Python 在for循环中更改像素颜色 导入cv 进口cv2 将matplotlib.pyplot作为plt导入 将matplotlib.image导入为mpimg 将numpy作为np导入 导入图像 cam=cv2.视频捕获（0） s、 img1=cam.read（

在for循环中更改像素颜色

导入cv
进口cv2
将matplotlib.pyplot作为plt导入
将matplotlib.image导入为mpimg
将numpy作为np导入
导入图像
cam=cv2.视频捕获（0）
s、 img1=cam.read（）
高度、宽度、深度=img1.shape
打印高度、宽度
对于范围内的i（0，高度）：
对于范围（0，宽度）内的j：
如果（img1[i，j][25,25,25]）all（）和（img1[i，j][50,50]）all（）和（img1[i，j][75,75,75]）all（）和（img1[i，j][100100100]）all（）和（img1[i，j][125125125]）all（）和（img1[i，j][150150150150]）all（）和（img1[i，j][175175175175]）all（）和（img1[i，j][200200]）all（）和（img1[i，j][2252125225]），all[i，j]
导入cv
进口cv2
将matplotlib.pyplot作为plt导入
将matplotlib.image导入为mpimg
将numpy作为np导入
导入图像
cam=cv2.视频捕获（0）
s、 img1=cam.read（）
imgg1=img1
高度、宽度、深度=img1.shape
打印高度、宽度
对于范围内的i（0，高度）：
对于范围（0，宽度）内的j：
如果（img1[i，j]您实际上正在做的是从一种像素颜色更改为另一种颜色，对吗？请检查3D查找表和颜色查找表。可能会对您有所帮助。/请查看cv2.LUT（）。您基本上在上面的代码中执行1d（灰度强度）查找。
import cv
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import Image
cam = cv2.VideoCapture(0)
s,img1 = cam.read()
height, width, depth = img1.shape
print height, width
for i in range(0,height):
   for j in range(0,width):
      if (img1[i, j] <= [25,25,25]).all():
        img1[i, j] = [255, 0, 128]
      elif ((img1[i, j] > [25,25,25]).all() and (img1[i, j] <= [50,50,50]).all()):
          img1[i,j] = [255, 255, 128]
      elif ((img1[i, j] > [50,50,50]).all() and (img1[i, j] <= [75,75,75]).all()):
          img1[i,j] = [255, 128, 0]
      elif ((img1[i, j] > [75,75,75]).all() and (img1[i, j] <= [100,100,100]).all()):
          img1[i,j] = [0, 255, 0]
      elif ((img1[i, j] > [100,100,100]).all() and (img1[i, j] <= [125,125,125]).all()):
          img1[i,j] = [68, 128, 251]      
      elif ((img1[i, j] > [125,125,125]).all() and (img1[i, j] <= [150,150,150]).all()):
          img1[i,j] = [0, 255, 255]
      elif ((img1[i, j] > [150,150,150]).all() and (img1[i, j] <= [175,175,175]).all()):
          img1[i,j] = [0, 0, 255]
      elif ((img1[i, j] > [175,175,175]).all() and (img1[i, j] <= [200,200,200]).all()):
          img1[i,j] = [128, 128, 128]
      elif ((img1[i, j] > [200,200,200]).all() and (img1[i, j] <= [225,225,225]).all()):
          img1[i,j] = [0, 0, 0]
      elif ((img1[i, j] > [225,225,225]).all() and (img1[i, j] <= [255,255,255]).all()):
          img1[i,j] = [255, 255, 255]
      else:
         img1[i,j] = [0, 60, 0]
      j=j+1
   i=i-1
m=1
while m<2:
  cv2.imshow('pseudocolor',img1)
  cv2.waitKey(10)

import cv
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import Image
cam = cv2.VideoCapture(0)
s,img1 = cam.read()
imgg1=img1
height, width, depth = img1.shape
print height, width
for i in range(0,height):
   for j in range(0,width):
      if (img1[i, j] <= [25,25,25]).all():
        img1[i,j] = [255, 255, 255]
        imgg1[i, j] = [255, 0, 128]
      elif (img1[i, j] <= [50,50,50]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [255, 255, 128]
      elif (img1[i, j] <= [75,75,75]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [255, 128, 0]
      elif (img1[i, j] <= [100,100,100]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [0, 255, 0]
      elif (img1[i, j] <= [125,125,125]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [68, 128, 251]      
      elif (img1[i, j] <= [150,150,150]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [0, 255, 255]
      elif (img1[i, j] <= [175,175,175]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [0, 0, 255]
      elif (img1[i, j] <= [200,200,200]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [128, 128, 128]
      elif (img1[i, j] <= [225,225,225]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [0, 0, 0]
      elif (img1[i, j] < [255,255,255]).all():
          img1[i,j] = [255, 255, 255]
          imgg1[i,j] = [255, 255, 255]
      else:
         imgg1[i,j] = [0, 60, 0]
      j=j+1
   i=i-1
m=1
while m<2:
  cv2.imshow('pseudocolor',imgg1)
  cv2.waitKey(10)