Comments (9)
bgr和rgb读取进去都是一样的,没有通道交换,rgb2bgr才会交换r和b通道
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模型都是insightface官方的,我直接用ncnn转的,没有修改过
from insightface_ncnn.
多谢您的回答,我的问题我可能没有表达清楚。我比较了几个mtcnn的C++代码,您的mtcnn代码的速度很快。 阅读代码的时候我发现您的输入图像的通道顺序和其他版本的不同。
其他的mtcnn的ncnn版本的代码, 通过OpenCV读入图像img1, 然后后再转为ncnn Mat矩阵ncnn_img1,使用的是ncnn::Mat ncnn_img1 = ncnn::Mat::from_pixels(img1.data, ncnn::Mat::PIXEL_BGR2RGB, img1.cols,
img1.rows), 该函数的第二个实参为ncnn::Mat::PIXEL_BGR2RGB,也就是说生成ncnn矩阵的同时把通道顺序从BGR转为RGB, 您的main函数中使用的是ncnn::Mat::PIXEL_BGR,对应的ncnn Mat的通道顺序仍为BGR。
卷积model是和input mat channel的顺序对应的,如果model中的channel顺序是RGB,那么input mat的通道顺序应该相同。
insighface模型是mxnet版本的,model的通道顺序也是RGB。那是不是说input mat的通道顺序也应该为RGB?
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另外,方便告诉一下mtcnn的原模型链接么?
也是从insightface 的github网页上的mtcnn模型转成成ncnn版本的么?
https://github.com/deepinsight/mxnet-mtcnn
https://github.com/deepinsight/mtcnn-ncnn ?
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https://github.com/deepinsight/insightface
当时实现的时候我两种读取方式都试了一下,发现用pixel_bgr模式读入能够得到于原作相同的网络输出,而pixel_bgr2rgb模式读入的图像得到的网络输出与原作不一致。具体原理我没有深入思考过,因为我没有训练过模型,具体模型接受怎样的输入,我也不知道。
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我刚刚仔细看了一下,在mtcnn部分是bgr方式,arcface部分应该是rgb方式,需要转换图片
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mtcnn应该是caffe model转过来的,caffe 的输入通道顺序是BGR,我测试的时候也是发现mtcnn在BGR通道顺序下检测率高。
from insightface_ncnn.
请问模型连接是哪个?
https://github.com/deepinsight/insightface/tree/master/deploy/mtcnn-model ?
https://github.com/deepinsight/insightface/tree/master/gender-age/mtcnn-model ?
这两个看起来都一样,跟
https://github.com/deepinsight/mxnet-mtcnn
里面的模型是一样的吧,但是我转来试了一下,没有你这里给出的4个ncnn模型效果好呢
from insightface_ncnn.
mtcnn模型https://github.com/deepinsight/insightface/tree/master/deploy/mtcnn-model
mobilefacenets模型deepinsight/insightface#214
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Related Issues (9)
- mergebn needed?
- Could you support mxnet model link corresponding to this ncnn mobilefacenet model HOT 4
- 关于arcface.cpp中preprocess输入的图像问题 HOT 1
- 大神,我使用官方提供的LResNet100E-IR模型转化为ncnn模型后测试精度很低? HOT 3
- 询问下ArcFace的代码,谢谢 HOT 1
- 请问您用的是什么版本的ncnn?使用我自行编译的ncnn,链接运行之后显示段错误
- Bbox value normalization
- How to install and run program?
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