覆盖不同大小和频道数的图像

Question

我尝试使用OpenCV和Python覆盖随机图像（自然场景图像应该覆盖符号图像）。它们可以在大小、文件扩展名和频道数量上有所不同（我想可能还有更多）。所以我要根据自然场景图像的大小调整标牌图像的大小，并将它们放在后者上。在

我已经实现了fireant在这里找到的代码：overlay a smaller image on a larger image python OpenCv

但它只适用于4个通道的图像。在

使用cv2.addWeighted（）总是将较大的图像（场景图像）裁剪为较小图像（符号图像）的大小。有人知道怎么做吗？非常感谢帮助。在

编辑：请参阅下面的预期输出。起初，逃生路线标志和背景是分开的图像。

这是我的代码，它正在工作，但由于我的许多图像似乎只有3个通道，我想让它也为那些工作。在

import cv2
import time
import math
import os

pathSigns = "/home/moritz/Schreibtisch/Signs"
pathScenes = "/home/moritz/Schreibtisch/Scenes"
i = 0

for fSigns in os.listdir(pathSigns):
    fSigns = os.path.join(pathSigns, fSigns)
    s_img = cv2.imread(fSigns, -1)

    for fScenes in os.listdir(pathScenes):
        try:
            l_img = cv2.imread(os.path.join(pathScenes, fScenes))
            l_height, l_width, l_channels = l_img.shape

            TARGET_PIXEL_AREA = (l_height * l_width) * 0.05

            ratio = float(s_img.shape[1]) / float(s_img.shape[0])
            s_new_h = int(math.sqrt(TARGET_PIXEL_AREA / ratio) + 0.5)
            s_new_w = int((s_new_h * ratio) + 0.5)

            s_img = cv2.resize(s_img,(s_new_w, s_new_h))

            x_offset=y_offset=50
            # l_img[y_offset:y_offset+s_img.shape[0], 
               x_offset:x_offset+s_img.shape[1]] = s_img

            y1, y2 = y_offset, y_offset + s_img.shape[0]
            x1, x2 = x_offset, x_offset + s_img.shape[1]

            height, width, channels = s_img.shape

            if channels <= 3:
                alpha_s = s_img[:, :, 2] / 255.0
                alpha_l = 1.0 - alpha_s
            else:
                alpha_s = s_img[:, :, 3] / 255.0
                alpha_l = 1.0 - alpha_s

            for c in range(0, 3):
                l_img[y1:y2, x1:x2, c] = (alpha_s * s_img[:, :, c] +
                  alpha_l * l_img[y1:y2, x1:x2, c])

            fResult = "/home/moritz/Schreibtisch/results/data_" + str(i) + 
                   ".png"
            i += 1
            cv2.imwrite(fResult, l_img)
        except IndexError:
            pass

Answer 1

多亏了@DanMašek hint和How to crop or remove white background from an image，我找到了一个解决方案。下面的代码将首先从较小的图像中删除白色背景，然后将所有图像设置为4个通道，然后用较小的图像覆盖较大的图像。对我有用。在

import cv2
import time
import math
import os
import numpy as np

pathSigns = "/home/moritz/Schreibtisch/Signs"
pathScenes = "/home/moritz/Schreibtisch/Scenes"
i = 0

for fSigns in os.listdir(pathSigns):
    fSigns = os.path.join(pathSigns, fSigns)
    s_img = cv2.imread(fSigns, -1)
    s_height, s_width, s_channels = s_img.shape

    # crop image
    gray = cv2.cvtColor(s_img, cv2.COLOR_BGR2GRAY)
    th, threshed = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
    morphed = cv2.morphologyEx(threshed, cv2.MORPH_CLOSE, kernel)

    _, cnts, _ = cv2.findContours(morphed, cv2.RETR_EXTERNAL, 
    cv2.CHAIN_APPROX_SIMPLE)
    cnt = sorted(cnts, key=cv2.contourArea)[-1]
    x,y,w,h = cv2.boundingRect(cnt)
    s_img = s_img[y:y+h, x:x+w]

    # set channels to 4
    if s_channels < 4:
        s_img = cv2.cvtColor(s_img, cv2.COLOR_BGR2BGRA)

    for fScenes in os.listdir(pathScenes):
        try:
            l_img = cv2.imread(os.path.join(pathScenes, fScenes))
            l_height, l_width, l_channels = l_img.shape

            if l_channels < 4:
                l_img = cv2.cvtColor(l_img, cv2.COLOR_BGR2BGRA)

            TARGET_PIXEL_AREA = (l_height * l_width) * 0.05

            ratio = float(s_img.shape[1]) / float(s_img.shape[0])
            s_new_h = int(math.sqrt(TARGET_PIXEL_AREA / ratio) + 0.5)
            s_new_w = int((s_new_h * ratio) + 0.5)

            s_img = cv2.resize(s_img,(s_new_w, s_new_h))

            x_offset=y_offset=50

            y1, y2 = y_offset, y_offset + s_img.shape[0]
            x1, x2 = x_offset, x_offset + s_img.shape[1]

            alpha_s = s_img[:, :, 3] / 255.0
            alpha_l = 1.0 - alpha_s

            for c in range(0, 3):
                l_img[y1:y2, x1:x2, c] = (alpha_s * s_img[:, :, c] + alpha_l * 
                 l_img[y1:y2, x1:x2, c])

            fResult = "/home/moritz/Schreibtisch/results/data_" + str(i) + ".png"
            i += 1
            cv2.imwrite(fResult, l_img)
        except IndexError:
            pass