# Transform the images into NumPy arrays (matrices)
wally = np.array(wally_img).astype(float)
the_crowd = np.array(the_crowd_img).astype(float)

# Normalize inputs
wally = (wally / 255.0) * 2 - 1
the_crowd = (the_crowd / 255.0) * 2 - 1

# Get the dimensions of the images
w, h, channels = wally.shape
W, H, _ = the_crowd.shape
n = float(w * h * channels)

# Calculates the crosscorrelation
cross_corr = np.zeros((W - w, H - h))
for x in range(W - w):
    for y in range(H - h):
        m = np.multiply(the_crowd[x:x+w,y:y+h,:], wally)
        s = np.sum(m)
        cross_corr[x,y] = float(s) / n

# Find the maximum crosscorrelation coordinates
wally_pos = np.unravel_index(np.argmax(cross_corr),cross_corr.shape)

# Normalize results
smallest = np.min(cross_corr)
largest = np.max(cross_corr)
cross_corr = (cross_corr - smallest) / (largest - smallest)

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

# Shows the resulting matched position
draw = ImageDraw.Draw(the_crowd_img)
x,y = wally_pos
x = x + w/2 # Adjusting coordinates to point
y = y + h/2 # to the center instead of corner
r = int(np.sqrt(w**2 + h**2))
for i in range(15):
    r = r + 0.5
    draw.ellipse( (y-r, x-r, y+r, x+r) )
ax.imshow(the_crowd_img)

sol = addJBFigure("sol", 0, 0, fig )
plt.close()

def find_template( img, tmpl ):
  img = (img / 255.0) * 2 - 1
  tmpl = (tmpl / 255.0) * 2 - 1
  w, h, channels = tmpl.shape
  W, H, _ = img.shape
  n = float(w * h * channels)

  cross_corr = np.zeros((W - w, H - h))
  for x in range(W - w):
      for y in range(H - h):
          m = np.multiply(img[x:x+w,y:y+h,:], tmpl)
          s = np.sum(m)
          cross_corr[x,y] = float(s) / n

  pos = np.unravel_index(np.argmax(cross_corr),cross_corr.shape)
  return pos