图片的灰度处理
方法1
import cv2
img0 = cv2.imread("11111.jpg",0) # 将图片读取进来
img1 = cv2.imread("11111.jpg",1) # 如果是0,则为灰度图片,如果是1,则为彩色图片。
print(img0.shape) #灰度图片,二维 (333, 500) 宽 高
print(img1.shape) #彩色图片,三维 (333, 500, 3) 宽 高 深度信息
cv2.imshow("src",img0) # 展示img0
cv2.waitKey(0) # 展示图片不消失
方法2
"""
方法2 cvtColor
如果读进来的时候是彩色图片
我们需要把它转化为灰度图片
"""
import cv2
img = cv2.imread("11111.jpg",1)
dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # 完成颜色空间转换
cv2.imshow("src",dst)
cv2.waitKey(0)
方法3,通过源码来完成
"""
灰度图片的RGB值和彩色图片的RGB区别?
灰度图片 R=G=B
可以将彩色图片RGB的均值当作当前的灰度值
"""
import cv2
import numpy as np
img = cv2.imread("11111.jpg",1)
# 获取当前图片的信息
imgInfo = img.shape
heigh = imgInfo[0]
width = imgInfo[1]
# dst 一般是新建值,目标图片
dst=np.zeros((heigh,width,3),np.uint8)
for i in range(0,heigh):
for j in range(0,width):
(b,g,r) = img[i,j]
gray = (int(b)+int(g)+int(r))/3 # 防止溢出,转化为int再计算
dst[i,j] = np.uint8(gray)
cv2.imshow("dst",dst)
cv2.waitKey(0)
方法4
"""
方法4 心理学的一个计算公式
gray = r*0.299 + g*0.587 + b*0.114
"""
import cv2
import numpy as np
img = cv2.imread("11111.jpg",1)
# 获取当前图片的信息
imgInfo = img.shape
heigh = imgInfo[0]
width = imgInfo[1]
# dst 一般是新建值,目标图片
dst=np.zeros((heigh,width,3),np.uint8)
for i in range(0,heigh):
for j in range(0,width):
(b,g,r) = img[i,j]
b=int(b)
g=int(g)
r=int(r)
gray = r*0.299 + g*0.587 + b*0.114 # 防止溢出,转化为int再计算
dst[i,j] = np.uint8(gray)
cv2.imshow("dst",dst)
cv2.waitKey(0)
灰度图片的颜色反转
"""
颜色反转
颜色范围 0~255
反转 255减去当前值
"""
import cv2
import numpy as np
img = cv2.imread("11111.jpg",1)
imgInfo = img.shape
heigh = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((heigh,width,1),np.uint8)
for i in range(0,heigh):
for j in range(0,width):
grayPixel = gray[i,j]
dst[i,j]=255-grayPixel
cv2.imshow("dst",dst)
cv2.waitKey(0)
彩色图片的颜色反转
import cv2
import numpy as np
img = cv2.imread("11111.jpg",1)
imgInfo = img.shape
heigh = imgInfo[0]
width = imgInfo[1]
dst = np.zeros((heigh,width,3),np.uint8)
for i in range(0,heigh):
for j in range(0,width):
(b,g,r) = img[i,j]
dst[i,j] = (255-b,255-j,255-r)
cv2.imshow("dst",dst)
cv2.waitKey(0)