Image Recognition with Confirmation

What You’ll Build

You’ll implement a photo-to-dishes flow where your app uploads a meal image, receives top-N dish candidates per region from LogMeal, displays them to the user, and asks for a quick confirmation before persisting the meal. This balances AI automation with a human-in-the-loop step that improves accuracy and user trust.

This pattern is ideal for diet logs and wellness apps: the model proposes dishes; the user confirms (or corrects) them; the app stores the final, validated result for downstream analytics and nutrition.

Personalization tip: Before first use, consider setting the user’s country and language to improve recognition labels and ranking. See POST /profile/modifyUserProfileInfo for details.

Prerequisites

• Plan: Analyse and above (see more info about LogMeal Plans)
• User type: 🔴 APIUser (see more info about User Types)
• Client library for HTTP (e.g., Python requests, Node axios, Java OkHttp)
• 1–2 sample food images for testing

Understanding Segmentation

Segmentation is the process of dividing a meal image into distinct regions, each representing a detected dish or food item.
This step allows the LogMeal API to identify multiple dishes within the same photo and generate separate top-N dish predictions for each region.

For example, a single photo containing pasta, salad, and bread will be segmented into three regions — one per food type — so each can be recognized and confirmed individually.
The segmentation step is performed automatically when calling POST /image/segmentation/complete.

ℹ️

In this documentation, we may use the terms “segmentation” and “detection” interchangeably, as both refer to identifying and isolating food regions within an image.

Sequence Diagrams

Main Flow (Food Segmentation + Candidate Dishes Confirmation)

sequenceDiagram
  participant User
  participant App
  participant LogMeal API

  User->>App: Capture/choose meal photo
  App->>LogMeal API: POST /v2/image/segmentation/complete <br> (Authorization: APIUser token, image)
  LogMeal API-->>App: 200 OK (imageId, segmentation results)
  App-->>User: Show top 5 candidate dishes per detected item
  User->>App: Confirm/adjust dish per item
  App->>LogMeal API: POST /v2/image/confirm/dish (imageId, confirmed items)
  LogMeal API-->>App: 200 OK (confirmed items)
  App-->>User: Saved ✅

Fallback Flow (If Something Wasn’t Detected)

sequenceDiagram
  participant User
  participant App
  participant LogMeal API

  App->>User: "Missing something? Search to add it."
  App->>LogMeal API: GET /v2/dataset/dishes (query)
  LogMeal API-->>App: 200 OK (complete list of existing dishes in the DB)
  User->>App: Search a dish in the list and select it
  App->>LogMeal API: POST /v2/image/confirm/dish (imageId, added dish)
  LogMeal API-->>App: 200 OK (updated items)

Implementation Guide

Implementation Examples (Recipes)

See dedicated Recipes for runnable code and JSON response examples:

1. Set profile data once to improve label language and regional dishes:

2. Send the image for segmentation to obtain regions and top dish candidates per region. Display the top 5 candidate dishes per region.

3. Ask the user to confirm or correct the dishes appearing on the image:

4. If a user reports a missing item or a wrong recognition, search the dataset and confirm the chosen dish into the target region:

User Interaction Recommendations

Recommendations:

• Show top-N candidates for each item/food region.
• Let users confirm or adjust before saving.
• Fallback search if item not detected.
• Use thumbnails for bounding boxes or segmented regions to help users identify dishes.
• Use processed_image_size to map bounding boxes to your displayed image size.

Here is an example on how we recommend displaying the suggested food items to the user for confirmation:

Related Endpoints

• POST /image/segmentation/complete
• POST /image/confirm/dish
• GET /dataset/dishes
• POST /profile/modifyUserProfileInfo

Remember to check applicable request limitations inside each of the endpoints.

Common Pitfalls & Tips

• Show candidates to users: human confirmation materially improves accuracy and downstream trust.
• Scaling segments: use processed_image_size to map bounding boxes to your displayed image size.
• Internationalization: set language for labels; set country for regional dishes.
• Error handling: manage 413 (payload too large) and 429 (rate limits). See Plans & Limits.

Optional Enhancements

• After confirmation, call ingredient endpoints to extract list of ingredients and quantities and/or nutritional endpoints to extract macros/micros.
• Persist user corrections.
• Track latency and add a background upload with a progress bar.

Next Steps

Pick your path based on what you want to build next: