When PCA transform doesn't help much

bad_pca.py

import numpy
import matplotlib.pyplot as plt

from sklearn.decomposition import PCA            
def pca(data):                          
    return PCA(n_components = 4).fit(data).transform(data)

a = numpy.zeros((100,12,12), dtype=numpy.float32)
for i in xrange(10):                             
    for j in xrange(10):
        a[i*10+j,i+1,j:j+3] = 1

b = numpy.zeros((100,12,12), dtype=numpy.float32)
for i in xrange(10):                             
    for j in xrange(10):
        b[i*10+j,i:i+3,j+1] = 1
vs=numpy.vstack([a,b]).reshape(200,12*12)
r=pca(vs)
plt.scatter(r[:100, 0], r[:100, 1], c = 'r')
plt.scatter(r[100:, 0], r[100:, 1], c = 'b')
plt.show()