Vyper 代码实现
在这部分代码中，我们将通过模拟图像数据并应用 Sobel 算子来计算图像的边缘。由于 Vyper 没有直接的图像处理库，我们只能通过二维数组来代表图像，并用简单的循环和数学计算来实现边缘检测。

vyper

Vyper code for edge detection simulation using Sobel operator

Example of a 5x5 grayscale image represented as a 2D array

image: public(map(uint256, map(uint256, uint256)))

Sobel X and Sobel Y kernels

sobel_x: constant(map(uint256, map(uint256, int256))) = {
0: {0: -1, 1: 0, 2: 1},
1: {0: -2, 1: 0, 2: 2},
2: {0: -1, 1: 0, 2: 1}
}

sobel_y: constant(map(uint256, map(uint256, int256))) = {
0: {0: -1, 1: -2, 2: -1},
1: {0: 0, 1: 0, 2: 0},
2: {0: 1, 1: 2, 2: 1}
}

Function to set an example image in the contract storage

@public
def set_image() -> bool:
self.image[0][0] = 100
self.image[0][1] = 100
self.image[0][2] = 100
self.image[0][3] = 100
self.image[0][4] = 100

self.image[1][0] = 100
self.image[1][1] = 255
self.image[1][2] = 255
self.image[1][3] = 255
self.image[1][4] = 100

self.image[2][0] = 100
self.image[2][1] = 255
self.image[2][2] = 0
self.image[2][3] = 255
self.image[2][4] = 100

self.image[3][0] = 100
self.image[3][1] = 255
self.image[3][2] = 255
self.image[3][3] = 255
self.image[3][4] = 100

self.image[4][0] = 100
self.image[4][1] = 100
self.image[4][2] = 100
self.image[4][3] = 100
self.image[4][4] = 100

return True

Function to apply Sobel edge detection

@public
def apply_sobel(x: uint256, y: uint256) -> uint256:
# Calculate gradients in X and Y direction using Sobel operator
gx: int256 = 0
gy: int256 = 0

# Apply Sobel X kernel
for i in range(3):
    for j in range(3):
        gx += self.sobel_x[i][j] * int256(self.image[x + i - 1][y + j - 1])

# Apply Sobel Y kernel
for i in range(3):
    for j in range(3):
        gy += self.sobel_y[i][j] * int256(self.image[x + i - 1][y + j - 1])

# Calculate the gradient magnitude
gradient: uint256 = uint256(sqrt(gx ** 2 + gy ** 2))

# Return gradient as an integer (max 255)
if gradient > 255:
    return 255
return gradient

Function to simulate edge detection on the whole image

@public
def edge_detection() -> bool:
# Apply Sobel filter to every pixel in the image (except for borders)
for i in range(1, 4): # Skip borders
for j in range(1, 4):
# Set the edge detected value back in the image (simulation)
self.image[i][j] = self.apply_sobel(i, j)

return True

步骤解析更多内容访问ttocr.com或联系1436423940
图像数据模拟： 使用 set_image 函数模拟了一个 5x5 的灰度图像，图像中间的像素是较亮的区域，四周是较暗的区域，目的是模拟图像中的边缘。

Sobel 算子： 我们定义了 Sobel X 和 Sobel Y 滤波器，它们分别用于计算水平方向和垂直方向的梯度。这些滤波器应用到图像数据上，帮助我们检测图像的边缘。

梯度计算： apply_sobel 函数计算每个像素在 X 和 Y 方向的梯度，然后计算这些梯度的平方和的平方根，得到最终的边缘强度。

边缘检测： edge_detection 函数遍历图像中的每个像素（排除边缘像素），并应用 Sobel 算子来计算每个像素的梯度。最后，更新图像中每个像素的值，反映边缘检测的结果。