In a raster analysis deployment, this tool runs a trained deep learning model on an input raster to produce a feature class containing the objects it identifies. The feature class can be shared as a hosted feature layer in your portal. The features can be bounding boxes or polygons around the objects found, or points at the centers of the objects.
Your raster analysis (RA) server Python environment must be configured with the proper deep learning framework Python API such as Tensorflow, CNTK, or similar.
With the tool running, your RA server calls a third-party deep learning Python API (such as TensorFlow or CNTK) and uses the specified Python raster function to process each raster tile.
The Input Model parameter will only use a deep learning package (.dlpk) item from the portal.
After the Input Model is selected or specified, the tool will obtain the model arguments information from your raster analysis server. The tool may fail to obtain such information if your input model is invalid or your raster analysis server isn’t properly configured with the deep learning framework.
Use the Non Maximum Suppression parameter to identify and remove duplicate features from the object detection.
For more information about deep learning, see Deep learning in ArcGIS Pro.
| Parameter | Explanation |
|---|---|
| inputRaster |
The input image used to detect objects. It can be an image service URL, a raster layer, an image service, a map server layer, or an internet tiled layer. |
| inputModel |
The input model can be a file or a URL of a deep learning package (.dlpk) item from the portal. |
| outputName |
The name of the output feature service of detected objects. |
| modelArguments (Optional) |
The function model arguments are defined in the Python raster function class referenced by the input model. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for fine tuning the sensitivity. The names of the arguments are populated by the tool from reading the Python module on the RA server. |
| runNMS (Optional) |
Specifies whether non maximum suppression, where duplicate objects are identified and the duplicate feature with a lower confidence value is removed, will be performed.
|
| confidenceScoreField (Optional) |
The field in the feature service that contains the confidence scores that will be used as output by the object detection method. This parameter is required when the Non Maximum Suppression parameter is checked. |
| classValueField (Optional) |
The name of the class value field in the feature service. If a field name is not specified, the tool will attempt to use a Classvalue or Value field. If these fields do not exist, the tool will identify all records as belonging to one class. |
| maxOverlapRatio (Optional) |
The maximum overlap ratio for two overlapping features, which is defined as the ratio of intersection area over union area. The default is 0. |
DetectObjectsUsingDeepLearning_ra example 1 (Python window)
This example creates a hosted feature layer in your portal based on object detection using the DetectObjectsUsingDeepLearning tool.
import arcpy
arcpy.DetectObjectsUsingDeepLearning_ra(
"https://myserver/rest/services/Farm/ImageServer",
"https://myportal/sharing/rest/content/items/itemId", "detectedTrees",
"score_threshold 0.6;padding 0", "NO_NMS")
DetectObjectsUsingDeepLearning example 2 (stand-alone script)
This example creates a hosted feature layer in your portal based on object detection using the DetectObjectsUsingDeepLearning tool.
#---------------------------------------------------------------------------
# Name: DetectObjectsUsingDeepLearning_example02.py
# Requirements: ArcGIS Image Server
# Import system modules
import arcpy
# Set local variables
inImage = "https://myserver/rest/services/coconutFarmImage/ImageServer"
inModel = "https://myportal/sharing/rest/content/items/itemId"
outName = "detectedTrees"
modelArgs = "score_threshold 0.6;padding 0"
runNMS = "NMS"
confScoreField = "Confidence"
classVField = "Class"
maxOverlapRatio = 0.15
# Execute Detect Objects Using raster analysis tool
arcpy.DetectObjectsUsingDeepLearning_ra(inImage, inModel, outName, modelArgs,
runNMS, confScoreField, ClassVField, maxOverlapRatio)
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