Python处理图片并实现生成天际线
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
#-*- 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
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
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
到此这篇关于Python处理图片并实现生成天际线的文章就介绍到这了,更多相关Python图片天际线内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家!
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