Scale_Stat_Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d11628e3",
   "metadata": {},
   "source": [
    "# IMPORTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e229892",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from natsort import natsorted\n",
    "import cv2\n",
    "import imutils\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from math import trunc\n",
    "import json\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.layers import Conv2D, Input, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D\n",
    "from keras.layers import add, concatenate\n",
    "from keras.models import Model\n",
    "import struct\n",
    "import sys\n",
    "import numpy as np\n",
    "from numpy import expand_dims\n",
    "from keras.models import load_model\n",
    "from tensorflow.keras.utils import load_img\n",
    "from tensorflow.keras.utils import img_to_array\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle                \n",
    "from scipy.io import savemat\n",
    "\n",
    "def listdir_fullpath(d):\n",
    "    return [os.path.join(d, f) for f in os.listdir(d)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37d17659",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DATASET IMPORT WITH PATHS AND BBOXES\n",
    "f = open(r'D:\\Scale_Stat_Evaluation\\Dataset\\labels.json')\n",
    "data = json.load(f)\n",
    "annotations = data['annotations']\n",
    "every_path = []\n",
    "rows = 200\n",
    "bbox_size = 4\n",
    "counters = [0] * rows\n",
    "bboxes = [[] for i in range(rows)] \n",
    "items = 0\n",
    "# Creation of the list of paths of images in the dataset and bboxes\n",
    "# This elements are extracted from the json file downloaded from coco with the dataset\n",
    "for entry in data['annotations']:\n",
    "    class_id = entry['category_id']\n",
    "    if (class_id == 1):\n",
    "        img_id = entry['image_id']\n",
    "        path = float(img_id)/pow(10,11)\n",
    "        dec_path = format(path, '.11f')\n",
    "        final_path = ''\n",
    "        final_path = \"D:\\\\Scale_Stat_Evaluation\\\\Dataset\\\\Person\\\\\"+ dec_path.replace('.', '') + '.jpg'\n",
    "        if(os.path.exists(final_path) == True):\n",
    "            if(every_path.count(final_path) == 0):\n",
    "                every_path.append(final_path)\n",
    "                bboxes[items].append(entry['bbox'])\n",
    "                items = items + 1\n",
    "            else:\n",
    "                index = every_path.index(final_path)\n",
    "                bboxes[index].append(entry['bbox'])\n",
    "                counters[index] = counters[index] + 1\n",
    "\n",
    "assert(len(bboxes)==len(every_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7795c96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "non_persons = listdir_fullpath(\"D:\\\\Scale_Stat_Evaluation\\\\Dataset\\\\nonPerson\")\n",
    "for i in range(0, len(non_persons)):\n",
    "    every_path.append(non_persons[i])\n",
    "    bboxes.append([])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74af9216",
   "metadata": {},
   "source": [
    "# HELPER FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70d43d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_single_image_results(gt_boxes, pred_boxes, iou_thr):\n",
    "    \n",
    "    \"\"\"Calculates number of true_pos, false_pos, false_neg from single batch of boxes.\n",
    "    Args:\n",
    "        gt_boxes (list of list of floats): list of locations of ground truth\n",
    "            objects as [xlest, yleft, width, height]\n",
    "        pred_boxes (dict): dict of dicts of 'boxes' (formatted like `gt_boxes`)\n",
    "            and 'scores'\n",
    "        iou_thr (float): value of IoU to consider as threshold for a\n",
    "            true prediction.\n",
    "    Returns:\n",
    "        dict: true positives (int), false positives (int), false negatives (int)\n",
    "    \"\"\"\n",
    "    \n",
    "    if len(gt_boxes)==0:\n",
    "        tp=0\n",
    "        fp=len(pred_boxes)\n",
    "        fn=0\n",
    "        return [tp, fp, fn]\n",
    "    \n",
    "    if len(pred_boxes)==0 & len(gt_boxes) != 0:\n",
    "        tp=0\n",
    "        fp=0\n",
    "        fn=len(gt_boxes)\n",
    "        return [tp, fp, fn]\n",
    "\n",
    "    if len(pred_boxes)==0 & len(gt_boxes) == 0:\n",
    "        tp=0\n",
    "        fp=0\n",
    "        fn=len(gt_boxes)\n",
    "        return [tp, fp, fn]\n",
    "    \n",
    "    tp = 0\n",
    "    \n",
    "    for i in range(0, len(gt_boxes)):\n",
    "        for h in range(0, len(pred_boxes)):\n",
    "            if(calc_iou(gt_boxes[i],pred_boxes[h]) >= iou_thr):\n",
    "                if(tp < len(pred_boxes)):\n",
    "                    tp += 1\n",
    "        fp = len(pred_boxes) - tp\n",
    "        fn = len(gt_boxes) - tp\n",
    "\n",
    "    return [tp, fp, fn]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa42db1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_iou(gt_bbox, pred_bbox):\n",
    "    '''\n",
    "    This function takes the predicted bounding box and ground truth bounding box and \n",
    "    returns the IoU ratio\n",
    "    '''\n",
    "    \n",
    "    x_topleft_gt, y_topleft_gt, x_bottomright_gt, y_bottomright_gt= gt_bbox[0] , gt_bbox[1], gt_bbox[0] + gt_bbox[2] , gt_bbox[1] + gt_bbox[3]\n",
    "    x_topleft_p, y_topleft_p, x_bottomright_p, y_bottomright_p= pred_bbox[0] , pred_bbox[1], pred_bbox[0] + pred_bbox[2] , pred_bbox[1] + pred_bbox[3]\n",
    "    \n",
    "    \n",
    "    if (x_topleft_gt > x_bottomright_gt) or (y_topleft_gt> y_bottomright_gt):\n",
    "        raise AssertionError(\"Ground Truth Bounding Box is not correct\")\n",
    "        \n",
    "    if (x_topleft_p > x_bottomright_p) or (y_topleft_p> y_bottomright_p):\n",
    "        raise AssertionError(\"Predicted Bounding Box is not correct\",x_topleft_p, x_bottomright_p,y_topleft_p,y_bottomright_gt)\n",
    "        \n",
    "        \n",
    "    #if the GT bbox and predcited BBox do not overlap then iou=0\n",
    "    if(x_bottomright_gt< x_topleft_p):\n",
    "        # If bottom right of x-coordinate  GT  bbox is less than or above the top left of x coordinate of  the predicted BBox\n",
    "        \n",
    "        return 0.0\n",
    "    if(y_bottomright_gt< y_topleft_p):  # If bottom right of y-coordinate  GT  bbox is less than or above the top left of y coordinate of  the predicted BBox\n",
    "        \n",
    "        return 0.0\n",
    "    if(x_topleft_gt> x_bottomright_p): # If bottom right of x-coordinate  GT  bbox is greater than or below the bottom right  of x coordinate of  the predcited BBox\n",
    "        \n",
    "        return 0.0\n",
    "    if(y_topleft_gt> y_bottomright_p): # If bottom right of y-coordinate  GT  bbox is greater than or below the bottom right  of y coordinate of  the predcited BBox\n",
    "        \n",
    "        return 0.0\n",
    "    \n",
    "    \n",
    "    GT_bbox_area = (x_bottomright_gt -  x_topleft_gt + 1) * (  y_bottomright_gt -y_topleft_gt + 1)\n",
    "    Pred_bbox_area =(x_bottomright_p - x_topleft_p + 1 ) * ( y_bottomright_p -y_topleft_p + 1)\n",
    "    \n",
    "    x_top_left =np.max([x_topleft_gt, x_topleft_p])\n",
    "    y_top_left = np.max([y_topleft_gt, y_topleft_p])\n",
    "    x_bottom_right = np.min([x_bottomright_gt, x_bottomright_p])\n",
    "    y_bottom_right = np.min([y_bottomright_gt, y_bottomright_p])\n",
    "    \n",
    "    intersection_area = (x_bottom_right- x_top_left + 1) * (y_bottom_right-y_top_left  + 1)\n",
    "    \n",
    "    union_area = (GT_bbox_area + Pred_bbox_area - intersection_area)\n",
    "   \n",
    "    return intersection_area/union_area"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f67018c",
   "metadata": {},
   "source": [
    "# HOG detector\n",
    "\n",
    "Get some statistics like precision,recall... by scaling down the original scale of the image to 1.0,0.9,0.8... Compare the results with the ground truth bounding boxes that have also been scaled down by the same scale factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "fca185b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_image(im_path, true_bboxes):\n",
    "    image = cv2.imread(im_path)\n",
    "    for (x, y, w, h) in true_bboxes:\n",
    "        cv2.rectangle(image, (int(x), int(y)), \n",
    "                    (int(x + w), int(y + h)), \n",
    "                    (0, 255, 0), 2)\n",
    "    imRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(imRGB)\n",
    "    plt.show()\n",
    "    cv2.imwrite(r'D:\\Scale_Stat_Evaluation\\challenging2.jpg', image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ab529939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 63  #63,28,54,92\n",
    "save_image(every_path[i], bboxes[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aeb7edb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hog_detector(im_path, scale): \n",
    "    # Initializing the HOG person\n",
    "    # detector\n",
    "    hog = cv2.HOGDescriptor()\n",
    "    hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n",
    "    \n",
    "    # Reading the Image\n",
    "    image = cv2.imread(im_path)\n",
    "    image = cv2.resize(image, (int(640*scale), int(480*scale)))\n",
    "    \n",
    "    # Detecting all the regions in the \n",
    "    # Image that have a pedestrians inside it\n",
    "    (regions, _) = hog.detectMultiScale(image, \n",
    "                                      winStride=(4, 4),\n",
    "                                      padding=(4, 4),\n",
    "                                      scale=1.05)\n",
    "    '''\n",
    "    # Drawing the regions in the Image\n",
    "    for (x, y, w, h) in regions:\n",
    "        cv2.rectangle(image, (x, y), \n",
    "                    (x + w, y + h), \n",
    "                    (0, 0, 255), 2)\n",
    "    for (x, y, w, h) in true_bboxes:\n",
    "        cv2.rectangle(image, (int(x*scale), int(y*scale)), \n",
    "                    (int((x + w)*scale), int((y + h)*scale)), \n",
    "                    (0, 255, 0), 2)\n",
    "    imRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(imRGB)\n",
    "    plt.show()\n",
    "    '''\n",
    "    return regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "432ad697",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = 1.0\n",
    "regions = []  #proposed regions\n",
    "scaled_bboxes = [] #scaled down version of ground truth boxes\n",
    "for i in range(0,7):\n",
    "    temp_regions = []\n",
    "    temp_boxes = []\n",
    "    for f in range(0,len(every_path)): #for every image\n",
    "        prop_regions = hog_detector(every_path[f], scale) #get proposed_regions at current scale\n",
    "        temp_regions.append(prop_regions)\n",
    "        temp_boxes_in = []\n",
    "        for x,y,w,h in bboxes[f]:\n",
    "            temp_boxes_in.append([x * scale, y * scale, w * scale, h * scale])\n",
    "        temp_boxes.append(temp_boxes_in)\n",
    "    regions.append(temp_regions)\n",
    "    scaled_bboxes.append(temp_boxes)\n",
    "    scale = scale - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42b3dc51",
   "metadata": {},
   "outputs": [],
   "source": [
    "hog_results = np.zeros((7,3))\n",
    "HOG_precision = np.zeros((7,))\n",
    "HOG_recall = np.zeros((7,))\n",
    "HOG_f1 = np.zeros((7,))\n",
    "HOG_missrate = np.zeros((7,))\n",
    "HOG_scales = []\n",
    "scale = 1.0\n",
    "for h in range(0,7):\n",
    "    HOG_scales.append(scale)\n",
    "    tp = 0\n",
    "    fp = 0\n",
    "    fn = 0 \n",
    "    for i in range(0, 18):\n",
    "        res = get_single_image_results(scaled_bboxes[h][i],regions[h][i],0.1)\n",
    "        tp += res[0]\n",
    "        fp += res[1]\n",
    "        fn += res[2]\n",
    "    hog_results[h][0] = tp\n",
    "    hog_results[h][1] = fp\n",
    "    hog_results[h][2] = fn\n",
    "    prec = tp / (tp + fp)\n",
    "    rec = tp / (tp + fn)\n",
    "    HOG_precision[h] = tp / (tp + fp)\n",
    "    HOG_recall[h] = tp / (tp + fn)\n",
    "    if(prec + rec != 0):\n",
    "        HOG_f1[h] = 2 * (prec * rec)/ (prec + rec)\n",
    "    HOG_missrate[h] = fn / (tp + fn) \n",
    "    scale = scale - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7b116f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRECISION\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECALL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 SCORE\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"PRECISION\")\n",
    "pyplot.plot(HOG_scales, HOG_precision)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"RECALL\")\n",
    "pyplot.plot(HOG_scales, HOG_recall)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"F1 SCORE\")\n",
    "pyplot.plot(HOG_scales, HOG_f1)\n",
    "pyplot.show()\n",
    "\n",
    "mdic = {\"scales\": HOG_scales}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\scales.mat\", mdic)\n",
    "\n",
    "mdic = {\"HOG_precision\": HOG_precision}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\HOG_precision.mat\", mdic)\n",
    "\n",
    "mdic = {\"HOG_recall\": HOG_recall}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\HOG_recall.mat\", mdic)\n",
    "\n",
    "mdic = {\"HOG_f1\": HOG_f1}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\HOG_f1.mat\", mdic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1a7fa7",
   "metadata": {},
   "source": [
    "# VIOLA one cascade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "463a0b75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def viola_detector(im_path, scale): \n",
    "    \n",
    "    #Reading the Image\n",
    "    image = cv2.imread(im_path)\n",
    "\n",
    "    image = cv2.resize(image,(int(640*scale),int(480*scale)))\n",
    "    #convert in gray_scale since the cascade classifier works on gray_scale images\n",
    "    grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    #load the cascade classifier, detect the bodies in the image and draw them for the user to see\n",
    "    body_cascade = cv2.CascadeClassifier(r'D:\\Stat Evaluation\\Model\\haarcascade_fullbody.xml')\n",
    "    detected_bodies = body_cascade.detectMultiScale(grayscale_image)\n",
    "    \n",
    "    '''\n",
    "    # Drawing the regions in the Image\n",
    "    for (x, y, w, h) in detected_bodies:\n",
    "        cv2.rectangle(image, (x, y), \n",
    "                    (x + w, y + h), \n",
    "                    (0, 0, 255), 2)\n",
    "    for (x, y, w, h) in true_bboxes:\n",
    "        cv2.rectangle(image, (int(x*scale), int(y*scale)), \n",
    "                    (int((x + w)*scale), int((y + h)*scale)), \n",
    "                    (0, 255, 0), 2)\n",
    "        \n",
    "    imRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(imRGB)\n",
    "    plt.show()\n",
    "    '''\n",
    "    \n",
    "    return detected_bodies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "747ba62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "viola_regions = []  #proposed regions\n",
    "scaled_boxes = []\n",
    "\n",
    "scales = []\n",
    "scale = 1.0\n",
    "for i in range(0,9):\n",
    "    temp_regions = []\n",
    "    temp_boxes = []\n",
    "    for f in range(0,len(every_path)): #for every image\n",
    "        prop_regions = viola_detector(every_path[f], scale) #get proposed_regions at current scale\n",
    "        temp_regions.append(prop_regions)\n",
    "        temp_boxes_in = []\n",
    "        for x,y,w,h in bboxes[f]:\n",
    "            temp_boxes_in.append([x * scale, y * scale, w * scale, h * scale])\n",
    "        temp_boxes.append(temp_boxes_in)\n",
    "    scaled_boxes.append(temp_boxes)\n",
    "    viola_regions.append(temp_regions)\n",
    "    scale = scale - 0.1\n",
    "    scales.append(scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c889aeef",
   "metadata": {},
   "outputs": [],
   "source": [
    "viola_results = np.zeros((9,3))\n",
    "viola_precision = np.zeros((9,))\n",
    "viola_recall = np.zeros((9,))\n",
    "viola_f1 = np.zeros((9,))\n",
    "for h in range(0,9):\n",
    "    tp = 0\n",
    "    fp = 0\n",
    "    fn = 0\n",
    "    for i in range(0, 268):\n",
    "        res = get_single_image_results(scaled_boxes[h][i],viola_regions[h][i],0.1)\n",
    "        tp += res[0]\n",
    "        fp += res[1]\n",
    "        fn += res[2]\n",
    "    viola_results[h][0] = tp\n",
    "    viola_results[h][1] = fp\n",
    "    viola_results[h][2] = fn\n",
    "    prec = tp / (tp + fp)\n",
    "    viola_precision[h] = tp / (tp + fp)\n",
    "    rec = tp / (tp + fn)\n",
    "    \n",
    "    viola_recall[h] = tp / (tp + fn)\n",
    "    \n",
    "    if(prec + rec != 0):\n",
    "        viola_f1[h] = 2 * (prec * rec)/ (prec + rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f6b16648",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRECISION\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECALL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"PRECISION\")\n",
    "pyplot.plot(scales, viola_precision)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"RECALL\")\n",
    "pyplot.plot(scales, viola_recall)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"F1 score\")\n",
    "pyplot.plot(scales, viola_f1)\n",
    "pyplot.show()\n",
    "\n",
    "from scipy.io import savemat\n",
    "\n",
    "mdic = {\"viola_precision\": viola_precision}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\viola_precision.mat\", mdic)\n",
    "\n",
    "mdic = {\"viola_recall\": viola_recall}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\viola_recall.mat\", mdic)\n",
    "\n",
    "mdic = {\"viola_f1\": viola_f1}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\viola_f1.mat\", mdic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24568a9d",
   "metadata": {},
   "source": [
    "# VIOLA three cascade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "899898d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def viola_detector_powered(im_path, scale): \n",
    "    \n",
    "    #Reading the Image\n",
    "    image = cv2.imread(im_path)\n",
    "  \n",
    "    image = cv2.resize(image,(int(640*scale),int(480*scale)))\n",
    "    \n",
    "    #convert in gray_scale since the cascade classifier works on gray_scale images\n",
    "    grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    #load the cascade classifier, detect the bodies in the image and draw them for the user to see\n",
    "    body_cascade = cv2.CascadeClassifier(r'D:\\Stat Evaluation\\Model\\haarcascade_fullbody.xml')\n",
    "    lbody_cascade = cv2.CascadeClassifier(r'D:\\Stat Evaluation\\Model\\haarcascade_lowerbody.xml') #lower body\n",
    "    ubody_cascade = cv2.CascadeClassifier(r'D:\\Stat Evaluation\\Model\\haarcascade_upperbody.xml') #upper body\n",
    "  \n",
    "    detected_bodies = body_cascade.detectMultiScale(grayscale_image)\n",
    "    ldetected_bodies = lbody_cascade.detectMultiScale(grayscale_image) #lower_body\n",
    "    udetected_bodies = ubody_cascade.detectMultiScale(grayscale_image) #upper_body\n",
    "  \n",
    "    tot_bodies = list(detected_bodies) + list(ldetected_bodies) + list(udetected_bodies) #concatenate all the detected bboxes\n",
    "    \n",
    "    '''\n",
    "    # Drawing the regions in the Image\n",
    "    for (x, y, w, h) in tot_bodies:\n",
    "        cv2.rectangle(image, (x, y), \n",
    "                    (x + w, y + h), \n",
    "                    (0, 0, 255), 2)\n",
    "    for (x, y, w, h) in true_bboxes:\n",
    "        cv2.rectangle(image, (int(x*scale), int(y*scale)), \n",
    "                    (int((x + w)*scale), int((y + h)*scale)), \n",
    "                    (0, 255, 0), 2)\n",
    "        \n",
    "    imRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(imRGB)\n",
    "    plt.show()\n",
    "    '''\n",
    "    \n",
    "    return tot_bodies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "72f58881",
   "metadata": {},
   "outputs": [],
   "source": [
    "viola_pow_regions = []  #proposed regions\n",
    "scaled_boxes = []\n",
    "scales = []\n",
    "scale = 1.0\n",
    "for i in range(0,9):\n",
    "    temp_regions = []\n",
    "    temp_boxes = []\n",
    "    for f in range(0,len(every_path)): #for every image\n",
    "        prop_regions = viola_detector_powered(every_path[f], scale) #get proposed_regions at current scale\n",
    "        temp_regions.append(prop_regions)\n",
    "        temp_boxes_in = []\n",
    "        for x,y,w,h in bboxes[f]:\n",
    "            temp_boxes_in.append([x * scale, y * scale, w * scale, h * scale])\n",
    "        temp_boxes.append(temp_boxes_in)\n",
    "    scaled_boxes.append(temp_boxes)\n",
    "    viola_pow_regions.append(temp_regions)\n",
    "    scale = scale - 0.1\n",
    "    scales.append(scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7844684a",
   "metadata": {},
   "outputs": [],
   "source": [
    "viola_pow_results = np.zeros((9,3))\n",
    "viola_pow_precision = np.zeros((9,))\n",
    "viola_pow_recall = np.zeros((9,))\n",
    "viola_pow_f1 = np.zeros((9,))\n",
    "for h in range(0,9):\n",
    "    tp = 0\n",
    "    fp = 0\n",
    "    fn = 0\n",
    "    for i in range(0, 268):\n",
    "        res = get_single_image_results(scaled_boxes[h][i],viola_pow_regions[h][i],0.1)\n",
    "        tp += res[0]\n",
    "        fp += res[1]\n",
    "        fn += res[2]\n",
    "    viola_pow_results[h][0] = tp\n",
    "    viola_pow_results[h][1] = fp\n",
    "    viola_pow_results[h][2] = fn\n",
    "    prec = tp / (tp + fp)\n",
    "    viola_pow_precision[h] = tp / (tp + fp)\n",
    "    rec = tp / (tp + fn)\n",
    "    \n",
    "    viola_pow_recall[h] = tp / (tp + fn)\n",
    "    \n",
    "    if(prec + rec != 0):\n",
    "        viola_pow_f1[h] = 2 * (prec * rec)/ (prec + rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3d5128c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRECISION\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECALL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"PRECISION\")\n",
    "pyplot.plot(scales, viola_pow_precision)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"RECALL\")\n",
    "pyplot.plot(scales, viola_pow_recall)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"F1 score\")\n",
    "pyplot.plot(scales, viola_pow_f1)\n",
    "pyplot.show()\n",
    "\n",
    "from scipy.io import savemat\n",
    "\n",
    "mdic = {\"viola_pow_precision\": viola_pow_precision}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\viola_pow_precision.mat\", mdic)\n",
    "\n",
    "mdic = {\"viola_pow_recall\": viola_pow_recall}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\viola_pow_recall.mat\", mdic)\n",
    "\n",
    "mdic = {\"viola_pow_f1\": viola_pow_f1}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\viola_pow_f1.mat\", mdic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0641cb5d",
   "metadata": {},
   "source": [
    "# Yolo v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "827aa831",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FUNCTIONS TO CREATE AND LOAD THE YOLO V3 model\n",
    "\n",
    "def _conv_block(inp, convs, skip=True):\n",
    "    x = inp\n",
    "    count = 0\n",
    "    \n",
    "    for conv in convs:\n",
    "        if count == (len(convs) - 2) and skip:\n",
    "            skip_connection = x\n",
    "        count += 1\n",
    "        \n",
    "        if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top\n",
    "        x = Conv2D(conv['filter'], \n",
    "                   conv['kernel'], \n",
    "                   strides=conv['stride'], \n",
    "                   padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top\n",
    "                   name='conv_' + str(conv['layer_idx']), \n",
    "                   use_bias=False if conv['bnorm'] else True)(x)\n",
    "        if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)\n",
    "        if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)\n",
    "\n",
    "    return add([skip_connection, x]) if skip else x\n",
    "\n",
    "\n",
    "def make_yolov3_model():\n",
    "    input_image = Input(shape=(None, None, 3))\n",
    "\n",
    "    # Layer  0 => 4\n",
    "    x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},\n",
    "                                  {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},\n",
    "                                  {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},\n",
    "                                  {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])\n",
    "\n",
    "    # Layer  5 => 8\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},\n",
    "                        {'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},\n",
    "                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])\n",
    "\n",
    "    # Layer  9 => 11\n",
    "    x = _conv_block(x, [{'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},\n",
    "                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])\n",
    "\n",
    "    # Layer 12 => 15\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},\n",
    "                        {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},\n",
    "                        {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])\n",
    "\n",
    "    # Layer 16 => 36\n",
    "    for i in range(7):\n",
    "        x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},\n",
    "                            {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])\n",
    "        \n",
    "    skip_36 = x\n",
    "        \n",
    "    # Layer 37 => 40\n",
    "    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])\n",
    "\n",
    "    # Layer 41 => 61\n",
    "    for i in range(7):\n",
    "        x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},\n",
    "                            {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])\n",
    "        \n",
    "    skip_61 = x\n",
    "        \n",
    "    # Layer 62 => 65\n",
    "    x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])\n",
    "\n",
    "    # Layer 66 => 74\n",
    "    for i in range(3):\n",
    "        x = _conv_block(x, [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},\n",
    "                            {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])\n",
    "        \n",
    "    # Layer 75 => 79\n",
    "    x = _conv_block(x, [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], skip=False)\n",
    "\n",
    "    # Layer 80 => 82\n",
    "    yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 80},\n",
    "                              {'filter':  255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], skip=False)\n",
    "\n",
    "    # Layer 83 => 86\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}], skip=False)\n",
    "    x = UpSampling2D(2)(x)\n",
    "    x = concatenate([x, skip_61])\n",
    "\n",
    "    # Layer 87 => 91\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 90},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], skip=False)\n",
    "\n",
    "    # Layer 92 => 94\n",
    "    yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 92},\n",
    "                              {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], skip=False)\n",
    "\n",
    "    # Layer 95 => 98\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True,   'layer_idx': 96}], skip=False)\n",
    "    x = UpSampling2D(2)(x)\n",
    "    x = concatenate([x, skip_36])\n",
    "\n",
    "    # Layer 99 => 106\n",
    "    yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 99},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 100},\n",
    "                               {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 101},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 102},\n",
    "                               {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 103},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 104},\n",
    "                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], skip=False)\n",
    "\n",
    "    model = Model(input_image, [yolo_82, yolo_94, yolo_106])    \n",
    "    return model\n",
    "\n",
    "class WeightReader:\n",
    "    def __init__(self, weight_file):\n",
    "        with open(weight_file, 'rb') as w_f:\n",
    "            major,    = struct.unpack('i', w_f.read(4))\n",
    "            minor,    = struct.unpack('i', w_f.read(4))\n",
    "            revision, = struct.unpack('i', w_f.read(4))\n",
    "\n",
    "            if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:\n",
    "                w_f.read(8)\n",
    "            else:\n",
    "                w_f.read(4)\n",
    "\n",
    "            transpose = (major > 1000) or (minor > 1000)\n",
    "            \n",
    "            binary = w_f.read()\n",
    "\n",
    "        self.offset = 0\n",
    "        self.all_weights = np.frombuffer(binary, dtype='float32')\n",
    "        \n",
    "    def read_bytes(self, size):\n",
    "        self.offset = self.offset + size\n",
    "        return self.all_weights[self.offset-size:self.offset]\n",
    "\n",
    "    def load_weights(self, model):\n",
    "        for i in range(106):\n",
    "            try:\n",
    "                conv_layer = model.get_layer('conv_' + str(i))\n",
    "                print(\"loading weights of convolution #\" + str(i))\n",
    "\n",
    "                if i not in [81, 93, 105]:\n",
    "                    norm_layer = model.get_layer('bnorm_' + str(i))\n",
    "\n",
    "                    size = np.prod(norm_layer.get_weights()[0].shape)\n",
    "\n",
    "                    beta  = self.read_bytes(size) # bias\n",
    "                    gamma = self.read_bytes(size) # scale\n",
    "                    mean  = self.read_bytes(size) # mean\n",
    "                    var   = self.read_bytes(size) # variance            \n",
    "\n",
    "                    weights = norm_layer.set_weights([gamma, beta, mean, var])  \n",
    "\n",
    "                if len(conv_layer.get_weights()) > 1:\n",
    "                    bias   = self.read_bytes(np.prod(conv_layer.get_weights()[1].shape))\n",
    "                    kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n",
    "                    \n",
    "                    kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n",
    "                    kernel = kernel.transpose([2,3,1,0])\n",
    "                    conv_layer.set_weights([kernel, bias])\n",
    "                else:\n",
    "                    kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n",
    "                    kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n",
    "                    kernel = kernel.transpose([2,3,1,0])\n",
    "                    conv_layer.set_weights([kernel])\n",
    "            except ValueError:\n",
    "                print(\"no convolution #\" + str(i))     \n",
    "    \n",
    "    def reset(self):\n",
    "        self.offset = 0\n",
    "\n",
    "class BoundBox:\n",
    "    def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):\n",
    "        self.xmin = xmin\n",
    "        self.ymin = ymin\n",
    "        self.xmax = xmax\n",
    "        self.ymax = ymax\n",
    "        \n",
    "        self.objness = objness\n",
    "        self.classes = classes\n",
    "\n",
    "        self.label = -1\n",
    "        self.score = -1\n",
    "\n",
    "    def get_label(self):\n",
    "        if self.label == -1:\n",
    "            self.label = np.argmax(self.classes)\n",
    "        \n",
    "        return self.label\n",
    "    \n",
    "    def get_score(self):\n",
    "        if self.score == -1:\n",
    "            self.score = self.classes[self.get_label()]\n",
    "            \n",
    "        return self.score\n",
    "\n",
    "def _sigmoid(x):\n",
    "    return 1. / (1. + np.exp(-x))\n",
    "\n",
    "def decode_netout(netout, anchors, obj_thresh, nms_thresh, net_h, net_w):\n",
    "    grid_h, grid_w = netout.shape[:2]\n",
    "    nb_box = 3\n",
    "    netout = netout.reshape((grid_h, grid_w, nb_box, -1))\n",
    "    nb_class = netout.shape[-1] - 5\n",
    "\n",
    "    boxes = []\n",
    "\n",
    "    netout[..., :2]  = _sigmoid(netout[..., :2])\n",
    "    netout[..., 4:]  = _sigmoid(netout[..., 4:])\n",
    "    netout[..., 5:]  = netout[..., 4][..., np.newaxis] * netout[..., 5:]\n",
    "    netout[..., 5:] *= netout[..., 5:] > obj_thresh\n",
    "\n",
    "    for i in range(grid_h*grid_w):\n",
    "        row = i / grid_w\n",
    "        col = i % grid_w\n",
    "        \n",
    "        for b in range(nb_box):\n",
    "            # 4th element is objectness score\n",
    "            objectness = netout[int(row)][int(col)][b][4]\n",
    "            #objectness = netout[..., :4]\n",
    "            \n",
    "            if(objectness.all() <= obj_thresh): continue\n",
    "            \n",
    "            # first 4 elements are x, y, w, and h\n",
    "            x, y, w, h = netout[int(row)][int(col)][b][:4]\n",
    "\n",
    "            x = (col + x) / grid_w # center position, unit: image width\n",
    "            y = (row + y) / grid_h # center position, unit: image height\n",
    "            w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width\n",
    "            h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height  \n",
    "            \n",
    "            # last elements are class probabilities\n",
    "            classes = netout[int(row)][col][b][5:]\n",
    "            \n",
    "            box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)\n",
    "            #box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, None, classes)\n",
    "\n",
    "            boxes.append(box)\n",
    "\n",
    "    return boxes\n",
    "\n",
    "def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):\n",
    "    if (float(net_w)/image_w) < (float(net_h)/image_h):\n",
    "        new_w = net_w\n",
    "        new_h = (image_h*net_w)/image_w\n",
    "    else:\n",
    "        new_h = net_w\n",
    "        new_w = (image_w*net_h)/image_h\n",
    "        \n",
    "    for i in range(len(boxes)):\n",
    "        x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w\n",
    "        y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h\n",
    "        \n",
    "        boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)\n",
    "        boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)\n",
    "        boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)\n",
    "        boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)\n",
    "\n",
    "def correct_yolo_boxes2(boxes, image_h, image_w, net_h, net_w):\n",
    "        \n",
    "    for i in range(len(boxes)):\n",
    "        x_scale = image_w/net_w\n",
    "        y_scale = image_h/net_h\n",
    "        \n",
    "        boxes[i].xmin = int(boxes[i].xmin * image_w)\n",
    "        boxes[i].xmax = int(boxes[i].xmax * image_w)\n",
    "        boxes[i].ymin = int(boxes[i].ymin * image_h)\n",
    "        boxes[i].ymax = int(boxes[i].ymax * image_h)\n",
    "\n",
    "def _interval_overlap(interval_a, interval_b):\n",
    "    x1, x2 = interval_a\n",
    "    x3, x4 = interval_b\n",
    "\n",
    "    if x3 < x1:\n",
    "        if x4 < x1:\n",
    "            return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x1\n",
    "    else:\n",
    "        if x2 < x3:\n",
    "             return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x3\n",
    "\n",
    "def bbox_iou(box1, box2):\n",
    "    intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\n",
    "    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\n",
    "    \n",
    "    intersect = intersect_w * intersect_h\n",
    "\n",
    "    w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\n",
    "    w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\n",
    "    \n",
    "    union = w1*h1 + w2*h2 - intersect\n",
    "    \n",
    "    return float(intersect) / union\n",
    "\n",
    "def do_nms(boxes, nms_thresh):\n",
    "    if len(boxes) > 0:\n",
    "        nb_class = len(boxes[0].classes)\n",
    "    else:\n",
    "        return\n",
    "        \n",
    "    for c in range(nb_class):\n",
    "        sorted_indices = np.argsort([-box.classes[c] for box in boxes])\n",
    "\n",
    "        for i in range(len(sorted_indices)):\n",
    "            index_i = sorted_indices[i]\n",
    "\n",
    "            if boxes[index_i].classes[c] == 0: continue\n",
    "\n",
    "            for j in range(i+1, len(sorted_indices)):\n",
    "                index_j = sorted_indices[j]\n",
    "\n",
    "                if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:\n",
    "                    boxes[index_j].classes[c] = 0 \n",
    "\n",
    "\n",
    "# get all of the results above a threshold\n",
    "def get_boxes(boxes, labels, thresh):\n",
    "\tv_boxes, v_labels, v_scores = list(), list(), list()\n",
    "\t# enumerate all boxes\n",
    "\tfor box in boxes:\n",
    "\t\t# enumerate all possible labels\n",
    "\t\tfor i in range(len(labels)):\n",
    "\t\t\t# check if the threshold for this label is high enough\n",
    "\t\t\tif box.classes[i] > thresh:\n",
    "\t\t\t\tv_boxes.append(box)\n",
    "\t\t\t\tv_labels.append(labels[i])\n",
    "\t\t\t\tv_scores.append(box.classes[i]*100)\n",
    "\t\t\t\t# don't break, many labels may trigger for one box\n",
    "\treturn v_boxes, v_labels, v_scores\n",
    "\n",
    "def correct_yolo_boxes2(boxes, image_h, image_w, net_h, net_w):\n",
    "        \n",
    "    for i in range(len(boxes)):\n",
    "        x_scale = image_w/net_w\n",
    "        y_scale = image_h/net_h\n",
    "        \n",
    "        boxes[i].xmin = int(boxes[i].xmin * image_w)\n",
    "        boxes[i].xmax = int(boxes[i].xmax * image_w)\n",
    "        boxes[i].ymin = int(boxes[i].ymin * image_h)\n",
    "        boxes[i].ymax = int(boxes[i].ymax * image_h)\n",
    "        \n",
    "# draw all results\n",
    "def draw_boxes(filename, v_boxes, v_labels, v_scores):\n",
    "\t# load the image\n",
    "\tdata = pyplot.imread(filename)\n",
    "\t# plot the image\n",
    "\tpyplot.imshow(data)\n",
    "\t# get the context for drawing boxes\n",
    "\tax = pyplot.gca()\n",
    "\t# plot each box\n",
    "\tfor i in range(len(v_boxes)):\n",
    "\t\tbox = v_boxes[i]\n",
    "\t\t# get coordinates\n",
    "\t\ty1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n",
    "\t\t# calculate width and height of the box\n",
    "\t\twidth, height = x2 - x1, y2 - y1\n",
    "\t\t# create the shape\n",
    "\t\trect = Rectangle((x1, y1), width, height, fill=False, color='white')\n",
    "\t\t# draw the box\n",
    "\t\tax.add_patch(rect)\n",
    "\t\t# draw text and score in top left corner\n",
    "\t\tlabel = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n",
    "\t\tpyplot.text(x1, y1, label, color='white')\n",
    "\t# show the plot\n",
    "\tpyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76064aa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading weights of convolution #0\n",
      "loading weights of convolution #1\n",
      "loading weights of convolution #2\n",
      "loading weights of convolution #3\n",
      "no convolution #4\n",
      "loading weights of convolution #5\n",
      "loading weights of convolution #6\n",
      "loading weights of convolution #7\n",
      "no convolution #8\n",
      "loading weights of convolution #9\n",
      "loading weights of convolution #10\n",
      "no convolution #11\n",
      "loading weights of convolution #12\n",
      "loading weights of convolution #13\n",
      "loading weights of convolution #14\n",
      "no convolution #15\n",
      "loading weights of convolution #16\n",
      "loading weights of convolution #17\n",
      "no convolution #18\n",
      "loading weights of convolution #19\n",
      "loading weights of convolution #20\n",
      "no convolution #21\n",
      "loading weights of convolution #22\n",
      "loading weights of convolution #23\n",
      "no convolution #24\n",
      "loading weights of convolution #25\n",
      "loading weights of convolution #26\n",
      "no convolution #27\n",
      "loading weights of convolution #28\n",
      "loading weights of convolution #29\n",
      "no convolution #30\n",
      "loading weights of convolution #31\n",
      "loading weights of convolution #32\n",
      "no convolution #33\n",
      "loading weights of convolution #34\n",
      "loading weights of convolution #35\n",
      "no convolution #36\n",
      "loading weights of convolution #37\n",
      "loading weights of convolution #38\n",
      "loading weights of convolution #39\n",
      "no convolution #40\n",
      "loading weights of convolution #41\n",
      "loading weights of convolution #42\n",
      "no convolution #43\n",
      "loading weights of convolution #44\n",
      "loading weights of convolution #45\n",
      "no convolution #46\n",
      "loading weights of convolution #47\n",
      "loading weights of convolution #48\n",
      "no convolution #49\n",
      "loading weights of convolution #50\n",
      "loading weights of convolution #51\n",
      "no convolution #52\n",
      "loading weights of convolution #53\n",
      "loading weights of convolution #54\n",
      "no convolution #55\n",
      "loading weights of convolution #56\n",
      "loading weights of convolution #57\n",
      "no convolution #58\n",
      "loading weights of convolution #59\n",
      "loading weights of convolution #60\n",
      "no convolution #61\n",
      "loading weights of convolution #62\n",
      "loading weights of convolution #63\n",
      "loading weights of convolution #64\n",
      "no convolution #65\n",
      "loading weights of convolution #66\n",
      "loading weights of convolution #67\n",
      "no convolution #68\n",
      "loading weights of convolution #69\n",
      "loading weights of convolution #70\n",
      "no convolution #71\n",
      "loading weights of convolution #72\n",
      "loading weights of convolution #73\n",
      "no convolution #74\n",
      "loading weights of convolution #75\n",
      "loading weights of convolution #76\n",
      "loading weights of convolution #77\n",
      "loading weights of convolution #78\n",
      "loading weights of convolution #79\n",
      "loading weights of convolution #80\n",
      "loading weights of convolution #81\n",
      "no convolution #82\n",
      "no convolution #83\n",
      "loading weights of convolution #84\n",
      "no convolution #85\n",
      "no convolution #86\n",
      "loading weights of convolution #87\n",
      "loading weights of convolution #88\n",
      "loading weights of convolution #89\n",
      "loading weights of convolution #90\n",
      "loading weights of convolution #91\n",
      "loading weights of convolution #92\n",
      "loading weights of convolution #93\n",
      "no convolution #94\n",
      "no convolution #95\n",
      "loading weights of convolution #96\n",
      "no convolution #97\n",
      "no convolution #98\n",
      "loading weights of convolution #99\n",
      "loading weights of convolution #100\n",
      "loading weights of convolution #101\n",
      "loading weights of convolution #102\n",
      "loading weights of convolution #103\n",
      "loading weights of convolution #104\n",
      "loading weights of convolution #105\n"
     ]
    }
   ],
   "source": [
    "# define the model\n",
    "model = make_yolov3_model()\n",
    "\n",
    "# load the model weights\n",
    "weight_reader = WeightReader(r'D:\\Stat Evaluation\\Model\\yolov3.weights')\n",
    "weight_reader.load_weights(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e63a6362",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the anchors and labels\n",
    "anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]# define the labels\n",
    "labels = [\"person\", \"bicycle\", \"car\", \"motorbike\", \"aeroplane\", \"bus\", \"train\", \"truck\",\n",
    "    \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\", \"parking meter\", \"bench\",\n",
    "    \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\", \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\",\n",
    "    \"backpack\", \"umbrella\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n",
    "    \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\", \"surfboard\",\n",
    "    \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\", \"knife\", \"spoon\", \"bowl\", \"banana\",\n",
    "    \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\",\n",
    "    \"chair\", \"sofa\", \"pottedplant\", \"bed\", \"diningtable\", \"toilet\", \"tvmonitor\", \"laptop\", \"mouse\",\n",
    "    \"remote\", \"keyboard\", \"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\",\n",
    "    \"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"]\n",
    "# get the details of the detected objects\n",
    "# define the probability threshold for detected objects\n",
    "class_threshold = 0.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a29920a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n"
     ]
    }
   ],
   "source": [
    "prop_regions_yolo = []\n",
    "scaled_bboxes_yolo = []\n",
    "scale = 1.0\n",
    "for i in range(0,10):\n",
    "    temp_regions = []\n",
    "    temp_boxes = []\n",
    "    for f in range(0,len(every_path)):\n",
    "        prop_regions = []\n",
    "        # load the image with the required size \n",
    "        image = load_img(every_path[f], target_size=(int(480*scale),int(640*scale)))\n",
    "        width, height = image.size\n",
    "        image = image.resize((416, 416))\n",
    "\n",
    "        # convert to numpy array\n",
    "        image = img_to_array(image)\n",
    "        # scale pixel values to [0, 1]\n",
    "        image = image.astype('float32')\n",
    "        image /= 255.0\n",
    "        image = expand_dims(image, 0)\n",
    "        # make prediction\n",
    "        yhat = model.predict(image)\n",
    "\n",
    "        boxes = list()\n",
    "        for j in range(len(yhat)):\n",
    "        # decode the output of the network\n",
    "            boxes += decode_netout(yhat[j][0], anchors[j], 0.6, class_threshold, 416, 416)\n",
    "        correct_yolo_boxes2(boxes, height, width, 416, 416)\n",
    "        # suppress non-maximal boxes\n",
    "        do_nms(boxes, class_threshold)\n",
    "\n",
    "        # get the details of the detected objects\n",
    "        v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)\n",
    "        for j in range(len(v_boxes)):\n",
    "            if v_labels[j] == \"person\":\n",
    "                box = v_boxes[j]\n",
    "                y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n",
    "                width_box, height_box = x2 - x1, y2 - y1\n",
    "                prop_regions.append([x1,y1,width_box,height_box]) #no need to resize since yolo calculates always on 416x416!!\n",
    "\n",
    "        temp_regions.append(prop_regions)\n",
    "        temp_boxes_in = []\n",
    "        for x,y,w,h in bboxes[f]:\n",
    "            temp_boxes_in.append([x*scale , y*scale , w*scale , h*scale])\n",
    "        temp_boxes.append(temp_boxes_in)\n",
    "\n",
    "    scaled_bboxes_yolo.append(temp_boxes)\n",
    "    prop_regions_yolo.append(temp_regions)\n",
    "    scale = scale - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1feab172",
   "metadata": {},
   "outputs": [],
   "source": [
    "yolo_results = np.zeros((10,3))\n",
    "yolo_precision = np.zeros((10,))\n",
    "yolo_recall = np.zeros((10,))\n",
    "yolo_f1 = np.zeros((10,))\n",
    "yolo_missrate = np.zeros((10,))\n",
    "scales = []\n",
    "scale = 1.0\n",
    "for h in range(0,10):\n",
    "    scales.append(scale)\n",
    "    tp = 0\n",
    "    fp = 0\n",
    "    fn = 0 \n",
    "    for i in range(0, 268):\n",
    "        res = get_single_image_results(scaled_bboxes_yolo[h][i],prop_regions_yolo[h][i],0.1)\n",
    "        tp += res[0]\n",
    "        fp += res[1]\n",
    "        fn += res[2]\n",
    "        yolo_results[h][0] = tp\n",
    "        yolo_results[h][1] = fp\n",
    "        yolo_results[h][2] = fn\n",
    "        if(tp+fp != 0):\n",
    "            prec = tp / (tp + fp)\n",
    "        else:\n",
    "            prec = 0\n",
    "        rec = tp / (tp + fn)\n",
    "        yolo_precision[h] = prec\n",
    "        yolo_recall[h] = tp / (tp + fn)\n",
    "        if(prec + rec != 0):\n",
    "            yolo_f1[h] = 2 * (prec * rec)/ (prec + rec)\n",
    "        yolo_missrate[h] = fn / (tp + fn) \n",
    "    scale = scale - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e01dac33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRECISION\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RECALL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"PRECISION\")\n",
    "pyplot.plot(scales, yolo_precision)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"RECALL\")\n",
    "pyplot.plot(scales, yolo_recall)\n",
    "pyplot.show()\n",
    "\n",
    "print(\"F1 score\")\n",
    "pyplot.plot(scales, yolo_f1)\n",
    "pyplot.show()\n",
    "\n",
    "mdic = {\"scales\": scales}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\scales.mat\", mdic)\n",
    "\n",
    "mdic = {\"yolo_precision\": yolo_precision}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\yolo_precision.mat\", mdic)\n",
    "\n",
    "mdic = {\"yolo_recall\": yolo_recall}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\yolo_recall.mat\", mdic)\n",
    "\n",
    "mdic = {\"yolo_f1\": yolo_f1}\n",
    "savemat(r\"D:\\Scale_Stat_Evaluation\\Results\\yolo_f1.mat\", mdic)"
   ]
  }
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