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authorJörg Frings-Fürst <debian@jff.email>2023-06-28 21:35:52 +0200
committerJörg Frings-Fürst <debian@jff.email>2023-06-28 21:35:52 +0200
commitb86540b743f1a87a163ffb811c8fe22a01fefa38 (patch)
treeb47cb3bb83c2377234226fb3987ab3320a987dd9 /subprojects
parentac6e0b731b9f0b2efd392e3309a5c07e2a66adad (diff)
parente905d8e16eec152d19797937f13ba3cf4b8f8aca (diff)
Merge branch 'release/debian/0.32.1-1'debian/0.32.1-1
Diffstat (limited to 'subprojects')
-rw-r--r--subprojects/shotwell-facedetect/deploy.prototxt1790
-rw-r--r--subprojects/shotwell-facedetect/facedetect-opencv.cpp281
-rw-r--r--subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml24350
-rw-r--r--subprojects/shotwell-facedetect/haarcascade_profileface.xml29690
-rw-r--r--subprojects/shotwell-facedetect/meson.build62
-rw-r--r--subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7bin0 -> 31510785 bytes
-rw-r--r--subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in7
-rw-r--r--subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in3
-rw-r--r--subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml42
-rw-r--r--subprojects/shotwell-facedetect/shotwell-facedetect.cpp167
-rw-r--r--subprojects/shotwell-facedetect/shotwell-facedetect.hpp33
11 files changed, 56425 insertions, 0 deletions
diff --git a/subprojects/shotwell-facedetect/deploy.prototxt b/subprojects/shotwell-facedetect/deploy.prototxt
new file mode 100644
index 0000000..a128515
--- /dev/null
+++ b/subprojects/shotwell-facedetect/deploy.prototxt
@@ -0,0 +1,1790 @@
+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
+ clip: 1
+ }
+}
diff --git a/subprojects/shotwell-facedetect/facedetect-opencv.cpp b/subprojects/shotwell-facedetect/facedetect-opencv.cpp
new file mode 100644
index 0000000..8b0ad10
--- /dev/null
+++ b/subprojects/shotwell-facedetect/facedetect-opencv.cpp
@@ -0,0 +1,281 @@
+// SPDX-License-Identifier: LGPL-2.1-or-later
+
+#include "shotwell-facedetect.hpp"
+
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <opencv2/objdetect/objdetect.hpp>
+
+#ifdef HAS_OPENCV_DNN
+ #include <opencv2/dnn.hpp>
+#endif
+
+#include <iostream>
+#include <string>
+#include <filesystem>
+
+// Global variable for DNN to generate vector out of face
+#ifdef HAS_OPENCV_DNN
+static cv::dnn::Net faceRecogNet;
+static cv::dnn::Net faceDetectNet;
+#endif
+
+static cv::CascadeClassifier cascade;
+static cv::CascadeClassifier cascade_profile;
+static bool disableDnn{ true };
+
+constexpr std::string_view PROTOTEXT_FILE{ "deploy.prototxt" };
+constexpr std::string_view OPENFACE_RECOG_TORCH_NET{ "openface.nn4.small2.v1.t7" };
+constexpr std::string_view RESNET_DETECT_CAFFE_NET{ "res10_300x300_ssd_iter_140000_fp16.caffemodel" };
+constexpr std::string_view HAARCASCADE{ "haarcascade_frontalface_alt.xml" };
+constexpr std::string_view HAARCASCADE_PROFILE{ "haarcascade_profileface.xml" };
+
+std::vector<cv::Rect> detectFacesMat(const cv::Mat &img);
+std::vector<double> faceToVecMat(const cv::Mat& img);
+
+// Detect faces in a photo
+std::vector<FaceRect> detectFaces(const cv::String &inputName, double scale, bool infer = false) {
+ if(cascade.empty()) {
+ g_warning("No cascade file loaded. Did you call loadNet()?");
+ return {};
+ }
+
+ if (inputName.empty()) {
+ g_warning("No file to process. aborting");
+ return {};
+ }
+
+ cv::Mat const img = cv::imread(inputName, 1);
+ if (img.empty()) {
+ g_warning("Failed to load the image file: %s", inputName.c_str());
+ return {};
+ }
+
+ std::vector<cv::Rect> faces;
+ cv::Size smallImgSize;
+
+#ifdef HAS_OPENCV_DNN
+ disableDnn = faceDetectNet.empty();
+#else
+ disableDnn = true;
+#endif
+ try {
+ if (disableDnn) {
+ // Classical face detection
+ cv::Mat gray;
+ cvtColor(img, gray, cv::COLOR_BGR2GRAY);
+
+ scale = 1.0;
+ cv::Mat smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);
+ smallImgSize = smallImg.size();
+
+ cv::resize(gray, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+ cv::equalizeHist(smallImg, smallImg);
+ constexpr double SCALE_FACTOR_FRONTAL{ 1.1 };
+ constexpr double SCALE_FACTOR_PROFILE{ 1.05 };
+ constexpr int MIN_NEIGHBOURS{ 2 };
+ constexpr int MIN_SIZE{ 30 };
+ cascade.detectMultiScale (smallImg,
+ faces,
+ SCALE_FACTOR_FRONTAL,
+ MIN_NEIGHBOURS,
+ cv::CASCADE_SCALE_IMAGE,
+ cv::Size (MIN_SIZE, MIN_SIZE));
+
+ // Run the cascade for profile faces, if available
+ if(not cascade_profile.empty()) {
+ g_debug("Running haarcascade detection for profile faces");
+ std::vector<cv::Rect> profiles;
+ cascade_profile.detectMultiScale (smallImg,
+ profiles,
+ SCALE_FACTOR_PROFILE,
+ MIN_NEIGHBOURS,
+ cv::CASCADE_SCALE_IMAGE,
+ cv::Size (MIN_SIZE, MIN_SIZE));
+ if(not profiles.empty()) {
+ faces.insert(faces.end(), profiles.begin(), profiles.end());
+ }
+
+ // Duplicate all rectangles so we can safely run groupRectangles with minimum 1 on it - otherwise
+ // OpenCV does weird things
+ faces.insert(faces.end(), faces.begin(), faces.end());
+
+ // Try to merge all overlapping rectangles
+ cv::groupRectangles(faces, 1);
+ }
+ } else {
+ #ifdef HAS_OPENCV_DNN
+ // DNN based face detection
+ faces = detectFacesMat(img);
+ smallImgSize = img.size(); // Not using the small image here
+ #endif
+ }
+ } catch (cv::Exception& ex) {
+ g_warning("Face detection failed: %s", ex.what());
+ return {};
+ }
+
+ std::vector<FaceRect> scaled;
+ for (std::vector<cv::Rect>::const_iterator r = faces.begin(); r != faces.end(); r++) {
+ FaceRect i;
+ i.x = (float) r->x / smallImgSize.width;
+ i.y = (float) r->y / smallImgSize.height;
+ i.width = (float) r->width / smallImgSize.width;
+ i.height = (float) r->height / smallImgSize.height;
+
+#ifdef HAS_OPENCV_DNN
+ try {
+ if (infer && !faceRecogNet.empty()) {
+ // Get colour image for vector generation
+ cv::Mat colourImg;
+ cv::resize(img, colourImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+ i.vec = faceToVecMat(colourImg(*r)); // Run vector conversion on the face
+ }
+ } catch (cv::Exception& ex) {
+ g_warning("Face recognition failed: %s", ex.what());
+ i.vec = {};
+ }
+#endif
+ scaled.push_back(i);
+ }
+
+ return scaled;
+}
+
+// Load network into global var
+bool loadNet(const cv::String &baseDir)
+{
+ // Split baseDir into multiple search paths
+ std::stringstream iss{ baseDir };
+ std::string path;
+ while(std::getline(iss, path, ':')) {
+ g_debug("Looking for face detection data files in %s", path.c_str());
+
+ std::filesystem::path const base_path{ path };
+
+ auto haarcascade = base_path / HAARCASCADE;
+ if(cascade.empty()) {
+ cascade.load(haarcascade);
+ }
+
+ if(cascade.empty()) {
+ g_info("%s not found", haarcascade.c_str());
+ }
+
+ auto haarcascade_profile = base_path / HAARCASCADE_PROFILE;
+ if(cascade_profile.empty()) {
+ cascade_profile.load(haarcascade_profile);
+ }
+
+ if(cascade_profile.empty()) {
+ g_info("%s not found", haarcascade_profile.c_str());
+ }
+
+#if HAS_OPENCV_DNN
+
+ if(faceDetectNet.empty()) {
+ try {
+ faceDetectNet =
+ cv::dnn::readNetFromCaffe(base_path / PROTOTEXT_FILE, base_path / RESNET_DETECT_CAFFE_NET);
+ } catch(cv::Exception &e) {
+ g_info("Failed to load face detect net: %s", e.what());
+ }
+ }
+
+ if(faceRecogNet.empty()) {
+ try {
+ faceRecogNet = cv::dnn::readNetFromTorch(base_path / OPENFACE_RECOG_TORCH_NET);
+ } catch(cv::Exception &e) {
+ g_info("Failed to load face recognition net: %s", e.what());
+ }
+ }
+#endif
+ }
+
+ if(cascade.empty() && cascade_profile.empty() && faceDetectNet.empty()) {
+ g_warning("No face detection method detected. Face detection fill not work.");
+ return false;
+ }
+
+#if HAS_OPENCV_DNN
+ // If there is no detection model, disable advanced face detection
+ disableDnn = faceDetectNet.empty();
+
+ if(faceRecogNet.empty()) {
+ g_warning("Face recognition net not available, disabling recognition");
+ }
+
+ return true;
+#else
+ return not cascade.empty() && not cascade_profile.empty();
+#endif
+}
+
+// Face detector
+// Adapted from OpenCV example:
+// https://github.com/opencv/opencv/blob/master/samples/dnn/js_face_recognition.html
+std::vector<cv::Rect> detectFacesMat(const cv::Mat& img) {
+ std::vector<cv::Rect> faces;
+#ifdef HAS_OPENCV_DNN
+ const cv::Mat blob = cv::dnn::blobFromImage(img, 1.0, cv::Size(128*8, 96*8),
+ cv::Scalar(104, 177, 123, 0), false, false);
+ faceDetectNet.setInput(blob);
+ cv::Mat out = faceDetectNet.forward();
+ // out is a 4D matrix [1 x 1 x n x 7]
+ // n - number of results
+ assert(out.dims == 4);
+ int outIdx[4] = { 0, 0, 0, 0 };
+ auto result_size = out.size[2];
+ for (auto i = 0; i < result_size; i++) {
+ outIdx[2] = i; outIdx[3] = 2;
+ const auto confidence = out.at<float>(outIdx);
+ outIdx[3]++;
+ auto left = out.at<float>(outIdx) * (double)img.cols;
+ outIdx[3]++;
+ auto top = out.at<float>(outIdx) * (double)img.rows;
+ outIdx[3]++;
+ auto right = out.at<float>(outIdx) * (double)img.cols;
+ outIdx[3]++;
+ auto bottom = out.at<float> (outIdx) * (double)img.rows;
+ left = std::clamp (left, 0.0, (double) img.cols - 1);
+ right = std::clamp (right, 0.0, (double) img.cols - 1);
+ bottom = std::clamp (bottom, 0.0, (double) img.rows - 1);
+ top = std::clamp (top, 0.0, (double) img.rows - 1);
+
+ constexpr double CONFIDENCE_THRESHOLD{ 0.98 };
+ if (confidence > CONFIDENCE_THRESHOLD && left < right && top < bottom) {
+ const cv::Rect rect (static_cast<int> (left),
+ static_cast<int> (top),
+ static_cast<int> (right - left),
+ static_cast<int> (bottom - top));
+ faces.push_back(rect);
+ }
+ }
+#endif // HAS_OPENCV_DNN
+ return faces;
+}
+
+// Face to vector converter
+// Adapted from OpenCV example:
+// https://github.com/opencv/opencv/blob/master/samples/dnn/js_face_recognition.html
+#ifdef HAS_OPENCV_DNN
+std::vector<double> faceToVecMat(const cv::Mat &img) {
+ std::vector<double> ret;
+ constexpr int SMALL_IMAGE_SIZE{ 96 };
+ cv::Mat smallImg(SMALL_IMAGE_SIZE, SMALL_IMAGE_SIZE, CV_8UC1);
+ const cv::Size smallImgSize = smallImg.size();
+
+ cv::resize(img, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
+ // Generate 128 element face vector using DNN
+ constexpr double SCALE_FACTOR{ 1.0 / 255.0 };
+ const cv::Mat blob = cv::dnn::blobFromImage (smallImg, SCALE_FACTOR, smallImgSize, cv::Scalar (), true, false);
+
+ faceRecogNet.setInput(blob);
+ cv::Mat vec = faceRecogNet.forward();
+ // Return vector
+ for (int i = 0; i < vec.rows; ++i) {
+ ret.insert(ret.end(), vec.ptr<float>(i), vec.ptr<float>(i) + vec.cols);
+ }
+ return ret;
+}
+#endif
diff --git a/subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml b/subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml
new file mode 100644
index 0000000..ade4b21
--- /dev/null
+++ b/subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml
@@ -0,0 +1,24350 @@
+<?xml version="1.0"?>
+<!--
+ Stump-based 20x20 gentle adaboost frontal face detector.
+ Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+ IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+ By downloading, copying, installing or using the software you agree to this license.
+ If you do not agree to this license, do not download, install,
+ copy or use the software.
+
+
+ Intel License Agreement
+ For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistribution's of source code must retain the above copyright notice,
+ this list of conditions and the following disclaimer.
+
+ * Redistribution's in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+ * The name of Intel Corporation may not be used to endorse or promote products
+ derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+ <featureType>HAAR</featureType>
+ <height>20</height>
+ <width>20</width>
+ <stageParams>
+ <maxWeakCount>213</maxWeakCount></stageParams>
+ <featureParams>
+ <maxCatCount>0</maxCatCount></featureParams>
+ <stageNum>22</stageNum>
+ <stages>
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diff --git a/subprojects/shotwell-facedetect/haarcascade_profileface.xml b/subprojects/shotwell-facedetect/haarcascade_profileface.xml
new file mode 100644
index 0000000..486d8e3
--- /dev/null
+++ b/subprojects/shotwell-facedetect/haarcascade_profileface.xml
@@ -0,0 +1,29690 @@
+<?xml version="1.0"?>
+<!--
+ 20x20 profile face detector.
+ Contributed by David Bradley from Princeton University.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+ IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+ By downloading, copying, installing or using the software you agree to this license.
+ If you do not agree to this license, do not download, install,
+ copy or use the software.
+
+
+ Intel License Agreement
+ For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistribution's of source code must retain the above copyright notice,
+ this list of conditions and the following disclaimer.
+
+ * Redistribution's in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+ * The name of Intel Corporation may not be used to endorse or promote products
+ derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
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+</opencv_storage>
diff --git a/subprojects/shotwell-facedetect/meson.build b/subprojects/shotwell-facedetect/meson.build
new file mode 100644
index 0000000..70570e4
--- /dev/null
+++ b/subprojects/shotwell-facedetect/meson.build
@@ -0,0 +1,62 @@
+project('shotwell-facedetect', ['c', 'cpp'], default_options : ['cpp_std=c++17'])
+gnome = import('gnome')
+facedetect_dep = dependency('opencv4', version : ['>= 4.0.0'], required : false)
+if not facedetect_dep.found()
+ facedetect_dep = dependency('opencv', version : ['>= 3.4.0'], required : true)
+endif
+cpp = meson.get_compiler('cpp')
+has_dnn = cpp.has_header('opencv2/dnn.hpp', dependencies: facedetect_dep)
+if has_dnn
+ dnn_define = declare_dependency(compile_args: '-DHAS_OPENCV_DNN')
+else
+ dnn_define = []
+endif
+
+libexecdir = join_paths(get_option('libexecdir'), 'shotwell')
+
+gio = dependency('gio-2.0', version: '>= 2.40')
+gio_unix = dependency('gio-unix-2.0', required : true)
+gdbus_src = gnome.gdbus_codegen('dbus-interface',
+ sources: 'org.gnome.ShotwellFaces1.xml',
+ interface_prefix : 'org.gnome.')
+
+con = configuration_data()
+con.set('libexecdir', join_paths(get_option('prefix'), libexecdir))
+
+if meson.is_subproject()
+ config_incdir = include_directories('../..')
+else
+ config_incdir = include_directories('.')
+ configure_file(
+ input: 'org.gnome.Shotwell.Faces1.desktop.in',
+ output: 'org.gnome.Shotwell.Faces1.desktop',
+ configuration: con,
+ install: true,
+ install_dir : join_paths(get_option('datadir'), 'applications')
+ )
+endif
+
+executable('shotwell-facedetect',
+ 'shotwell-facedetect.cpp', 'facedetect-opencv.cpp', gdbus_src,
+ dependencies : [facedetect_dep, gio, gio_unix, dnn_define],
+ install : true,
+ include_directories: config_incdir,
+ install_dir : libexecdir)
+install_data('haarcascade_frontalface_alt.xml',
+ install_dir : join_paths(get_option('datadir'), 'shotwell', 'facedetect'))
+install_data('haarcascade_profileface.xml',
+ install_dir : join_paths(get_option('datadir'), 'shotwell', 'facedetect'))
+install_data(
+ 'openface.nn4.small2.v1.t7',
+ install_dir: join_paths(get_option('datadir'), 'shotwell', 'facedetect')
+)
+
+install_data('deploy.prototxt', install_dir: join_paths(get_option('datadir'), 'shotwell', 'facedetect'))
+
+configure_file(
+ input : 'org.gnome.Shotwell.Faces1.service.in',
+ output : 'org.gnome.Shotwell.Faces1.service',
+ configuration: con,
+ install: true,
+ install_dir : join_paths(get_option('datadir'), 'dbus-1', 'services')
+ )
diff --git a/subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7 b/subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7
new file mode 100644
index 0000000..8a0d019
--- /dev/null
+++ b/subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7
Binary files differ
diff --git a/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in
new file mode 100644
index 0000000..d64714e
--- /dev/null
+++ b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in
@@ -0,0 +1,7 @@
+[Desktop Entry]
+Version=1.0
+Name=Shotwell Face detection
+Comment=Shotwell face detection and recognition backend
+NoDisplay=true
+Type=Application
+Exec=@libexecdir@/shotwell-facedetect
diff --git a/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in
new file mode 100644
index 0000000..127d744
--- /dev/null
+++ b/subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in
@@ -0,0 +1,3 @@
+[D-BUS Service]
+Name=org.gnome.Shotwell.Faces1
+Exec=@libexecdir@/shotwell-facedetect \ No newline at end of file
diff --git a/subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml b/subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml
new file mode 100644
index 0000000..3bf1071
--- /dev/null
+++ b/subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml
@@ -0,0 +1,42 @@
+<!DOCTYPE node PUBLIC
+'-//freedesktop//DTD D-BUS Object Introspection 1.0//EN'
+'http://www.freedesktop.org/standards/dbus/1.0/introspect.dtd'>
+<node>
+ <!--
+ org.gnome.Shotwell.Faces1:
+ @short_description: Face detection/recognition
+ -->
+ <interface name="org.gnome.Shotwell.Faces1">
+ <!--
+ DetectFaces
+ @image: Image file to run face detection on
+ @cascade: Cascade XML file - unused
+ @scale: Scaling to apply on image
+ @infer: Provide an
+ Returns an array of face bounding boxes (x,y,w,h) in dimensionless units
+ -->
+ <method name="DetectFaces">
+ <arg type="s" name="image" direction="in" />
+ <arg type="s" name="cascade" direction="in" />
+ <arg type="d" name="scale" direction="in" />
+ <arg type="b" name="infer" direction="in" />
+ <arg type="a(ddddad)" name="faces" direction="out" />
+ </method>
+
+ <!--
+ LoadNet
+ @net: path to folder containing the DNN
+ Returns non-zero on any error
+ -->
+ <method name="LoadNet">
+ <arg type="s" name="net" direction="in" />
+ <arg type="b" name="ret" direction="out" />
+ </method>
+
+ <!--
+ Terminate
+ -->
+ <method name="Terminate">
+ </method>
+ </interface>
+</node>
diff --git a/subprojects/shotwell-facedetect/shotwell-facedetect.cpp b/subprojects/shotwell-facedetect/shotwell-facedetect.cpp
new file mode 100644
index 0000000..3f8edeb
--- /dev/null
+++ b/subprojects/shotwell-facedetect/shotwell-facedetect.cpp
@@ -0,0 +1,167 @@
+/* Copyright 2016 Software Freedom Conservancy Inc.
+ *
+ * Copyright 2011 Valentín Barros Puertas <valentin(at)sanva(dot)net>
+ * Copyright 2018 Ricardo Fantin da Costa <ricardofantin(at)gmail(dot)com>
+ * Copyright 2018 Narendra A <narendra_m_a(at)yahoo(dot)com>
+ *
+ * This software is licensed under the GNU LGPL (version 2.1 or later).
+ * See the COPYING file in this distribution.
+ */
+
+#include "shotwell-facedetect.hpp"
+#include "dbus-interface.h"
+
+#include <gio/gio.h>
+#include <glib.h>
+
+#include <iostream>
+
+constexpr std::string_view FACEDETECT_INTERFACE_NAME{ "org.gnome.Shotwell.Faces1" };
+constexpr std::string_view FACEDETECT_PATH{ "/org/gnome/shotwell/faces" };
+
+GVariant *FaceRect::serialize() const
+{
+ return g_variant_new("(dddd@ad)", x, y, width, height,
+ g_variant_new_fixed_array(G_VARIANT_TYPE_DOUBLE, vec.data(), vec.size(), sizeof(double)));
+}
+
+// DBus binding functions
+static gboolean on_handle_detect_faces(ShotwellFaces1 *object, GDBusMethodInvocation *invocation,
+ [[maybe_unused]]const gchar *arg_image, const gchar *arg_cascade, gdouble arg_scale,
+ gboolean arg_infer)
+{
+ g_auto(GVariantBuilder) builder = G_VARIANT_BUILDER_INIT(G_VARIANT_TYPE("a(ddddad)"));
+ auto rects = detectFaces(arg_image, arg_scale, arg_infer == TRUE);
+
+ // Construct return value
+ for(const auto &rect : rects) {
+ g_variant_builder_add(&builder, "@(ddddad)", rect.serialize());
+ g_debug("Returning %f,%f-%f", rect.x, rect.y, rect.vec.back());
+ }
+
+ // Call return
+ shotwell_faces1_complete_detect_faces(object, invocation, g_variant_builder_end(&builder));
+ return TRUE;
+}
+
+static gboolean on_handle_load_net(ShotwellFaces1 *object, GDBusMethodInvocation *invocation, const gchar *arg_net)
+{
+ // Call return
+ shotwell_faces1_complete_load_net(object, invocation, loadNet(arg_net) ? TRUE : FALSE);
+ return TRUE;
+}
+
+static gboolean on_handle_terminate(ShotwellFaces1 *object, GDBusMethodInvocation *invocation, gpointer user_data)
+{
+ g_debug("Exiting...");
+ shotwell_faces1_complete_terminate(object, invocation);
+ g_main_loop_quit(static_cast<GMainLoop *>(user_data));
+
+ return TRUE;
+}
+
+static void on_name_acquired(GDBusConnection *connection,
+ const gchar *name, gpointer user_data) {
+ g_debug("Got name %s", name);
+
+ auto *interface = shotwell_faces1_skeleton_new();
+ g_signal_connect(interface, "handle-detect-faces", G_CALLBACK (on_handle_detect_faces), nullptr);
+ g_signal_connect(interface, "handle-terminate", G_CALLBACK (on_handle_terminate), user_data);
+ g_signal_connect(interface, "handle-load-net", G_CALLBACK (on_handle_load_net), nullptr);
+
+ g_autoptr(GError) error = nullptr;
+ g_dbus_interface_skeleton_export(G_DBUS_INTERFACE_SKELETON(interface), connection, FACEDETECT_PATH.data(), &error);
+ if (error != nullptr) {
+ g_print("Failed to export interface: %s", error->message);
+ }
+}
+
+static void on_name_lost(GDBusConnection *connection,
+ const gchar *name, gpointer user_data) {
+ if (connection == nullptr) {
+ g_debug("Unable to establish connection for name %s", name);
+ } else {
+ g_debug("Connection for name %s disconnected", name);
+ }
+ g_main_loop_quit(static_cast<GMainLoop *>(user_data));
+}
+
+static char* address = nullptr;
+
+static GOptionEntry entries[] = {
+ { "address", 'a', 0, G_OPTION_ARG_STRING, &address, "Use private DBus ADDRESS instead of session", "ADDRESS" },
+ { nullptr }
+};
+
+static gboolean on_authorize_authenticated_peer([[maybe_unused]] GIOStream *iostream, GCredentials *credentials,
+ [[maybe_unused]] gpointer user_data)
+{
+ g_autoptr(GCredentials) own_credentials = nullptr;
+
+ g_debug("Authorizing peer with credentials %s\n", g_credentials_to_string(credentials));
+
+ if(credentials == nullptr) {
+ return FALSE;
+ }
+
+ own_credentials = g_credentials_new();
+
+ {
+ g_autoptr(GError) error = nullptr;
+
+ if(g_credentials_is_same_user(credentials, own_credentials, &error) == FALSE) {
+ g_warning("Unable to authorize peer: %s", error->message);
+
+ return FALSE;
+ }
+ }
+
+ return TRUE;
+}
+
+int main(int argc, char **argv) {
+ GMainLoop *loop;
+ GError *error = nullptr;
+ GOptionContext *context;
+
+ context = g_option_context_new ("- Shotwell face detection helper service");
+ g_option_context_add_main_entries (context, entries, "shotwell");
+ if (g_option_context_parse (context, &argc, &argv, &error) == FALSE) {
+ g_print ("Failed to parse options: %s\n", error->message);
+ exit(1);
+ }
+
+ loop = g_main_loop_new (nullptr, FALSE);
+
+
+ // We are running on the session bus
+ if (address == nullptr) {
+ g_debug("Starting %s on G_BUS_TYPE_SESSION", argv[0]);
+ g_bus_own_name(G_BUS_TYPE_SESSION, FACEDETECT_INTERFACE_NAME.data(), G_BUS_NAME_OWNER_FLAGS_NONE,
+ nullptr, on_name_acquired, on_name_lost, loop, nullptr);
+
+ } else {
+ g_debug("Starting %s on %s", argv[0], address);
+ GDBusAuthObserver *observer = g_dbus_auth_observer_new ();
+ g_signal_connect (G_OBJECT (observer), "authorize-authenticated-peer",
+ G_CALLBACK (on_authorize_authenticated_peer), nullptr);
+
+ GDBusConnection *connection = g_dbus_connection_new_for_address_sync (address,
+ G_DBUS_CONNECTION_FLAGS_AUTHENTICATION_CLIENT,
+ observer,
+ nullptr,
+ &error);
+ if (connection != nullptr) {
+ on_name_acquired (connection, FACEDETECT_INTERFACE_NAME.data (), loop);
+ }
+ }
+
+ if (error != nullptr) {
+ g_error("Failed to get connection on %s bus: %s",
+ address == nullptr ? "session" : "private",
+ error->message);
+ }
+
+ g_main_loop_run (loop);
+ return 0;
+}
diff --git a/subprojects/shotwell-facedetect/shotwell-facedetect.hpp b/subprojects/shotwell-facedetect/shotwell-facedetect.hpp
new file mode 100644
index 0000000..abb5a65
--- /dev/null
+++ b/subprojects/shotwell-facedetect/shotwell-facedetect.hpp
@@ -0,0 +1,33 @@
+/*
+ * Copyright 2018 Narendra A (narendra_m_a(at)yahoo dot com)
+ *
+ * This software is licensed under the GNU LGPL (version 2.1 or later).
+ * See the COPYING file in this distribution.
+ *
+ * Header file for facedetect/recognition routines
+ */
+
+#pragma once
+
+#include <opencv2/core/core.hpp>
+
+#include <gio/gio.h>
+
+#include <vector>
+
+struct FaceRect {
+ FaceRect()
+ : vec(128, 0)
+ {
+ }
+ float x{ 0.0F };
+ float y{ 0.0F };
+ float width{ 0.0F };
+ float height{ 0.0F };
+ std::vector<double> vec;
+
+ GVariant *serialize() const;
+};
+
+bool loadNet(const cv::String& netFile);
+std::vector<FaceRect> detectFaces(const cv::String& inputName, double scale, bool infer);