1、天际线简介
天际线 （SkyLine）顾名思义就是天空与地面的边界线，人站在不同的高度，会看到不同的景色和地平线，天空与地面建筑物分离的标记线，不得不说，每天抬头仰望天空，相信大家都可以看到，它的的确确客观存在，美丽值得欣赏。
2、Python代码
python
#-*- coding:utf-8 -*-
import sys
from os.path import exists
import cv2
import numpy as np
def getImage(height, width, channels):
image = np.zeros([height, width, 3], np.uint8) # 三通道顺序是BGR
# 三层循环逐个修改像素点
for row in range(height):
for col in range(width):
for c in range(channels):
image[row, col, c] = 0
return image
def isWhite(pixel_value, threshold): #阈值可以取10、20、30、50、100
res = False
if pixel_value[0] > threshold and pixel_value[1] > threshold and pixel_value[2] > threshold: # 10、10、10 50、50、50 这里是天空和地面楼山的分界线，需要调参
res = True
return res
def isPureWhite(pixel_value):
res = False
if pixel_value[0] == 255 and pixel_value[1] == 255 and pixel_value[2] == 255: # >3|>3|>3 10、10、10
res = True
return res
def getRowNumberSpecificCol(image, col):
res_row = -1
height, width = image.shape[0:2]
if col >= 0 and col < width:
for row in range(0, height):
pv = image[row][col]
if(pv[0] > 0 and pv[1] > 0 and pv[2] >0):
res_row = row
break
return res_row
def getEnhancedEdgeImageFromEdgeImage(edge_Image):
edge_SrcImage = edge_Image
height, width = edge_SrcImage.shape[0:2]
for col in range(1, width):
for row in range(0, height):
pixel_value = edge_SrcImage[row][col] # 计算红绿蓝三波段的平均值
if isPureWhite(pixel_value):
r_last = getRowNumberSpecificCol(edge_SrcImage, col - 1)
if r_last:
if row > r_last:
minR, maxR = r_last, row
for k in range(minR, maxR):
edge_SrcImage[k][col - 1][0] = 255
edge_SrcImage[k][col - 1][1] = 255
edge_SrcImage[k][col - 1][2] = 255
else:
minR, maxR = row, r_last
for k in range(minR, maxR):
edge_SrcImage[k][col][0] = 255
edge_SrcImage[k][col][1] = 255
edge_SrcImage[k][col][2] = 255
# cv2.imshow("Enhanced-edge-image", edge_SrcImage)
return edge_SrcImage
def getFileExtensionname(filename):
res = ".png"
dot_index = -1
for i in range(len(filename), 0):
if filename[i] == '.':
dot_index = i
break
if dot_index != -1:
res = filename[dot_index: len(filename)-1]
return res
if __name__ == '__main__':
origin_pic_filename = "D:/test.png"
sky_ground_threshold = 30
isDownSampling = False
if (len(sys.argv) == 1):
print(sys.argv[0])
origin_pic_filename = ""
elif(len(sys.argv) == 2):
origin_pic_filename = str(sys.argv[1])
elif(len(sys.argv) == 3):
origin_pic_filename = str(sys.argv[1])
sky_ground_threshold = int(sys.argv[2])
elif (len(sys.argv) == 4):
origin_pic_filename = str(sys.argv[1])
sky_ground_threshold = int(sys.argv[2])
if(int(sys.argv[3]) == 1):
isDownSampling = True
if origin_pic_filename != "" and sky_ground_threshold > 0:
print(("输入图片文件名为：{0}").format(origin_pic_filename))
print(("天空地面分界灰度阈值为：{0}").format(sky_ground_threshold))
suffix_name = getFileExtensionname(origin_pic_filename)
print(("后缀名为：{0}").format(suffix_name))
srcImage = cv2.imread(origin_pic_filename)
inputSrcImage = srcImage
if isDownSampling:
inputSrcImage = cv2.pyrDown(inputSrcImage)
height, width = inputSrcImage.shape[0:2]
print(("高度：{0}, 宽度：{1}").format(height, width))
cv2.namedWindow('downsampling-image', cv2.WINDOW_AUTOSIZE)
cv2.imshow("downsampling-image", inputSrcImage)
Sobelx = cv2.Sobel(inputSrcImage, cv2.CV_64F, 1, 0)
Sobely = cv2.Sobel(inputSrcImage, cv2.CV_64F, 0, 1)
Sobelx = cv2.convertScaleAbs(Sobelx)
Sobely = cv2.convertScaleAbs(Sobely)
# cv2.imshow("sobel-x-Abs", Sobelx)
# cv2.imshow("sobel-y-Abs", Sobely)
Sobelxy = cv2.addWeighted(Sobelx, 0.5, Sobely, 0.5, 0)
cv2.namedWindow('sobel-xy', cv2.WINDOW_AUTOSIZE)
cv2.imshow('sobel-xy', Sobelxy)
edgeImage = getImage(height, width, 3)
for col in range(0, width):
for row in range(0, height):
pixel_value = Sobelxy[row][col] # 计算红绿蓝三波段的平均值
if isWhite(pixel_value, sky_ground_threshold):
edgeImage[row][col][0] = 255
edgeImage[row][col][1] = 255
edgeImage[row][col][2] = 255
break
cv2.namedWindow('edge-image', cv2.WINDOW_AUTOSIZE)
cv2.imshow('edge-image', edgeImage)
cv2.imwrite(origin_pic_filename.replace(suffix_name, "-ZGetEdge.png"), edgeImage)
enhanced_edgeImage = getEnhancedEdgeImageFromEdgeImage(edgeImage)
cv2.namedWindow('enhanced-edge-image', cv2.WINDOW_AUTOSIZE)
cv2.imshow('enhanced-edge-image', enhanced_edgeImage)
cv2.imwrite(origin_pic_filename.replace(suffix_name, "-EnhancedEdge.png"), enhanced_edgeImage)
for col in range(0, width):
for row in range(0, height):
pixel_value = enhanced_edgeImage[row][col] # 计算红绿蓝三波段的平均值
if isPureWhite(pixel_value):
if row+2 < height:
inputSrcImage[row+2][col][0] = 0
inputSrcImage[row+2][col][1] = 0
inputSrcImage[row+2][col][2] = 255
else:
inputSrcImage[row][col][0] = 0
inputSrcImage[row][col][1] = 0
inputSrcImage[row][col][2] = 255
# inputSrcImage[row][col][0] = 0
# inputSrcImage[row][col][1] = 0
# inputSrcImage[row][col][2] = 255
# break #最开始从每列遍历从上到下找第一个分界点就停止才用break
cv2.namedWindow('RedEdge-image', cv2.WINDOW_AUTOSIZE)
cv2.imshow('RedEdge-image', inputSrcImage)
cv2.imwrite(origin_pic_filename.replace(suffix_name, "-RedEdge.png"), inputSrcImage)
cv2.waitKey(0)
cv2.destroyAllWindows()
print('Success!')
cv2.waitKey()
cv2.destroyAllWindows()3、运行结果
3.1 非下采样+边缘检测
cpp
python GetSkyLine.py test.jpg 100|--------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| 
原始图片 | 
边缘点图片 |
| 
边缘增强图片 | 
sobel-xy处理后图片 |
| 
downloadsampling图片 | 
红色边缘叠加图片 |
3.2 下采样+边缘检测
cpp
python GetSkyLine.py test.jpg 50 1
原始图片
|--------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| 
边缘点图片 | 
边缘增强图片 |
| 
downloadsampling图片 | 
sobel-xy处理后图片 |
红色边缘叠加图片
4、小结
在这个人世间，每个人都是独立的个体，身处浩荡洪流之中，难免身不由己，时而坚定，时而困惑，但我们还是应该永远相信美好的事情终将发生，要心怀一颗感恩的心，相信家人，相信自己，相信未来，坦然面对生活，接受平凡。
关关难过关关过，前路漫漫亦灿灿！