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python - 将二维数组插入另一个二维数组，同时考虑值

问题描述

我试图在某些坐标上将一张图片（透明 .png）插入另一张图片。虽然如何使用 x,y 坐标在另一个图像上添加图像的解决方案？ frame[y: y+insert_size[1], x: x+insert_size[0]] = image（其中 insert_size - 插入图片的宽度和高度）有效，我也不想要最终图像上的黑色像素（这就是 opencv 表示透明像素的方式）。

我编写了一个逐像素迭代的函数，虽然它工作 - 它非常慢（它每秒完成大约 2 个图像插入），代码：

def insert_image(frame, image, insert_coordinates, masked_value):
    img_height = len(image)
    img_width = len(image[0])
    mask = np.ndarray((3,), np.uint8, buffer=np.array(masked_value))
    y_diff = 0 #current vertical position in insert picture
    for y, line in enumerate(frame):
        if y_diff == img_height-1:
            continue #interested until last row
        if y < insert_coordinates[1] or y > insert_coordinates[1]+img_height:
            continue #interested only in rows that will be changed
        else:
            x_diff = 0 #current horizontal position in insert picture
            for x, col in enumerate(line):
                if x_diff == img_width-1:
                    continue #interested until last column
                if x < insert_coordinates[0] or x > insert_coordinates[0]+img_width:
                    continue #interested only in columns that will be changed
                else:
                    if (image[y_diff][x_diff] != mask).all():
                        frame[y][x] = image[y_diff][x_diff]  #setting pixel value if its not of masked value
                    x_diff += 1
        y_diff += 1
    return frame

也许有更聪明的方法可以做到这一点？opencv 版本 4.5.0 numpy 版本 1.20.0rc1

更新：“插入”是指将图像中的像素值分配给帧的某个像素。我为可重现的示例添加了数据和代码（也修改了函数，所以它有点快）：

  1. “帧” - 原始图片，该图像将被添加到，在 (100,100) 坐标处具有红色正方形大小 (500,500)
  2. "image" - 透明的 .png，大小为 (500,500)，将被“插入”到原始帧中
  3. “result1” - 结果，其中红色像素被插入图像中的黑色“透明”像素替换
  4. "result2" - 想要的结果

原始帧，该图像将是 任意图像，这将是 result1 - 来自插入图像的黑色透明像素 result2 - 期望的结果

代码，需要 opencv-python 和 numpy 模块：example.py

import cv2
import numpy as np
import copy


def insert_image_v2(frame, image, insert_coordinates, masked_value):
    img_height = len(image)
    img_width = len(image[0])
    mask = np.ndarray((3,), np.uint8, buffer=np.array(masked_value))
    y_diff = 0
    for y in range(insert_coordinates[1], insert_coordinates[1]+img_height, 1):
        x_diff = 0
        for x in range(insert_coordinates[0], insert_coordinates[0]+img_width, 1):
            if (image[y_diff][x_diff] != mask).all():
                frame[y][x] = image[y_diff][x_diff]
            x_diff += 1
        y_diff += 1
    return frame


if __name__ == "__main__":
    frame = cv2.imread('frame.png')
    image = cv2.imread('image.png')
    insert_size = (image.shape[0], image.shape[1])
    insert_coordinates = (100, 100)
    x = insert_coordinates[0]
    y = insert_coordinates[1]
    result1 = copy.deepcopy(frame)
    result1[y: y+insert_size[1], x: x+insert_size[0]] = image
    result2 = insert_image_v2(frame, image, insert_coordinates, [0,0,0])
    cv2.imshow('result1', result1)
    cv2.imshow('result2', result2)
    cv2.imwrite('result1.jpg', result1)
    cv2.imwrite('result2.jpg', result2)
    print()

标签： pythonarraysnumpyopencv

解决方案

在Image alpha composition with OpenCV中找到了一个解决方案，它比我的速度快 20 倍。代码：

import cv2
import numpy as np
import time


def insert_image_v2(frame, image, insert_coordinates):
    x = insert_coordinates[0]
    y = insert_coordinates[1]
    insert_size = (image.shape[1], image.shape[0])
    background = frame[y: y+insert_size[1], x: x+insert_size[0]]
    foreground = image
    kernel = np.ones((5,5), np.uint8)
    image_gray = cv2.cvtColor(foreground, cv2.COLOR_BGR2GRAY)
    ret, mask = cv2.threshold(image_gray, 1, 255, cv2.THRESH_BINARY_INV)
    opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    output = np.zeros(foreground.shape, dtype=foreground.dtype)
    for i in range(3):
        output[:,:,i] = background[:,:,i] * (opening/255)+foreground[:,:,i]*(1-opening/255)
    frame[y: y+insert_size[1], x: x+insert_size[0]] = output
    return frame


if __name__ == "__main__":
    frame = cv2.imread('frame.png')
    image = cv2.imread('image_1.png')
    insert_size = (image.shape[0], image.shape[1])
    insert_coordinates = (100, 100)
    t1 = time.time()
    frame = insert_image_v2(frame, image, insert_coordinates)
    t2 = time.time()
    print(f"{t2-t1}")
    cv2.imshow('img', frame)
    cv2.waitKey(0)

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