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134 lines
5.3 KiB
Python
134 lines
5.3 KiB
Python
import numpy as np
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from datumaro.components.extractor import (Extractor, DatasetItem, Label,
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Mask, Bbox, Points, Caption)
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from datumaro.components.project import Dataset
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from datumaro.components.operations import mean_std, compute_ann_statistics
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from unittest import TestCase
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class TestOperations(TestCase):
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def test_mean_std(self):
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expected_mean = [100, 50, 150]
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expected_std = [20, 50, 10]
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class TestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.random.normal(
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expected_mean, expected_std,
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size=(w, h, 3))
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)
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for i, (w, h) in enumerate([
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(3000, 100), (800, 600), (400, 200), (700, 300)
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])
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])
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actual_mean, actual_std = mean_std(TestExtractor())
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for em, am in zip(expected_mean, actual_mean):
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self.assertAlmostEqual(em, am, places=0)
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for estd, astd in zip(expected_std, actual_std):
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self.assertAlmostEqual(estd, astd, places=0)
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def test_stats(self):
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dataset = Dataset.from_iterable([
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DatasetItem(id=1, image=np.ones((5, 5, 3)), annotations=[
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Caption('hello'),
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Caption('world'),
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Label(2, attributes={ 'x': 1, 'y': '2', }),
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Bbox(1, 2, 2, 2, label=2, attributes={ 'score': 0.5, }),
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Bbox(5, 6, 2, 2, attributes={
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'x': 1, 'y': '3', 'occluded': True,
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}),
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Points([1, 2, 2, 0, 1, 1], label=0),
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Mask(label=3, image=np.array([
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[0, 0, 1, 1, 1],
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[0, 0, 1, 1, 1],
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[0, 0, 1, 1, 1],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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])),
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]),
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DatasetItem(id=2, image=np.ones((2, 4, 3)), annotations=[
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Label(2, attributes={ 'x': 2, 'y': '2', }),
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Bbox(1, 2, 2, 2, label=3, attributes={ 'score': 0.5, }),
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Bbox(5, 6, 2, 2, attributes={
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'x': 2, 'y': '3', 'occluded': False,
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}),
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]),
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DatasetItem(id=3),
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], categories=['label_%s' % i for i in range(4)])
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expected = {
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'images count': 3,
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'annotations count': 10,
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'unannotated images count': 1,
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'unannotated images': ['3'],
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'annotations by type': {
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'label': { 'count': 2, },
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'polygon': { 'count': 0, },
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'polyline': { 'count': 0, },
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'bbox': { 'count': 4, },
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'mask': { 'count': 1, },
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'points': { 'count': 1, },
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'caption': { 'count': 2, },
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},
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'annotations': {
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'labels': {
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'count': 6,
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'distribution': {
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'label_0': [1, 1/6],
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'label_1': [0, 0.0],
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'label_2': [3, 3/6],
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'label_3': [2, 2/6],
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},
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'attributes': {
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'x': {
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'count': 2, # unnotations with no label are skipped
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'values count': 2,
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'values present': ['1', '2'],
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'distribution': {
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'1': [1, 1/2],
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'2': [1, 1/2],
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},
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},
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'y': {
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'count': 2, # unnotations with no label are skipped
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'values count': 1,
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'values present': ['2'],
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'distribution': {
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'2': [2, 2/2],
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},
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},
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# must not include "special" attributes like "occluded"
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}
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},
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'segments': {
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'avg. area': (4 * 2 + 9 * 1) / 3,
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'area distribution': [
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{'min': 4.0, 'max': 4.5, 'count': 2, 'percent': 2/3},
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{'min': 4.5, 'max': 5.0, 'count': 0, 'percent': 0.0},
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{'min': 5.0, 'max': 5.5, 'count': 0, 'percent': 0.0},
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{'min': 5.5, 'max': 6.0, 'count': 0, 'percent': 0.0},
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{'min': 6.0, 'max': 6.5, 'count': 0, 'percent': 0.0},
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{'min': 6.5, 'max': 7.0, 'count': 0, 'percent': 0.0},
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{'min': 7.0, 'max': 7.5, 'count': 0, 'percent': 0.0},
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{'min': 7.5, 'max': 8.0, 'count': 0, 'percent': 0.0},
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{'min': 8.0, 'max': 8.5, 'count': 0, 'percent': 0.0},
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{'min': 8.5, 'max': 9.0, 'count': 1, 'percent': 1/3},
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],
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'pixel distribution': {
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'label_0': [0, 0.0],
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'label_1': [0, 0.0],
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'label_2': [4, 4/17],
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'label_3': [13, 13/17],
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},
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}
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},
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}
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actual = compute_ann_statistics(dataset)
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self.assertEqual(expected, actual) |