Visualize The Detection Results On Waymo Images

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Introduction

Visualizing the detection results on Waymo images is a crucial step in understanding the performance of object detection models. The Waymo dataset is a large-scale dataset for autonomous driving, and visualizing the detection results can help in identifying the strengths and weaknesses of the models. In this article, we will discuss how to modify the code to visualize the corresponding 3D detection results in Waymo images at the same time.

Understanding the Waymo Dataset

The Waymo dataset is a large-scale dataset for autonomous driving, which consists of 1000 scenes, each with 20 seconds of driving data. The dataset includes 3D point cloud data, camera images, and labels for various objects such as cars, pedestrians, and cyclists. The dataset is divided into training and validation sets, with 800 scenes for training and 200 scenes for validation.

Visualizing Point Cloud Data

To visualize the point cloud data, we can use the following command:

python demo.py --cfg_file cfgs/waymo_models/pv_rcnn_plusplus.yaml --ckpt [pretrained_model].pth --data_path ../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000.npy --ext .npy

This command uses the demo.py script to visualize the point cloud data. The --cfg_file option specifies the configuration file for the model, the --ckpt option specifies the pre-trained model, and the --data_path option specifies the path to the point cloud data.

Visualizing 3D Detection Results

To visualize the 3D detection results, we need to modify the code to include the camera images and the 3D detection results. We can use the following code to visualize the 3D detection results:

import cv2
import numpy as np
from waymo_open_dataset import dataset_pb2

# Load the point cloud data
pc = np.load('../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000.npy')

# Load the camera images
cam_images = []
for i in range(6):
    cam_image = cv2.imread('../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000_image_{}.jpg'.format(i))
    cam_images.append(cam_image)

# Load the 3D detection results
detections = []
with open('../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000_labels.json') as f:
    data = json.load(f)
    for obj in data['objects']:
        detections.append(obj)

# Visualize the 3D detection results
for i in range(len(detections)):
    detection = detections[i]
    cam_image = cam_images[detection['camera_index']]
    cv2.rectangle(cam_image, (int(detection['x']), int(detection['y'])), (int(detection['x'] + detection['width']), int(detection['y'] + detection['height'])), (0, 255, 0), 2)
    cv2.putText(cam_image, str(detection['type']), (int(detection['x']), int(detection['y'] - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
    cv2.imshow('Detection', cam_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

This code loads the point cloud data, camera images, and 3D detection results. It then visualizes the 3D detection results by drawing rectangles around the detected objects and displaying the object type.

Conclusion

Visualizing the detection results on Waymo images is a crucial step in understanding the performance of object detection models. By modifying the code to include the camera images and the 3D detection results, we can visualize the 3D detection results in Waymo images at the same time. This can help in identifying the strengths and weaknesses of the models and improving their performance.

Future Work

In the future, we can improve the visualization of the 3D detection results by adding more features such as:

  • 3D bounding box visualization: We can visualize the 3D bounding boxes around the detected objects to provide a better understanding of the object's size and orientation.
  • Object classification: We can classify the detected objects into different categories such as cars, pedestrians, and cyclists to provide a better understanding of the object's type.
  • Object tracking: We can track the detected objects over time to provide a better understanding of the object's movement and behavior.

Q: What is the Waymo dataset?

A: The Waymo dataset is a large-scale dataset for autonomous driving, which consists of 1000 scenes, each with 20 seconds of driving data. The dataset includes 3D point cloud data, camera images, and labels for various objects such as cars, pedestrians, and cyclists.

Q: How can I visualize the point cloud data in the Waymo dataset?

A: You can use the following command to visualize the point cloud data:

python demo.py --cfg_file cfgs/waymo_models/pv_rcnn_plusplus.yaml --ckpt [pretrained_model].pth --data_path ../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000.npy --ext .npy

Q: How can I visualize the 3D detection results in the Waymo dataset?

A: You can use the following code to visualize the 3D detection results:

import cv2
import numpy as np
from waymo_open_dataset import dataset_pb2

# Load the point cloud data
pc = np.load('../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000.npy')

# Load the camera images
cam_images = []
for i in range(6):
    cam_image = cv2.imread('../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000_image_{}.jpg'.format(i))
    cam_images.append(cam_image)

# Load the 3D detection results
detections = []
with open('../data/waymo/waymo_processed_data_v0_5_0/segment-10023947602400723454_1120_000_1140_000_with_camera_labels/0000_labels.json') as f:
    data = json.load(f)
    for obj in data['objects']:
        detections.append(obj)

# Visualize the 3D detection results
for i in range(len(detections)):
    detection = detections[i]
    cam_image = cam_images[detection['camera_index']]
    cv2.rectangle(cam_image, (int(detection['x']), int(detection['y'])), (int(detection['x'] + detection['width']), int(detection['y'] + detection['height'])), (0, 255, 0), 2)
    cv2.putText(cam_image, str(detection['type']), (int(detection['x']), int(detection['y'] - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
    cv2.imshow('Detection', cam_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

Q: What are the benefits of visualizing the 3D detection results in the Waymo dataset?

A: Visualizing the 3D detection results in the Waymo dataset can help in identifying the strengths and weaknesses of the models and improving performance. It can also provide a better understanding of the object's size and orientation, and help in object classification and tracking.

Q: How can I improve the visualization of the 3D detection results in the Waymo dataset?

A: You can improve the visualization of the 3D detection results by adding more features such as:

  • 3D bounding box visualization: You can visualize the 3D bounding boxes around the detected objects to provide a better understanding of the object's size and orientation.
  • Object classification: You can classify the detected objects into different categories such as cars, pedestrians, and cyclists to provide a better understanding of the object's type.
  • Object tracking: You can track the detected objects over time to provide a better understanding of the object's movement and behavior.

Q: What are the challenges of visualizing the 3D detection results in the Waymo dataset?

A: The challenges of visualizing the 3D detection results in the Waymo dataset include:

  • Data complexity: The Waymo dataset is a large-scale dataset with complex data, which can make it challenging to visualize the 3D detection results.
  • Model complexity: The object detection models used in the Waymo dataset can be complex, which can make it challenging to visualize the 3D detection results.
  • Computational resources: Visualizing the 3D detection results in the Waymo dataset can require significant computational resources, which can be a challenge.

Q: How can I overcome the challenges of visualizing the 3D detection results in the Waymo dataset?

A: You can overcome the challenges of visualizing the 3D detection results in the Waymo dataset by:

  • Using efficient algorithms: You can use efficient algorithms to visualize the 3D detection results, such as using GPU acceleration or parallel processing.
  • Using simplified models: You can use simplified models to visualize the 3D detection results, such as using 2D bounding boxes instead of 3D bounding boxes.
  • Using data augmentation: You can use data augmentation to increase the size of the dataset and make it easier to visualize the 3D detection results.