不確定這是否是正確的術語。基本上嘗試拍攝黑白影像并首先對其進行轉換，以使與黑色像素相鄰的所有白色像素保持白色，否則變為黑色。該程式的那部分作業正常，并在 find_edges 中完成。接下來我需要計算影像中每個元素到最近的白色像素的距離。現在我正在使用一個非常慢的 for 回圈來做到這一點。有沒有辦法讓 find_nearest_edge 函式只用 numpy 撰寫，而不需要 for 回圈在每個元素上呼叫它？謝謝。
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from PIL import Image
import numpy as np
from scipy.ndimage import binary_erosion
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def find_nearest_edge(arr, point):
w, h = arr.shape
x, y = point
xcoords, ycoords = np.meshgrid(np.arange(w), np.arange(h))
target = np.sqrt((xcoords - x)**2 (ycoords - y)**2)
target[arr == 0] = np.inf
shortest_distance = np.min(target[target > 0.0])
return shortest_distance
def find_edges(img):
img = img.convert('L')
img_np = np.array(img)
kernel = np.ones((3,3))
edges = img_np - binary_erosion(img_np, kernel)*255
return edges
a = Image.open('a.png')
x, y = a.size
edges = find_edges(a)
out = Image.fromarray(edges.astype('uint8'), 'L')
out.save('b.png')
dists =[]
for _x in range(x):
for _y in range(y):
dist = find_nearest_edge(edges,(_x,_y))
dists.append(dist)
print(dists)
圖片：
uj5u.com熱心網友回復：
您可以使用 KDTree 快速計算距離。
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import binary_erosion
from scipy.spatial import KDTree
def find_edges(img):
img_np = np.array(img)
kernel = np.ones((3,3))
edges = img_np - binary_erosion(img_np, kernel)*255
return edges
def find_closest_distance(img):
# NOTE: assuming input is binary image and white is any non-zero value!
white_pixel_points = np.array(np.where(img))
tree = KDTree(white_pixel_points.T)
img_meshgrid = np.array(np.meshgrid(np.arange(img.shape[0]), np.arange(img.shape[1]))).T
distances, _ = tree.query(img_meshgrid)
return distances
test_image = np.zeros((200, 200))
rectangle = np.ones((30, 80))
test_image[20:50, 60:140] = rectangle
test_image[150:180, 60:140] = rectangle
test_image[60:140, 20:50] = rectangle.T
test_image[60:140, 150:180] = rectangle.T
test_image = test_image * 255
edge_image = find_edges(test_image)
distance_image = find_closest_distance(edge_image)
fig, axes = plt.subplots(1, 3, figsize=(12, 5))
axes[0].imshow(test_image, cmap='Greys_r')
axes[1].imshow(edge_image, cmap='Greys_r')
axes[2].imshow(distance_image, cmap='Greys_r')
plt.show()
uj5u.com熱心網友回復：
find_nearest_edge您可以通過如下更改使您的代碼速度提高 25 倍。許多其他優化是可能的，但這是代碼中最大的瓶頸。
from numba import njit
@njit
def find_nearest_edge(arr, point):
x, y = point
shortest_distance = np.inf
for i in range(arr.shape[0]):
for j in range(arr.shape[1]):
if arr[i,j] == 0: continue
shortest_distance = min(shortest_distance, (i-x)**2 (j-y)**2)
return np.sqrt(shortest_distance)
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