This code snippet appears to load and preprocess an image, make a prediction using a machine learning model, and then display the original image and print the predicted label. The image is loaded from a specified path, preprocessed using a function ef, and then passed to a model for prediction, which selects a label from a list or dictionary based on the predicted output.
image = 'images/train/happy/7.jpg'
print("original image is of happy")
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')def load_image(image_path):
"""
Loads an image from the given path using PIL.
Args:
image_path (str): Path to the image file.
Returns:
numpy.ndarray: The loaded image as a numpy array.
"""
from PIL import Image
import numpy as np
image = Image.open(image_path)
image = np.array(image)
return image
def preprocess_image(image):
"""
Resizes the image to a fixed size of 48x48.
Args:
image (numpy.ndarray): The image to preprocess.
Returns:
numpy.ndarray: The preprocessed image.
"""
import numpy as np
from PIL import Image
# Reshape the image to 48x48
image = image.reshape(48, 48)
# Convert the image to grayscale
image = Image.fromarray(image).convert('L')
image = np.array(image)
return image
def display_image(image):
"""
Displays the image using matplotlib.
Args:
image (numpy.ndarray): The image to display.
"""
import matplotlib.pyplot as plt
plt.imshow(image, cmap='gray')
plt.show()
def main():
image_path = 'images/train/happy/7.jpg'
print("Original image is of happy")
try:
# Load the image
image = load_image(image_path)
# Preprocess the image
image = preprocess_image(image)
# Load the deep learning model
import torch
model = torch.load('trained_model.pth')
# Make a prediction using the model
import torch.nn as nn
pred = model(image)
pred_label = nn.functional.softmax(pred, dim=1).argmax().item()
print("Model prediction is", label[pred_label])
# Display the image
display_image(image)
except Exception as e:
print(f"An error occurred: {str(e)}")
if __name__ == "__main__":
main()The necessary modules are not imported in this code snippet, but they are likely imported elsewhere in the codebase. The required modules are:
matplotlib.pyplot for displaying images (plt.imshow)numpy or pandas for reshaping the image array (img.reshape)image = 'images/train/happy/7.jpg': Loads an image file path from the specified location.img = ef(image): Calls a function ef with the image path as an argument, but the function name is not descriptive. It likely loads and preprocesses the image for use with a machine learning model.pred = model.predict(img): Passes the preprocessed image to a machine learning model for prediction.pred_label = label[pred.argmax()]: Uses the predicted output to select a label from a list or dictionary called label. The argmax function returns the index of the maximum value in the prediction output.plt.imshow(img.reshape(48,48),cmap='gray'): Displays the original image data in a 48x48 grayscale format using Matplotlib.print("model prediction is ",pred_label): Prints the predicted label to the console.print("original image is of happy"): Prints a message indicating the original image is of a "happy" subject.