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627-1189950-半透明蒙版的应用.sy.md

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opencv

回忆

  • 上次设置了各种渐变的效果
    • 可以控制灰度的渐变
    • 也可以控制彩色的渐变
    • 还可以配合滚动条制作渐变
  • 还能玩点什么呢？🤔

图片描述

蒙版掩映遮罩的应用

import numpy as np        
import cv2                
img = cv2.imread("kun1.png")
print(img.shape)          
mask = np.zeros((305,483,1),np.uint8)                                        
mask[75:118,214:255,:] = 255
black = np.zeros((305,483,3),np.uint8)
final = cv2.add(img,black,mask=mask)
cv2.imshow("final",final) 
cv2.waitKey()             
cv2.destroyAllWindows()

圆形遮罩

#coding:utf-8
import cv2  
import numpy as np  
 
#读取图片 
img = cv2.imread("/usr/local/support/lena.png", cv2.IMREAD_GRAYSCALE)

#获取图像宽和高
rows, cols = img.shape[:2]
print(rows, cols)

#画圆形
circle = np.zeros((rows, cols), dtype="uint8")
cv2.circle(circle, (int(rows/2.0), int(cols/2)), 80, 255, -1)
print(circle.shape)
print(img.size, circle.size)

#OpenCV图像与运算
result = cv2.bitwise_and(img, circle)

#显示图像
cv2.imshow("original", img)
cv2.imshow("circle", circle)
cv2.imshow("result", result)

#等待显示
cv2.waitKey(0)
cv2.destroyAllWindows()
  • 效果

图片描述

径向渐变图层蒙版

import cv2
import numpy as np
import math

#获取图像宽和高
img = cv2.imread("lena.png", cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape[:2]

def draw_gradient(pos_x,pos_y,radius,image):
    for x in range(0,cols):
        for y in range(rows):
            dx = abs(x - pos_x)
            dy = abs(y - pos_y)
            dis = (dy**2 + dx**2) ** 0.5
            if dis < radius:
                color = dis / radius * 255
                color = 255 - int(color)
                image[y,x] = color

#画圆形

mask = np.zeros((rows, cols), dtype="uint8")
draw_gradient(int(rows/2.0), int(cols/2), 80, mask)

#OpenCV图像与运算
result = np.zeros((rows, cols), dtype="uint8")
for x in range(cols):
    for y in range(rows):
        result[y, x] = int ( mask[y,x]/255* img[y,x])

#显示图像
cv2.imshow("original", img)
cv2.imshow("mask", mask)
cv2.imshow("result", result)

#等待显示
cv2.waitKey(0)
cv2.destroyAllWindows()
  • 结果

图片描述

  • 这个蒙版可以应用吗？

蒙版应用

import cv2
import numpy as np
import math

#获取图像宽和高
img = cv2.imread("lena.png", cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape[:2]

def draw_gradient(pos_x,pos_y,radius,image):
    for x in range(0,cols):
        for y in range(rows):
            dx = abs(x - pos_x)
            dy = abs(y - pos_y)
            dis = (dy**2 + dx**2) ** 0.5
            if dis < radius:
                color = dis / radius * 255
                color = 255 - int(color)
                image[y,x] = color

#画圆形
img2 = cv2.imread("gear.jpg", cv2.IMREAD_GRAYSCALE)
mask = np.zeros((rows, cols), dtype="uint8")
draw_gradient(int(rows/2.0), int(cols/2), 80, mask)

#OpenCV图像与运算
result = img2
rows, cols = img2.shape[:2]
dy = 100
dx = 100
for x in range(cols):
    for y in range(rows):
        percent = mask[y+dy,x+dx] / 255
        result2[y, x] = int(percent*img[y+dy,x+dx] + (1-percent)*img2[y,x])

#显示图像
cv2.imshow("original", img)
cv2.imshow("mask", mask)
cv2.imshow("result", result)

#等待显示
cv2.waitKey(0)
cv2.destroyAllWindows()
  • 效果

图片描述

  • 可以对比 软硬蒙版的效果的不同吗？

两种蒙版

import cv2
import numpy as np
import math

#获取图像宽和高
img = cv2.imread("lena.png", cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape[:2]

def draw_gradient(pos_x,pos_y,radius,image):
    for x in range(0,cols):
        for y in range(rows):
            dx = abs(x - pos_x)
            dy = abs(y - pos_y)
            dis = (dy**2 + dx**2) ** 0.5
            if dis < radius:
                color = dis / radius * 255
                color = 255 - int(color)
                image[y,x] = color

#画圆形
img2 = cv2.imread("gear.jpg", cv2.IMREAD_GRAYSCALE)
mask = np.zeros((rows, cols), dtype="uint8")
draw_gradient(int(rows/2.0), int(cols/2), 80, mask)

#OpenCV图像与运算
result = img2
dest_rows, dest_cols = img2.shape[:2]
dy = 100
dx = 100
for x in range(dest_cols):
    for y in range(dest_rows):
        percent = mask[y+dy,x+dx] / 255
        result2[y, x] = int(percent*img[y+dy,x+dx] + (1-percent)*img2[y,x])

#显示图像
cv2.imshow("result", result)

result2 = img2
mask = np.zeros((rows,cols),dtype="uint8")
mask = cv2.circle(mask,(int(rows/2),int(cols/2)),80,255,-1)
cv2.imshow("mask", mask)
for x in range(dest_cols):
    for y in range(dest_rows):
        percent = mask[y+dy,x+dx] / 255
        result2[y, x] = int(percent*img[y+dy,x+dx] + (1-percent)*img2[y,x])

#显示图像
cv2.imshow("result2", result2)


#等待显示
cv2.waitKey(0)
cv2.destroyAllWindows()
  • 可以做一个滑块看效果吗？

滑块效果

import cv2
import numpy as np
import math

#获取图像宽和高
img = cv2.imread("lena.png", cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape[:2]

def draw_gradient(pos_x,pos_y,radius,image):
    for x in range(0,cols):
        for y in range(rows):
            dx = abs(x - pos_x)
            dy = abs(y - pos_y)
            dis = (dy**2 + dx**2) ** 0.5
            if dis < radius:
                color = dis / radius * 255
                color = 255 - int(color)
                image[y,x] = color

#画圆形
img2 = cv2.imread("gear.jpg", cv2.IMREAD_GRAYSCALE)
mask = np.zeros((rows, cols), dtype="uint8")
draw_gradient(int(rows/2.0), int(cols/2), 80, mask)

#OpenCV图像与运算
result = img2
dest_rows, dest_cols = img2.shape[:2]
dy = 100
dx = 100
for x in range(dest_cols):
    for y in range(dest_rows):
        result[y, x] = int ( mask[y+dy,x+dx]/255* img[y+dy,x+dx]) + img2[y,x]

#显示图像
def callback(value):
    dx = value
    img2 = cv2.imread("gear.jpg", cv2.IMREAD_GRAYSCALE)
    mask = np.zeros((rows, cols), dtype="uint8")
    draw_gradient(int(rows/2.0), int(cols/2), 80, mask)
    for x in range(dest_cols):
        for y in range(dest_rows):
            percent = mask[y+dy,x+dx] / 255
            result[y, x] = int(percent*img[y+dy,x+dx] + (1-percent)*img2[y,x])
    cv2.imshow("result", result)

def callback2(value):
    dx = value
    img2 = cv2.imread("gear.jpg", cv2.IMREAD_GRAYSCALE)
    mask = np.zeros((rows, cols), dtype="uint8")
    mask = cv2.circle(mask,(int(rows/2),int(cols/2)),80,255,-1)
    for x in range(dest_cols):
        for y in range(dest_rows):
            percent = mask[y+dy,x+dx] / 255
            result2[y, x] = int(percent*img[y+dy,x+dx] + (1-percent)*img2[y,x])
    cv2.imshow("result2", result2)

result2 = img2.copy()
cv2.imshow("result", result)
cv2.createTrackbar('x', 'result', 0, 100, callback)
mask = np.zeros((rows,cols),dtype="uint8")
mask = cv2.circle(mask,(int(rows/2),int(cols/2)),80,255,-1)
for x in range(dest_cols):
    for y in range(dest_rows):
        percent = mask[y+dy,x+dx] / 255
        result2[y, x] = int(percent*img[y+dy,x+dx] + (1-percent)*img2[y,x])

#显示图像
cv2.imshow("result2", result2)
cv2.createTrackbar('x', 'result2', 0, 100, callback2)


#等待显示
cv2.waitKey(0)
cv2.destroyAllWindows()
  • 结果

图片描述

  • 可以控制x
  • 可以控制y，radius或者蒙版的硬度吗？

总结 🤔

  • 这次了解了 蒙版的用法
    • 可以控制蒙版的
      • 位置
      • 软硬
      • 半径
      • 形状
  • 还有什么好玩的吗？
  • 下次再说👋