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.

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: