I'm implementing the LBP characteristics for images with Python. I'm using it for clasifying pedestrians but I don't obtain good results and it lasts too long because of for loops nested (about 3 hours).
Can anyone see a mistake in the implementation?
And any suggestions for make it more efficient?
The function is this:
I'm using blocks of 16x16 and moving it 8 pixels.
def LBP_img_basic(img):
size2, size1 = img.shape
numbers = []
dx = 8
dy = 8
cell_x = 16
cell_y = 16
x = 0
y = 0
hist_list = []
while y + cell_y <= size2:
for i in range(y, y + cell_y):
for j in range(x, x + cell_x):
hood = np.zeros((3,3), dtype = int)
if j == 0 and i == 0:
hood[1:3, 1:3] = img[i:i+2, j:j+2]
elif i == 0 and j == size1 - 1:
hood[1:3, 0:2] = img[i:i+2, j-1:j+1]
elif j == 0 and i == size2 - 1:
hood[0:2, 1:3] = img[i-1:i+1, j:j+2]
elif i == size2 - 1 and j == size1 - 1:
hood[0:2, 0:2] = img[i-1 : i+1, j-1:j+1]
elif i == 0:
hood[1:3,0:3] = img[i:i+2, j-1:j+2]
elif j == 0:
hood[0:3, 1:3] = img[i-1:i+2, j:j+2]
elif i == size2 - 1:
hood[0:2, 0:3] = img[i-1 : i+1, j-1:j+2]
elif j == size1 - 1:
hood[0:3, 0:2] = img[i-1 : i+2, j-1:j+1]
else:
hood = img[i-1 : i+2, j-1:j+2]
ordered_hood = np.concatenate((hood[0], [hood[1,2], hood[2,2], hood[2,1], hood[2,0], hood[1,0]]))
for k in range(len(ordered_hood)):
if ordered_hood[k] < hood [1,1]:
ordered_hood[k] = 0
else:
ordered_hood[k] = 1
binary = ""
for digit in ordered_hood:
binary += str(digit)
integer = int(binary, 2)
numbers.append(integer)
hist = np.zeros(256)
for l in numbers:
hist[l] += 1
hist_list = np.concatenate((hist_list, hist))
if x + dx + cell_x > size1:
x = 0
y = y + dy
else:
x = x + dx
return hist_list
