diff options
Diffstat (limited to 'subprojects/shotwell-facedetect')
| -rw-r--r-- | subprojects/shotwell-facedetect/deploy.prototxt | 1790 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/facedetect-opencv.cpp | 281 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/haarcascade_frontalface_alt.xml | 24350 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/haarcascade_profileface.xml | 29690 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/meson.build | 62 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7 | bin | 0 -> 31510785 bytes | |||
| -rw-r--r-- | subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.desktop.in | 7 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/org.gnome.Shotwell.Faces1.service.in | 3 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/org.gnome.ShotwellFaces1.xml | 42 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/shotwell-facedetect.cpp | 167 | ||||
| -rw-r--r-- | subprojects/shotwell-facedetect/shotwell-facedetect.hpp | 33 |
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> + <_> + <maxWeakCount>3</maxWeakCount> + <stageThreshold>8.2268941402435303e-01</stageThreshold> + <weakClassifiers> + <_> + <internalNodes> + 0 -1 0 4.0141958743333817e-03</internalNodes> + <leafValues> + 3.3794190734624863e-02 8.3781069517135620e-01</leafValues></_> + <_> + <internalNodes> + 0 -1 1 1.5151339583098888e-02</internalNodes> + 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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 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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 Binary files differnew file mode 100644 index 0000000..8a0d019 --- /dev/null +++ b/subprojects/shotwell-facedetect/openface.nn4.small2.v1.t7 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 ValentÃn 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); |