我已经达到了这个双线性插值代码(在此处添加),但我想将此代码改进为 3D,这意味着将其更新为使用 RGB 图像(3D,而不仅仅是 2D)。
如果您有任何建议,我很想知道。
这是一维线性插值:
import math
def linear1D_resize(in_array, size):
"""
`in_array` is the input array.
`size` is the desired size.
"""
ratio = (len(in_array) - 1) / (size - 1)
out_array = []
for i in range(size):
low = math.floor(ratio * i)
high = math.ceil(ratio * i)
weight = ratio * i - low
a = in_array[low]
b = in_array[high]
out_array.append(a * (1 - weight) + b * weight)
return out_array
这对于 2D:
import math
def bilinear_resize(image, height, width):
"""
`image` is a 2-D numpy array
`height` and `width` are the desired spatial dimension of the new 2-D array.
"""
img_height, img_width = image.shape[:2]
resized = np.empty([height, width])
x_ratio = float(img_width - 1) / (width - 1) if width > 1 else 0
y_ratio = float(img_height - 1) / (height - 1) if height > 1 else 0
for i in range(height):
for j in range(width):
x_l, y_l = math.floor(x_ratio * j), math.floor(y_ratio * i)
x_h, y_h = math.ceil(x_ratio * j), math.ceil(y_ratio * i)
x_weight = (x_ratio * j) - x_l
y_weight = (y_ratio * i) - y_l
a = image[y_l, x_l]
b = image[y_l, x_h]
c = image[y_h, x_l]
d = image[y_h, x_h]
pixel = a * (1 - x_weight) * (1 - y_weight) + b * x_weight * (1 - y_weight) + c * y_weight * (1 - x_weight) + d * x_weight * y_weight
resized[i][j] = pixel # pixel is the scalar with the value comptued by the interpolation
return resized