main.py

import os
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from PIL import Image

cifar10 = tf.keras.datasets.cifar10
(training_images, training_labels), (testing_images, testing_labels) = cifar10.load_data()
training_images, testing_images = training_images / 255, testing_images / 255

class_names = ['Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']

# for i in range(16):
#     plt.subplot(4,4,i+1)
#     plt.xticks([])
#     plt.yticks([])
#     plt.imshow(training_images[i], cmap=plt.cm.binary)
#     plt.xlabel(class_names[training_labels[i][0]])
#
# plt.show()

# training_images = training_images[:20000]
# training_labels = training_labels[:20000]
# testing_images = testing_images[:4000]
# testing_labels = testing_labels[:4000]
#
# model = tf.keras.models.Sequential()
# model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
# model.add(tf.keras.layers.MaxPooling2D((2, 2)))
# model.add(tf.keras.layers.Conv2D(64, (3,3), activation='relu'))
# model.add(tf.keras.layers.MaxPooling2D((2,2)))
# model.add(tf.keras.layers.Conv2D(64, (3,3), activation='relu'))
# model.add(tf.keras.layers.Flatten())
# model.add(tf.keras.layers.Dense(64, activation='relu'))
# model.add(tf.keras.layers.Dense(10, activation='softmax'))
#
# model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
#
# model.fit(training_images, training_labels, epochs=10, validation_data=(testing_images, testing_labels))
#
# loss, accuracy = model.evaluate(testing_images, testing_labels)
# print(f"Loss: {loss}")
# print(f"Accuracy: {accuracy}")
#
# model.save('image_recog_model1.model')

model2 = tf.keras.models.load_model('image_recog_model2.model')

image_number = 1
while os.path.isfile(f"images/{image_number}.jpg"):
    try:
        if os.path.exists(f"resized_img_{image_number}.jpg"):
            img = cv.imread(f"resized_img_{image_number}.jpg")
            img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
            prediction2 = model2.predict(np.array([img]) / 255)
            index2 = np.argmax(prediction2)
            print(f"This photo/image is probably a {class_names[index2]}")
            plt.imshow(img, cmap=plt.cm.binary)
            plt.show()
        else:
            resized_img = Image.open(f"images/{image_number}.jpg")
            if resized_img.width == 32 and resized_img.height == 32:
                img = cv.imread(f"images/{image_number}.jpg")
                img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
                prediction2 = model2.predict(np.array([img]) / 255)
                index2 = np.argmax(prediction2)
                print(f"This photo/image is probably a {class_names[index2]}")
                plt.imshow(img, cmap=plt.cm.binary)
                plt.show()
            else:
                resized_img = resized_img.resize((32, 32))
                resized_img.save(f"resized_img_{image_number}.jpg")
                img = cv.imread(f"resized_img_{image_number}.jpg")
                img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
                prediction2 = model2.predict(np.array([img]) / 255)
                index2 = np.argmax(prediction2)
                print(f"This photo/image is probably a {class_names[index2]}")
                plt.imshow(img, cmap=plt.cm.binary)
                plt.show()
    finally:
        image_number += 1