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metadata
title: horizon-metrics
tags:
  - evaluate
  - metric
description: >-
  This huggingface metric calculates horizon evaluation metrics using
  `seametrics.horizon.HorizonMetrics`.
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
emoji: 🌅

SEA-AI/horizon-metrics

This huggingface metric uses seametrics.horizon.HorizonMetrics under the hood to calculate the slope and midpoint errors.

How to Use

To utilize horizon-metrics effectively, start by installing the necessary dependencies using the provided pip command. Once installed, import the evaluate library into your Python environment. Then, use the SEA-AI/horizon-metrics metric to evaluate your horizon prediction models. Ensure that both ground truth and prediction points are correctly formatted before computing the result. Finally, analyze the computed result to gain insights into the performance of your prediction models.

Getting Started

To get started with horizon-metrics, make sure you have the necessary dependencies installed. This metric relies on the evaluate and seametrics libraries.

Installation 

  pip install evaluate git+https://github.com/SEA-AI/seametrics@develop

Basic Usage

This is how you can quickly evaluate your horizon prediction models using SEA-AI/horizon-metrics:

The vertical_fov_degrees and height parameters are required. The default value for roll_threshold is 0.5 and for pitch_threshold it is 0.1.

The horizon metric should be calculated per sequence. Make sure that the vertical_fov and height are consistent across the inputs and do not change.

Use artificial data for testing 
ground_truth_points = [[[0.0, 0.5384765625], [1.0, 0.4931640625]],
                       [[0.0, 0.53796875], [1.0, 0.4928515625]],
                       [[0.0, 0.5374609375], [1.0, 0.4925390625]],
                       [[0.0, 0.536953125], [1.0, 0.4922265625]],
                       [[0.0, 0.5364453125], [1.0, 0.4919140625]]]

prediction_points = [[[0.0, 0.5428930956049597], [1.0, 0.4642497615378973]],
                     [[0.0, 0.5428930956049597], [1.0, 0.4642497615378973]],
                     [[0.0, 0.523573113510805], [1.0, 0.47642688648919496]],
                     [[0.0, 0.5200016849393765], [1.0, 0.4728554579177664]],
                     [[0.0, 0.523573113510805], [1.0, 0.47642688648919496]]]
Load data from fiftyone 
# Load data from fiftyone
sequence = "Sentry_2022_11_PROACT_CELADON_7.5M_MOB_2022_11_25_12_29_48"
dataset_name = "SENTRY_VIDEOS_DATASET_QA"
sequence_view = fo.load_dataset(dataset_name).match(F("sequence") == sequence)
sequence_view = sequence_view.select_group_slices("thermal_wide")

# Get the ground truth points
polylines_gt = sequence_view.values("frames.ground_truth_pl")
ground_truth_points = [
    line["polylines"][0]["points"][0] if line is not None else None
    for line in polylines_gt[0]
]

# Get the predicted points
polylines_pred = sequence_view.values(
    "frames.ahoy-IR-b2-whales__XAVIER-AGX-JP46_pl_TnFoV")
prediction_points = [
    line["polylines"][0]["points"][0] if line is not None else None
    for line in polylines_pred[0]
Calculate horizon metrics

Input parameters

  • vertical_fov_degrees (in degrees): vertical field of view

  • height (in pixels): height of the images

  • roll_threshold (in degrees): This value is internally converted to a slope threshold. It is used to determine the number of slope error jumps. A slope error jump is counted if the difference in slope error between two successive frames exceeds this threshold.

  • pitch_threshold (in degrees): This value is internally converted to the midpoint. It is used as a threshold to determine the number of midpoint error jumps. A midpoint error jump is counted if the difference in midpoint error between two successive frames exceeds this threshold.

import evaluate

module = evaluate.load("SEA-AI/horizon-metrics", vertical_fov_degrees=25.6, height=512, roll_threshold=0.5, pitch_threshold=0.1)
module.add(predictions=prediction_points, references=ground_truth_points)
module.compute()

This is output the evaluation metrics for your horizon prediction model:

{'average_slope_error': 0.39394822776758726,
 'average_midpoint_error': 0.0935801366906932,
 'average_midpoint_error_px': 1.871602733813864,
 'stddev_slope_error': 0.3809031270343266,
 'stddev_midpoint_error': 0.23003871087476538,
 'stddev_midpoint_error_px': 4.6007742174953075,
 'max_slope_error': 3.5549008029526132,
 'max_midpoint_error': 2.515424321301225,
 'max_midpoint_error_px': 50.3084864260245,
 'num_slope_error_jumps': 173,
 'num_midpoint_error_jumps': 205,
 'detection_rate': 0.2606486908948808}

Output Values

SEA-AI/horizon-metrics provides the following performance metrics for horizon prediction:

  • average_slope_error: Measures the average difference in slope between the predicted and ground truth horizon in degree.
  • average_midpoint_error: Represents the average difference in midpoint position between the predicted and ground truth horizon.
  • average_midpoint_error_px: Represents the average difference in midpoint position between the predicted and ground truth horizon, measured in pixels.
  • stddev_slope_error: Indicates the variability of errors in slope between the predicted and ground truth horizon in degree.
  • stddev_midpoint_error: Quantifies the variability of errors in midpoint position between the predicted and ground truth horizon in degree.
  • stddev_midpoint_error_px: Quantifies the variability of errors in midpoint position between the predicted and ground truth horizon, measured in pixels.
  • max_slope_error: Represents the maximum difference in slope between the predicted and ground truth horizon in degree.
  • max_midpoint_error: Indicates the maximum difference in midpoint position between the predicted and ground truth horizon in degree.
  • max_midpoint_error_px: Indicates the maximum difference in midpoint position between the predicted and ground truth horizon, measured in pixels.
  • num_slope_error_jumps: Calculates the differences between errors in successive frames for the slope. It then counts the number of jumps in these errors by comparing the absolute differences to a specified threshold.
  • num_midpoint_error_jumps: Calculates the differences between errors in successive frames for the midpoint. It then counts the number of jumps in these errors by comparing the absolute differences to a specified threshold.
  • detection_rate: Measures the proportion of frames in which the horizon is successfully detected out of the total number of frames.

Further References

Explore the seametrics GitHub repository for more details on the underlying library.

Contribution

Your contributions are welcome! If you'd like to improve SEA-AI/horizon-metrics or add new features, please feel free to fork the repository, make your changes, and submit a pull request.