This code loads an image using OpenCV, makes a prediction on it using a model, and prints the predicted label. The image is then displayed using Matplotlib, reshaped to a 48x48 matrix and mapped to grayscale colors.

Cell 26

image = 'images/train/sad/42.jpg'
print("original image is of sad")
img = ef(image)
pred = model.predict(img)
pred_label = label[pred.argmax()]
print("model prediction is ",pred_label)
plt.imshow(img.reshape(48,48),cmap='gray')

# Import necessary libraries for image processing and visualization

Code Breakdown

Importing Modules (Assumed)

import cv2 as ef  # Assuming 'ef' is an alias for OpenCV
import matplotlib.pyplot as plt

Code

# Load image from file
image = 'images/train/sad/42.jpg'

# Print original image label
print("original image is of sad")

# Load image using OpenCV
img = ef.imread(image)

# Make prediction using a model
pred = model.predict(img)

# Get predicted label
pred_label = label[pred.argmax()]

# Print predicted label
print("model prediction is ", pred_label)

# Display image using Matplotlib
plt.imshow(img.reshape(48,48), cmap='gray')

Notes

• The image is loaded using OpenCV's imread function.
• The model's prediction is made on the original image.
• The predicted label is obtained using the argmax method on the prediction output.
• The image is displayed using Matplotlib's imshow function, reshaped to a 48x48 matrix and with grayscale color mapping.