OpenCV像素操作与通道处理实战：从基础修改到透明通道详解

# USAGE
# python opencv_getting_setting.py --image fjdj.png

# 导入必要的包
import argparse

import cv2
import imutils
import numpy as np

# 构建命令行参数及解析
# --image 磁盘图片路径，默认名称为当前py文件同级目录：fjdj.jpg


ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", type=str, default="fjdj.jpg",
                help="path to the input image")
args = vars(ap.parse_args())
ap = argparse.ArgumentParser()

# 加载图像，获取空间维度（宽度、高度），展示原始图像到屏幕
image = cv2.imread(args["image"])
image = imutils.resize(image, width=430)
origin = image.copy()
(h, w) = image.shape[:2]
cv2.imshow("Original", image)

# 图像以Numpy数组存在，获取左上角，图像索引从0开始
# 图像以BGR通道表示，因为最开始BGR是标准，后来调整为RGB
(b, g, r) = image[0, 0]
print("Pixel at (0, 0) - Red: {}, Green: {}, Blue: {}".format(r, g, b))

# 获取x=380，y=380的像素值，图像想象为M*N的矩阵，M为行，N为列
(b, g, r) = image[380, 380]
print("Pixel at (380, 380) - Red: {}, Green: {}, Blue: {}".format(r, g, b))

# 更新x=50，y=20的像素为红色
image[380, 380] = (0, 0, 255)
(b, g, r) = image[380, 380]
print("Pixel at (380, 380) - Red: {}, Green: {}, Blue: {}".format(r, g, b))

# 计算图像的中心
(cX, cY) = (w // 2, h // 2)

# 使用数组切片获取左上角1/4的部分
tl = image[0:cY, 0:cX]
cv2.imshow("Top-Left Corner", tl)

# 同样的，用数组切片裁剪 右上角、左下角、右下角部分，并展示
tr = image[0:cY, cX:w]
br = image[cY:h, cX:w]
bl = image[cY:h, 0:cX]
cv2.imshow("Top-Right Corner", tr)
cv2.imshow("Bottom-Right Corner", br)
cv2.imshow("Bottom-Left Corner", bl)

# 使用像素切片来更改像素区域的颜色
image[0:cY, 0:cX] = (0, 255, 0)

# 展示更新像素后的图片
cv2.imshow("Updated (Top-Left Corner to Green)", image)

gray = cv2.cvtColor(origin, cv2.COLOR_BGR2GRAY)
cv2.imshow("Gray", gray)

(h, w) = origin.shape[:2]
zeros = np.zeros((h, w), dtype="uint8")
# 将origin分离为红色，绿色和蓝色通道， 然后我们使用Numpy 零数组分别构造每个通道的表示形式
(B, G, R) = cv2.split(origin)
R = cv2.merge([zeros, zeros, R])
G = cv2.merge([zeros, G, zeros])
B = cv2.merge([B, zeros, zeros])
cv2.imshow("B G R", np.hstack([B, G, R]))
# 构建输出帧  原图在左上角 红色通道右上角 绿色通道右下角 蓝色通道左下角
output = np.zeros((h * 2, w * 2, 3), dtype="uint8")
output[0:h, 0:w] = origin
output[0:h, w:w * 2] = R
output[h:h * 2, 0:w] = G
output[h:h * 2, w:w * 2] = B
cv2.imshow("origin vs R vs G vs B", imutils.resize(output, width=700))

alpha0 = np.dstack([origin, np.ones((h, w), dtype="uint8") * 0])
cv2.imshow("alph 0", alpha0)
cv2.imwrite("alph 0.png", alpha0)

alpha1 = np.dstack([origin, np.ones((h, w), dtype="uint8") * 255])
cv2.imshow("alph 255", alpha1)
cv2.imwrite("alph 255.png", alpha1)
cv2.waitKey(0)