python opencv 找出图片中最亮和最暗的的行、列、区域

python opencv 找出图片中最亮和最暗的的行、列、区域

最亮的列

import cv2
import numpy as np
#得到图片路径image_path的宽wide、高heigh和深度deep
def wide_heigh(image_path):img = cv2.imread(image_path)(heigh,wide,deep) = img.shapeprint('图片的高是：',heigh)print('图片的宽是：',wide)print('图片的深是：', deep)return heigh,wide,deep
#最亮的列
def brightestColumn(img_path):# 图片的高、宽、深heigh, wide, deep = wide_heigh(img_path)# 读取图片img = cv2.imread(img_path)#列亮度列表bright_list = []# 遍历宽for i in range(wide):# 提取每一列Column_i = img[:,i:i+1]# 列的亮度求和bright_sum = Column_i.sum()# 列亮度加到列亮度列表中bright_list.append(bright_sum)#找出列表bright_list中最大索引（就是图片的最亮的列数）brightest_Column = bright_list.index(max(bright_list))# 找出列表bright_list中最小索引（就是图片的最暗的列数）# dark_Column = bright_list.index(min(bright_list))# 最亮列起点的坐标ptStart = (brightest_Column, 0)# 最亮列终点的坐标ptEnd = (brightest_Column, heigh)# 最亮列的颜色point_color = (0, 255, 0)  # BGR# 最亮列的线条宽度thickness = 1# lineType：最亮线的类型#          - 8( or omitted) ： 8 - connectedline#          - 4：4 - connectedline#          - CV_AA - antialiased linelineType = 4# 画出最亮列cv2.line(img, ptStart, ptEnd, point_color, thickness, lineType)cv2.imshow('img', img)cv2.waitKey(0)return brightest_Column
if __name__ == '__main__':img_path = '1.jpg'brightestColumn(img_path)

效果如下：

最亮的行

import cv2
import numpy as np
#得到图片路径image_path的宽wide、高heigh和深度deep
def wide_heigh(image_path):img = cv2.imread(image_path)(heigh,wide,deep) = img.shapeprint('图片的高是：',heigh)print('图片的宽是：',wide)print('图片的深是：', deep)return heigh,wide,deep
#最亮的行
def brightestColumn(img_path):# 图片的高、宽、深heigh, wide, deep = wide_heigh(img_path)# 读取图片img = cv2.imread(img_path)#行亮度行表bright_list = []# 遍历宽for i in range(heigh):# 提取每一行Column_i = img[i:i+1]# 行的亮度求和bright_sum = Column_i.sum()# 行亮度加到行亮度行表中bright_list.append(bright_sum)#找出行表bright_list中最大索引（就是图片的最亮的行数）brightest_Column = bright_list.index(max(bright_list))# 找出行表bright_list中最小索引（就是图片的最暗的行数）# dark_Column = bright_list.index(min(bright_list))# 最亮行起点的坐标ptStart = (0,brightest_Column)# 最亮行终点的坐标ptEnd = (wide,brightest_Column)# 最亮行的颜色point_color = (0, 255, 0)  # BGR# 最亮行的线条宽度thickness = 1# lineType：最亮线的类型#          - 8( or omitted) ： 8 - connectedline#          - 4：4 - connectedline#          - CV_AA - antialiased linelineType = 4# 画出最亮行cv2.line(img, ptStart, ptEnd, point_color, thickness, lineType)cv2.imshow('img', img)cv2.waitKey(0)return brightest_Column
if __name__ == '__main__':img_path = '1.jpg'brightestColumn(img_path)

效果如下：

最亮点和最亮区域

# coding=utf-8
# 导入python包
import numpy as np
import cv2
# 读取图片并将其转化为灰度图片
image = cv2.imread('1.jpg')
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 利用cv2.minMaxLoc寻找到图像中最亮和最暗的点
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
# 在图像中绘制结果
cv2.circle(image, maxLoc, 5, (255, 0, 0), 2)
# 应用高斯模糊进行预处理（由找点变成找区域）
gray = cv2.GaussianBlur(gray, (59,59), 0)
# 利用cv2.minMaxLoc寻找到图像中最亮和最暗的区域
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
image1 = orig.copy()
cv2.circle(image1, maxLoc, 59, (255, 0, 0), 2)
# 显示结果
result = np.hstack([orig, image, image1])
cv2.imwrite("2.jpg", result)
cv2.imshow("Robust", result)
cv2.waitKey(0)

效果如下：