如何使用OpenCV计算两幅图像之间的增量E

Question

我目前正试图确定我们的输出图像与使用OpenCV的Python绘制的莫奈绘画之间的颜色差异

通过我的研究，我发现Delta E是测定色差的最佳方法。我试着提取两幅图像的BGR通道，然后取平均值“蓝色”、“绿色”和“红色”，用于计算每个颜色通道的差异

output_chans = cv2.split(image)
monet_chans = cv2.split(best_painting)
colors = ("Blue", "Green", "Red")

for (output_chan, monet_chan, color) in zip(output_chans, monet_chans, colors):
    output_mean = np.mean(output_chan)
    monet_mean = np.mean(monet_chan)

    color1_rgb = None
    color2_rgb = None

    if color == "Blue":
            color1_rgb = sRGBColor(0.0, 0.0, output_mean)
            color2_rgb = sRGBColor(0.0, 0.0, monet_mean)
    elif color == "Green":
            color1_rgb = sRGBColor(0.0, output_mean, 0.0);
            color2_rgb = sRGBColor(0.0, monet_mean, 0.0);
    elif color == "Red":
            color1_rgb = sRGBColor(output_mean, 0.0, 0.0);
            color2_rgb = sRGBColor(monet_mean, 0.0, 0.0);

    # Convert from RGB to Lab Color Space
    color1_lab = convert_color(color1_rgb, LabColor);

    # Convert from RGB to Lab Color Space
    color2_lab = convert_color(color2_rgb, LabColor);

    # Find the color difference
    delta_e = delta_e_cie2000(color1_lab, color2_lab);

    print("Delta E of the Mean of %s Channel: %f" % (color, delta_e))

我收到了每个颜色通道的色差的输出，但是我的教授建议我可能做得不对，因为我应该只得到整个图像的色差的一个值，而不是每三个颜色通道的一个值。在这种情况下，有没有其他方法或正确的方法来计算两幅图像的Delta E

这是到我们的测试图像样本的链接：https://imgur.com/a/KToggFS

还有一个指向绘画样本的链接：https://imgur.com/a/vi1SFax

Answer 1

以上答案是正确的。但是，如果您正在寻找更简化的计算Delta E的方法，即通过避免额外的依赖关系。您可以简单地计算两幅图像之间的欧几里德距离并取平均值

def deltaE(img1, img2, colorspace = cv2.COLOR_BGR2LAB):
   # check the two images are of the same size, else resize the two images
   (h1, w1) = img1.shape[:2]
   (h2, w2) = img1.shape[:2]
    h, w = None, None
    # check the height
    if h1 > h2:
        h = h1
    else:
        h = h2
    #check the width
    if w1 > w2:
        w = w1
    else:
        w = w2
    
    img1 = cv2.resize(img1, (h,w))
    img2 = cv2.resize(img2, (h,w))
    # Convert BGR images to specified colorspace
    img1 = cv2.cvtColor(img1, colorspace)
    img2 = cv2.cvtColor(img2, colorspace)
    # compute the Euclidean distance with pixels of two images 
    return np.sqrt(np.sum((img1 - img2) ** 2, axis=-1))/255.