/Food-Calories-Estimation-Using-Image-Processing

Primary LanguagePythonThe UnlicenseUnlicense

Food Calories Estimation Using Image Processing

python

Vinayak Sable

  • Problem

The problem can be simply stated as, given a set of food images with calibration object thumb with the food name and an unlabeled set of food images from the same group of food, identify food and estimate food volume and calories intake.

  • Objectives

  1. To detect food type by using Convolutional Neural Network (CNN)
  2. To estimate food weight and calories of food

  • Data collection

For this project I used two datasets:

  1. FOODD
  2. ECUST Food Dataset (ECUSTFD)

In this project I used 7 food items like apple, banana, carrot, cucumber, onion, orange and tomato which details given in table below

Food type

Fruits Density Calorie Label Shape
Apple 0.609 52 1 Sphere
Banana 0.94 89 2 Cylinder
Carrot 0.641 41 3 Cylinder
Cucumber 0.641 16 4 Cylinder
Onion 0.513 40 5 Sphere
Orange 0.482 47 6 Sphere
Tomato 0.481 18 7 Sphere

Sample food images in dataset:

python

Recognition method

Food Recognition deals with recognition of food item when given an image. For this problem I used Convolutional Neural Network (CNN). The Architecture of CNN given below figure python

all this work done in cnn.py file change the directory to food-calories-estimation-using-Image-processing-master folder and give sufficient information to cnn.py python file and run

Model representation

python

Accuracy

python

Loss

python

Requirements

  • Windows 10 Pro CPU
  • Anaconda Distribution 4.6.11
  • Python
  • Tensorflow
  • tflearn

Testing

Google colab link for testing click here for testing our model run

python demo.py

Training

Download data from above FOODD link and create forlder in repo FOODD and run

python train.py

Estimation Method:

  • Image Segmentation:

A mixture of methods including canny edge detection, watershed segmentation, morphological operators and Otsu’s method were used to segment the food item to obtain the contour of the fruit and the contour of the thumb. We use the thumb finger for calibration purposes. The thumb is placed next to the dish while clicking the photo and this thumb gives us the estimate of the real-life size of the food item and helps estimate volume accurately.

all this done image_segment.py and calorie.py

Result

Fruits Calorie Estimated Calories
Apple 53.96 40.42
Banana 170.88 188.81
Carrot 31.16 26.28
Cucumber 29.44 37.65
Onion 44.88 37.13
Orange 69.09 71.92
Tomato 17.46 13.82

Limitation and Scope

  • Limitations:

    1. Actual weight and calories can’t find due to image quality
    2. Difficult to find appropriate angle between fruit And camera
    3. Lighting condition i.e pixel changes with respect to light

  • Scope:

    1. Estimate the calorie from all types of fruits.
    2. Minimize error of calories estimation

Reference:

  1. P.Pouladzadeh, S.Shirmohammadi, and R.Almaghrabi, “Measuring Calorie and Nutrition from Food Image”, IEEE Transactions on Instrumentation & Measurement, Vol.63, No.8, p.p. 1947 – 1956, August 2014.

  2. Parisa Pouladzadeh, Abdulsalam Yassine, and Shervin Shirmohammadi, “Foodd: An image-based food detection dataset for calorie measurement,” in InternationalConferenceonMultimediaAssistedDietaryManagement, 2015

  3. Meghana M Reddy, “Calorie-estimation-from-food-images-opencv”, Git repo , May 2016

@vinayak What do you think about these ?