python-kerasHow can I use Python, OpenCV, and Keras together to build a machine learning model?

Python, OpenCV, and Keras can be used together to build a machine learning model. First, you need to load the images and labels into Python using OpenCV. Then, you can use Keras to create a convolutional neural network (CNN). Here is an example of how to do this:

# Load images and labels into Python
import cv2
import numpy as np

# Load labels
labels = np.loadtxt('labels.txt', dtype='str')

# Load images
images = []
for label in labels:
    img = cv2.imread(label + '.jpg')
    images.append(img)

# Create a CNN
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten

model = Sequential()

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

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

model.fit(np.array(images), labels, batch_size=32, epochs=10)

This will train the CNN on the images and labels, and output the accuracy of the model.

Code explanation

1. Load images and labels into Python using OpenCV
2. Create a CNN using Keras
3. Compile the model
4. Train the model on the images and labels

Helpful links

• OpenCV Documentation
• Keras Documentation

Edit this code on GitHub