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Readme.md

Lab_1

What you will learn :

  • Image Filtering
  • Image PCA & Classification

Directory Tree

|__Lab1
|  |__ Readme.md
|  |__ Face_dataset/
|  |__ fig.jpg

Requirement

You can only use the python packages below:

  • numpy
  • cv2 (opencv)
  • matplotlib
  • sklearn
  • math

Evaluation

For your output image, you could use eval.py to check your answer.

$ python3 eval.py --gt GT/1-1/fig_grad_x.jpg --out output/1-1/fig_grad_y.jpg

It will show that you "PASS!" or "FAIL." this question.

1. Image Filtering

  1. Load "fig.jpg" with gray scale.
  2. Compute and save the results of gradient generated by horizontal Sobel filter and vertical Sobel filter as fig_grad_x.jpg and fig_grad_y.jpg (with cv2.filter2D).
  3. Compute and save the gradient magnitude as fig_grad_m.jpg with the results in step 2.

2. Image PCA Analysis

The dataset you need is under Face_dataset/ directory, which contains 56×46 pixel face images of 40 different subjects (classes), and 10 images available for each subject. Note that, i_j.png means personi_imagej . Now you have to split the dataset into two subsets (i.e., training and test sets). The first subset contains the first 6 images of each subject, while the second subset include the remaining images. Thus, a total of 6 × 40 = 240 images are in the training set, while 160 images in the test set.

  1. Perform PCA on the training set. Save the mean face and the first three eigenfaces.

    • save to [mean.png], [1.png], [2.png], [3.png]
    • Be careful the values in eigenvectors after PCA ! (sklearn.decomposition.PCA, you don't need to specify n_components value)
    • Bonus: you can implement your own PCA function !!
    • hint: 　255 * (shifted x/ max(shifted x))

  2. Take 7_2.png, and project it onto the above PCA eigenspace. Reconstruct this image using the first n = 3, 100 eigenfaces. For each n, compute the mean square error (MSE) between the reconstructed face image and 7_2.png. Please save these reconstructed images , [7_2_n3.png] and [7_2_n100.png], and record the corresponding MSE values.

  3. Apply the k-nearest neighbors classifier to recognize test set images. For simplicity, try k={1,3} and n = {3,100}. Report the recognition rate on the test set with different (k,n) pairs.

    • hint: sklearn.neighbors.KNeighborsClassifier()