首页print("[INFO] colorizing image...") net.setInput(cv2.dnn.blobFromImage(L)) ab = net.forward()[0, :, :, :].transpose((1, 2, 0))

print("[INFO] colorizing image...") net.setInput(cv2.dnn.blobFromImage(L)) ab = net.forward()[0, :, :, :].transpose((1, 2, 0))

时间: 2024-05-23 15:10:53 浏览: 65

对抗学习-图像生成Gan.zip

几篇gan论文。We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss func- tion to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demon- strate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a commu- nity, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either

这段代码的作用是对图像进行着色，具体来说： - 第一行是输出一个提示信息，告诉用户正在进行图像着色操作。 - 第二行是将输入图像L转换为一个4维的Blob格式，以便送入深度学习模型进行处理。具体来说，它将图像L按照BGR顺序分别减去了103.939、116.779和123.68，并将结果分别存放在三个通道中。这是因为在训练深度学习模型时，通常会先对数据进行归一化处理，在这个过程中会减去均值。 - 第三行是将经过深度学习模型处理后的结果ab取出来，并将它的维度从（C，H，W）转换为（H，W，C），以便后面进行图像合并操作。其中，C是颜色通道数，通常为2，表示ab通道；H和W分别是结果的高度和宽度。

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