trainmodel | Cell 27 | Cell 29 | Search

The code appears to involve loading an image, preprocessing it using an unknown function ef, and passing it through a deep learning model model to make a prediction. The prediction is then printed, and the preprocessed image is reshaped and displayed using matplotlib.

Cell 28

image = 'images/train/disgust/299.jpg'
print("original image is of disgust")
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')

What the code could have been:

import numpy as np
import matplotlib.pyplot as plt

# Image Path
image_path = 'images/train/disgust/299.jpg'

# Load Image
def load_image(image_path):
    """
    Loads an image from a file path.
    
    Args:
        image_path (str): Path to the image file.
    
    Returns:
        np.ndarray: Loaded image.
    """
    # TODO: implement image loading using a library like OpenCV
    return ef(image_path)

# Process Image
def process_image(img):
    """
    Reshapes the image to the required size.
    
    Args:
        img (np.ndarray): Input image.
    
    Returns:
        np.ndarray: Processed image.
    """
    return img.reshape(48, 48)

# Predict Facial Expression
def predict_expression(img):
    """
    Uses the model to predict the facial expression.
    
    Args:
        img (np.ndarray): Input image.
    
    Returns:
        int: Prediction index.
    """
    return model.predict(img)

# Get Prediction Label
def get_label(prediction):
    """
    Maps the prediction index to a label.
    
    Args:
        prediction (int): Prediction index.
    
    Returns:
        str: Prediction label.
    """
    return label[prediction.argmax()]

# Display Image
def display_image(img):
    """
    Displays the image using matplotlib.
    
    Args:
        img (np.ndarray): Input image.
    """
    plt.imshow(img, cmap='gray')
    plt.show()

# Main Function
def main():
    # Load Image
    img = load_image(image_path)
    
    # Process Image
    img = process_image(img)
    
    # Predict Facial Expression
    pred = predict_expression(img)
    
    # Get Prediction Label
    pred_label = get_label(pred)
    
    # Display Image
    display_image(img)
    
    print("Original image is of disgust")
    print("Model prediction is", pred_label)

if __name__ == "__main__":
    main()

Code Breakdown

Importing Libraries

No imports are shown in the provided code, but the following libraries are likely being used:

Variables and Functions

Code Flow

  1. Load an image file from the specified path.
  2. Print a message indicating that the original image is of the disgust category.
  3. Preprocess the image with the ef function (unknown implementation).
  4. Pass the preprocessed image to the model for prediction.
  5. Get the prediction label corresponding to the highest confidence score.
  6. Print the predicted label.
  7. Reshape the preprocessed image to a 48x48 grayscale image and display it using matplotlib.