一、讀入圖片獲得像素點的像素值、改變像素值、改變單個通道像素值、獲得圖像的行、列、圖像數據類型、像素點、ROI區域.
import cv2
import numpy as np
#讀取一個彩色圖像
img = cv2.imread('C:/Users/NWPU/Desktop/1.jpg')
#cv2.imshow('image',img)
#獲得某個像素點的像素值
px = img[200,200]
print(px)
#只獲取綠色通道的像素值
green = img[200,200,1]
print(green)
#修改像素值
img[200,200] = [255,255,255]
print(img[200,200])
#使用Numpy數組的處理方法更好的獲取像素點的值和編輯像素點的值
#獲得指定像素點的紅色通道的值
img_r = img.item(100,100,2)
print(img_r)
#修改指定像素點的紅色通道的值
img.itemset((100,100,2),100)
img_rnew = img.item(100,100,2)
print(img_rnew)
#獲取圖片的信息:行數、列數、通道數、圖像數據類型、像素數等
#獲得圖片的形狀
print(img.shape) #(768,1024,3):768*1024大小的圖像,彩色圖像三通道
#查詢像素總數
print(img.size)
#圖片的數據類型,img.dtype在調試過程中很重要,因爲很多opencv+python代碼中的問題都是不合法的數據類型造成的
print(img.dtype) #實驗圖片爲uint8數據類型
#圖片的ROI:獲得原始圖片的一部分,將此部分複製到圖片的另一個指定區域
img_ROI = img[280:340, 330:390]
img[273:333, 100:160] = img_ROI
cv2.rectangle(img,(280,330),(340,390),(255,255,255),1)
cv2.rectangle(img,(273,100),(333,160),(255,255,255),1)
cv2.imshow('image1',img)
#OpenCV存儲彩色圖片的格式是BGR模式,下面進行通道分離和合並
#使用split()函數進行通道分離,很耗時
b,g,r = cv2.split(img)
#使用merge()函數進行通道合併
img = cv2.merge((b,g,r))
#也可以直接操作Numpy數組來達到這一目的
b = np.zeros((img.shape[0],img.shape[1]),dtype = img.dtype)
g = np.zeros((img.shape[0],img.shape[1]),dtype = img.dtype)
r = np.zeros((img.shape[0],img.shape[1]),dtype = img.dtype)
b[:,:] = img[:,:,0]
g[:,:] = img[:,:,1]
r[:,:] = img[:,:,2]
運行結果:
二、圖像相加
img1:
img2:
import cv2
import numpy as np
img1 = cv2.imread("C:/Users/NWPU/Desktop/1.jpg")
img2 = cv2.imread("C:/Users/NWPU/Desktop/2.jpg")
#圖像相加:cv2.add()函數
rows, cols = img2.shape[:2] #獲取img2的高度和寬度
img1_roi = img1[100:rows+100,100:cols+100]
img_plus = cv2.add(img1_roi,img2)
img1_copy_plus = img1.copy()
img1_copy_plus[100:rows+100,100:cols+100] = img_plus
cv2.imshow('img_plus',img1_copy_plus)
cv2.waitKey(0)
三、圖像混合
import cv2
import numpy as np
img1 = cv2.imread("C:/Users/NWPU/Desktop/1.jpg")
img2 = cv2.imread("C:/Users/NWPU/Desktop/2.jpg")
#圖像混合:cv2.addWeighted()函數
rows, cols = img2.shape[:2] #獲取img2的高度和寬度
img1_roi = img1[100:rows+100, 100:cols+100]
img_mix = cv2.addWeighted(img1_roi, 0.3, img2, 0.7, 0)
img1_copy = img1.copy()
img1_copy[100:rows+100, 100:cols+100] = img_mix
cv2.imshow('img_mix',img1_copy)
cv2.waitKey(0)
結果:
四、圖像的位運算
#圖像的位操作有與、或、非、異或操作
'''
cv2.bitwise_and
cv2.bitwise_or
cv2.bitwsie_not
cv2.bitwise_xor
'''
import cv2
import numpy as np
img1 = cv2.imread("C:/Users/NWPU/Desktop/1.jpg") #768*1024
img2 = cv2.imread("C:/Users/NWPU/Desktop/2.jpg") #300*450
rows, cols = img2.shape[:2]
img1_roi = img1[100:rows+100, 100:cols+100]
img1_copy = img1.copy()
#與運算
img_add = cv2.bitwise_and(img1_roi,img2)
img1_copy[100:rows+100, 100:cols+100] = img_add
cv2.imshow('img_add', img1_copy)
#或運算
img_or = cv2.bitwise_or(img1_roi,img2)
img1_copy[100:rows+100, 100:cols+100] = img_or
cv2.imshow('img_or', img1_copy)
#非運算
img_not = cv2.bitwise_not(img1_roi,img2)
img1_copy[100:rows+100, 100:cols+100] = img_not
cv2.imshow('img_not', img1_copy)
#異或運算
img_xor = cv2.bitwise_xor(img1_roi,img2)
img1_copy[100:rows+100, 100:cols+100] = img_xor
cv2.imshow('img_xor', img1_copy)
cv2.waitKey(0)
結果:
與:
或:
非:
異或: