Sobel filter
として畳み込み
def conv_filter(img, stride, padding, kernel):
h, w = img.shape
f, f = kernel.shape
n_h = int(int(h + 2 * padding - f) / stride + 1)
n_w = int(int(w + 2 * padding - f) / stride + 1)
z = np.zeros([n_h, n_w])
img_pad = zero_pad(img, padding)
for h in range(n_h):
vertical_start = stride * h
verttical_end = vertical_start + f
for w in range(n_w):
horizontal_start = stride * w
horizontal_end = horizontal_start + f
target = img_pad[vertical_start:verttical_end, horizontal_start:horizontal_end]
conv = np.multiply(target, kernel)
conv_sum = np.sum(conv)
z[h, w] = conv_sum
return z