faceid_beta.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "faceid_beta.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "jUg8VqdmZX8f",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# FaceID recreation using face embeddings and RGBD images.\n",
        "Made by [Norman Di Palo](https://medium.com/@normandipalo), March 2018."
      ]
    },
    {
      "metadata": {
        "id": "jj7vG-q2XjuG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Let's start by downloading the dataset."
      ]
    },
    {
      "metadata": {
        "id": "H_8TUMLAmn8P",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 2
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "515058f5-0773-48d1-8b47-338d8e1f487d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520874536552,
          "user_tz": -60,
          "elapsed": 1416,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!rm -r faceid_train\n",
        "!rm -r faceid_val"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'faceid_train': No such file or directory\n",
            "rm: cannot remove 'faceid_val': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v3s164uzmrXW",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a1954e21-068c-4062-bcca-c4ea744e7726",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520874537595,
          "user_tz": -60,
          "elapsed": 874,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datalab\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ovZVY_1L0gEz",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!mkdir faceid_train\n",
        "!mkdir faceid_val"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YZQsKxXI0lei",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 4
            },
            {
              "item_id": 5
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "e28176ad-0ed4-4b85-c435-91cee82f3fbe",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520874572816,
          "user_tz": -60,
          "elapsed": 33902,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# https://keras.io/\n",
        "!pip install -U keras\n",
        "import keras"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keras\n",
            "  Downloading Keras-2.1.5-py2.py3-none-any.whl (334kB)\n",
            "\u001b[K    100% |████████████████████████████████| 337kB 2.3MB/s \n",
            "\u001b[?25hCollecting scipy>=0.14 (from keras)\n",
            "  Downloading scipy-1.0.0-cp36-cp36m-manylinux1_x86_64.whl (50.0MB)\n",
            "\u001b[K    100% |████████████████████████████████| 50.0MB 27kB/s \n",
            "\u001b[?25hCollecting pyyaml (from keras)\n",
            "  Downloading PyYAML-3.12.tar.gz (253kB)\n",
            "\u001b[K    100% |████████████████████████████████| 256kB 3.4MB/s \n",
            "\u001b[?25hRequirement already up-to-date: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras)\n",
            "Collecting numpy>=1.9.1 (from keras)\n",
            "  Downloading numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl (12.2MB)\n",
            "\u001b[K    100% |████████████████████████████████| 12.2MB 114kB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyyaml\n",
            "  Running setup.py bdist_wheel for pyyaml ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/2c/f7/79/13f3a12cd723892437c0cfbde1230ab4d82947ff7b3839a4fc\n",
            "Successfully built pyyaml\n",
            "Installing collected packages: numpy, scipy, pyyaml, keras\n",
            "  Found existing installation: numpy 1.14.0\n",
            "    Uninstalling numpy-1.14.0:\n",
            "      Successfully uninstalled numpy-1.14.0\n",
            "  Found existing installation: scipy 0.19.1\n",
            "    Uninstalling scipy-0.19.1:\n",
            "      Successfully uninstalled scipy-0.19.1\n",
            "  Found existing installation: PyYAML 3.11\n",
            "    Uninstalling PyYAML-3.11:\n",
            "      Successfully uninstalled PyYAML-3.11\n",
            "Successfully installed keras-2.1.5 numpy-1.14.1 pyyaml-3.12 scipy-1.0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "mtBhj2akp_0O",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "link_list=[\"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(151751).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(153054).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(154211).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(160440).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(160931).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(161342).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(163349).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-16)(164248).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-17)(141550).zip\", \\\n",
        "          \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-17)(142154).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-17)(142457).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-17)(143016).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(132824).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(133201).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(133846).zip\", \\\n",
        "          \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(134239).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(134757).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(140516).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(143345).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(144316).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(145150).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(145623).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(150303).zip\", \\\n",
        "          \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(150650).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(151337).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(151650).zip\"]\n",
        "val_list=[\"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(152717).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(153532).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(154129).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(154728).zip\", \"http://vap.aau.dk/wp-content/uploads/VAPRBGD/(2012-05-18)(155357).zip\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3muEPTwDkQ5J",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import requests, zipfile, io\n",
        "for link in link_list:\n",
        "  r = requests.get(link, stream=True)\n",
        "  z = zipfile.ZipFile(io.BytesIO(r.content))\n",
        "  z.extractall(\"faceid_train\")\n",
        "for link in val_list:\n",
        "  r = requests.get(link, stream=True)\n",
        "  z = zipfile.ZipFile(io.BytesIO(r.content))\n",
        "  z.extractall(\"faceid_val\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8oO8m58qXro4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Input preprocessing.\n",
        "Here we create some functions that will create the input couple for our model, both correct and wrong couples. I created functions to have both depth-only input and RGBD inputs."
      ]
    },
    {
      "metadata": {
        "id": "-1wK9iPVkshW",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TfBuWehTl5hF",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def create_couple(file_path):\n",
        "    folder=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    while folder == \"datalab\":\n",
        "      folder=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "  #  print(folder)\n",
        "    mat=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue \n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat = np.asarray(mat)\n",
        "    mat_small=mat[140:340,220:420]\n",
        "    mat_small=(mat_small-np.mean(mat_small))/np.max(mat_small)\n",
        "#    plt.imshow(mat_small)\n",
        "#    plt.show()\n",
        "    \n",
        "    mat2=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue \n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat2[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat2 = np.asarray(mat2)\n",
        "    mat2_small=mat2[140:340,220:420]\n",
        "    mat2_small=(mat2_small-np.mean(mat2_small))/np.max(mat2_small)\n",
        "#    plt.imshow(mat2_small)\n",
        "#    plt.show()\n",
        "    return np.array([mat_small, mat2_small])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xuVaH9UvBmTl",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "2fc81cfc-7677-4554-de66-5205c5fa02c1",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875650316,
          "user_tz": -60,
          "elapsed": 2380,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "print(create_couple(\"faceid_train/\"))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[ 0.10215505  0.10215505  0.10215505 ...  0.10215505  0.10215505\n",
            "    0.10215505]\n",
            "  [ 0.10215505  0.10215505  0.10215505 ...  0.10215505  0.10215505\n",
            "    0.10215505]\n",
            "  [ 0.10215505  0.10215505  0.10215505 ...  0.10215505  0.10215505\n",
            "    0.10215505]\n",
            "  ...\n",
            "  [-0.06117828 -0.06117828 -0.06367829 ...  0.10215505  0.10215505\n",
            "    0.10215505]\n",
            "  [-0.06367829 -0.06617828 -0.06617828 ...  0.10215505  0.10215505\n",
            "    0.10215505]\n",
            "  [-0.06617828 -0.06617828 -0.06867828 ...  0.10215505  0.10215505\n",
            "    0.10215505]]\n",
            "\n",
            " [[ 0.09494985  0.09494985  0.09494985 ...  0.09494985  0.09494985\n",
            "    0.09494985]\n",
            "  [ 0.09494985  0.09494985  0.09494985 ...  0.09494985  0.09494985\n",
            "    0.09494985]\n",
            "  [ 0.09494985  0.09494985  0.09494985 ...  0.09494985  0.09494985\n",
            "    0.09494985]\n",
            "  ...\n",
            "  [-0.06338348 -0.06338348 -0.06588349 ...  0.09494985  0.09494985\n",
            "    0.09494985]\n",
            "  [-0.06588349 -0.06588349 -0.06588349 ...  0.09494985  0.09494985\n",
            "    0.09494985]\n",
            "  [-0.06588349 -0.06588349 -0.06838349 ...  0.09494985  0.09494985\n",
            "    0.09494985]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Fouazlib4jj7",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def create_couple_rgbd(file_path):\n",
        "    folder=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    while folder == \"datalab\":\n",
        "      folder=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "  #  print(folder)\n",
        "    mat=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue    \n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat = np.asarray(mat)\n",
        "    mat_small=mat[140:340,220:420]\n",
        "    img = Image.open(depth_file[:-5] + \"c.bmp\")\n",
        "    img.thumbnail((640,480))\n",
        "    img = np.asarray(img)\n",
        "    img = img[140:340,220:420]\n",
        "    mat_small=(mat_small-np.mean(mat_small))/np.max(mat_small)\n",
        "#    plt.imshow(mat_small)\n",
        "#    plt.show()\n",
        "#    plt.imshow(img)\n",
        "#    plt.show()\n",
        "    \n",
        "    \n",
        "    mat2=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue\n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat2[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat2 = np.asarray(mat2)\n",
        "    mat2_small=mat2[140:340,220:420]\n",
        "    img2 = Image.open(depth_file[:-5] + \"c.bmp\")\n",
        "    img2.thumbnail((640,480))\n",
        "    img2 = np.asarray(img2)\n",
        "    img2 = img2[160:360,240:440]\n",
        "\n",
        " #   plt.imshow(img2)\n",
        " #   plt.show()\n",
        "    mat2_small=(mat2_small-np.mean(mat2_small))/np.max(mat2_small)\n",
        " #   plt.imshow(mat2_small)\n",
        " #   plt.show()\n",
        "    \n",
        "    full1 = np.zeros((200,200,4))\n",
        "    full1[:,:,:3] = img[:,:,:3]\n",
        "    full1[:,:,3] = mat_small\n",
        "    \n",
        "    full2 = np.zeros((200,200,4))\n",
        "    full2[:,:,:3] = img2[:,:,:3]\n",
        "    full2[:,:,3] = mat2_small\n",
        "    return np.array([full1, full2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "icTcfztOl9wY",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 2941
        },
        "outputId": "be73b5b7-47fd-4355-944d-c62afe620588",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875654430,
          "user_tz": -60,
          "elapsed": 1543,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "create_couple_rgbd(\"faceid_val/\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[[ 1.85000000e+02,  1.75000000e+02,  1.49000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.86000000e+02,  1.73000000e+02,  1.54000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.87000000e+02,  1.73000000e+02,  1.54000000e+02,\n",
              "           7.03941882e-02],\n",
              "         ...,\n",
              "         [ 1.65000000e+02,  1.62000000e+02,  1.45000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.63000000e+02,  1.61000000e+02,  1.43000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.62000000e+02,  1.60000000e+02,  1.41000000e+02,\n",
              "           7.03941882e-02]],\n",
              "\n",
              "        [[ 1.86000000e+02,  1.75000000e+02,  1.49000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.87000000e+02,  1.72000000e+02,  1.52000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.87000000e+02,  1.73000000e+02,  1.54000000e+02,\n",
              "           7.03941882e-02],\n",
              "         ...,\n",
              "         [ 1.65000000e+02,  1.62000000e+02,  1.41000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.65000000e+02,  1.61000000e+02,  1.39000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.64000000e+02,  1.60000000e+02,  1.39000000e+02,\n",
              "           7.03941882e-02]],\n",
              "\n",
              "        [[ 1.86000000e+02,  1.73000000e+02,  1.49000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.87000000e+02,  1.72000000e+02,  1.49000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.87000000e+02,  1.73000000e+02,  1.53000000e+02,\n",
              "           7.03941882e-02],\n",
              "         ...,\n",
              "         [ 1.64000000e+02,  1.60000000e+02,  1.40000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.64000000e+02,  1.58000000e+02,  1.40000000e+02,\n",
              "           7.03941882e-02],\n",
              "         [ 1.65000000e+02,  1.60000000e+02,  1.40000000e+02,\n",
              "           7.03941882e-02]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[ 4.40000000e+01,  4.40000000e+01,  5.30000000e+01,\n",
              "          -6.54391497e-02],\n",
              "         [ 3.90000000e+01,  4.30000000e+01,  5.20000000e+01,\n",
              "          -6.29391447e-02],\n",
              "         [ 3.80000000e+01,  4.00000000e+01,  5.20000000e+01,\n",
              "          -6.54391497e-02],\n",
              "         ...,\n",
              "         [ 2.70000000e+01,  3.20000000e+01,  4.60000000e+01,\n",
              "          -7.54391477e-02],\n",
              "         [ 2.60000000e+01,  3.10000000e+01,  4.50000000e+01,\n",
              "          -7.54391477e-02],\n",
              "         [ 2.80000000e+01,  3.00000000e+01,  4.30000000e+01,\n",
              "          -7.54391477e-02]],\n",
              "\n",
              "        [[ 4.20000000e+01,  4.30000000e+01,  5.20000000e+01,\n",
              "          -6.54391497e-02],\n",
              "         [ 3.90000000e+01,  4.30000000e+01,  5.20000000e+01,\n",
              "          -6.54391497e-02],\n",
              "         [ 3.70000000e+01,  4.20000000e+01,  5.10000000e+01,\n",
              "          -6.54391497e-02],\n",
              "         ...,\n",
              "         [ 2.90000000e+01,  3.00000000e+01,  4.40000000e+01,\n",
              "          -7.79391453e-02],\n",
              "         [ 2.90000000e+01,  2.90000000e+01,  4.30000000e+01,\n",
              "          -7.54391477e-02],\n",
              "         [ 2.60000000e+01,  3.00000000e+01,  4.50000000e+01,\n",
              "          -7.54391477e-02]],\n",
              "\n",
              "        [[ 4.40000000e+01,  4.20000000e+01,  5.30000000e+01,\n",
              "          -6.79391474e-02],\n",
              "         [ 4.10000000e+01,  4.30000000e+01,  5.30000000e+01,\n",
              "          -6.79391474e-02],\n",
              "         [ 3.90000000e+01,  4.30000000e+01,  5.00000000e+01,\n",
              "          -6.79391474e-02],\n",
              "         ...,\n",
              "         [ 3.00000000e+01,  2.90000000e+01,  4.10000000e+01,\n",
              "          -7.79391453e-02],\n",
              "         [ 2.90000000e+01,  2.90000000e+01,  4.00000000e+01,\n",
              "          -7.79391453e-02],\n",
              "         [ 2.80000000e+01,  3.00000000e+01,  4.30000000e+01,\n",
              "          -7.79391453e-02]]],\n",
              "\n",
              "\n",
              "       [[[ 1.80000000e+02,  1.75000000e+02,  1.47000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.82000000e+02,  1.72000000e+02,  1.48000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.83000000e+02,  1.72000000e+02,  1.52000000e+02,\n",
              "           6.64661676e-02],\n",
              "         ...,\n",
              "         [ 1.66000000e+02,  1.60000000e+02,  1.39000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.65000000e+02,  1.59000000e+02,  1.39000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.65000000e+02,  1.60000000e+02,  1.36000000e+02,\n",
              "           6.64661676e-02]],\n",
              "\n",
              "        [[ 1.81000000e+02,  1.75000000e+02,  1.48000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.82000000e+02,  1.75000000e+02,  1.50000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.82000000e+02,  1.76000000e+02,  1.51000000e+02,\n",
              "           6.64661676e-02],\n",
              "         ...,\n",
              "         [ 1.66000000e+02,  1.61000000e+02,  1.38000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.67000000e+02,  1.60000000e+02,  1.35000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.67000000e+02,  1.58000000e+02,  1.35000000e+02,\n",
              "           6.64661676e-02]],\n",
              "\n",
              "        [[ 1.82000000e+02,  1.75000000e+02,  1.51000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.83000000e+02,  1.77000000e+02,  1.54000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.83000000e+02,  1.77000000e+02,  1.53000000e+02,\n",
              "           6.64661676e-02],\n",
              "         ...,\n",
              "         [ 1.66000000e+02,  1.59000000e+02,  1.41000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.69000000e+02,  1.59000000e+02,  1.40000000e+02,\n",
              "           6.64661676e-02],\n",
              "         [ 1.69000000e+02,  1.58000000e+02,  1.41000000e+02,\n",
              "           6.64661676e-02]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[ 3.10000000e+01,  3.50000000e+01,  4.00000000e+01,\n",
              "          -6.35338351e-02],\n",
              "         [ 3.00000000e+01,  3.40000000e+01,  3.70000000e+01,\n",
              "          -6.35338351e-02],\n",
              "         [ 3.00000000e+01,  3.30000000e+01,  3.90000000e+01,\n",
              "          -6.35338351e-02],\n",
              "         ...,\n",
              "         [ 3.00000000e+01,  3.00000000e+01,  4.20000000e+01,\n",
              "          -7.18671679e-02],\n",
              "         [ 3.00000000e+01,  3.20000000e+01,  4.90000000e+01,\n",
              "          -7.18671679e-02],\n",
              "         [ 3.20000000e+01,  3.20000000e+01,  5.10000000e+01,\n",
              "          -7.18671679e-02]],\n",
              "\n",
              "        [[ 3.00000000e+01,  3.50000000e+01,  4.30000000e+01,\n",
              "          -6.68671653e-02],\n",
              "         [ 2.80000000e+01,  3.40000000e+01,  4.10000000e+01,\n",
              "          -6.35338351e-02],\n",
              "         [ 2.70000000e+01,  3.30000000e+01,  4.20000000e+01,\n",
              "          -6.35338351e-02],\n",
              "         ...,\n",
              "         [ 3.10000000e+01,  3.00000000e+01,  4.50000000e+01,\n",
              "          -7.18671679e-02],\n",
              "         [ 3.00000000e+01,  3.00000000e+01,  4.70000000e+01,\n",
              "          -7.18671679e-02],\n",
              "         [ 3.10000000e+01,  3.30000000e+01,  4.80000000e+01,\n",
              "          -7.18671679e-02]],\n",
              "\n",
              "        [[ 3.20000000e+01,  3.30000000e+01,  4.60000000e+01,\n",
              "          -6.68671653e-02],\n",
              "         [ 2.90000000e+01,  3.30000000e+01,  4.20000000e+01,\n",
              "          -6.68671653e-02],\n",
              "         [ 2.70000000e+01,  3.20000000e+01,  4.10000000e+01,\n",
              "          -6.68671653e-02],\n",
              "         ...,\n",
              "         [ 3.10000000e+01,  2.90000000e+01,  4.70000000e+01,\n",
              "          -7.43671656e-02],\n",
              "         [ 3.00000000e+01,  3.10000000e+01,  4.60000000e+01,\n",
              "          -7.43671656e-02],\n",
              "         [ 3.10000000e+01,  3.30000000e+01,  4.70000000e+01,\n",
              "          -7.18671679e-02]]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "nNpd2wwKnT4b",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def create_wrong(file_path):\n",
        "    folder=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    while folder == \"datalab\":\n",
        "      folder=np.random.choice(glob.glob(file_path + \"*\"))    \n",
        "    mat=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue \n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat = np.asarray(mat)\n",
        "    mat_small=mat[140:340,220:420]\n",
        "    mat_small=(mat_small-np.mean(mat_small))/np.max(mat_small)\n",
        " #   plt.imshow(mat_small)\n",
        " #   plt.show()\n",
        "    \n",
        "    folder2=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    while folder==folder2 or folder2==\"datalab\": #it activates if it chose the same folder\n",
        "        folder2=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    mat2=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder2 + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue\n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat2[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat2 = np.asarray(mat2)\n",
        "    mat2_small=mat2[140:340,220:420]\n",
        "    mat2_small=(mat2_small-np.mean(mat2_small))/np.max(mat2_small)\n",
        " #   plt.imshow(mat2_small)\n",
        " #   plt.show()\n",
        "  \n",
        "    \n",
        "    return np.array([mat_small, mat2_small])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NzsUozpkEp2s",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "fcd17e6e-45a9-4d6f-8a33-4aebba4aec44",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875659583,
          "user_tz": -60,
          "elapsed": 1810,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "create_wrong(\"faceid_train/\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0.15714498,  0.15714498,  0.15714498, ...,  0.15714498,\n",
              "          0.15714498,  0.15714498],\n",
              "        [ 0.15714498,  0.15714498,  0.15714498, ...,  0.15714498,\n",
              "          0.15714498,  0.15714498],\n",
              "        [ 0.15714498,  0.15714498,  0.15714498, ...,  0.15714498,\n",
              "          0.15714498,  0.15714498],\n",
              "        ...,\n",
              "        [-0.02285502, -0.02285502, -0.02285502, ..., -0.04785502,\n",
              "         -0.04785502, -0.04535502],\n",
              "        [-0.02285502, -0.02535502, -0.02535502, ..., -0.04952169,\n",
              "         -0.04785502, -0.04785502],\n",
              "        [-0.02785502, -0.02785502, -0.02952169, ..., -0.04952169,\n",
              "         -0.04952169, -0.04952169]],\n",
              "\n",
              "       [[ 0.06464213,  0.06464213,  0.06464213, ...,  0.06464213,\n",
              "          0.06464213,  0.06464213],\n",
              "        [ 0.06464213,  0.06464213,  0.06464213, ...,  0.06464213,\n",
              "          0.06464213,  0.06464213],\n",
              "        [ 0.06464213,  0.06464213,  0.06464213, ...,  0.06464213,\n",
              "          0.06464213,  0.06464213],\n",
              "        ...,\n",
              "        [ 0.06464213,  0.06464213,  0.06464213, ...,  0.06464213,\n",
              "          0.06464213,  0.06464213],\n",
              "        [ 0.06464213,  0.06464213,  0.06464213, ...,  0.06464213,\n",
              "          0.06464213,  0.06464213],\n",
              "        [ 0.06464213,  0.06464213,  0.06464213, ...,  0.06464213,\n",
              "          0.06464213,  0.06464213]]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "kbcdYKNj-cou",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def create_wrong_rgbd(file_path):\n",
        "    folder=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    while folder == \"datalab\":\n",
        "      folder=np.random.choice(glob.glob(file_path + \"*\"))    \n",
        "    mat=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue\n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat = np.asarray(mat)\n",
        "    mat_small=mat[140:340,220:420]\n",
        "    img = Image.open(depth_file[:-5] + \"c.bmp\")\n",
        "    img.thumbnail((640,480))\n",
        "    img = np.asarray(img)\n",
        "    img = img[140:340,220:420]\n",
        "    mat_small=(mat_small-np.mean(mat_small))/np.max(mat_small)\n",
        "  #  plt.imshow(img)\n",
        "  #  plt.show()\n",
        "  #  plt.imshow(mat_small)\n",
        "  #  plt.show()\n",
        "    folder2=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    while folder==folder2 or folder2==\"datalab\": #it activates if it chose the same folder\n",
        "        folder2=np.random.choice(glob.glob(file_path + \"*\"))\n",
        "    mat2=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = np.random.choice(glob.glob(folder2 + \"/*.dat\"))\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue \n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat2[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat2 = np.asarray(mat2)\n",
        "    mat2_small=mat2[140:340,220:420]\n",
        "    img2 = Image.open(depth_file[:-5] + \"c.bmp\")\n",
        "    img2.thumbnail((640,480))\n",
        "    img2 = np.asarray(img2)\n",
        "    img2 = img2[140:340,220:420]\n",
        "    mat2_small=(mat2_small-np.mean(mat2_small))/np.max(mat2_small)\n",
        " #   plt.imshow(img2)\n",
        " #   plt.show()\n",
        " #   plt.imshow(mat2_small)\n",
        " #   plt.show()\n",
        "    full1 = np.zeros((200,200,4))\n",
        "    full1[:,:,:3] = img[:,:,:3]\n",
        "    full1[:,:,3] = mat_small\n",
        "    \n",
        "    full2 = np.zeros((200,200,4))\n",
        "    full2[:,:,:3] = img2[:,:,:3]\n",
        "    full2[:,:,3] = mat2_small\n",
        "    return np.array([full1, full2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sH8L8uaknZqw",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "374180f4-5f09-40d7-e4b8-c0250cfc5117",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875662648,
          "user_tz": -60,
          "elapsed": 1722,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "create_wrong_rgbd(\"faceid_val/\")[0].shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 200, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "ypQwUJXPX8IM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Network crafting.\n",
        "Now we create the network. We first manually create the *constrative loss*, then we define the network architecture starting from the SqueezeNet architecture, and then using it as a siamese-network for embedding faces into a manifold. (the network for now is very big and could be heavily optimized, but I just wanted to show a proof-of-concept)"
      ]
    },
    {
      "metadata": {
        "id": "Hb8GAKvQocVw",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, Lambda, ELU, concatenate, GlobalAveragePooling2D, Input, BatchNormalization, SeparableConv2D, Subtract, concatenate\n",
        "from keras.activations import relu, softmax\n",
        "from keras.layers.convolutional import Convolution2D\n",
        "from keras.layers.pooling import MaxPooling2D, AveragePooling2D\n",
        "from keras.optimizers import Adam, RMSprop, SGD\n",
        "from keras.regularizers import l2\n",
        "from keras import backend as K"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e37tdjTcooS-",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def euclidean_distance(inputs):\n",
        "    assert len(inputs) == 2, \\\n",
        "        'Euclidean distance needs 2 inputs, %d given' % len(inputs)\n",
        "    u, v = inputs\n",
        "    return K.sqrt(K.sum((K.square(u - v)), axis=1, keepdims=True))\n",
        "        \n",
        "\n",
        "def contrastive_loss(y_true,y_pred):\n",
        "    margin=1.\n",
        "    return K.mean((1. - y_true) * K.square(y_pred) + y_true * K.square(K.maximum(margin - y_pred, 0.)))\n",
        "   # return K.mean( K.square(y_pred) )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KhcLtqEWo3Wh",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def fire(x, squeeze=16, expand=64):\n",
        "    x = Convolution2D(squeeze, (1,1), padding='valid')(x)\n",
        "    x = Activation('relu')(x)\n",
        "    \n",
        "    left = Convolution2D(expand, (1,1), padding='valid')(x)\n",
        "    left = Activation('relu')(left)\n",
        "    \n",
        "    right = Convolution2D(expand, (3,3), padding='same')(x)\n",
        "    right = Activation('relu')(right)\n",
        "    \n",
        "    x = concatenate([left, right], axis=3)\n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JKA3EcVyo_C8",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 2516
        },
        "outputId": "e84349d1-4269-499b-add4-a5038c7eee4b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875946309,
          "user_tz": -60,
          "elapsed": 1665,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "img_input=Input(shape=(200,200,4))\n",
        "\n",
        "x = Convolution2D(64, (5, 5), strides=(2, 2), padding='valid')(img_input)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)\n",
        "\n",
        "x = fire(x, squeeze=16, expand=16)\n",
        "\n",
        "x = fire(x, squeeze=16, expand=16)\n",
        "\n",
        "x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)\n",
        "\n",
        "\n",
        "x = fire(x, squeeze=32, expand=32)\n",
        "\n",
        "x = fire(x, squeeze=32, expand=32)\n",
        "\n",
        "x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)\n",
        "\n",
        "\n",
        "x = fire(x, squeeze=48, expand=48)\n",
        "\n",
        "x = fire(x, squeeze=48, expand=48)\n",
        "\n",
        "x = fire(x, squeeze=64, expand=64)\n",
        "\n",
        "x = fire(x, squeeze=64, expand=64)\n",
        "\n",
        "x = Dropout(0.2)(x)\n",
        "\n",
        "x = Convolution2D(512, (1, 1), padding='same')(x)\n",
        "out = Activation('relu')(x)\n",
        "\n",
        "\n",
        "modelsqueeze= Model(img_input, out)\n",
        "\n",
        "modelsqueeze.summary()\n",
        "\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 200, 200, 4)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 98, 98, 64)   6464        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 98, 98, 64)   256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 98, 98, 64)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 48, 48, 64)   0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 48, 48, 16)   1040        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 48, 48, 16)   0           conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 48, 48, 16)   272         activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 48, 48, 16)   2320        activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 48, 48, 16)   0           conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 48, 48, 16)   0           conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 48, 48, 32)   0           activation_3[0][0]               \n",
            "                                                                 activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 48, 48, 16)   528         concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 48, 48, 16)   0           conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 48, 48, 16)   272         activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 48, 48, 16)   2320        activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 48, 48, 16)   0           conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 48, 48, 16)   0           conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 48, 48, 32)   0           activation_6[0][0]               \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 23, 23, 32)   0           concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 23, 23, 32)   1056        max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 23, 23, 32)   0           conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 23, 23, 32)   1056        activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 23, 23, 32)   9248        activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 23, 23, 32)   0           conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 23, 23, 32)   0           conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 23, 23, 64)   0           activation_9[0][0]               \n",
            "                                                                 activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 23, 23, 32)   2080        concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 23, 23, 32)   0           conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 23, 23, 32)   1056        activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 23, 23, 32)   9248        activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 23, 23, 32)   0           conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 23, 23, 32)   0           conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, 23, 23, 64)   0           activation_12[0][0]              \n",
            "                                                                 activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 11, 11, 64)   0           concatenate_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 11, 11, 48)   3120        max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 11, 11, 48)   0           conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 11, 11, 48)   2352        activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 11, 11, 48)   20784       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 11, 11, 48)   0           conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 11, 11, 48)   0           conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_5 (Concatenate)     (None, 11, 11, 96)   0           activation_15[0][0]              \n",
            "                                                                 activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 11, 11, 48)   4656        concatenate_5[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 11, 11, 48)   0           conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 11, 11, 48)   2352        activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 11, 11, 48)   20784       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 11, 11, 48)   0           conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 11, 11, 48)   0           conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_6 (Concatenate)     (None, 11, 11, 96)   0           activation_18[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 11, 11, 64)   6208        concatenate_6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 11, 11, 64)   0           conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 11, 11, 64)   4160        activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 11, 11, 64)   36928       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 11, 11, 64)   0           conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 11, 11, 64)   0           conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_7 (Concatenate)     (None, 11, 11, 128)  0           activation_21[0][0]              \n",
            "                                                                 activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 11, 11, 64)   8256        concatenate_7[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 11, 11, 64)   0           conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 11, 11, 64)   4160        activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 11, 11, 64)   36928       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 11, 11, 64)   0           conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 11, 11, 64)   0           conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_8 (Concatenate)     (None, 11, 11, 128)  0           activation_24[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 11, 11, 128)  0           concatenate_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 11, 11, 512)  66048       dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 11, 11, 512)  0           conv2d_26[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 253,952\n",
            "Trainable params: 253,824\n",
            "Non-trainable params: 128\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XSy2AIxBpTG3",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "outputId": "c7891a94-7ed3-4cd0-8054-fc7f795ff52b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875947670,
          "user_tz": -60,
          "elapsed": 1202,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "im_in = Input(shape=(200,200,4))\n",
        "#wrong = Input(shape=(200,200,3))\n",
        "\n",
        "x1 = modelsqueeze(im_in)\n",
        "#x = Convolution2D(64, (5, 5), padding='valid', strides =(2,2))(x)\n",
        "\n",
        "#x1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x1)\n",
        "\n",
        "\"\"\"\n",
        "x1 = Convolution2D(256, (3,3), padding='valid', activation=\"relu\")(x1)\n",
        "x1 = Dropout(0.4)(x1)\n",
        "\n",
        "x1 = MaxPooling2D(pool_size=(3, 3), strides=(1, 1))(x1)\n",
        "\n",
        "x1 = Convolution2D(256, (3,3), padding='valid', activation=\"relu\")(x1)\n",
        "x1 = BatchNormalization()(x1)\n",
        "x1 = Dropout(0.4)(x1)\n",
        "\n",
        "x1 = Convolution2D(64, (1,1), padding='same', activation=\"relu\")(x1)\n",
        "x1 = BatchNormalization()(x1)\n",
        "x1 = Dropout(0.4)(x1)\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "\n",
        "x1 = Flatten()(x1)\n",
        "\n",
        "x1 = Dense(512, activation=\"relu\")(x1)\n",
        "x1 = Dropout(0.2)(x1)\n",
        "#x1 = BatchNormalization()(x1)\n",
        "feat_x = Dense(128, activation=\"linear\")(x1)\n",
        "feat_x = Lambda(lambda  x: K.l2_normalize(x,axis=1))(feat_x)\n",
        "\n",
        "\n",
        "model_top = Model(inputs = [im_in], outputs = feat_x)\n",
        "\n",
        "model_top.summary()\n",
        "\n",
        "im_in1 = Input(shape=(200,200,4))\n",
        "im_in2 = Input(shape=(200,200,4))\n",
        "\n",
        "feat_x1 = model_top(im_in1)\n",
        "feat_x2 = model_top(im_in2)\n",
        "\n",
        "\n",
        "lambda_merge = Lambda(euclidean_distance)([feat_x1, feat_x2])\n",
        "\n",
        "\n",
        "model_final = Model(inputs = [im_in1, im_in2], outputs = lambda_merge)\n",
        "\n",
        "model_final.summary()\n",
        "\n",
        "adam = Adam(lr=0.001)\n",
        "\n",
        "sgd = SGD(lr=0.001, momentum=0.9)\n",
        "\n",
        "model_final.compile(optimizer=adam, loss=contrastive_loss)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 200, 200, 4)       0         \n",
            "_________________________________________________________________\n",
            "model_1 (Model)              (None, 11, 11, 512)       253952    \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 61952)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               31719936  \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               65664     \n",
            "_________________________________________________________________\n",
            "lambda_1 (Lambda)            (None, 128)               0         \n",
            "=================================================================\n",
            "Total params: 32,039,552\n",
            "Trainable params: 32,039,424\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            (None, 200, 200, 4)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_4 (InputLayer)            (None, 200, 200, 4)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_2 (Model)                 (None, 128)          32039552    input_3[0][0]                    \n",
            "                                                                 input_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_2 (Lambda)               (None, 1)            0           model_2[1][0]                    \n",
            "                                                                 model_2[2][0]                    \n",
            "==================================================================================================\n",
            "Total params: 32,039,552\n",
            "Trainable params: 32,039,424\n",
            "Non-trainable params: 128\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "o37-2YMQYU3m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Learning phase.\n",
        "We write the generators that will give our model batches of data to train on, then we run the training."
      ]
    },
    {
      "metadata": {
        "id": "PYSFqo-ppTnJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def generator(batch_size):\n",
        "  \n",
        "  while 1:\n",
        "    X=[]\n",
        "    y=[]\n",
        "    switch=True\n",
        "    for _ in range(batch_size):\n",
        "   #   switch += 1\n",
        "      if switch:\n",
        "     #   print(\"correct\")\n",
        "        X.append(create_couple_rgbd(\"faceid_train/\").reshape((2,200,200,4)))\n",
        "        y.append(np.array([0.]))\n",
        "      else:\n",
        "     #   print(\"wrong\")\n",
        "        X.append(create_wrong_rgbd(\"faceid_train/\").reshape((2,200,200,4)))\n",
        "        y.append(np.array([1.]))\n",
        "      switch=not switch\n",
        "    X = np.asarray(X)\n",
        "    y = np.asarray(y)\n",
        "    XX1=X[0,:]\n",
        "    XX2=X[1,:]\n",
        "    yield [X[:,0],X[:,1]],y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zs8Ktvcbjq31",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def val_generator(batch_size):\n",
        "  \n",
        "  while 1:\n",
        "    X=[]\n",
        "    y=[]\n",
        "    switch=True\n",
        "    for _ in range(batch_size):\n",
        "      if switch:\n",
        "        X.append(create_couple_rgbd(\"faceid_val/\").reshape((2,200,200,4)))\n",
        "        y.append(np.array([0.]))\n",
        "      else:\n",
        "        X.append(create_wrong_rgbd(\"faceid_val/\").reshape((2,200,200,4)))\n",
        "        y.append(np.array([1.]))\n",
        "      switch=not switch\n",
        "    X = np.asarray(X)\n",
        "    y = np.asarray(y)\n",
        "    XX1=X[0,:]\n",
        "    XX2=X[1,:]\n",
        "    yield [X[:,0],X[:,1]],y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "x_tsUiyAslYj",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "gen = generator(16)\n",
        "val_gen = val_generator(4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UcV1AqgQs0tr",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {},
            {},
            {},
            {},
            {},
            {},
            {},
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 949
        },
        "outputId": "34152b5d-8c8d-49ae-be55-444ea20a88d4",
        "executionInfo": {
          "status": "error",
          "timestamp": 1520706651566,
          "user_tz": -60,
          "elapsed": 3837783,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "outputs = model_final.fit_generator(gen, steps_per_epoch=30, epochs=50, validation_data = val_gen, validation_steps=20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "30/30 [==============================] - 515s 17s/step - loss: 0.0994 - val_loss: 0.1148\n",
            "Epoch 2/50\n",
            "30/30 [==============================] - 425s 14s/step - loss: 0.0842 - val_loss: 0.0719\n",
            "Epoch 3/50\n",
            " 4/30 [===>..........................] - ETA: 11s - loss: 0.0794"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.226792). Check your callbacks.\n",
            "  % delta_t_median)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 5/30 [====>.........................] - ETA: 10s - loss: 0.0912"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.233295). Check your callbacks.\n",
            "  % delta_t_median)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "30/30 [==============================] - 427s 14s/step - loss: 0.0841 - val_loss: 0.1185\n",
            "Epoch 4/50\n",
            "30/30 [==============================] - 438s 15s/step - loss: 0.0748 - val_loss: 0.0817\n",
            "Epoch 5/50\n",
            "30/30 [==============================] - 444s 15s/step - loss: 0.0752 - val_loss: 0.1171\n",
            "Epoch 6/50\n",
            "30/30 [==============================] - 439s 15s/step - loss: 0.0704 - val_loss: 0.0689\n",
            "Epoch 7/50\n",
            "30/30 [==============================] - 434s 14s/step - loss: 0.0776 - val_loss: 0.0992\n",
            "Epoch 8/50\n",
            "30/30 [==============================] - 438s 15s/step - loss: 0.0800 - val_loss: 0.0904\n",
            "Epoch 9/50\n",
            " 5/30 [====>.........................] - ETA: 11s - loss: 0.0760"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.245728). Check your callbacks.\n",
            "  % delta_t_median)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "26/30 [=========================>....] - ETA: 40s - loss: 0.0728"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-7119c2f8ee50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     90\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   2190\u001b[0m                 \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2191\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2192\u001b[0;31m                     \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2194\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    785\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m                     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m         \u001b[0;31m# Make sure to rethrow the first exception in the queue, if any\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "e0ANrU9zYdwq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Some model tests.\n"
      ]
    },
    {
      "metadata": {
        "id": "mBv4yjz958KR",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "97fc3075-7639-47dc-a7fe-c68f2e887123",
        "executionInfo": {
          "status": "error",
          "timestamp": 1520676724725,
          "user_tz": -60,
          "elapsed": 652,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "cop = create_couple(\"faceid_val/\")\n",
        "model_final.evaluate([cop[0].reshape((1,200,200,1)), cop[1].reshape((1,200,200,1))], np.array([0.]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-467176cf82e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_couple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"faceid_val/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel_final\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'create_couple' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "OQyEXogj7OVI",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b27a2c8b-38a3-4ed5-c1d9-dbf6ad386c12",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520706715007,
          "user_tz": -60,
          "elapsed": 1404,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "cop = create_wrong_rgbd(\"faceid_val/\")\n",
        "model_final.predict([cop[0].reshape((1,200,200,4)), cop[1].reshape((1,200,200,4))])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.4120153]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "metadata": {
        "id": "ZHsdoBlPYhYg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Saving and loading the model.\n",
        "The next cells show both how to save the model weights and upload them into your Drive, and then how to retrieve those weights from the Drive to load a pre-trained model."
      ]
    },
    {
      "metadata": {
        "id": "vZwXpSCQpLwd",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "model_final.save(\"faceid_big_rgbd_2.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R44vdVVdyiYm",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "53xZa7JXykeQ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fffab0ce-29f7-4cb0-8545-0f10b7f7aadb",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520706780161,
          "user_tz": -60,
          "elapsed": 38935,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Install the PyDrive wrapper & import libraries.\n",
        "# This only needs to be done once in a notebook.\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once in a notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# Create & upload a file.\n",
        "uploaded = drive.CreateFile({'title': 'faceid_big_rgbd.h5'})\n",
        "uploaded.SetContentFile('faceid_big_rgbd.h5')\n",
        "uploaded.Upload()\n",
        "print('Uploaded file with ID {}'.format(uploaded.get('id')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uploaded file with ID 17Lo_ZxYcKO751iYs4XRyIvVXME8Lyc75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-soDN2VJs5T_",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "# Install the PyDrive wrapper & import libraries.\n",
        "# This only needs to be done once per notebook.\n",
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once per notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# Download a file based on its file ID.\n",
        "#\n",
        "# A file ID looks like: laggVyWshwcyP6kEI-y_W3P8D26sz\n",
        "file_id = '17Lo_ZxYcKO751iYs4XRyIvVXME8Lyc75'\n",
        "downloaded = drive.CreateFile({'id': file_id})\n",
        "#print('Downloaded content \"{}\"'.format(downloaded.GetContentString()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2J6b7vzqtSGU",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "downloaded.GetContentFile('pesi.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jvO_pCGQugnR",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1JcUbcJbuoak",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "model_final.load_weights('pesi.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VWIiy1gNYuyU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Raw output.\n",
        "Here we create a model that outputs the embedding of an input face instead of the distance between two embeddings, so we can map those outputs."
      ]
    },
    {
      "metadata": {
        "id": "mscCbMwEvI9Y",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1e253bbc-0c22-43cf-afb7-474e6d86dc6e",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875977798,
          "user_tz": -60,
          "elapsed": 845,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "im_in1 = Input(shape=(200,200,4))\n",
        "#im_in2 = Input(shape=(200,200,4))\n",
        "\n",
        "feat_x1 = model_top(im_in1)\n",
        "#feat_x2 = model_top(im_in2)\n",
        "\n",
        "\n",
        "\n",
        "model_output = Model(inputs = im_in1, outputs = feat_x1)\n",
        "\n",
        "model_output.summary()\n",
        "\n",
        "adam = Adam(lr=0.001)\n",
        "\n",
        "sgd = SGD(lr=0.001, momentum=0.9)\n",
        "\n",
        "model_output.compile(optimizer=adam, loss=contrastive_loss)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_5 (InputLayer)         (None, 200, 200, 4)       0         \n",
            "_________________________________________________________________\n",
            "model_2 (Model)              (None, 128)               32039552  \n",
            "=================================================================\n",
            "Total params: 32,039,552\n",
            "Trainable params: 32,039,424\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qZuWJKRhwJRZ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "f2880290-02be-4551-89c3-1ffb6062f10f",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520875987230,
          "user_tz": -60,
          "elapsed": 1669,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "cop = create_couple_rgbd(\"faceid_val/\")\n",
        "model_output.predict(cop[0].reshape((1,200,200,4)))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.16414395, -0.13870355,  0.01013375,  0.00533672,  0.12819481,\n",
              "         0.00950897,  0.0581798 ,  0.0125026 , -0.14719632, -0.0320079 ,\n",
              "         0.07111354,  0.05890286,  0.00266691, -0.01223555, -0.00381333,\n",
              "         0.00116326,  0.04405256,  0.07326933, -0.02592516, -0.05166509,\n",
              "         0.18017435, -0.06742426,  0.04962699,  0.105292  , -0.0898006 ,\n",
              "         0.0411743 ,  0.11524671, -0.16427691, -0.07334423, -0.08005075,\n",
              "         0.12541303, -0.22259332,  0.05284468, -0.09389317, -0.04294442,\n",
              "        -0.05872821,  0.10885703, -0.1348703 ,  0.16432703,  0.04767004,\n",
              "         0.14579478,  0.02009899,  0.02733266, -0.07123698,  0.13352446,\n",
              "        -0.07614288, -0.03760444,  0.05777392,  0.10022782,  0.04174699,\n",
              "         0.06108676,  0.04703509, -0.05310335, -0.0403618 ,  0.12924859,\n",
              "        -0.00420733,  0.06204433,  0.02973575, -0.08997695,  0.04855238,\n",
              "         0.01506527, -0.07634371,  0.09674198, -0.05066067,  0.13670339,\n",
              "         0.08011758, -0.04623083, -0.00054593,  0.01456751, -0.03225679,\n",
              "        -0.00324295, -0.07304905, -0.10909945, -0.14082356,  0.10926367,\n",
              "        -0.06141786,  0.05206886,  0.00635697,  0.07088174, -0.15124643,\n",
              "         0.05503514,  0.0272195 , -0.04793534, -0.02090729,  0.04632349,\n",
              "        -0.07887188,  0.08320936, -0.02269304, -0.05766648,  0.07911541,\n",
              "         0.12780257, -0.06953883, -0.05881255, -0.04288266,  0.10334536,\n",
              "        -0.07210722, -0.10482919, -0.07910118,  0.24839257,  0.05464309,\n",
              "        -0.09011574, -0.14515159,  0.00683772, -0.03234436, -0.03015377,\n",
              "        -0.16184358,  0.1080156 , -0.05953241, -0.03290075,  0.0801215 ,\n",
              "         0.0865816 , -0.04650388,  0.05712184,  0.04291632, -0.18441448,\n",
              "        -0.0605873 ,  0.04139114, -0.10240612,  0.04476995, -0.05173467,\n",
              "         0.14993647,  0.00845173,  0.14346528, -0.00341601, -0.15364479,\n",
              "        -0.06985486, -0.0282876 , -0.14075774]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "LrVE78tyvFoQ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def create_input_rgbd(file_path):\n",
        "  #  print(folder)\n",
        "    mat=np.zeros((480,640), dtype='float32')\n",
        "    i=0\n",
        "    j=0\n",
        "    depth_file = file_path\n",
        "    with open(depth_file) as file:\n",
        "        for line in file:\n",
        "            vals = line.split('\\t')\n",
        "            for val in vals:\n",
        "                if val == \"\\n\": continue    \n",
        "                if int(val) > 1200 or int(val) == -1: val= 1200\n",
        "                mat[i][j]=float(int(val))\n",
        "                j+=1\n",
        "                j=j%640\n",
        "\n",
        "            i+=1\n",
        "        mat = np.asarray(mat)\n",
        "    mat_small=mat[140:340,220:420]\n",
        "    img = Image.open(depth_file[:-5] + \"c.bmp\")\n",
        "    img.thumbnail((640,480))\n",
        "    img = np.asarray(img)\n",
        "    img = img[140:340,220:420]\n",
        "    mat_small=(mat_small-np.mean(mat_small))/np.max(mat_small)\n",
        "    plt.figure(figsize=(8,8))\n",
        "    plt.grid(True)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(mat_small)\n",
        "    plt.show()\n",
        "    plt.figure(figsize=(8,8))\n",
        "    plt.grid(True)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(img)\n",
        "    plt.show()\n",
        "    \n",
        "    \n",
        "    \n",
        "    full1 = np.zeros((200,200,4))\n",
        "    full1[:,:,:3] = img[:,:,:3]\n",
        "    full1[:,:,3] = mat_small\n",
        "    \n",
        "    return np.array([full1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PHM4NdyYY7dg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data visualization.\n",
        "Here we store the embeddings for all the faces in the dataset. Then, using both **t-SNE** and **PCA**, we visualize the embeddings going from 128 to 2 dimensions. "
      ]
    },
    {
      "metadata": {
        "id": "wV9Vfj12w9tq",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 918
        },
        "outputId": "584c3613-22d7-4453-ccc1-810f4b7dc0a7",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520851881246,
          "user_tz": -60,
          "elapsed": 565824,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "outputs=[]\n",
        "n=0\n",
        "for folder in glob.glob('faceid_train/*'):\n",
        "  i=0\n",
        "  for file in glob.glob(folder + '/*.dat'):\n",
        "    i+=1\n",
        "    outputs.append(model_output.predict(create_input_rgbd(file).reshape((1,200,200,4))))\n",
        "  print(i)\n",
        "  n+=1\n",
        "  print(\"Folder \", n, \" of \", len(glob.glob('faceid_train/*')))\n",
        "print(len(outputs))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "51\n",
            "Folder  1  of  26\n",
            "51\n",
            "Folder  1  of  26\n",
            "51\n",
            "Folder  1  of  26\n",
            "51\n",
            "Folder  1  of  26\n",
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            "Folder  1  of  26\n",
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            "Folder  1  of  26\n",
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            "51\n",
            "Folder  1  of  26\n",
            "1326\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xHWd60a2yTDt",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3e831841-be05-4bdf-84e4-054c4676d9cf",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520852122459,
          "user_tz": -60,
          "elapsed": 646,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "outputs= np.asarray(outputs)\n",
        "outputs = outputs.reshape((-1,128))\n",
        "outputs.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1326, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "metadata": {
        "id": "Bi5voxYF1kon",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "from sklearn.manifold import TSNE\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R8DUpgGa1pzg",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3ed7628d-9744-46fd-d9bb-6c31d937e12c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520853394538,
          "user_tz": -60,
          "elapsed": 20571,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "X_embedded = TSNE(2).fit_transform(outputs)\n",
        "X_embedded.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1326, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "metadata": {
        "id": "kE8K5L226qXM",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1b60b83b-9138-4798-fc07-5b081474a11a",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520853532457,
          "user_tz": -60,
          "elapsed": 691,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "X_PCA = PCA(3).fit_transform(outputs)\n",
        "print(X_PCA.shape)\n",
        "\n",
        "#X_embedded = TSNE(2).fit_transform(X_PCA)\n",
        "#print(X_embedded.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1326, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XJjBf2Lm2dMV",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FOTZ9fEr2lGK",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {}
          ],
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "146076e0-3761-45af-c9b4-e15eafe8b15c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520853507543,
          "user_tz": -60,
          "elapsed": 7832,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "color = 0\n",
        "for i in range(len((X_embedded))):\n",
        "  el = X_embedded[i]\n",
        "  if i % 51 == 0 and not i==0:\n",
        "    color+=1\n",
        "    color=color%10\n",
        "  plt.scatter(el[0], el[1], color=\"C\" + str(color))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OH67pOQAg9pt3EXt3N9iuroqbvtLoTbtNDpvJEONcgWpbp7zjbVMUTem2SxWd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DLJpJu+gTYk30Y+cu8t7N3ZU3IzfuJTqwo+T5AkOrZ/hrZPPkUmEmHdoQF2Hir1yhfFLliGtK3w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\nGf8ftq2qgELULkMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f67215e5668>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "sKnEVXGmZK01",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Distance between two arbitrary RGBD pictures."
      ]
    },
    {
      "metadata": {
        "id": "h9w-piJV11-g",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "output_extras": [
            {
              "item_id": 1
            },
            {
              "item_id": 2
            },
            {
              "item_id": 3
            },
            {
              "item_id": 4
            },
            {
              "item_id": 5
            }
          ],
          "base_uri": "https://localhost:8080/",
          "height": 1830
        },
        "outputId": "ed13ad90-0992-4d49-f55a-d5211d30f84c",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1520878039179,
          "user_tz": -60,
          "elapsed": 2548,
          "user": {
            "displayName": "Norman Di Palo",
            "photoUrl": "//lh5.googleusercontent.com/-hwpkiYzSctg/AAAAAAAAAAI/AAAAAAAAAkg/I59DXttMdKw/s50-c-k-no/photo.jpg",
            "userId": "109405231849327541723"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "file1 = ('faceid_train/(2012-05-16)(154211)/015_1_d.dat')\n",
        "inp1 = create_input_rgbd(file1)\n",
        "file1 = ('faceid_train/(2012-05-16)(154211)/011_1_d.dat')\n",
        "inp2 = create_input_rgbd(file1)\n",
        "\n",
        "model_final.predict([inp1, inp2])\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rJEEAwLBIghs2/eRdUvpsDr1nfPYYR6yReh49xu20JNUyk9Vqt4VVpk1+L84I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\n3njjsT+ncknKGu+puZcSnnG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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f91237ab860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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epnbA3h861PW1Eiy95X3Xzil7oJZVC1NORZ3dnErok+j8CTE7+T/KbrcO+3ky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UpKdwwOXd2gOY/53Kc5EcibkIzLu9jvlDXFZ+TX7ECChx122GGHvVn7Be5MzmPE30wzRfta766F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\n7gvjoTJJxbyzFhnwxMcRQTD6LGU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bsOjhu0313q2FD5a2iegU85Tfw2a5MLQf7E1la5Re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\nly5dutxZ6T+CXbp06dLlzsr/D+4C4dinIzFYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9123c4dfd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c7ujQucydAGv0Krq9VLqaq393zrJmS7+mQAmSIABgWCTBlUh9e15jMuslV6Ek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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f912376e198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ESHVVHrB0Hfl3lEIp9EHR7GY/F0iCAmvfR2YwpmM0uNx8CvUQ2KoBl2v+IoVv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JEL57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\nmZm986PfMjO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ced5aYk7ORjUj4po+Qdxr2gPui0MaC6Nnd3yf3GLF0lMDVwdhWiFwYsVw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\nCHbp0qVLl1sr/UewS5cuXbrcWvn/AZifeyxRV9smAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9123679ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.30020022]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "id": "g2XhC-G6C08z",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}