//tag::ref-doc[] :image-root: https://github.com/spring-cloud/stream-applications/raw/master/applications/processor/object-detection-processor/src/test/resources/images = Object Detection Processor The Object Detection processor provides out-of-the-box support for the https://github.com/tensorflow/models/blob/master/research/object_detection/README.md[TensorFlow Object Detection API]. It allows for real-time localization and identification of multiple objects in a single image or image stream. The Object Detection processor is built on top of the https://github.com/spring-cloud/stream-applications/tree/master/functions/function/object-detection-function[Object Detection Function]. You have to provide the Processor with a pre-trained https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md[object detection model], and the corresponding https://github.com/tensorflow/models/tree/865c14c/research/object_detection/data[object labels]. Here are some sensible configuration defaults: * `object.detection.model` : `https://storage.googleapis.com/scdf-tensorflow-models/object-detection/faster_rcnn_resnet101_coco_2018_01_28_frozen_inference_graph.pb` * `object.detection.labels` : `https://storage.googleapis.com/scdf-tensorflow-models/object-detection/mscoco_label_map.pbtxt` * `object.detection.with-masks` : `false` The following diagram shows a https://dataflow.spring.io/docs/concepts/streams/[Spring Cloud Data Flow], streaming pipeline, that predicts, in real-time, the object types in input image stream. image::{image-root}/scdf-tensorflow-object-detection-arch.png[] Processor's input is an image byte array, and the output is an augmented image, and a header, called `detected_objects`, that provides textual description of the detected objects: ```json { "labels" : [ {"name":"person", "confidence":0.9996774,"x1":0.0,"y1":0.3940161,"x2":0.9465165,"y2":0.5592592,"cid":1}, {"name":"person", "confidence":0.9996604,"x1":0.047891676,"y1":0.03169123,"x2":0.941098,"y2":0.2085562,"cid":1}, {"name":"backpack", "confidence":0.96534747,"x1":0.15588468,"y1":0.85957795,"x2":0.5091308,"y2":0.9908878,"cid":23}, {"name":"backpack", "confidence":0.963343,"x1":0.1273736,"y1":0.57658505,"x2":0.47765,"y2":0.6986431,"cid":23} ] } ``` The `detected_objects` header format is: * *object-name*:**confidence** - human readable name of the detected object (e.g. label) with its confidence as a float between [0-1] * *x1*, *y1*, *x2*, *y2* - Response also provides the bounding box of the detected objects represented as `(x1, y1, x2, y2)`. The coordinates are relative to the size of the image size. * *cid* - Classification identifier as defined in the provided https://github.com/tensorflow/models/tree/865c14c/research/object_detection/data[labels] configuration file. === Payload The incoming type is `byte[]`, and the content type is `application/octet-stream`. The processor processes the input `byte[]` image and outputs an augmented `byte[]` image payload and a JSON header (`detected_objects`). == Options //tag::configuration-properties[] $$object.detection.cache-model$$:: $$$$ *($$Boolean$$, default: `$$true$$`)* $$object.detection.confidence$$:: $$$$ *($$Float$$, default: `$$0.4$$`)* $$object.detection.debug-output$$:: $$$$ *($$Boolean$$, default: `$$false$$`)* $$object.detection.debug-output-path$$:: $$$$ *($$String$$, default: `$$object-detection-result.png$$`)* $$object.detection.labels$$:: $$Labels URI.$$ *($$String$$, default: `$$https://storage.googleapis.com/scdf-tensorflow-models/object-detection/mscoco_label_map.pbtxt$$`)* $$object.detection.model$$:: $$pre-trained tensorflow object detection model.$$ *($$String$$, default: `$$https://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz#frozen_inference_graph.pb$$`)* $$object.detection.response-size$$:: $$$$ *($$Integer$$, default: `$$$$`)* $$object.detection.with-masks$$:: $$$$ *($$Boolean$$, default: `$$false$$`)* //end::configuration-properties[] //end::ref-doc[]