the one commit

dataset/age_deploy.prototxt

@@ -0,0 +1,175 @@
+name: "CaffeNet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 227
+input_dim: 227
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 96
+    kernel_size: 7
+    stride: 4
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers{
+  name: "relu3" 
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  inner_product_param {
+    num_output: 512
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  inner_product_param {
+    num_output: 512
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8"
+  inner_product_param {
+    num_output: 8
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8"
+  top: "prob"
+}

dataset/facial_expression_model_structure.json

@@ -0,0 +1 @@
+{"class_name": "Sequential", "backend": "tensorflow", "config": [{"class_name": "Conv2D", "config": {"dtype": "float32", "padding": "valid", "filters": 64, "trainable": true, "activation": "relu", "bias_constraint": null, "strides": [1, 1], "name": "conv2d_1", "dilation_rate": [1, 1], "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_constraint": null, "kernel_regularizer": null, "data_format": "channels_last", "use_bias": true, "batch_input_shape": [null, 48, 48, 1], "activity_regularizer": null, "kernel_size": [5, 5], "bias_initializer": {"class_name": "Zeros", "config": {}}}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "padding": "valid", "pool_size": [5, 5], "data_format": "channels_last", "trainable": true, "strides": [2, 2]}}, {"class_name": "Conv2D", "config": {"padding": "valid", "filters": 64, "trainable": true, "activation": "relu", "bias_constraint": null, "strides": [1, 1], "name": "conv2d_2", "dilation_rate": [1, 1], "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_constraint": null, "kernel_regularizer": null, "data_format": "channels_last", "use_bias": true, "activity_regularizer": null, "kernel_size": [3, 3], "bias_initializer": {"class_name": "Zeros", "config": {}}}}, {"class_name": "Conv2D", "config": {"padding": "valid", "filters": 64, "trainable": true, "activation": "relu", "bias_constraint": null, "strides": [1, 1], "name": "conv2d_3", "dilation_rate": [1, 1], "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_constraint": null, "kernel_regularizer": null, "data_format": "channels_last", "use_bias": true, "activity_regularizer": null, "kernel_size": [3, 3], "bias_initializer": {"class_name": "Zeros", "config": {}}}}, {"class_name": "AveragePooling2D", "config": {"name": "average_pooling2d_1", "padding": "valid", "pool_size": [3, 3], "data_format": "channels_last", "trainable": true, "strides": [2, 2]}}, {"class_name": "Conv2D", "config": {"padding": "valid", "filters": 128, "trainable": true, "activation": "relu", "bias_constraint": null, "strides": [1, 1], "name": "conv2d_4", "dilation_rate": [1, 1], "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_constraint": null, "kernel_regularizer": null, "data_format": "channels_last", "use_bias": true, "activity_regularizer": null, "kernel_size": [3, 3], "bias_initializer": {"class_name": "Zeros", "config": {}}}}, {"class_name": "Conv2D", "config": {"padding": "valid", "filters": 128, "trainable": true, "activation": "relu", "bias_constraint": null, "strides": [1, 1], "name": "conv2d_5", "dilation_rate": [1, 1], "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_constraint": null, "kernel_regularizer": null, "data_format": "channels_last", "use_bias": true, "activity_regularizer": null, "kernel_size": [3, 3], "bias_initializer": {"class_name": "Zeros", "config": {}}}}, {"class_name": "AveragePooling2D", "config": {"name": "average_pooling2d_2", "padding": "valid", "pool_size": [3, 3], "data_format": "channels_last", "trainable": true, "strides": [2, 2]}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true}}, {"class_name": "Dense", "config": {"use_bias": true, "trainable": true, "activation": "relu", "activity_regularizer": null, "name": "dense_1", "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_regularizer": null, "units": 1024, "bias_constraint": null, "kernel_constraint": null, "bias_initializer": {"class_name": "Zeros", "config": {}}}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "rate": 0.2}}, {"class_name": "Dense", "config": {"use_bias": true, "trainable": true, "activation": "relu", "activity_regularizer": null, "name": "dense_2", "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_regularizer": null, "units": 1024, "bias_constraint": null, "kernel_constraint": null, "bias_initializer": {"class_name": "Zeros", "config": {}}}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "rate": 0.2}}, {"class_name": "Dense", "config": {"use_bias": true, "trainable": true, "activation": "softmax", "activity_regularizer": null, "name": "dense_3", "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "mode": "fan_avg", "scale": 1.0, "seed": null}}, "bias_regularizer": null, "kernel_regularizer": null, "units": 7, "bias_constraint": null, "kernel_constraint": null, "bias_initializer": {"class_name": "Zeros", "config": {}}}}], "keras_version": "2.0.8"}

dataset/gender_deploy.prototxt

@@ -0,0 +1,175 @@
+name: "CaffeNet"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 227
+input_dim: 227
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 96
+    kernel_size: 7
+    stride: 4
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers{
+  name: "relu3" 
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  inner_product_param {
+    num_output: 512
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  inner_product_param {
+    num_output: 512
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8"
+  inner_product_param {
+    num_output: 2
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8"
+  top: "prob"
+}

main.py

@@ -0,0 +1,19 @@
+import cv2
+import face_recognition
+
+image_to_detect = cv2.imread("images/veran2.png")
+
+all_face_locations = face_recognition.face_locations(
+    image_to_detect, model="hog")
+
+print("There are {} faces detected in this image".format(len(all_face_locations)))
+
+for index, current_face_location in enumerate(all_face_locations):
+    top_pos, right_pos, bottom_pos, left_pos = current_face_location
+    print("Found face {} at top: {}, right: {}, bottom: {}, left: {}".format(
+        index + 1, top_pos, right_pos, bottom_pos, left_pos))
+    current_face_image = image_to_detect[top_pos:bottom_pos,
+                                         left_pos:right_pos]
+    cv2.imshow("Face n" + str(index), current_face_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()