
T_composed = T1.compose(T2)
print(f"Composed translation: {T_composed.translation}")  # [1, 1, 0]
```

--------------------------------

### Export detections to feather file

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/object_detection_evaluation.ipynb

Exports the processed detections DataFrame to a feather file named 'detections.feather'. This format is suitable for submission to the evaluation server.

```python
dts.to_feather("detections.feather")
```

--------------------------------

### PinholeCamera Motion Compensation Projection

Source: https://context7.com/argoverse/av2-api/llms.txt

Projects 3D points to image coordinates with motion compensation, aligning LiDAR data to camera timestamps. Requires SE3 objects for camera and LiDAR poses at a given time.

```python
city_SE3_ego_cam_t = SE3(rotation=np.eye(3), translation=np.array([100, 200, 0]))
city_SE3_ego_lidar_t = SE3(rotation=np.eye(3), translation=np.array([100.1, 200.05, 0]))

uv_compensated, points_cam_compensated, is_valid_compensated = (
    camera.project_ego_to_img_motion_compensated(
        points_lidar_time=points_ego,
        city_SE3_ego_cam_t=city_SE3_ego_cam_t,
        city_SE3_ego_lidar_t=city_SE3_ego_lidar_t,
    )
)
```

--------------------------------

### Define data root and log ID

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Specifies the root directory for Argoverse 2.0 data and a unique log identifier.

```python
# path to where the logs live
dataroot = "/Users/jlambert/Downloads/av2-v7-2022-01-15"

# unique log identifier
log_id = "adcf7d18-0510-35b0-a2fa-b4cea13a6d76"
```

--------------------------------

### Perform SE3 Rigid Transformations

Source: https://context7.com/argoverse/av2-api/llms.txt

Defines rigid body transformations in 3D space using the SE3 class. Requires rotation and translation components.

```python
import numpy as np
from av2.geometry.se3 import SE3

# Create an SE3 transformation
rotation = np.array([
    [1, 0, 0],
    [0, np.cos(np.pi/4), -np.sin(np.pi/4)],
    [0, np.sin(np.pi/4), np.cos(np.pi/4)]
])
translation = np.array([10.0, 5.0, 2.0])
transform = SE3(rotation=rotation, translation=translation)
```

--------------------------------

### Define RGB Color Constants

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Define color constants scaled for matplotlib usage.

```python
# scaled to [0,1] for matplotlib.
PURPLE_RGB = [201, 71, 245]
PURPLE_RGB_MPL = np.array(PURPLE_RGB) / 255

DARK_GRAY_RGB = [40, 39, 38]
DARK_GRAY_RGB_MPL = np.array(DARK_GRAY_RGB) / 255
```

--------------------------------

### Plot Lane Segments

Source: https://github.com/argoverse/av2-api/blob/main/tutorials/map_tutorial.ipynb

Plots lane segments with specific boundary types and styling.

```python
def plot_lane_segments(
    ax: matplotlib.axes.Axes,
    lane_segments: List[LaneSegment],
    lane_color: np.ndarray = DARK_GRAY_RGB_MPL,
) -> None:
    """Plot lane segments."""
    for ls in lane_segments:
        pts_city = ls.polygon_boundary
        ALPHA = 1.0  # 0.1
        vector_plotting_utils.plot_polygon_patch_mpl(
            polygon_pts=pts_city, ax=ax, color=lane_color, alpha=ALPHA, zorder=1
        )

        for bound_type, bound_city in zip(
            [ls.left_mark_type, ls.right_mark_type],
            [ls.left_lane_boundary, ls.right_lane_boundary],
        ):
            if "YELLOW" in bound_type:
                mark_color = "y"
            elif "WHITE" in bound_type:
                mark_color = "w"
            else:
                mark_color = "grey"  # "b" lane_color #

            LOOSELY_DASHED = (0, (5, 10))

            if "DASHED" in bound_type:
                linestyle = LOOSELY_DASHED
            else:
                linestyle = "solid"

            if "DOUBLE" in bound_type:
                left, right = polyline_utils.get_double_polylines(
                    polyline=bound_city.xyz[:, :2], width_scaling_factor=0.1
                )
                ax.plot(
                    left[:, 0],
                    left[:, 1],
                    mark_color,
                    alpha=ALPHA,
                    linestyle=linestyle,
                    zorder=2,
                )
                ax.plot(
                    right[:, 0],
                    right[:, 1],
                    mark_color,
                    alpha=ALPHA,
                    linestyle=linestyle,
                    zorder=2,
                )
            else:
                ax.plot(
                    bound_city.xyz[:, 0],
                    bound_city.xyz[:, 1],
                    mark_color,
                    alpha=ALPHA,
                    linestyle=linestyle,
                    zorder=2,
                )
```

--------------------------------

### Query Static Map Lane Data

Source: https://context7.com/argoverse/av2-api/llms.txt

Access lane segment properties, centerlines, and drivable areas from a static map. Requires a loaded static map object.

```python
lane_ids = static_map.get_scenario_lane_segment_ids()
for lane_id in lane_ids[:3]:
    # Get centerline polyline
    centerline = static_map.get_lane_segment_centerline(lane_id)
    print(f"Lane {lane_id} centerline shape: {centerline.shape}")  # (N, 3)

    # Get lane polygon boundary
    polygon = static_map.get_lane_segment_polygon(lane_id)

    # Check lane properties
    is_intersection = static_map.lane_is_in_intersection(lane_id)
    successors = static_map.get_lane_segment_successor_ids(lane_id)
    left_neighbor = static_map.get_lane_segment_left_neighbor_id(lane_id)
    right_neighbor = static_map.get_lane_segment_right_neighbor_id(lane_id)

# Get nearby lanes
query_point = np.array([100.0, 200.0])
nearby_lanes = static_map.get_nearby_lane_segments(query_point, search_radius_m=50.0)
print(f"Found {len(nearby_lanes)} lanes within 50m")

# Get drivable areas
drivable_areas = static_map.get_scenario_vector_drivable_areas()
print(f"Number of drivable area polygons: {len(drivable_areas)}")

# Get pedestrian crossings
ped_crossings = static_map.get_scenario_ped_crossings()
print(f"Number of pedestrian crossings: {len(ped_crossings)}")

# Filter points to drivable area (requires build_raster=True)
points_xyz = np.random.randn(1000, 3) * 50
drivable_points = static_map.remove_non_drivable_area_points(points_xyz)
print(f"Points in drivable area: {len(drivable_points)}/{len(points_xyz)}")

# Remove ground surface points
non_ground_points = static_map.remove_ground_surface(points_xyz)

# Check if points are on ground
is_ground = static_map.get_ground_points_boolean(points_xyz)
```

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