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Commit 39a68897 authored by Alexander Alekhin's avatar Alexander Alekhin
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Merge pull request #15118 from dkurt:fix_15106

parents 0db4fb18 a2125594
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modules/dnn/test/test_caffe_importer.cpp
View file @ 39a68897
...@@ -561,7 +561,7 @@ TEST(Test_Caffe, shared_weights) ...@@ -561,7 +561,7 @@ TEST(Test_Caffe, shared_weights)
typedef testing::TestWithParam<tuple<std::string, Target> > opencv_face_detector; typedef testing::TestWithParam<tuple<std::string, Target> > opencv_face_detector;
TEST_P(opencv_face_detector, Accuracy) TEST_P(opencv_face_detector, Accuracy)
{ {
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt", false); std::string proto = findDataFile("dnn/opencv_face_detector.prototxt");
std::string model = findDataFile(get<0>(GetParam()), false); std::string model = findDataFile(get<0>(GetParam()), false);
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam()); dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
...@@ -584,6 +584,29 @@ TEST_P(opencv_face_detector, Accuracy) ...@@ -584,6 +584,29 @@ TEST_P(opencv_face_detector, Accuracy)
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801); 0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
normAssertDetections(ref, out, "", 0.5, 1e-5, 2e-4); normAssertDetections(ref, out, "", 0.5, 1e-5, 2e-4);
} }
// False positives bug for large faces: https://github.com/opencv/opencv/issues/15106
TEST_P(opencv_face_detector, issue_15106)
{
std::string proto = findDataFile("dnn/opencv_face_detector.prototxt");
std::string model = findDataFile(get<0>(GetParam()), false);
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("cv/shared/lena.png"));
img = img.rowRange(img.rows / 4, 3 * img.rows / 4).colRange(img.cols / 4, 3 * img.cols / 4);
Mat blob = blobFromImage(img, 1.0, Size(300, 300), Scalar(104.0, 177.0, 123.0), false, false);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(targetId);
net.setInput(blob);
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
Mat ref = (Mat_<float>(1, 7) << 0, 1, 0.9149431, 0.30424616, 0.26964942, 0.88733053, 0.99815309);
normAssertDetections(ref, out, "", 0.2, 6e-5, 1e-4);
}
INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector, INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
Combine( Combine(
Values("dnn/opencv_face_detector.caffemodel", Values("dnn/opencv_face_detector.caffemodel",
......
samples/dnn/face_detector/deploy.prototxt
View file @ 39a68897
...@@ -892,7 +892,7 @@ layer { ...@@ -892,7 +892,7 @@ layer {
} }
convolution_param { convolution_param {
num_output: 128 num_output: 128
pad: 1 pad: 0
kernel_size: 3 kernel_size: 3
stride: 1 stride: 1
weight_filler { weight_filler {
...@@ -958,7 +958,7 @@ layer { ...@@ -958,7 +958,7 @@ layer {
} }
convolution_param { convolution_param {
num_output: 128 num_output: 128
pad: 1 pad: 0
kernel_size: 3 kernel_size: 3
stride: 1 stride: 1
weight_filler { weight_filler {
......
samples/dnn/face_detector/deploy_lowres.prototxt 0 → 100644
View file @ 39a68897
# This file is based on deploy.prototxt but might be used for input resolution less than 300x300
input: "data"
input_shape {
dim: 1
dim: 3
dim: 300
dim: 300
}
layer {
name: "data_bn"
type: "BatchNorm"
bottom: "data"
top: "data_bn"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "data_scale"
type: "Scale"
bottom: "data_bn"
top: "data_bn"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv1_h"
type: "Convolution"
bottom: "data_bn"
top: "conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "msra"
variance_norm: FAN_OUT
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv1_bn_h"
type: "BatchNorm"
bottom: "conv1_h"
top: "conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "conv1_scale_h"
type: "Scale"
bottom: "conv1_h"
top: "conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv1_relu"
type: "ReLU"
bottom: "conv1_h"
top: "conv1_h"
}
layer {
name: "conv1_pool"
type: "Pooling"
bottom: "conv1_h"
top: "conv1_pool"
pooling_param {
kernel_size: 3
stride: 2
}
}
layer {
name: "layer_64_1_conv1_h"
type: "Convolution"
bottom: "conv1_pool"
top: "layer_64_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_64_1_bn2_h"
type: "BatchNorm"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_64_1_scale2_h"
type: "Scale"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_64_1_relu2"
type: "ReLU"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv1_h"
}
layer {
name: "layer_64_1_conv2_h"
type: "Convolution"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv2_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_64_1_sum"
type: "Eltwise"
bottom: "layer_64_1_conv2_h"
bottom: "conv1_pool"
top: "layer_64_1_sum"
}
layer {
name: "layer_128_1_bn1_h"
type: "BatchNorm"
bottom: "layer_64_1_sum"
top: "layer_128_1_bn1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_128_1_scale1_h"
type: "Scale"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_bn1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_128_1_relu1"
type: "ReLU"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_bn1_h"
}
layer {
name: "layer_128_1_conv1_h"
type: "Convolution"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_128_1_bn2"
type: "BatchNorm"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_128_1_scale2"
type: "Scale"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_128_1_relu2"
type: "ReLU"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv1_h"
}
layer {
name: "layer_128_1_conv2"
type: "Convolution"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_128_1_conv_expand_h"
type: "Convolution"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_conv_expand_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 0
kernel_size: 1
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_128_1_sum"
type: "Eltwise"
bottom: "layer_128_1_conv2"
bottom: "layer_128_1_conv_expand_h"
top: "layer_128_1_sum"
}
layer {
name: "layer_256_1_bn1"
type: "BatchNorm"
bottom: "layer_128_1_sum"
top: "layer_256_1_bn1"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_256_1_scale1"
type: "Scale"
bottom: "layer_256_1_bn1"
top: "layer_256_1_bn1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_256_1_relu1"
type: "ReLU"
bottom: "layer_256_1_bn1"
top: "layer_256_1_bn1"
}
layer {
name: "layer_256_1_conv1"
type: "Convolution"
bottom: "layer_256_1_bn1"
top: "layer_256_1_conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_256_1_bn2"
type: "BatchNorm"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv1"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_256_1_scale2"
type: "Scale"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_256_1_relu2"
type: "ReLU"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv1"
}
layer {
name: "layer_256_1_conv2"
type: "Convolution"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_256_1_conv_expand"
type: "Convolution"
bottom: "layer_256_1_bn1"
top: "layer_256_1_conv_expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 0
kernel_size: 1
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_256_1_sum"
type: "Eltwise"
bottom: "layer_256_1_conv2"
bottom: "layer_256_1_conv_expand"
top: "layer_256_1_sum"
}
layer {
name: "layer_512_1_bn1"
type: "BatchNorm"
bottom: "layer_256_1_sum"
top: "layer_512_1_bn1"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_512_1_scale1"
type: "Scale"
bottom: "layer_512_1_bn1"
top: "layer_512_1_bn1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_512_1_relu1"
type: "ReLU"
bottom: "layer_512_1_bn1"
top: "layer_512_1_bn1"
}
layer {
name: "layer_512_1_conv1_h"
type: "Convolution"
bottom: "layer_512_1_bn1"
top: "layer_512_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1 # 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_512_1_bn2_h"
type: "BatchNorm"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_512_1_scale2_h"
type: "Scale"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_512_1_relu2"
type: "ReLU"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv1_h"
}
layer {
name: "layer_512_1_conv2_h"
type: "Convolution"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv2_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 2 # 1
kernel_size: 3
stride: 1
dilation: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_512_1_conv_expand_h"
type: "Convolution"
bottom: "layer_512_1_bn1"
top: "layer_512_1_conv_expand_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 0
kernel_size: 1
stride: 1 # 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_512_1_sum"
type: "Eltwise"
bottom: "layer_512_1_conv2_h"
bottom: "layer_512_1_conv_expand_h"
top: "layer_512_1_sum"
}
layer {
name: "last_bn_h"
type: "BatchNorm"
bottom: "layer_512_1_sum"
top: "layer_512_1_sum"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "last_scale_h"
type: "Scale"
bottom: "layer_512_1_sum"
top: "layer_512_1_sum"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "last_relu"
type: "ReLU"
bottom: "layer_512_1_sum"
top: "fc7"
}
layer {
name: "conv6_1_h"
type: "Convolution"
bottom: "fc7"
top: "conv6_1_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_1_relu"
type: "ReLU"
bottom: "conv6_1_h"
top: "conv6_1_h"
}
layer {
name: "conv6_2_h"
type: "Convolution"
bottom: "conv6_1_h"
top: "conv6_2_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_relu"
type: "ReLU"
bottom: "conv6_2_h"
top: "conv6_2_h"
}
layer {
name: "conv7_1_h"
type: "Convolution"
bottom: "conv6_2_h"
top: "conv7_1_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_1_relu"
type: "ReLU"
bottom: "conv7_1_h"
top: "conv7_1_h"
}
layer {
name: "conv7_2_h"
type: "Convolution"
bottom: "conv7_1_h"
top: "conv7_2_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_relu"
type: "ReLU"
bottom: "conv7_2_h"
top: "conv7_2_h"
}
layer {
name: "conv8_1_h"
type: "Convolution"
bottom: "conv7_2_h"
top: "conv8_1_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_1_relu"
type: "ReLU"
bottom: "conv8_1_h"
top: "conv8_1_h"
}
layer {
name: "conv8_2_h"
type: "Convolution"
bottom: "conv8_1_h"
top: "conv8_2_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_relu"
type: "ReLU"
bottom: "conv8_2_h"
top: "conv8_2_h"
}
# layer {
# name: "conv9_1_h"
# type: "Convolution"
# bottom: "conv8_2_h"
# top: "conv9_1_h"
# param {
# lr_mult: 1
# decay_mult: 1
# }
# param {
# lr_mult: 2
# decay_mult: 0
# }
# convolution_param {
# num_output: 64
# pad: 0
# kernel_size: 1
# stride: 1
# weight_filler {
# type: "xavier"
# }
# bias_filler {
# type: "constant"
# value: 0
# }
# }
# }
# layer {
# name: "conv9_1_relu"
# type: "ReLU"
# bottom: "conv9_1_h"
# top: "conv9_1_h"
# }
# layer {
# name: "conv9_2_h"
# type: "Convolution"
# bottom: "conv9_1_h"
# top: "conv9_2_h"
# param {
# lr_mult: 1
# decay_mult: 1
# }
# param {
# lr_mult: 2
# decay_mult: 0
# }
# convolution_param {
# num_output: 128
# pad: 0
# kernel_size: 3
# stride: 1
# weight_filler {
# type: "xavier"
# }
# bias_filler {
# type: "constant"
# value: 0
# }
# }
# }
# layer {
# name: "conv9_2_relu"
# type: "ReLU"
# bottom: "conv9_2_h"
# top: "conv9_2_h"
# }
layer {
name: "conv4_3_norm"
type: "Normalize"
bottom: "layer_256_1_bn1"
top: "conv4_3_norm"
norm_param {
across_spatial: false
scale_filler {
type: "constant"
value: 20
}
channel_shared: false
}
}
layer {
name: "conv4_3_norm_mbox_loc"
type: "Convolution"
bottom: "conv4_3_norm"
top: "conv4_3_norm_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4_3_norm_mbox_loc_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_loc"
top: "conv4_3_norm_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv4_3_norm_mbox_loc_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_loc_perm"
top: "conv4_3_norm_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv4_3_norm_mbox_conf"
type: "Convolution"
bottom: "conv4_3_norm"
top: "conv4_3_norm_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8 # 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4_3_norm_mbox_conf_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_conf"
top: "conv4_3_norm_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv4_3_norm_mbox_conf_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_conf_perm"
top: "conv4_3_norm_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv4_3_norm_mbox_priorbox"
type: "PriorBox"
bottom: "conv4_3_norm"
bottom: "data"
top: "conv4_3_norm_mbox_priorbox"
prior_box_param {
min_size: 30.0
max_size: 60.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 8
offset: 0.5
}
}
layer {
name: "fc7_mbox_loc"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc7_mbox_loc_perm"
type: "Permute"
bottom: "fc7_mbox_loc"
top: "fc7_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "fc7_mbox_loc_flat"
type: "Flatten"
bottom: "fc7_mbox_loc_perm"
top: "fc7_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "fc7_mbox_conf"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 12 # 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc7_mbox_conf_perm"
type: "Permute"
bottom: "fc7_mbox_conf"
top: "fc7_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "fc7_mbox_conf_flat"
type: "Flatten"
bottom: "fc7_mbox_conf_perm"
top: "fc7_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "fc7_mbox_priorbox"
type: "PriorBox"
bottom: "fc7"
bottom: "data"
top: "fc7_mbox_priorbox"
prior_box_param {
min_size: 60.0
max_size: 111.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 16
offset: 0.5
}
}
layer {
name: "conv6_2_mbox_loc"
type: "Convolution"
bottom: "conv6_2_h"
top: "conv6_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_mbox_loc_perm"
type: "Permute"
bottom: "conv6_2_mbox_loc"
top: "conv6_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv6_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv6_2_mbox_loc_perm"
top: "conv6_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv6_2_mbox_conf"
type: "Convolution"
bottom: "conv6_2_h"
top: "conv6_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 12 # 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_mbox_conf_perm"
type: "Permute"
bottom: "conv6_2_mbox_conf"
top: "conv6_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv6_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv6_2_mbox_conf_perm"
top: "conv6_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv6_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv6_2_h"
bottom: "data"
top: "conv6_2_mbox_priorbox"
prior_box_param {
min_size: 111.0
max_size: 162.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 32
offset: 0.5
}
}
layer {
name: "conv7_2_mbox_loc"
type: "Convolution"
bottom: "conv7_2_h"
top: "conv7_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_mbox_loc_perm"
type: "Permute"
bottom: "conv7_2_mbox_loc"
top: "conv7_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv7_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv7_2_mbox_loc_perm"
top: "conv7_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv7_2_mbox_conf"
type: "Convolution"
bottom: "conv7_2_h"
top: "conv7_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 12 # 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_mbox_conf_perm"
type: "Permute"
bottom: "conv7_2_mbox_conf"
top: "conv7_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv7_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv7_2_mbox_conf_perm"
top: "conv7_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv7_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv7_2_h"
bottom: "data"
top: "conv7_2_mbox_priorbox"
prior_box_param {
min_size: 162.0
max_size: 213.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 64
offset: 0.5
}
}
layer {
name: "conv8_2_mbox_loc"
type: "Convolution"
bottom: "conv8_2_h"
top: "conv8_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_mbox_loc_perm"
type: "Permute"
bottom: "conv8_2_mbox_loc"
top: "conv8_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv8_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv8_2_mbox_loc_perm"
top: "conv8_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv8_2_mbox_conf"
type: "Convolution"
bottom: "conv8_2_h"
top: "conv8_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8 # 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_mbox_conf_perm"
type: "Permute"
bottom: "conv8_2_mbox_conf"
top: "conv8_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv8_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv8_2_mbox_conf_perm"
top: "conv8_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv8_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv8_2_h"
bottom: "data"
top: "conv8_2_mbox_priorbox"
prior_box_param {
min_size: 213.0
max_size: 264.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 100
offset: 0.5
}
}
# layer {
# name: "conv9_2_mbox_loc"
# type: "Convolution"
# bottom: "conv9_2_h"
# top: "conv9_2_mbox_loc"
# param {
# lr_mult: 1
# decay_mult: 1
# }
# param {
# lr_mult: 2
# decay_mult: 0
# }
# convolution_param {
# num_output: 16
# pad: 1
# kernel_size: 3
# stride: 1
# weight_filler {
# type: "xavier"
# }
# bias_filler {
# type: "constant"
# value: 0
# }
# }
# }
# layer {
# name: "conv9_2_mbox_loc_perm"
# type: "Permute"
# bottom: "conv9_2_mbox_loc"
# top: "conv9_2_mbox_loc_perm"
# permute_param {
# order: 0
# order: 2
# order: 3
# order: 1
# }
# }
# layer {
# name: "conv9_2_mbox_loc_flat"
# type: "Flatten"
# bottom: "conv9_2_mbox_loc_perm"
# top: "conv9_2_mbox_loc_flat"
# flatten_param {
# axis: 1
# }
# }
# layer {
# name: "conv9_2_mbox_conf"
# type: "Convolution"
# bottom: "conv9_2_h"
# top: "conv9_2_mbox_conf"
# param {
# lr_mult: 1
# decay_mult: 1
# }
# param {
# lr_mult: 2
# decay_mult: 0
# }
# convolution_param {
# num_output: 8 # 84
# pad: 1
# kernel_size: 3
# stride: 1
# weight_filler {
# type: "xavier"
# }
# bias_filler {
# type: "constant"
# value: 0
# }
# }
# }
# layer {
# name: "conv9_2_mbox_conf_perm"
# type: "Permute"
# bottom: "conv9_2_mbox_conf"
# top: "conv9_2_mbox_conf_perm"
# permute_param {
# order: 0
# order: 2
# order: 3
# order: 1
# }
# }
# layer {
# name: "conv9_2_mbox_conf_flat"
# type: "Flatten"
# bottom: "conv9_2_mbox_conf_perm"
# top: "conv9_2_mbox_conf_flat"
# flatten_param {
# axis: 1
# }
# }
# layer {
# name: "conv9_2_mbox_priorbox"
# type: "PriorBox"
# bottom: "conv9_2_h"
# bottom: "data"
# top: "conv9_2_mbox_priorbox"
# prior_box_param {
# min_size: 264.0
# max_size: 315.0
# aspect_ratio: 2
# flip: true
# clip: false
# variance: 0.1
# variance: 0.1
# variance: 0.2
# variance: 0.2
# step: 300
# offset: 0.5
# }
# }
layer {
name: "mbox_loc"
type: "Concat"
bottom: "conv4_3_norm_mbox_loc_flat"
bottom: "fc7_mbox_loc_flat"
bottom: "conv6_2_mbox_loc_flat"
bottom: "conv7_2_mbox_loc_flat"
bottom: "conv8_2_mbox_loc_flat"
# bottom: "conv9_2_mbox_loc_flat"
top: "mbox_loc"
concat_param {
axis: 1
}
}
layer {
name: "mbox_conf"
type: "Concat"
bottom: "conv4_3_norm_mbox_conf_flat"
bottom: "fc7_mbox_conf_flat"
bottom: "conv6_2_mbox_conf_flat"
bottom: "conv7_2_mbox_conf_flat"
bottom: "conv8_2_mbox_conf_flat"
# bottom: "conv9_2_mbox_conf_flat"
top: "mbox_conf"
concat_param {
axis: 1
}
}
layer {
name: "mbox_priorbox"
type: "Concat"
bottom: "conv4_3_norm_mbox_priorbox"
bottom: "fc7_mbox_priorbox"
bottom: "conv6_2_mbox_priorbox"
bottom: "conv7_2_mbox_priorbox"
bottom: "conv8_2_mbox_priorbox"
# bottom: "conv9_2_mbox_priorbox"
top: "mbox_priorbox"
concat_param {
axis: 2
}
}
layer {
name: "mbox_conf_reshape"
type: "Reshape"
bottom: "mbox_conf"
top: "mbox_conf_reshape"
reshape_param {
shape {
dim: 0
dim: -1
dim: 2
}
}
}
layer {
name: "mbox_conf_softmax"
type: "Softmax"
bottom: "mbox_conf_reshape"
top: "mbox_conf_softmax"
softmax_param {
axis: 2
}
}
layer {
name: "mbox_conf_flatten"
type: "Flatten"
bottom: "mbox_conf_softmax"
top: "mbox_conf_flatten"
flatten_param {
axis: 1
}
}
layer {
name: "detection_out"
type: "DetectionOutput"
bottom: "mbox_loc"
bottom: "mbox_conf_flatten"
bottom: "mbox_priorbox"
top: "detection_out"
include {
phase: TEST
}
detection_output_param {
num_classes: 2
share_location: true
background_label_id: 0
nms_param {
nms_threshold: 0.45
top_k: 400
}
code_type: CENTER_SIZE
keep_top_k: 200
confidence_threshold: 0.01
}
}
samples/dnn/js_face_recognition.html
View file @ 39a68897
...@@ -69,7 +69,7 @@ function recognize(face) { ...@@ -69,7 +69,7 @@ function recognize(face) {
function loadModels(callback) { function loadModels(callback) {
var utils = new Utils(''); var utils = new Utils('');
var proto = 'https://raw.githubusercontent.com/opencv/opencv/3.4/samples/dnn/face_detector/deploy.prototxt'; var proto = 'https://raw.githubusercontent.com/opencv/opencv/3.4/samples/dnn/face_detector/deploy_lowres.prototxt';
var weights = 'https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel'; var weights = 'https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel';
var recognModel = 'https://raw.githubusercontent.com/pyannote/pyannote-data/master/openface.nn4.small2.v1.t7'; var recognModel = 'https://raw.githubusercontent.com/pyannote/pyannote-data/master/openface.nn4.small2.v1.t7';
utils.createFileFromUrl('face_detector.prototxt', proto, () => { utils.createFileFromUrl('face_detector.prototxt', proto, () => {
......
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