:images-asciidoc: https://raw.githubusercontent.com/spring-cloud/stream-applications/master/functions/function/object-detection-function/src/main/resources/images/
# Object Detection Function
Java model inference library for the https://github.com/tensorflow/models/blob/master/research/object_detection/README.md[TensorFlow Object Detection API]. Allows real-time localization and identification of multiple objects in a single or batch of images. Works with all https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md[pre-trained zoo models] and ttps://github.com/tensorflow/models/tree/865c14c/research/object_detection/data[object labels].
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|===
| image:{images-asciidoc}/object_detection_1.jpg[alt=Object Detection 1, width=100%]
|The https://github.com/spring-cloud/stream-applications/blob/master/functions/function/object-detection-function/src/main/java/org/springframework/cloud/fn/object/detection/ObjectDetectionService.java[ObjectDetectionService]
takes an image or a batch of images and outputs a list of predicted objects bounding boxes
represented by https://github.com/spring-cloud/stream-applications/blob/master/functions/function/object-detection-function/src/main/java/org/springframework/cloud/fn/object/detection/domain/ObjectDetection.java[ObjectDetection].
For the models supporting https://github.com/tensorflow/models/tree/master/research/object_detection#february-9-2018[Instance Segmentation],
the `ObjectDetectionService` can predict the instance segmentation `masks` in addition to object bounding boxes.
The https://github.com/spring-cloud/stream-applications/blob/master/functions/common/tensorflow-common/src/main/java/org/springframework/cloud/fn/common/tensorflow/deprecated/JsonMapperFunction.java[JsonMapperFunction] permits
converting the `List<ObjectDetection>` into JSON objects and the
https://github.com/spring-cloud/stream-applications/blob/master/functions/function/object-detection-function/src/main/java/org/springframework/cloud/fn/object/detection/ObjectDetectionImageAugmenter.java[ObjectDetectionImageAugmenter]
allow to augment the input image with the detected bounding boxes and segmentation masks.
|===
## Usage
Add the `object-detection` dependency to the pom (use the latest version available):
[source,xml]
----
<dependency>
<groupId>org.springframework.cloud.fn</groupId>
<artifactId>object-detection-function</artifactId>
<version>${spring-cloud-fn.version}</version>
</dependency>
----
#### Example 1: Object Detection
The https://github.com/spring-cloud/stream-applications/blob/master/functions/function/object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleObjectDetection.java[ExampleObjectDetection.java]
sample demonstrates how to use the `ObjectDetectionService` for detecting objects in input images. It also shows how to
convert the result into JSON format and augment the input image with the detected object bounding boxes.
[source,java,linenums]
----
ObjectDetectionService detectionService = new ObjectDetectionService(
"http://download.tensorflow.org/models/object_detection/faster_rcnn_nas_coco_2018_01_28.tar.gz#frozen_inference_graph.pb", //<1>
"https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt", //<2>
0.4f, //<3>
false, //<4>
true); //<5>
byte[] image = GraphicsUtils.loadAsByteArray("classpath:/images/object-detection.jpg"); //<6>
List<ObjectDetection> detectedObjects = detectionService.detect(image); //<7>
----
<1> Downloads and loads a pre-trained `frozen_inference_graph.pb` model directly from the `faster_rcnn_nas_coco.tar.gz` archive in the
Tensorflow model zoo. Mind that on first attempt it will download few hundreds of MBs. The consecutive runs will use the
cached copy (5) instead.
<2> Object category labels (e.g. names) for the model
<3> Confidence threshold - Only object with estimate above the threshold are returned
<4> Indicate that this is not a `mask` (e.g. not an instance segmentation) model type
<5> Cache the model on the local file system.
<6> Load the input image to evaluate
<7> Detect the objects in the image and represent the result as a list of ObjectDetection instances.
Next you can convert the result in JSON format.
[source,java,linenums]
----
String jsonObjectDetections = new JsonMapperFunction().apply(detectedObjects);
System.out.println(jsonObjectDetections);
----
.Sample Object Detection JSON representation
[source,json]
----
[{"name":"person","estimate":0.998,"x1":0.160,"y1":0.774,"x2":0.201,"y2":0.946,"cid":1},
{"name":"kite","estimate":0.998,"x1":0.437,"y1":0.089,"x2":0.495,"y2":0.169,"cid":38},
{"name":"person","estimate":0.997,"x1":0.084,"y1":0.681,"x2":0.121,"y2":0.848,"cid":1},
{"name":"kite","estimate":0.988,"x1":0.206,"y1":0.263,"x2":0.225,"y2":0.314,"cid":38}]]
----
Use the https://github.com/spring-cloud/stream-applications/blob/master/functions/function/object-detection-function/src/main/java/org/springframework/cloud/fn/object/detection/ObjectDetectionImageAugmenter.java[ObjectDetectionImageAugmenter]
to draw the detected objects on top of the input image.
[source,java,linenums]
----
byte[] annotatedImage = new ObjectDetectionImageAugmenter().apply(image, detectedObjects); // <1>
IOUtils.write(annotatedImage, new FileOutputStream("./object-detection-function/target/object-detection-augmented.jpg")); //<2>
----
<1> Augment the image with the detected object bounding boxes (Uses Java2D internally).
<2> Stores the augmented image as `object-detection-augmented.jpg` image file.
.Augmented object-detection-augmented.jpg file
image:{images-asciidoc}/object-detection-augmented.jpg[alt=Object Detection, width=60%]
TIP: Set the `ObjectDetectionImageAugmenter#agnosticColors` property to `true` to use a monochrome color schema.
#### Example 2: Instance Segmentation
The https://github.com/spring-cloud/stream-applications/blob/master/functions/function/object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleInstanceSegmentation.java[ExampleInstanceSegmentation.java]
sample shows how to use the `ObjectDetectionService` for `Instance Segmentation`.
NOTE: It requires a trained model that supports `Masks` as well as setting the instance segmentation (e.g. `useMasks`) flag to `true`.
[source,java,linenums]
----
ObjectDetectionService detectionService = new ObjectDetectionService(
"http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar.gz#frozen_inference_graph.pb", // <1>
"https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt", // <2>
0.4f, // <3>
true, // <4>
true); // <5>
byte[] image = GraphicsUtils.loadAsByteArray("classpath:/images/object-detection.jpg");
List<ObjectDetection> detectedObjects = detectionService.detect(image); // <6>
String jsonObjectDetections = new JsonMapperFunction().apply(detectedObjects); // <7>
System.out.println(jsonObjectDetections);
byte[] annotatedImage = new ObjectDetectionImageAugmenter(true) // <8>
.apply(image, detectedObjects);
IOUtils.write(annotatedImage, new FileOutputStream("./object-detection-function/target/object-detection-segmentation-augmented.jpg"));
----
<1> Uses one of the 4 MASK pre-trained models
<2> Object category labels (e.g. names) for the model
<3> Confidence threshold - Only object with estimate above the threshold are returned.
<4> Use masks output - For the pre-trained models instruct to use the extended fetch names that include instance segmentation masks as well.
<5> Cache model - Create a local copy of the model to speed up consecutive runs.
<6> Evaluate the model to predict the object in the input image.
<7> Convert the detected object in to JSON array. NOTE: that with mask there is an additional field: `mask`
<8> Draw the detected object on top of the input image. Mind the `true` constructor parameter stands for draw detected masks.
If false only the bounding boxes will be shown.
.Result augmented object-detection-segmentation-augmented.jpg file
image:{images-asciidoc}/object-detection-segmentation-augmented.jpg[alt=Object Detection Augmented, width=60%]
## Models
All pre-trained https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md[detection_model_zoo.md]
models are supported. Following URI notation can be used to download any of the models directly from the zoo.
----
http://<zoo model tar.gz url>#frozen_inference_graph.pb
----
The `frozen_inference_graph.pb` is the frozen model file name within the archive.
NOTE: For some models this name may differ. You have to download and open the archive to find the real name.
TIP: To speedup the bootstrap performance you may consider extracting the `frozen_inference_graph.pb` and caching it
locally. Then you can use the `file://path-to-my-local-copy` URI schema to access it.
Following models can be used for `Instance Segmentation` as well:
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|===
| http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar.gz[mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar.gz]
| http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz[mask_rcnn_inception_v2_coco_2018_01_28.tar.gz]
| http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet101_atrous_coco_2018_01_28.tar.gz[mask_rcnn_resnet101_atrous_coco_2018_01_28.tar.gz]
| http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz[mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz]
|===
In addition to the model, the `ObjectDetectionService` requires a list of labels that correspond to the categories detectable by the selected model.
All labels files are available in the https://github.com/tensorflow/models/tree/master/research/object_detection/data[object_detection/data] folder.
NOTE: It is important to use the labels that correspond to the model being used! Table below highlights this mapping.
.Relationsip between trained model types and category labels
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|===
| Model
| Labels
| https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models[coco]
| https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt[mscoco_label_map.pbtxt]
| https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#kitti-trained-models[kitti]
| https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/kitti_label_map.pbtxt[kitti_label_map.pbtxt]
| https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#open-images-trained-models[open-images]
| https://github.com/tensorflow/models/blob/master/research/object_detection/data/oid_bbox_trainable_label_map.pbtxt[oid_bbox_trainable_label_map.pbtxt]
| https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#inaturalist-species-trained-models[inaturalist-species]
| https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/fgvc_2854_classes_label_map.pbtxt[fgvc_2854_classes_label_map.pbtxt]
| https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#ava-v21-trained-models[ava]
| https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/ava_label_map_v2.1.pbtxt[ava_label_map_v2.1.pbtxt]
|===
TIP: For performance reasons you may consider downloading the required label files to the local file system.
