FoodGenie/data.py at main · bh4vya/FoodGenie · GitHub

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import os
import cv2
import numpy as np
from numpy import save

classNames = ["banana", "apple", "pear", "grapes", "orange", "kiwi", "watermelon",
             "pomegranate", "pineapple", "mango", "cucumber", "carrot", "capsicum", "onion", "potato", "lemon", "tomato", "raddish",
             "beetroot", "cabbage", "lettuce", "spinach", "soy beans", "cauliflower", "bell pepper","chilli pepper",
             "turnip", "corn", "sweetcorn", "sweetpotato", "paprika", "jalepeno", "ginger", "garlic", "peas", "eggplant"]

#apple, bell pepper, cabbage, capsicum, carrot, cauliflower, chilli pepper, corn, cucumber, eggplant
#garlic, ginger, grapes, jalepeno, kiwi, lemon, lettuce, mango, onion, orange, paprika, pear,
#peas, pineapple, pomegranate, potato, raddish, soy beans, spinach, sweetcorn, sweetpotato
#tomato, turnip, watermelon,

folder_path = 'c:/tempFolder'

if not os.path.exists(folder_path):
    os.makedirs(folder_path)

trainDataArray = []

trainFeaturesArray = []

print("loading the train data")

imgDir = "C:/Users/pra_d/OneDrive/Desktop/Anmol/Projects/WebDev/repos/OpticGenie/archive/train"

for subDirectory, directory, files in os.walk(imgDir):

    for theFile in files:
        theImg = cv2.imread(os.path.join(subDirectory, theFile))

        if(theImg is None):
            print("invalid image")
        else:
            print(subDirectory, theFile)

            resizedImage = cv2.resize(theImg,(28,28), interpolation=cv2.INTER_AREA)
            checkTheSize = resizedImage.shape[0]

            if(checkTheSize==28):
                trainDataArray.append(resizedImage)
                theIndex = classNames.index(os.path.basename(subDirectory))
                trainFeaturesArray.append(theIndex)

trainData = np.array(trainDataArray)
trainFeatures = np.array(trainFeaturesArray)

print("Finished loading.")
print("Number of training records: ", trainData.shape[0])

print(trainData.shape)
print(trainFeatures.shape)


# testImage = trainData[4]
# cv2.imshow("Test image", testImage)
# theIndex2 = trainFeatures[4]
# print(classNames[theIndex2])

# testImage2 = trainData[5]
# cv2.imshow("Test image", testImage2)
# theIndex3 = trainFeatures[5]
# print(classNames[theIndex3])

# cv2.waitKey(0)

save('c:/tempFolder/trainData.npy', trainData)
save('c:/tempFolder/trainFeatures.npy', trainFeatures)


imgDir = "C:/Users/pra_d/OneDrive/Desktop/Anmol/Projects/WebDev/repos/OpticGenie/archive/test"

testDataArray = []

testFeaturesArray = []

largeTestDataArray = []

for subDirectory, directory, files in os.walk(imgDir):

    for theFile in files:
        theImg = cv2.imread(os.path.join(subDirectory, theFile))

        if(theImg is None):
            print("invalid image")
        else:
            print(subDirectory, theFile)
            largeResizedImage = resizedImage = cv2.resize(theImg,(280,280), interpolation=cv2.INTER_AREA)

            resizedImage = cv2.resize(theImg,(28,28), interpolation=cv2.INTER_AREA)
            checkTheSize = resizedImage.shape[0]

            if(checkTheSize==28):
                testDataArray.append(resizedImage)
                largeTestDataArray.append(largeResizedImage)
                theIndex = classNames.index(os.path.basename(subDirectory))
                testFeaturesArray.append(theIndex)

testData = np.array(testDataArray)
largeTestData = np.array(largeTestDataArray)
testFeatures = np.array(testFeaturesArray)

print("Finished loading test data")
print("Number of test records: ", testData.shape[0])
print(testData.shape)
print(testFeatures.shape)

save('c:/tempFolder/testData.npy', testData)
save('c:/tempFolder/largeTestData.npy', largeTestData)
save('c:/tempFolder/testFeatures.npy', testFeatures)