Card_Predictor.py

import keras
from keras.preprocessing import image
from keras.applications.xception import (Xception, preprocess_input, decode_predictions)
from keras import models
from keras.models import load_model
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense

import numpy as np
import pandas as pd
import os
from os import listdir
from PIL import Image

class_list = ['1DGE',
 '1DGF',
 '1DGS',
 '1DPE',
 '1DPF',
 '1DPS',
 '1DRE',
 '1DRF',
 '1DRS',
 '1OGE',
 '1OGF',
 '1OGS',
 '1OPE',
 '1OPF',
 '1OPS',
 '1ORE',
 '1ORF',
 '1ORS',
 '1SGE',
 '1SGF',
 '1SGS',
 '1SPE',
 '1SPF',
 '1SPS',
 '1SRE',
 '1SRF',
 '1SRS',
 '2DGE',
 '2DGF',
 '2DGS',
 '2DPE',
 '2DPF',
 '2DPS',
 '2DRE',
 '2DRF',
 '2DRS',
 '2OGE',
 '2OGF',
 '2OGS',
 '2OPE',
 '2OPF',
 '2OPS',
 '2ORE',
 '2ORF',
 '2ORS',
 '2SGE',
 '2SGF',
 '2SGS',
 '2SPE',
 '2SPF',
 '2SPS',
 '2SRE',
 '2SRF',
 '2SRS',
 '3DGE',
 '3DGF',
 '3DGS',
 '3DPE',
 '3DPF',
 '3DPS',
 '3DRE',
 '3DRF',
 '3DRS',
 '3OGE',
 '3OGF',
 '3OGS',
 '3OPE',
 '3OPF',
 '3OPS',
 '3ORE',
 '3ORF',
 '3ORS',
 '3SGE',
 '3SGF',
 '3SGS',
 '3SPE',
 '3SPF',
 '3SPS',
 '3SRE',
 '3SRF',
 '3SRS']

model_path = 'static/model/final.h5'
image_size = (150, 150)
folderpath = 'static/images/cropped'

#Create Model and Load Weights

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(81))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.load_weights(model_path)

# Predict the image

def predictor(image_path):
    img = image.load_img(image_path, target_size=image_size)
    x = image.img_to_array(img)
    # print(x.shape)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    predictions = model.predict(x)
    # print(max(predictions[0]))
    list_a = predictions[0]
    list_b = class_list
    zip_test = list(zip(list_a, list_b))
    return max(zip_test)

def find_cropped_images(folderpath):
    images = [folderpath + '/' + v for v in os.listdir(folderpath) if v != '.DS_Store']
    return images

def loadImages(imagepath):
    imagesList = imagepath
    loadedImages = []
    for image in imagesList:
        img = Image.open(image)
        loadedImages.append(img)
    return loadedImages


def prediction_tuples():
     predictions = [predictor(png) for png in find_cropped_images(folderpath)]
     return predictions

print(prediction_tuples())