opencv_test.txt

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np

import tensorflow as tf

from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

cap = cv2.VideoCapture(0)
ret,frame1 = cap.read()
print(frame1)

#train_data
cap.release()
#cv2.destroyAllWindows()

cap = cv2.VideoCapture(0)
ret,frame2 = cap.read()
print(frame2)
cap.release()
cv2.destroyAllWindows()

cap = cv2.VideoCapture(0)
ret,frame3 = cap.read()
print(frame3)
cap.release()
cv2.destroyAllWindows()


#model
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()

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

history = model.fit(P, train_labels, epochs=10, 
                    validation_data=(test_images, test_labels))