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Shelf planogram compliance (preview)

  • Artikkeli

A planogram is a diagram that indicates the correct placement of retail products on shelves. The Planogram Compliance API lets you compare analysis results from a photo to the store's planogram input. It returns an account of all the positions in the planogram, and whether a product was found in each position.

Photo of a retail shelf with detected products outlined and planogram position rectangles outlined separately.

Note

The brands shown in the images are not affiliated with Microsoft and do not indicate any form of endorsement of Microsoft or Microsoft products by the brand owners, or an endorsement of the brand owners or their products by Microsoft.

Prerequisites

  • You must have already set up and run basic Product Understanding analysis with the Product Understanding API.
  • cURL installed. Or, you can use a different REST platform, like Swagger or the REST Client extension for VS Code.

Prepare a planogram schema

You need to have your planogram data in a specific JSON format. See the sections below for field definitions.

"planogram": {
  "width": 100.0,
  "height": 50.0,
  "products": [
    {
      "id": "string",
      "name": "string",
      "w": 12.34,
      "h": 123.4
    }
  ],
  "fixtures": [
    {
      "id": "string",
      "w": 2.0,
      "h": 10.0,
      "x": 0.0,
      "y": 3.0
    }
  ],
  "positions": [
    {
      "id": "string",
      "productId": "string",
      "fixtureId": "string",
      "x": 12.0,
      "y": 34.0
    }
  ]
}

The X and Y coordinates are relative to a top-left origin, and the width and height extend each bounding box down and to the right. The following diagram shows examples of the coordinate system.

Diagram of a shelf image with fixtures and products highlighted and their coordinates shown.

Note

The brands shown in the images are not affiliated with Microsoft and do not indicate any form of endorsement of Microsoft or Microsoft products by the brand owners, or an endorsement of the brand owners or their products by Microsoft.

Quantities in the planogram schema are in nonspecific units. They can correspond to inches, centimeters, or any other unit of measurement. The matching algorithm calculates the relationship between the photo analysis units (pixels) and the planogram units.

Planogram API model

Describes the planogram for planogram matching operations.

Name Type Description Required
width double Width of the planogram. Yes
height double Height of the planogram. Yes
products ProductApiModel List of products in the planogram. Yes
fixtures FixtureApiModel List of fixtures in the planogram. Yes
positions PositionApiModel List of positions in the planogram. Yes

Product API model

Describes a product in the planogram.

Name Type Description Required
id string ID of the product. Yes
name string Name of the product. Yes
w double Width of the product. Yes
h double Height of the fixture. Yes

Fixture API model

Describes a fixture (shelf or similar hardware) in a planogram.

Name Type Description Required
id string ID of the fixture. Yes
w double Width of the fixture. Yes
h double Height of the fixture. Yes
x double Left offset from the origin, in units of in inches or centimeters. Yes
y double Top offset from the origin, in units of inches or centimeters. Yes

Position API model

Describes a product's position in a planogram.

Name Type Description Required
id string ID of the position. Yes
productId string ID of the product. Yes
fixtureId string ID of the fixture that the product is on. Yes
x double Left offset from the origin, in units of in inches or centimeters. Yes
y double Top offset from the origin, in units of inches or centimeters. Yes

Get analysis results

Next, you need to do a Product Understanding API call with a custom model.

The returned JSON text should be a "detectedProducts" structure. It shows all the products that were detected on the shelf, with the product-specific labels you used in the training stage.

"detectedProducts": {
  "imageMetadata": {
    "width": 21,
    "height": 25
  },
  "products": [
    {
      "id": "01",
      "boundingBox": {
        "x": 123,
        "y": 234,
        "w": 34,
        "h": 45
      },
      "classifications": [
        {
          "confidence": 0.8,
          "label": "Product1"
        }
      ]
    }
  ],
  "gaps": [
    {
      "id": "02",
      "boundingBox": {
        "x": 12,
        "y": 123,
        "w": 1234,
        "h": 123
      },
      "classifications": [
        {
          "confidence": 0.9,
          "label": "Product1"
        }
      ]
    }
  ]
}

Prepare the matching request

Join the JSON content of your planogram schema with the JSON content of the analysis results, like this:

"planogram": {
  "width": 100.0,
  "height": 50.0,
  "products": [
    {
      "id": "string",
      "name": "string",
      "w": 12.34,
      "h": 123.4
    }
  ],
  "fixtures": [
    {
      "id": "string",
      "w": 2.0,
      "h": 10.0,
      "x": 0.0,
      "y": 3.0
    }
  ],
  "positions": [
    {
      "id": "string",
      "productId": "string",
      "fixtureId": "string",
      "x": 12.0,
      "y": 34.0
    }
  ]
},
"detectedProducts": {
  "imageMetadata": {
    "width": 21,
    "height": 25
  },
  "products": [
    {
      "id": "01",
      "boundingBox": {
        "x": 123,
        "y": 234,
        "w": 34,
        "h": 45
      },
      "classifications": [
        {
          "confidence": 0.8,
          "label": "Product1"
        }
      ]
    }
  ],
  "gaps": [
    {
      "id": "02",
      "boundingBox": {
        "x": 12,
        "y": 123,
        "w": 1234,
        "h": 123
      },
      "classifications": [
        {
          "confidence": 0.9,
          "label": "Product1"
        }
      ]
    }
  ]
}

This is the text you'll use in your API request body.

Call the planogram matching API

  1. Copy the following curl command into a text editor.

    curl.exe -H "Ocp-Apim-Subscription-Key: <subscriptionKey>" -H "Content-Type: application/json" "<endpoint>/computervision/planogramcompliance:match?api-version=2023-04-01-preview" -d "<body>"

  2. Make the following changes in the command where needed:

    1. Replace the value of <subscriptionKey> with your Vision resource key.
    2. Replace the value of <endpoint> with your Vision resource endpoint. For example: https://YourResourceName.cognitiveservices.azure.com.
    3. Replace the value of <body> with the joined JSON string you prepared in the previous section.

  3. Open a command prompt window.

  4. Paste your edited curl command from the text editor into the command prompt window, and then run the command.

Examine the response

A successful response is returned in JSON, showing the products (or gaps) detected at each planogram position. See the sections below for field definitions.

{
  "matchedResultsPerPosition": [
    {
      "positionId": "01",
      "detectedObject": {
        "id": "01",
        "boundingBox": {
          "x": 12,
          "y": 1234,
          "w": 123,
          "h": 12345
        },
        "classifications": [
          {
            "confidence": 0.9,
            "label": "Product1"
          }
        ]
      }
    }
  ]
}

Planogram matching position API model

Paired planogram position ID and corresponding detected object from product understanding result.

Name Type Description Required
positionId string The position ID from the planogram matched to the corresponding detected object. No
detectedObject DetectedObjectApiModel Describes a detected object in an image. No

Next steps