src/AFEW_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=1\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR='/home/HDD6TB/datasets/emotions/EmotiW/AFEW/'\n",
    "emotion_to_index = {'Angry':0, 'Disgust':1, 'Fear':2, 'Happy':3, 'Neutral':4, 'Sad':5, 'Surprise':6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "import cv2\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm,metrics,preprocessing\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from collections import defaultdict\n",
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "import pathlib\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.resnet import preprocess_input\n",
    "from tensorflow.keras.layers import TimeDistributed, GRU, Dense, Dropout, Flatten, LSTM, Activation, MaxPooling2D\n",
    "from tensorflow.keras.regularizers import l2 as L2_reg\n",
    "from tensorflow.keras.layers import Conv2D, BatchNormalization, \\\n",
    "    MaxPool2D, GlobalMaxPool2D, Input, Masking, Conv3D, MaxPooling3D, GlobalMaxPool3D\n",
    "from tensorflow.keras.optimizers import SGD, Adam\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.compat.v1.keras.backend import set_session \n",
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess=tf.compat.v1.Session(config=config)\n",
    "set_session(sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. copy /home/kdemochkin/emotions/train/Angry\n",
    "        /home/kdemochkin/emotions/train/Disgust\n",
    "        /home/kdemochkin/emotions/train/Fear\n",
    "        /home/kdemochkin/emotions/train/Happy\n",
    "        /home/kdemochkin/emotions/train/Neutral\n",
    "        /home/kdemochkin/emotions/train/Sad\n",
    "        /home/kdemochkin/emotions/train/Surprise\n",
    "        to the features/train_data folder\n",
    "        \n",
    "2. copy /home/kdemochkin/emotions/val/Angry\n",
    "        /home/kdemochkin/emotions/val/Disgust\n",
    "        /home/kdemochkin/emotions/val/Fear\n",
    "        /home/kdemochkin/emotions/val/Happy\n",
    "        /home/kdemochkin/emotions/val/Neutral\n",
    "        /home/kdemochkin/emotions/val/Sad\n",
    "        /home/kdemochkin/emotions/val/Surprise\n",
    "        to the features/val_data folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for emotion in ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']:\n",
    "    for filename in os.listdir('/home/kdemochkin/emotions/train/' + emotion):\n",
    "        if not os.path.exists('/home/kdemochkin/emotions/train/AlignedFaces_LBPTOP_Points/frames/' + str(os.path.splitext(filename)[0])):\n",
    "            os.mkdir('/home/kdemochkin/emotions/train/AlignedFaces_LBPTOP_Points/frames/' + str(os.path.splitext(filename)[0]))\n",
    "        command = \"ffmpeg -r 1 -i /home/kdemochkin/emotions/train/\" + emotion + str(filename) + \" -r 1 '/home/kdemochkin/emotions/train/AlignedFaces_LBPTOP_Points/frames/\" + str(os.path.splitext(filename)[0]) + \"/%03d.png'\"\n",
    "        os.system(command=command)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect faces in frames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MTCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/HDD6TB/avsavchenko/src/face-emotion-recognition/src/facial_analysis.py:152: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if method is 'Min':\n"
     ]
    }
   ],
   "source": [
    "from facial_analysis import FacialImageProcessing\n",
    "imgProcessing=FacialImageProcessing(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_SIZE = (224,224)\n",
    "def save_faces(source_path,save_path):\n",
    "    if not os.path.exists(save_path):\n",
    "        os.mkdir(save_path)\n",
    "    for folder in tqdm(os.listdir(source_path)):\n",
    "        if not os.path.exists(os.path.join(save_path, folder)):\n",
    "            os.mkdir(os.path.join(save_path, folder))\n",
    "\n",
    "            for image in os.listdir(os.path.join(source_path, folder)):\n",
    "                filename = os.path.join(source_path, folder, image)\n",
    "                frame_bgr = cv2.imread(filename)\n",
    "                frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)\n",
    "                bounding_boxes, _ = imgProcessing.detect_faces(frame)\n",
    "\n",
    "                if len(bounding_boxes)==0:\n",
    "                    print('No faces found for ',filename)\n",
    "                    face_img = frame_bgr\n",
    "                    faceFound='noface'\n",
    "                else:\n",
    "                    if len(bounding_boxes)>1:\n",
    "                        print('Too many faces (',len(bounding_boxes),') found for ',filename)\n",
    "                        bounding_boxes=bounding_boxes[:1]\n",
    "\n",
    "                    b=[int(bi) for bi in bounding_boxes[0]]\n",
    "                    x1,y1,x2,y2=b[0:4]\n",
    "                    face_img=frame_bgr[y1:y2,x1:x2,:]\n",
    "\n",
    "                    if np.prod(face_img.shape)==0:\n",
    "                        print('Empty face ',b,' found for ',filename)\n",
    "                        continue\n",
    "                    \n",
    "                    faceFound=''\n",
    "\n",
    "                #face_img=cv2.resize(face_img,INPUT_SIZE)\n",
    "                root,ext=os.path.splitext(image)\n",
    "                cv2.imwrite(os.path.join(save_path, folder, root+faceFound+ext), face_img) \n",
    "        else:\n",
    "            print(folder)\n",
    "        \n",
    "#source_path = \"/home/kdemochkin/emotions/train/AlignedFaces_LBPTOP_Points/frames/\"\n",
    "#save_path = \"/home/kdemochkin/emotions/train/AlignedFaces_LBPTOP_Points/frames_mtcnn/\"\n",
    "save_faces(os.path.join(DATA_DIR,'val/AlignedFaces_LBPTOP_Points_Val/frames/'),os.path.join(DATA_DIR,'val/AlignedFaces_LBPTOP_Points_Val/frames_mtcnn_cropped/'))\n",
    "save_faces(os.path.join(DATA_DIR,'train/AlignedFaces_LBPTOP_Points/frames/'),os.path.join(DATA_DIR,'train/AlignedFaces_LBPTOP_Points/frames_mtcnn_cropped/'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aligned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import math\n",
    "from skimage import transform as trans\n",
    "def get_iou(bb1, bb2):\n",
    "    \"\"\"\n",
    "    Calculate the Intersection over Union (IoU) of two bounding boxes.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    bb1 : array\n",
    "        order: {'x1', 'y1', 'x2', 'y2'}\n",
    "        The (x1, y1) position is at the top left corner,\n",
    "        the (x2, y2) position is at the bottom right corner\n",
    "    bb2 : array\n",
    "        order: {'x1', 'y1', 'x2', 'y2'}\n",
    "        The (x1, y1) position is at the top left corner,\n",
    "        the (x2, y2) position is at the bottom right corner\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        in [0, 1]\n",
    "    \"\"\"\n",
    "\n",
    "    # determine the coordinates of the intersection rectangle\n",
    "    x_left = max(bb1[0], bb2[0])\n",
    "    y_top = max(bb1[1], bb2[1])\n",
    "    x_right = min(bb1[2], bb2[2])\n",
    "    y_bottom = min(bb1[3], bb2[3])\n",
    "\n",
    "    if x_right < x_left or y_bottom < y_top:\n",
    "        return 0.0\n",
    "\n",
    "    # The intersection of two axis-aligned bounding boxes is always an\n",
    "    # axis-aligned bounding box\n",
    "    intersection_area = (x_right - x_left) * (y_bottom - y_top)\n",
    "\n",
    "    # compute the area of both AABBs\n",
    "    bb1_area = (bb1[2] - bb1[0]) * (bb1[3] - bb1[1])\n",
    "    bb2_area = (bb2[2] - bb2[0]) * (bb2[3] - bb2[1])\n",
    "\n",
    "    # compute the intersection over union by taking the intersection\n",
    "    # area and dividing it by the sum of prediction + ground-truth\n",
    "    # areas - the interesection area\n",
    "    iou = intersection_area / float(bb1_area + bb2_area - intersection_area)\n",
    "    return iou\n",
    "\n",
    "#print(get_iou([10,10,20,20],[15,15,25,25]))\n",
    "\n",
    "def preprocess(img, bbox=None, landmark=None, **kwargs):\n",
    "    M = None\n",
    "    image_size = [224,224]\n",
    "    src = np.array([\n",
    "      [30.2946, 51.6963],\n",
    "      [65.5318, 51.5014],\n",
    "      [48.0252, 71.7366],\n",
    "      [33.5493, 92.3655],\n",
    "      [62.7299, 92.2041] ], dtype=np.float32 )\n",
    "    if image_size[1]==224:\n",
    "        src[:,0] += 8.0\n",
    "    src*=2\n",
    "    if landmark is not None:\n",
    "        dst = landmark.astype(np.float32)\n",
    "\n",
    "        tform = trans.SimilarityTransform()\n",
    "        #dst=dst[:3]\n",
    "        #src=src[:3]\n",
    "        #print(dst.shape,src.shape,dst,src)\n",
    "        tform.estimate(dst, src)\n",
    "        M = tform.params[0:2,:]\n",
    "        #M = cv2.estimateRigidTransform( dst.reshape(1,5,2), src.reshape(1,5,2), False)\n",
    "        #print(M)\n",
    "\n",
    "    if M is None:\n",
    "        if bbox is None: #use center crop\n",
    "            det = np.zeros(4, dtype=np.int32)\n",
    "            det[0] = int(img.shape[1]*0.0625)\n",
    "            det[1] = int(img.shape[0]*0.0625)\n",
    "            det[2] = img.shape[1] - det[0]\n",
    "            det[3] = img.shape[0] - det[1]\n",
    "        else:\n",
    "              det = bbox\n",
    "        margin = kwargs.get('margin', 44)\n",
    "        bb = np.zeros(4, dtype=np.int32)\n",
    "        bb[0] = np.maximum(det[0]-margin//2, 0)\n",
    "        bb[1] = np.maximum(det[1]-margin//2, 0)\n",
    "        bb[2] = np.minimum(det[2]+margin//2, img.shape[1])\n",
    "        bb[3] = np.minimum(det[3]+margin//2, img.shape[0])\n",
    "        ret = img[bb[1]:bb[3],bb[0]:bb[2],:]\n",
    "        if len(image_size)>0:\n",
    "              ret = cv2.resize(ret, (image_size[1], image_size[0]))\n",
    "        return ret \n",
    "    else: #do align using landmark\n",
    "        assert len(image_size)==2\n",
    "        warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)\n",
    "        return warped\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fpath='/home/HDD6TB/datasets/emotions/EmotiW/AFEW/val/AlignedFaces_LBPTOP_Points_Val/frames/001404920/001.png'\n",
    "#fpath='/home/HDD6TB/datasets/emotions/EmotiW/AFEW/val/AlignedFaces_LBPTOP_Points_Val/frames/010255520/017.png'\n",
    "#fpath='/home/HDD6TB/datasets/emotions/EmotiW/AFEW/val/AlignedFaces_LBPTOP_Points_Val/frames/012705800/030.png'\n",
    "frame_bgr=cv2.imread(fpath)\n",
    "plt.figure(figsize=(5, 5))\n",
    "frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)\n",
    "plt.imshow(frame)\n",
    "bounding_boxes, points = imgProcessing.detect_faces(frame)\n",
    "points = points.T\n",
    "for bbox,p in zip(bounding_boxes, points):\n",
    "    box = bbox.astype(np.int)\n",
    "    x1,y1,x2,y2=box[0:4]    \n",
    "    #face_img=frame[y1:y2,x1:x2,:]\n",
    "\n",
    "    #face_img=extract_image_chip(frame,p)\n",
    "    p = p.reshape((2,5)).T\n",
    "        \n",
    "    plt.figure(figsize=(5, 5))\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    face_img=preprocess(frame,box,None) #p)\n",
    "    ax1.set_title('Cropped')\n",
    "    ax1.imshow(face_img)\n",
    "    \n",
    "    face_img=preprocess(frame,box,p)\n",
    "    ax2.set_title('Aligned')\n",
    "    ax2.imshow(face_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_aligned_faces(source_path,save_path):\n",
    "    if not os.path.exists(save_path):\n",
    "        os.mkdir(save_path)\n",
    "\n",
    "    for folder in tqdm(os.listdir(source_path)):\n",
    "        if not os.path.exists(os.path.join(save_path, folder)):\n",
    "            os.mkdir(os.path.join(save_path, folder))\n",
    "\n",
    "            prev_b = None\n",
    "            counter = 0\n",
    "\n",
    "            for image in sorted(os.listdir(os.path.join(source_path, folder))):\n",
    "                filename = os.path.join(source_path, folder, image)\n",
    "                frame = cv2.imread(filename)\n",
    "                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "                bounding_boxes, points = imgProcessing.detect_faces(frame)\n",
    "                points = points.T\n",
    "\n",
    "                best_ind=None\n",
    "                if len(bounding_boxes)==0:\n",
    "                    print('No faces found for ',filename)\n",
    "                    counter+=1\n",
    "                    if prev_b is None or counter>3:\n",
    "                        continue\n",
    "                    else:\n",
    "                        b=prev_b\n",
    "                elif len(bounding_boxes)>1:\n",
    "                    print('Too many faces (',len(bounding_boxes),') found for ',filename)\n",
    "                    if prev_b is None:\n",
    "                        #continue\n",
    "                        best_ind=0\n",
    "                        b=[int(bi) for bi in bounding_boxes[best_ind]]\n",
    "                        counter=0\n",
    "                    else:\n",
    "                        best_iou=0\n",
    "                        for i in range(len(bounding_boxes)):\n",
    "                            iou=get_iou(bounding_boxes[i],prev_b)\n",
    "                            if iou>best_iou:\n",
    "                                best_iou=iou\n",
    "                                best_ind=i\n",
    "                        if best_iou>0:\n",
    "                            b=[int(bi) for bi in bounding_boxes[best_ind]]\n",
    "                            print('best_iou (',best_iou,') best_bb ',bounding_boxes[best_ind])\n",
    "                        else:\n",
    "                            #continue\n",
    "                            best_ind=0\n",
    "                            b=[int(bi) for bi in bounding_boxes[best_ind]]\n",
    "                            counter=0\n",
    "                else:\n",
    "                    best_ind=0\n",
    "                    b=[int(bi) for bi in bounding_boxes[best_ind]]\n",
    "                    counter=0\n",
    "                prev_b=b\n",
    "\n",
    "                if True:\n",
    "                    p=None\n",
    "                    if best_ind is not None:\n",
    "                        p=points[best_ind]\n",
    "                        if True: #not USE_RETINA_FACE:\n",
    "                            p = p.reshape((2,5)).T\n",
    "                    face_img=preprocess(frame,b,None) #p)\n",
    "                else:\n",
    "                    x1,y1,x2,y2=b[0:4]\n",
    "                    face_img=frame[y1:y2,x1:x2,:]\n",
    "                if np.prod(face_img.shape)==0:\n",
    "                    print('Empty face ',b,' found for ',filename)\n",
    "                    continue\n",
    "\n",
    "                cv2.imwrite(os.path.join(save_path, folder, image), cv2.cvtColor(face_img, cv2.COLOR_RGB2BGR)) \n",
    "\n",
    "#aligned\n",
    "save_aligned_faces(os.path.join(DATA_DIR,'val/AlignedFaces_LBPTOP_Points_Val/frames/'),os.path.join(DATA_DIR,'val/AlignedFaces_LBPTOP_Points_Val/frames_mtcnn_cropped/'))\n",
    "save_aligned_faces(os.path.join(DATA_DIR,'train/AlignedFaces_LBPTOP_Points/frames/'),os.path.join(DATA_DIR,'train/AlignedFaces_LBPTOP_Points/frames_mtcnn_cropped/'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dlib\n",
    "\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "\n",
    "source_path = \"/home/kdemochkin/emotions/train/AlignedFaces_LBPTOP_Points/frames/\"\n",
    "save_path = \"/home/kdemochkin/emotions/train/AlignedFaces_LBPTOP_Points/frames_dlib/\"\n",
    "INPUT_SIZE = (224, 224)\n",
    "\n",
    "for folder in tqdm(os.listdir(source_path)):\n",
    "    if not os.path.exists(os.path.join(save_path, folder)):\n",
    "        os.mkdir(os.path.join(save_path, folder))\n",
    "\n",
    "        for image in sorted(os.listdir(os.path.join(source_path, folder))):\n",
    "            filename = os.path.join(source_path, folder, image)\n",
    "            frame = cv2.imread(filename)\n",
    "            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "            faces,scores, idx = detector.run(gray,1,-1)\n",
    "            bounding_boxes=[[face.left(),face.top(),face.right(),face.bottom()] for face in faces]\n",
    "\n",
    "            if len(bounding_boxes)==0:\n",
    "                print('No faces found for ',filename)\n",
    "                face_img = frame\n",
    "                    \n",
    "            elif len(bounding_boxes)>1:\n",
    "                print('Too many faces (',len(bounding_boxes),') found for ',filename)\n",
    "                bounding_boxes=bounding_boxes[:1]\n",
    "                \n",
    "                b=[int(bi) for bi in bounding_boxes[0]]\n",
    "                x1,y1,x2,y2=b[0:4]\n",
    "                face_img=frame[y1:y2,x1:x2,:]\n",
    "                \n",
    "                if np.prod(face_img.shape)==0:\n",
    "                    print('Empty face ',b,' found for ',filename)\n",
    "                    continue\n",
    "            \n",
    "            else:\n",
    "                print('One face found for ', filename)\n",
    "                b=[int(bi) for bi in bounding_boxes[0]]\n",
    "                x1,y1,x2,y2=b[0:4]\n",
    "                face_img=frame[y1:y2,x1:x2,:]\n",
    "                \n",
    "                if np.prod(face_img.shape)==0:\n",
    "                    print('Empty face ',b,' found for ',filename)\n",
    "                    continue\n",
    "\n",
    "            face_img=cv2.resize(face_img,INPUT_SIZE)\n",
    "            cv2.imwrite(os.path.join(save_path, folder, image), cv2.cvtColor(face_img, cv2.COLOR_RGB2BGR)) \n",
    "    else:\n",
    "        print(folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AffectNet features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import mobilenet\n",
    "from tensorflow.keras.models import load_model,Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
      "_________________________________________________________________\n",
      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
      "_________________________________________________________________\n",
      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
      "_________________________________________________________________\n",
      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
      "_________________________________________________________________\n",
      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
      "_________________________________________________________________\n",
      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
      "_________________________________________________________________\n",
      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
      "_________________________________________________________________\n",
      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "global_pooling (GlobalAverag (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "feats (Dense)                (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "emotion_preds (Dense)        (None, 7)                 1799      \n",
      "=================================================================\n",
      "Total params: 3,493,063\n",
      "Trainable params: 3,471,175\n",
      "Non-trainable params: 21,888\n",
      "_________________________________________________________________\n",
      "224 224\n"
     ]
    }
   ],
   "source": [
    "base_model=load_model('../models/affectnet_emotions/mobilenet_7.h5')\n",
    "feature_extractor_model=Model(base_model.input,[base_model.get_layer('global_pooling').output,base_model.get_layer('feats').output,base_model.output])\n",
    "feature_extractor_model.summary()\n",
    "_,w,h,_=feature_extractor_model.input.shape\n",
    "print(w,h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mobilenet_preprocess_input(x,**kwargs):\n",
    "    x[..., 0] -= 103.939\n",
    "    x[..., 1] -= 116.779\n",
    "    x[..., 2] -= 123.68\n",
    "    return x\n",
    "\n",
    "preprocessing_function=mobilenet_preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 773/773 [03:57<00:00,  3.26it/s]\n"
     ]
    }
   ],
   "source": [
    "def get_features_scores(data_dir):\n",
    "    filename2features={}\n",
    "    for filename in tqdm(os.listdir(data_dir)):\n",
    "        frames_dir=os.path.join(data_dir,filename)\n",
    "        X_global_features,X_feats,X_scores,X_isface=[],[],[],[]\n",
    "        imgs=[]\n",
    "        for img_name in os.listdir(frames_dir):\n",
    "            img=cv2.imread(os.path.join(frames_dir,img_name))\n",
    "            X_isface.append('noface' not in img_name)\n",
    "                \n",
    "            if img.size:\n",
    "                img=cv2.resize(img,(w,h))\n",
    "                img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "                imgs.append(img)\n",
    "                if len(imgs)>=512:        \n",
    "                    inp = preprocessing_function(np.array(imgs, dtype=np.float32))\n",
    "                    global_features,feats,scores=feature_extractor_model.predict(inp)\n",
    "                    #print(global_features.shape,feats.shape,scores.shape)\n",
    "                    if len(X_feats)==0:\n",
    "                        X_feats=feats\n",
    "                        X_global_features=global_features\n",
    "                        X_scores=scores\n",
    "                    else:\n",
    "                        X_feats=np.concatenate((X_feats,feats),axis=0)\n",
    "                        X_global_features=np.concatenate((X_global_features,global_features),axis=0)\n",
    "                        X_scores=np.concatenate((X_scores,scores),axis=0)\n",
    "                    \n",
    "                    imgs=[]\n",
    "\n",
    "        if len(imgs)>0:        \n",
    "            inp = preprocessing_function(np.array(imgs, dtype=np.float32))\n",
    "            global_features,feats,scores=feature_extractor_model.predict(inp)\n",
    "            #print(global_features.shape,feats.shape,scores.shape)\n",
    "            if len(X_feats)==0:\n",
    "                X_feats=feats\n",
    "                X_global_features=global_features\n",
    "                X_scores=scores\n",
    "            else:\n",
    "                X_feats=np.concatenate((X_feats,feats),axis=0)\n",
    "                X_global_features=np.concatenate((X_global_features,global_features),axis=0)\n",
    "                X_scores=np.concatenate((X_scores,scores),axis=0)\n",
    "\n",
    "        X_isface=np.array(X_isface)\n",
    "        #print(X_global_features.shape,X_feats.shape,X_scores.shape)\n",
    "        filename2features[filename]=(X_global_features,X_feats,X_scores,X_isface)\n",
    "    return filename2features\n",
    "\n",
    "#filename2Allfeatures_val=get_features_scores(os.path.join(DATA_DIR,'val/AlignedFaces_LBPTOP_Points_Val/frames_mtcnn_cropped/'))\n",
    "filename2Allfeatures_train=get_features_scores(os.path.join(DATA_DIR,'train/AlignedFaces_LBPTOP_Points/frames_mtcnn_cropped/')) #_cropped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save/load features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_name='mobilenet_v1_ft'\n",
    "model_name='mymobilenet_7_ft_sgd_model'\n",
    "MODEL2EMOTIW_FEATURES=model_name+'_feat_emotiw.pickle' #'_feat_bgr_emotiw.pickle'\n",
    "#MODEL2EMOTIW_FEATURES=model_name+'scores_emotiw.pickle'\n",
    "#MODEL2EMOTIW_FEATURES=model_name+'_feat_emotiw_aligned.pickle'\n",
    "#MODEL2EMOTIW_FEATURES=model_name+'_feat_emotiw_cropped.pickle'\n",
    "\n",
    "#MODEL2EMOTIW_FEATURES='enet0_vggface2_pt_feat_emotiw.pickle'\n",
    "print(MODEL2EMOTIW_FEATURES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(MODEL2EMOTIW_FEATURES, 'wb') as handle:\n",
    "    #pickle.dump([filename2features_train,filename2features_val], handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    pickle.dump([filename2Allfeatures_train,filename2Allfeatures_val], handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "773 383\n"
     ]
    }
   ],
   "source": [
    "if False:\n",
    "    with open(MODEL2EMOTIW_FEATURES, 'rb') as handle:\n",
    "        filename2features_train,filename2features_val=pickle.load(handle)\n",
    "    print(len(filename2features_train),len(filename2features_val))\n",
    "else:\n",
    "    with open(MODEL2EMOTIW_FEATURES, 'rb') as handle:\n",
    "        filename2Allfeatures_train,filename2Allfeatures_val=pickle.load(handle)\n",
    "    print(len(filename2Allfeatures_train),len(filename2Allfeatures_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(773, 4096) (773,)\n",
      "(383, 4096) (383,)\n"
     ]
    }
   ],
   "source": [
    "USE_ALL_FEATURES=True\n",
    "def create_dataset(filename2features,data_dir):\n",
    "    x = []\n",
    "    y = []\n",
    "    has_faces=[]\n",
    "    ind=0\n",
    "    for category in emotion_to_index:\n",
    "        for filename in os.listdir(os.path.join(data_dir,category)):\n",
    "            fn=os.path.splitext(filename)[0]\n",
    "            if not fn in filename2features:\n",
    "                continue\n",
    "            features=filename2features[fn]\n",
    "            total_features=None\n",
    "            if USE_ALL_FEATURES and True:\n",
    "                for face in [1,0]:\n",
    "                    cur_features=features[ind][features[-1]==face]\n",
    "                    if len(cur_features)==0:\n",
    "                        continue\n",
    "                    weight=len(cur_features)/len(features[ind])\n",
    "                    mean_features = np.mean(cur_features, axis=0)\n",
    "                    std_features = np.std(cur_features, axis=0)\n",
    "                    max_features = np.max(cur_features, axis=0)\n",
    "                    min_features = np.min(cur_features, axis=0)\n",
    "\n",
    "                    # join several features together\n",
    "                    feature = np.concatenate((mean_features, std_features, min_features, max_features), axis=None)                    \n",
    "                    #feature = np.concatenate((mean_features, std_features, min_features), axis=None)\n",
    "                    #feature = np.concatenate((mean_features, min_features, max_features), axis=None)\n",
    "                    #feature=mean_features\n",
    "                    #feature=max_features\n",
    "\n",
    "                    if total_features is None:\n",
    "                        total_features=weight*feature\n",
    "                    else:\n",
    "                        total_features+=weight*feature\n",
    "                has_faces.append(1)\n",
    "            else:\n",
    "                if USE_ALL_FEATURES:\n",
    "                    cur_features=features[0][features[-1]==1]\n",
    "                else:\n",
    "                    cur_features=features\n",
    "                if len(cur_features)==0:\n",
    "                    has_faces.append(0)\n",
    "                    total_features=np.zeros_like(feature)\n",
    "                else:\n",
    "                    has_faces.append(1)\n",
    "                    #mean_features=features.mean(axis=0)\n",
    "                    mean_features = np.mean(cur_features, axis=0)\n",
    "                    std_features = np.std(cur_features, axis=0)\n",
    "                    max_features = np.max(cur_features, axis=0)\n",
    "                    min_features = np.min(cur_features, axis=0)\n",
    "\n",
    "                    # join several features together\n",
    "                    feature = np.concatenate((mean_features, std_features, min_features, max_features), axis=None)                    \n",
    "                    #feature = np.concatenate((mean_features, std_features, max_features), axis=None)\n",
    "                    #feature = np.concatenate((mean_features, min_features, max_features), axis=None)\n",
    "                    #feature = np.concatenate((mean_features, std_features), axis=None)\n",
    "                    #feature = np.concatenate((max_features, std_features), axis=None)\n",
    "                    #feature=max_features\n",
    "                    #feature=mean_features\n",
    "                    #feature=cur_features[-1]\n",
    "                    #feature=np.percentile(cur_features, 100,axis=0)\n",
    "\n",
    "                total_features=feature\n",
    "            \n",
    "            if total_features is not None:\n",
    "                x.append(total_features)\n",
    "                y.append(emotion_to_index[category])\n",
    "    x=np.array(x)\n",
    "    y=np.array(y)\n",
    "    has_faces=np.array(has_faces)\n",
    "    print(x.shape,y.shape)\n",
    "    return x,y,has_faces\n",
    "\n",
    "\n",
    "if USE_ALL_FEATURES:\n",
    "    x_train, y_train, has_faces_train = create_dataset(filename2Allfeatures_train,os.path.join(DATA_DIR,'train'))\n",
    "    x_test, y_test, has_faces_test = create_dataset(filename2Allfeatures_val,os.path.join(DATA_DIR,'val'))\n",
    "else:\n",
    "    x_train, y_train, has_faces_train = create_dataset(filename2features_train,os.path.join(DATA_DIR,'train'))\n",
    "    x_test, y_test, has_faces_test = create_dataset(filename2features_val,os.path.join(DATA_DIR,'val'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "if True:\n",
    "    x_train_norm=preprocessing.normalize(x_train,norm='l2')\n",
    "    x_test_norm=preprocessing.normalize(x_test,norm='l2')\n",
    "else:\n",
    "    # standartize features\n",
    "    scaler = preprocessing.StandardScaler()\n",
    "    x_train_norm = scaler.fit_transform(x_train)\n",
    "    x_test_norm = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5535248041775457\n",
      "Complete accuracy: 0.5535248041775457\n"
     ]
    }
   ],
   "source": [
    "#clf = svm.LinearSVC(C=0.07)\n",
    "#clf = svm.SVC(kernel='rbf')\n",
    "np.random.seed(1)\n",
    "clf=RandomForestClassifier(n_estimators=1000,max_depth=7, n_jobs=-1)\n",
    "#clf=KNeighborsClassifier(n_neighbors=9,p=2)\n",
    "\n",
    "#import xgboost as xgb\n",
    "#clf = xgb.XGBClassifier(n_estimators=1000,use_label_encoder=False)\n",
    "\n",
    "if True:    \n",
    "    clf.fit(x_train_norm[has_faces_train==1], y_train[has_faces_train==1])\n",
    "    y_pred = clf.predict(x_test_norm)\n",
    "elif False:\n",
    "    clf.fit(preprocessing.normalize(x_train[has_faces_train==1],norm='l2'), y_train[has_faces_train==1])\n",
    "    y_pred = clf.predict(preprocessing.normalize(x_test,norm='l2'))\n",
    "else:\n",
    "    clf.fit(x_train[has_faces_train==1], y_train[has_faces_train==1])\n",
    "    y_pred = clf.predict(x_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test[has_faces_test==1], y_pred[has_faces_test==1]))\n",
    "print(\"Complete accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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