Face completion with a Multi-output Estimators in Scikit Learn

Q: What is Face completion with a Multi-output Estimators in Scikit Learn?

In this article, we will learn Face completion with a Multi-output Estimators in Scikit Learn,This free Python tutorial for complete beginners will help you learn Python from scratch.

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Face completion is a fascinating application of machine learning where the goal is to predict missing parts of an image, typically the face, using the existing data. Scikit-learn provides multi-output estimators which are useful for this kind of task. This post is a step-by-step tutorial on how to perform face completion using multi-output estimators in Scikit-learn.

Table of Content

Step-by-Step Tutorial: Face Completion with Multi-output Estimators in Scikit-learn

1. Import Libraries
2. Load and Prepare the Data
3. Create Training and Test Sets
4. Mask Parts of the Images
5. Train the Multi-output Regressor
6. Predict the Missing Parts
7. Visualize the Results

Complete Code for Face completion with a Multi-output Estimators in Scikit Learn

Output

The challenge of face completion in computer vision involves predicting and reconstructing the missing portions of a facial picture. Applications like picture editing, augmented reality, and recovering old photos may all benefit greatly from this. Multi-output estimators, a kind of machine learning, allow us to build models that can handle many target variables at once, which makes them appropriate for applications such as face completion.

Step-by-Step Tutorial: Face Completion with Multi-output Estimators in Scikit-learn

1. Import Libraries

First, import the necessary libraries.

Python

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.model_selection import train_test_split
from sklearn.multioutput import MultiOutputRegressor
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error

Explanation

NumPy: Used for numerical operations.
Matplotlib: Used for plotting images.
Scikit-learn datasets: To fetch the Olivetti faces dataset.
Scikit-learn model selection: For splitting the data into training and testing sets.
Multi-output estimators: For handling multi-output regression.
Ridge regression: Used as the base estimator.
Metrics: To calculate the mean squared error.

2. Load and Prepare the Data

Load the Olivetti faces dataset.

Python

# Load the dataset
faces = fetch_olivetti_faces()
X = faces.data
y = faces.target
images = faces.images

# Display a sample face
plt.imshow(images[0], cmap='gray')
plt.title("Sample Face")
plt.show()

Explanation

fetch_olivetti_faces(): Fetches the Olivetti faces dataset.
X: Contains the flattened face images.
y: Contains the target labels (not used in this task).
images: Contains the face images in their original 2D shape (64×64 pixels).
imshow: Displays a sample face for visual confirmation.

3. Create Training and Test Sets

Split the dataset into training and test sets.

Python

# Split the data into training and testing sets
X_train, X_test, _, _ = train_test_split(X, y, test_size=0.25, random_state=42)

Explanation

train_test_split: Splits the data into training (75%) and testing (25%) sets.
random_state: Ensures reproducibility of the results.

4. Mask Parts of the Images

For face completion, mask parts of the images to create incomplete versions of the faces.

Python

# Mask a random part of the faces
n_pixels = X.shape[1]
mask = np.random.randint(0, 2, (X_train.shape[0], n_pixels), dtype=bool)

X_train_incomplete = X_train.copy()
X_test_incomplete = X_test.copy()

X_train_incomplete[mask] = 0
X_test_incomplete[mask[:len(X_test)]] = 0

# Display a sample masked face
plt.imshow(X_train_incomplete[0].reshape(64, 64), cmap='gray')
plt.title("Sample Incomplete Face")
plt.show()

Explanation

n_pixels: Total number of pixels in each face image (64*64=4096).
mask: Creates a random mask of the same shape as the training data, where each element is either True or False (randomly).
X_train_incomplete and X_test_incomplete: Copies of the training and testing sets where masked pixels are set to zero.
imshow: Displays a sample masked face for visual confirmation.

5. Train the Multi-output Regressor

Train a multi-output regressor using Ridge regression as the base estimator.

Python

# Train the model
model = MultiOutputRegressor(Ridge(alpha=1.0))
model.fit(X_train_incomplete, X_train)

Explanation

MultiOutputRegressor: Allows Ridge regression to handle multiple outputs (pixels in this case).
Ridge(alpha=1.0): Ridge regression model with regularization parameter alpha set to 1.0.
fit: Trains the model using incomplete training data and the original training data as the target.

6. Predict the Missing Parts

Use the trained model to predict the missing parts of the test images.

Python

# Predict the missing parts
X_test_predict = model.predict(X_test_incomplete)

Explanation

predict: Uses the trained model to predict the missing parts of the test images based on the incomplete test images.

7. Visualize the Results

Visualize the original, incomplete, and completed faces. Calculate the mean squared error for evaluation.

Python

# Calculate mean squared error
mse = mean_squared_error(X_test, X_test_predict)
print(f"Mean Squared Error: {mse}")

# Display original, incomplete, and completed faces
n_faces = 5
fig, axes = plt.subplots(n_faces, 3, figsize=(15, 10))

for i in range(n_faces):
    # Original face
    axes[i, 0].imshow(X_test[i].reshape(64, 64), cmap='gray')
    axes[i, 0].set_title("Original")
    axes[i, 0].axis('off')

    # Incomplete face
    axes[i, 1].imshow(X_test_incomplete[i].reshape(64, 64), cmap='gray')
    axes[i, 1].set_title("Incomplete")
    axes[i, 1].axis('off')

    # Completed face
    completed_face = X_test_predict[i].reshape(64, 64)
    axes[i, 2].imshow(completed_face, cmap='gray')
    axes[i, 2].set_title("Completed")
    axes[i, 2].axis('off')

plt.suptitle(f"Mean Squared Error: {mse}")
plt.show()

Explanation

mean_squared_error: Calculates the mean squared error between the original and predicted test images.
print: Outputs the mean squared error for evaluation.
plt.subplots: Creates a grid of subplots to display the faces.
imshow: Displays the original, incomplete, and completed faces.
suptitle: Adds a title to the figure displaying the mean squared error.

Results

After running the code, you should see plots showing the original faces, incomplete faces, and the faces with the missing parts completed by the model. The mean squared error will be displayed to indicate the performance of the model.

Complete Code for Face completion with a Multi-output Estimators in Scikit Learn

Here’s the complete code for the entire process:

Python

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.model_selection import train_test_split
from sklearn.multioutput import MultiOutputRegressor
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error

# Load the dataset
faces = fetch_olivetti_faces()
X = faces.data
y = faces.target
images = faces.images

# Display a sample face
plt.imshow(images[0], cmap='gray')
plt.title("Sample Face")
plt.show()

# Split the data into training and testing sets
X_train, X_test, _, _ = train_test_split(X, y, test_size=0.25, random_state=42)

# Mask a random part of the faces
n_pixels = X.shape[1]
mask = np.random.randint(0, 2, (X_train.shape[0], n_pixels), dtype=bool)

X_train_incomplete = X_train.copy()
X_test_incomplete = X_test.copy()

X_train_incomplete[mask] = 0
X_test_incomplete[mask[:len(X_test)]] = 0

# Display a sample masked face
plt.imshow(X_train_incomplete[0].reshape(64, 64), cmap='gray')
plt.title("Sample Incomplete Face")
plt.show()

# Train the model
model = MultiOutputRegressor(Ridge(alpha=1.0))
model.fit(X_train_incomplete, X_train)

# Predict the missing parts
X_test_predict = model.predict(X_test_incomplete)

# Calculate mean squared error
mse = mean_squared_error(X_test, X_test_predict)
print(f"Mean Squared Error: {mse}")

# Display original, incomplete, and completed faces
n_faces = 5
fig, axes = plt.subplots(n_faces, 3, figsize=(15, 10))

for i in range(n_faces):
    # Original face
    axes[i, 0].imshow(X_test[i].reshape(64, 64), cmap='gray')
    axes[i, 0].set_title("Original")
    axes[i, 0].axis('off')

    # Incomplete face
    axes[i, 1].imshow(X_test_incomplete[i].reshape(64, 64), cmap='gray')
    axes[i, 1].set_title("Incomplete")
    axes[i, 1].axis('off')

    # Completed face
    completed_face = X_test_predict[i].reshape(64, 64)
    axes[i, 2].imshow(completed_face, cmap='gray')
    axes[i, 2].set_title("Completed")
    axes[i, 2].axis('off')

plt.suptitle(f"Mean Squared Error: {mse}")
plt.show()

This complete code will load the Olivetti faces dataset, mask parts of the faces, train a multi-output regressor to predict the missing parts, and display the results with the mean squared error

Output

Conclusion

A useful method for reconstructing missing portions of face photographs is face completion, which makes use of multi-output estimators in Scikit-Learn. We can efficiently anticipate and fill in missing pixels, restoring the images to their original state, by utilizing models that can manage numerous target variables. By experimenting with various models and preprocessing methods to increase the precision and caliber of the final photos, this strategy may be further refined.

Tags:

#Python scikit-module #AI-ML-DS #Machine Learning #Machine Learning

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