CalorieMe-aws/Test.py at main · MuhammadS25/CalorieMe-aws · GitHub

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import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow
import os
import Food_Model_Load
import sys
from ID_segmentation import getIdCard
# sys.path.insert(1, 'yolov5')
# from detect import run

def getFoodWeight(foodImgPath='',id_pixel_count=0):
    # imgpath = 'Images/mid2.png'

    # #initiating ID Model
    # ID_modelpath = 'ID_card Model/unet_model_whole_100epochs.h5'

    # idModel = ID_Model_Load.IdModel(ID_modelpath, imgpath)
    # id_pixel_count = idModel.predict()
    # print("ID Pixels",id_pixel_count)


    #initiating Food Model
    Food_modelpath = 'Food_Model/cp2.h5'
    foodModel= Food_Model_Load.FoodModel(Food_modelpath)
    model = foodModel.loadmodel()
    image, ah, aw = foodModel.read_image("Food_Model/img.jpg")
    mask = foodModel.get_mask(image, model, ah, aw)
    print(mask.shape)
    cat_values = np.unique(mask)

    labels = {}
    categories = {}

    with open('category.txt', 'r') as f:
        categories = dict(enumerate(f.read().splitlines()))

    #Id card real dimensions in cm
    id_card_width = 8.56
    id_card_height = 5.398
    Density = 1.38

    print(cat_values)
    foodWhite_pixels = 0
    for cat in cat_values:
        if cat == 0:
            continue

        pixels = np.count_nonzero(mask == cat)
        Reference_Volume = id_card_height * id_card_width * 0.1
        Food_Size = (pixels / int(id_pixel_count)) * id_card_height * id_card_width
        Food_Weight = Food_Size**3 * Density / Reference_Volume

        labels[categories[cat-1]] = Food_Weight
        print("Pixels of ", categories[cat-1], pixels)
        foodWhite_pixels = max(foodModel.getSizeOfMask(mask, cat),foodWhite_pixels)

    print("Food Pixels",foodWhite_pixels)

    return labels


def getFoodWeightV2(conf, ref_pixels):

    if os.path.exists('yolov5/runs/detect/exp/'):
        os.system('rm -r yolov5/runs/detect/*')


    if not os.path.exists('Food_Model/img.jpg'):
        print("Image not found")
        return
    os.system('python3 yolov5/detect.py --source Food_Model/img.jpg --weights Food_Model/yolov5_best_2.pt --img 413 --augment --save-txt --conf-thres {}'.format(conf))
    # run(source = "Food_Model/img.jpg", weights = "Food_Model/yolov5_best_2.pt", imgsz= (413,413), save_txt= True, augment= True, conf_thres= conf)

    modelpath = 'Food_Model/cp2.h5'
    yolo_dir = 'yolov5'
    foodmodel= Food_Model_Load.FoodModel(modelpath)

    model = foodmodel.loadmodel()
    image, ah, aw = foodmodel.read_image("Food_Model/img.jpg")
    mask = foodmodel.get_mask(image, model, ah, aw)

    # check if the image has any food in it
    if not os.path.exists('{}/runs/detect/exp/labels/img.txt'.format(yolo_dir)):
        return

    bbox = foodmodel.read_bbox_file('{}/runs/detect/exp/labels/img.txt'.format(yolo_dir))
    mask = foodmodel.match_mask_with_bbox(mask, bbox, ah, aw)

    img = tensorflow.image.resize(image, (ah, aw), method='nearest')
    img = img[0].numpy()

    if foodmodel.get_cat_percentage(mask, 101) >= 2:
        return

    #Id card real dimensions in cm
    id_card_width = 8.57
    id_card_height = 5.4
    actual_ref_size = id_card_height * id_card_width

    labels = {}
    categories = {}

    with open('category.txt', 'r') as f:
        categories = dict(enumerate(f.read().splitlines()))

    cat_values = np.unique(mask)
    print(cat_values)
    foodWhite_pixels = 0

    for cat in cat_values:
        if cat == 0:
            continue
        white_pixels_percentage = foodmodel.get_cat_percentage(mask, cat)
        print("percentage of category {}: {}%".format(cat, white_pixels_percentage))

        if white_pixels_percentage < 2:
            continue

        pixels = np.count_nonzero(mask == cat)
        Food_Weight = (pixels * actual_ref_size) / ref_pixels

        print("Food Weight", Food_Weight)
        labels[categories[cat-1]] = Food_Weight
        print("Pixels of ", categories[cat-1], pixels)
        foodWhite_pixels = max(foodmodel.getSizeOfMask(mask, cat),foodWhite_pixels)

    print("label", labels)
    return labels