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| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """TODO: Add a description here.""" | |
| import evaluate | |
| import datasets | |
| import numpy as np | |
| #from seametrics.horizon.utils import * | |
| # TODO: Add BibTeX citation | |
| _CITATION = """\ | |
| @InProceedings{huggingface:module, | |
| title = {A great new module}, | |
| authors={huggingface, Inc.}, | |
| year={2020} | |
| } | |
| """ | |
| # TODO: Add description of the module here | |
| _DESCRIPTION = """\ | |
| This new module is designed to solve this great ML task and is crafted with a lot of care. | |
| """ | |
| # TODO: Add description of the arguments of the module here | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates how good are predictions given some references, using certain scores | |
| Args: | |
| predictions: list of predictions to score. Each predictions | |
| should be a string with tokens separated by spaces. | |
| references: list of reference for each prediction. Each | |
| reference should be a string with tokens separated by spaces. | |
| Returns: | |
| accuracy: description of the first score, | |
| another_score: description of the second score, | |
| Examples: | |
| Examples should be written in doctest format, and should illustrate how | |
| to use the function. | |
| >>> my_new_module = evaluate.load("my_new_module") | |
| >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1]) | |
| >>> print(results) | |
| {'accuracy': 1.0} | |
| """ | |
| # TODO: Define external resources urls if needed | |
| BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt" | |
| # begin utils | |
| def xy_points_to_slope_midpoint(xy_points): | |
| """ | |
| Given two points, return the slope and midpoint of the line | |
| Args: | |
| xy_points: list of two points, each point is a list of two elements | |
| Points are in the form of [x, y], where x and y are normalized to [0, 1] | |
| Returns: | |
| slope: Slope of the line | |
| midpoint : Midpoint is in the form of [x,y], and is also normalized to [0, 1] | |
| """ | |
| #x1, y1, x2, y2 = xy_points[0][0], xy_points[0][1], xy_points[1][ | |
| # 0], xy_points[1][1] | |
| x1, y1, x2, y2 = xy_points[0], xy_points[1], xy_points[2], xy_points[3] | |
| slope = (y2 - y1) / (x2 - x1) | |
| midpoint_x = 0.5 | |
| midpoint_y = slope * (0.5 - x1) + y1 | |
| midpoint = [midpoint_x, midpoint_y] | |
| return slope, midpoint | |
| def calculate_horizon_error(annotated_horizon, proposed_horizon): | |
| """ | |
| Calculate the error between the annotated horizon and the proposed horizon | |
| Args: | |
| annotated_horizon: list of two points, each point is a list of two elements | |
| Points are in the form of [x, y], where x and y are normalized to [0, 1] | |
| proposed_horizon: list of two points, each point is a list of two elements | |
| Points are in the form of [x, y], where x and y are normalized to [0, 1] | |
| Returns: | |
| slope_error: Error in the slope of the lines | |
| midpoint_error: Error in the midpoint_y of the lines | |
| """ | |
| slope_annotated, midpoint_annotated = xy_points_to_slope_midpoint( | |
| annotated_horizon) | |
| slope_proposed, midpoint_proposed = xy_points_to_slope_midpoint( | |
| proposed_horizon) | |
| slope_error = abs(slope_annotated - slope_proposed) | |
| midpoint_error = abs(midpoint_annotated[1] - midpoint_proposed[1]) | |
| return slope_error, midpoint_error | |
| def calculate_horizon_error_across_sequence(slope_error_list, | |
| midpoint_error_list, | |
| slope_error_jump_threshold, | |
| midpoint_error_jump_threshold): | |
| """ | |
| Calculate the error statistics across a sequence of frames | |
| Args: | |
| slope_error_list: List of errors in the slope of the lines | |
| midpoint_error_list: List of errors in the midpoint_y of the lines | |
| Returns: | |
| average_slope_error: Average error in the slope of the lines | |
| average_midpoint_error: Average error in the midpoint_y of the lines | |
| """ | |
| # Calculate the average and standard deviation of the errors | |
| average_slope_error = np.mean(slope_error_list) | |
| average_midpoint_error = np.mean(midpoint_error_list) | |
| stddev_slope_error = np.std(slope_error_list) | |
| stddev_midpoint_error = np.std(midpoint_error_list) | |
| # Calculate the maximum errors | |
| max_slope_error = np.max(slope_error_list) | |
| max_midpoint_error = np.max(midpoint_error_list) | |
| # Calculate the differences between errors in successive frames | |
| diff_slope_error = np.abs(np.diff(slope_error_list)) | |
| diff_midpoint_error = np.abs(np.diff(midpoint_error_list)) | |
| # Calculate the number of jumps in the errors | |
| num_slope_error_jumps = np.sum( | |
| diff_slope_error > slope_error_jump_threshold) | |
| num_midpoint_error_jumps = np.sum( | |
| diff_midpoint_error > midpoint_error_jump_threshold) | |
| # Create a dictionary to store the results | |
| sequence_results = { | |
| 'average_slope_error': average_slope_error, | |
| 'average_midpoint_error': average_midpoint_error, | |
| 'stddev_slope_error': stddev_slope_error, | |
| 'stddev_midpoint_error': stddev_midpoint_error, | |
| 'max_slope_error': max_slope_error, | |
| 'max_midpoint_error': max_midpoint_error, | |
| 'num_slope_error_jumps': num_slope_error_jumps, | |
| 'num_midpoint_error_jumps': num_midpoint_error_jumps | |
| } | |
| return sequence_results | |
| def xy_points_to_slope_midpoint(xy_points): | |
| """ | |
| Given two points, return the slope and midpoint of the line | |
| Args: | |
| xy_points: list of two points, each point is a list of two elements | |
| Points are in the form of [x, y], where x and y are normalized to [0, 1] | |
| Returns: | |
| slope: Slope of the line | |
| midpoint : Midpoint is in the form of [x,y], and is also normalized to [0, 1] | |
| """ | |
| x1, y1, x2, y2 = xy_points[0][0], xy_points[0][1], xy_points[1][ | |
| 0], xy_points[1][1] | |
| slope = (y2 - y1) / (x2 - x1) | |
| midpoint_x = 0.5 | |
| midpoint_y = slope * (0.5 - x1) + y1 | |
| midpoint = [midpoint_x, midpoint_y] | |
| return slope, midpoint | |
| def calculate_horizon_error(annotated_horizon, proposed_horizon): | |
| """ | |
| Calculate the error between the annotated horizon and the proposed horizon | |
| Args: | |
| annotated_horizon: list of two points, each point is a list of two elements | |
| Points are in the form of [x, y], where x and y are normalized to [0, 1] | |
| proposed_horizon: list of two points, each point is a list of two elements | |
| Points are in the form of [x, y], where x and y are normalized to [0, 1] | |
| Returns: | |
| slope_error: Error in the slope of the lines | |
| midpoint_error: Error in the midpoint_y of the lines | |
| """ | |
| slope_annotated, midpoint_annotated = xy_points_to_slope_midpoint( | |
| annotated_horizon) | |
| slope_proposed, midpoint_proposed = xy_points_to_slope_midpoint( | |
| proposed_horizon) | |
| slope_error = abs(slope_annotated - slope_proposed) | |
| midpoint_error = abs(midpoint_annotated[1] - midpoint_proposed[1]) | |
| return slope_error, midpoint_error | |
| def calculate_horizon_error_across_sequence(slope_error_list, | |
| midpoint_error_list, | |
| slope_error_jump_threshold, | |
| midpoint_error_jump_threshold): | |
| """ | |
| Calculate the error statistics across a sequence of frames | |
| Args: | |
| slope_error_list: List of errors in the slope of the lines | |
| midpoint_error_list: List of errors in the midpoint_y of the lines | |
| Returns: | |
| average_slope_error: Average error in the slope of the lines | |
| average_midpoint_error: Average error in the midpoint_y of the lines | |
| """ | |
| # Calculate the average and standard deviation of the errors | |
| average_slope_error = np.mean(slope_error_list) | |
| average_midpoint_error = np.mean(midpoint_error_list) | |
| stddev_slope_error = np.std(slope_error_list) | |
| stddev_midpoint_error = np.std(midpoint_error_list) | |
| # Calculate the maximum errors | |
| max_slope_error = np.max(slope_error_list) | |
| max_midpoint_error = np.max(midpoint_error_list) | |
| # Calculate the differences between errors in successive frames | |
| diff_slope_error = np.abs(np.diff(slope_error_list)) | |
| diff_midpoint_error = np.abs(np.diff(midpoint_error_list)) | |
| # Calculate the number of jumps in the errors | |
| num_slope_error_jumps = np.sum( | |
| diff_slope_error > slope_error_jump_threshold) | |
| num_midpoint_error_jumps = np.sum( | |
| diff_midpoint_error > midpoint_error_jump_threshold) | |
| # Create a dictionary to store the results | |
| sequence_results = { | |
| 'average_slope_error': average_slope_error, | |
| 'average_midpoint_error': average_midpoint_error, | |
| 'stddev_slope_error': stddev_slope_error, | |
| 'stddev_midpoint_error': stddev_midpoint_error, | |
| 'max_slope_error': max_slope_error, | |
| 'max_midpoint_error': max_midpoint_error, | |
| 'num_slope_error_jumps': num_slope_error_jumps, | |
| 'num_midpoint_error_jumps': num_midpoint_error_jumps | |
| } | |
| return sequence_results | |
| def slope_to_roll(slope): | |
| """ | |
| Convert the slope of the horizon to roll | |
| Args: | |
| slope: Slope of the horizon | |
| Returns: | |
| roll: Roll in degrees | |
| """ | |
| roll = np.arctan(slope) * 180 / np.pi | |
| return roll | |
| def roll_to_slope(roll): | |
| """ | |
| Convert the roll of the horizon to slope | |
| Args: | |
| roll: Roll of the horizon in degrees | |
| Returns: | |
| slope: Slope of the horizon | |
| """ | |
| slope = np.tan(roll * np.pi / 180) | |
| return slope | |
| def midpoint_to_pitch(midpoint, vertical_fov_degrees): | |
| """ | |
| Convert the midpoint of the horizon to pitch | |
| Args: | |
| midpoint: Midpoint of the horizon | |
| vertical_fov_degrees: Vertical field of view of the camera in degrees | |
| Returns: | |
| pitch: Pitch in degrees | |
| """ | |
| pitch = midpoint * vertical_fov_degrees | |
| return pitch | |
| def pitch_to_midpoint(pitch, vertical_fov_degrees): | |
| """ | |
| Convert the pitch of the horizon to midpoint | |
| Args: | |
| pitch: Pitch of the horizon in degrees | |
| vertical_fov_degrees: Vertical field of view of the camera in degrees | |
| Returns: | |
| midpoint: Midpoint of the horizon | |
| """ | |
| midpoint = pitch / vertical_fov_degrees | |
| return midpoint | |
| # end utils | |
| class horizonmetrics(evaluate.Metric): | |
| """TODO: Short description of my evaluation module.""" | |
| def __init__(self, | |
| slope_threshold=0.1, | |
| midpoint_threshold=0.1, | |
| vertical_fov_degrees=25.6, | |
| **kwargs): | |
| super().__init__(**kwargs) | |
| self.slope_threshold = slope_threshold | |
| self.midpoint_threshold = midpoint_threshold | |
| self.vertical_fov_degrees = vertical_fov_degrees | |
| self.predictions = None | |
| self.ground_truth_det = None | |
| self.slope_error_list = None | |
| self.midpoint_error_list = None | |
| def _info(self): | |
| # TODO: Specifies the evaluate.EvaluationModuleInfo object | |
| return evaluate.MetricInfo( | |
| # This is the description that will appear on the modules page. | |
| module_type="metric", | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| # This defines the format of each prediction and reference | |
| features=datasets.Features({ | |
| 'predictions': datasets.Value('int64'), | |
| 'references': datasets.Value('int64'), | |
| }), | |
| # Homepage of the module for documentation | |
| homepage="http://module.homepage", | |
| # Additional links to the codebase or references | |
| codebase_urls=["http://github.com/path/to/codebase/of/new_module"], | |
| reference_urls=["http://path.to.reference.url/new_module"]) | |
| def add(self, *, predictions, references, **kwargs): | |
| """ | |
| Update the predictions and ground truth detections. | |
| Parameters | |
| ---------- | |
| predictions : list | |
| List of predicted horizons. | |
| ground_truth_det : list | |
| List of ground truth horizons. | |
| """ | |
| self.predictions = predictions | |
| self.ground_truth_det = references | |
| self.slope_error_list = [] | |
| self.midpoint_error_list = [] | |
| for annotated_horizon, proposed_horizon in zip(self.ground_truth_det, | |
| self.predictions): | |
| slope_error, midpoint_error = calculate_horizon_error( | |
| annotated_horizon, proposed_horizon) | |
| self.slope_error_list.append(slope_error) | |
| self.midpoint_error_list.append(midpoint_error) | |
| # does not impact the metric, but is required for the interface x_x | |
| super(evaluate.Metric, self).add(prediction=predictions, | |
| references=references, | |
| **kwargs) | |
| def _compute(self): | |
| """ | |
| Compute the horizon error across the sequence. | |
| Returns | |
| ------- | |
| float | |
| The computed horizon error. | |
| """ | |
| return calculate_horizon_error_across_sequence( | |
| self.slope_error_list, self.midpoint_error_list, | |
| self.slope_threshold, self.midpoint_threshold) | |
| def _download_and_prepare(self, dl_manager): | |
| """Optional: download external resources useful to compute the scores""" | |
| # TODO: Download external resources if needed | |
| pass | |