-
Notifications
You must be signed in to change notification settings - Fork 3.8k
/
TestObjectDetectionRecordReader.java
245 lines (206 loc) · 10.3 KB
/
TestObjectDetectionRecordReader.java
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.datavec.image.recordreader;
import org.datavec.api.records.Record;
import org.datavec.api.records.metadata.RecordMetaData;
import org.datavec.api.records.metadata.RecordMetaDataImageURI;
import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.split.CollectionInputSplit;
import org.datavec.api.split.FileSplit;
import org.datavec.api.writable.NDArrayWritable;
import org.datavec.api.writable.Writable;
import org.datavec.image.recordreader.objdetect.ImageObject;
import org.datavec.image.recordreader.objdetect.ImageObjectLabelProvider;
import org.datavec.image.recordreader.objdetect.ObjectDetectionRecordReader;
import org.datavec.image.transform.FlipImageTransform;
import org.datavec.image.transform.ImageTransform;
import org.datavec.image.transform.PipelineImageTransform;
import org.datavec.image.transform.ResizeImageTransform;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.conditions.Conditions;
import org.nd4j.linalg.io.ClassPathResource;
import java.io.File;
import java.net.URI;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import static org.junit.Assert.*;
public class TestObjectDetectionRecordReader {
@Rule
public TemporaryFolder testDir = new TemporaryFolder();
@Test
public void test() throws Exception {
ImageObjectLabelProvider lp = new TestImageObjectDetectionLabelProvider();
File f = testDir.newFolder();
new ClassPathResource("datavec-data-image/objdetect/").copyDirectory(f);
String path = new File(f, "000012.jpg").getParent();
int h = 32;
int w = 32;
int c = 3;
int gW = 13;
int gH = 10;
//Enforce consistent iteration order for tests
URI[] u = new FileSplit(new File(path)).locations();
Arrays.sort(u);
RecordReader rr = new ObjectDetectionRecordReader(h, w, c, gH, gW, lp);
rr.initialize(new CollectionInputSplit(u));
RecordReader imgRR = new ImageRecordReader(h, w, c);
imgRR.initialize(new CollectionInputSplit(u));
List<String> labels = rr.getLabels();
assertEquals(Arrays.asList("car", "cat"), labels);
//000012.jpg - originally 500x333
//000019.jpg - originally 500x375
double[] origW = new double[]{500, 500};
double[] origH = new double[]{333, 375};
List<List<ImageObject>> l = Arrays.asList(
Collections.singletonList(new ImageObject(156, 97, 351, 270, "car")),
Arrays.asList(new ImageObject(11, 113, 266, 259, "cat"), new ImageObject(231, 88, 483, 256, "cat"))
);
for (int idx = 0; idx < 2; idx++) {
assertTrue(rr.hasNext());
List<Writable> next = rr.next();
List<Writable> nextImgRR = imgRR.next();
//Check features:
assertEquals(next.get(0), nextImgRR.get(0));
//Check labels
assertEquals(2, next.size());
assertTrue(next.get(0) instanceof NDArrayWritable);
assertTrue(next.get(1) instanceof NDArrayWritable);
List<ImageObject> objects = l.get(idx);
INDArray expLabels = Nd4j.create(1, 4 + 2, gH, gW);
for (ImageObject io : objects) {
double fracImageX1 = io.getX1() / origW[idx];
double fracImageY1 = io.getY1() / origH[idx];
double fracImageX2 = io.getX2() / origW[idx];
double fracImageY2 = io.getY2() / origH[idx];
double x1C = (fracImageX1 + fracImageX2) / 2.0;
double y1C = (fracImageY1 + fracImageY2) / 2.0;
int labelGridX = (int) (x1C * gW);
int labelGridY = (int) (y1C * gH);
int labelIdx;
if (io.getLabel().equals("car")) {
labelIdx = 4;
} else {
labelIdx = 5;
}
expLabels.putScalar(0, labelIdx, labelGridY, labelGridX, 1.0);
expLabels.putScalar(0, 0, labelGridY, labelGridX, fracImageX1 * gW);
expLabels.putScalar(0, 1, labelGridY, labelGridX, fracImageY1 * gH);
expLabels.putScalar(0, 2, labelGridY, labelGridX, fracImageX2 * gW);
expLabels.putScalar(0, 3, labelGridY, labelGridX, fracImageY2 * gH);
}
INDArray lArr = ((NDArrayWritable) next.get(1)).get();
assertArrayEquals(new long[]{1, 4 + 2, gH, gW}, lArr.shape());
assertEquals(expLabels, lArr);
}
rr.reset();
Record record = rr.nextRecord();
RecordMetaDataImageURI metadata = (RecordMetaDataImageURI)record.getMetaData();
assertEquals(new File(path, "000012.jpg"), new File(metadata.getURI()));
assertEquals(3, metadata.getOrigC());
assertEquals((int)origH[0], metadata.getOrigH());
assertEquals((int)origW[0], metadata.getOrigW());
List<Record> out = new ArrayList<>();
List<RecordMetaData> meta = new ArrayList<>();
out.add(record);
meta.add(metadata);
record = rr.nextRecord();
metadata = (RecordMetaDataImageURI)record.getMetaData();
out.add(record);
meta.add(metadata);
List<Record> fromMeta = rr.loadFromMetaData(meta);
assertEquals(out, fromMeta);
// make sure we don't lose objects just by explicitly resizing
int i = 0;
int[] nonzeroCount = {5, 10};
ImageTransform transform = new ResizeImageTransform(37, 42);
RecordReader rrTransform = new ObjectDetectionRecordReader(42, 37, c, gH, gW, lp, transform);
rrTransform.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform.hasNext()) {
List<Writable> next = rrTransform.next();
assertEquals(37, transform.getCurrentImage().getWidth());
assertEquals(42, transform.getCurrentImage().getHeight());
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
ImageTransform transform2 = new ResizeImageTransform(1024, 2048);
RecordReader rrTransform2 = new ObjectDetectionRecordReader(2048, 1024, c, gH, gW, lp, transform2);
rrTransform2.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform2.hasNext()) {
List<Writable> next = rrTransform2.next();
assertEquals(1024, transform2.getCurrentImage().getWidth());
assertEquals(2048, transform2.getCurrentImage().getHeight());
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
//Make sure image flip does not break labels and are correct for new image size dimensions:
ImageTransform transform3 = new PipelineImageTransform(
new ResizeImageTransform(2048, 4096),
new FlipImageTransform(-1)
);
RecordReader rrTransform3 = new ObjectDetectionRecordReader(2048, 1024, c, gH, gW, lp, transform3);
rrTransform3.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform3.hasNext()) {
List<Writable> next = rrTransform3.next();
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
//Test that doing a downscale with the native image loader directly instead of a transform does not cause an exception:
ImageTransform transform4 = new FlipImageTransform(-1);
RecordReader rrTransform4 = new ObjectDetectionRecordReader(128, 128, c, gH, gW, lp, transform4);
rrTransform4.initialize(new CollectionInputSplit(u));
i = 0;
while (rrTransform4.hasNext()) {
List<Writable> next = rrTransform4.next();
assertEquals((int) origW[i], transform4.getCurrentImage().getWidth());
assertEquals((int) origH[i], transform4.getCurrentImage().getHeight());
INDArray labelArray = ((NDArrayWritable)next.get(1)).get();
BooleanIndexing.replaceWhere(labelArray, 1, Conditions.notEquals(0));
assertEquals(nonzeroCount[i++], labelArray.sum().getInt(0));
}
}
//2 images: 000012.jpg and 000019.jpg
private static class TestImageObjectDetectionLabelProvider implements ImageObjectLabelProvider {
@Override
public List<ImageObject> getImageObjectsForPath(URI uri) {
return getImageObjectsForPath(uri.getPath());
}
@Override
public List<ImageObject> getImageObjectsForPath(String path) {
if (path.endsWith("000012.jpg")) {
return Collections.singletonList(new ImageObject(156, 97, 351, 270, "car"));
} else if (path.endsWith("000019.jpg")) {
return Arrays.asList(
new ImageObject(11, 113, 266, 259, "cat"),
new ImageObject(231, 88, 483, 256, "cat"));
} else {
throw new RuntimeException();
}
}
}
}