IOU Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "component1 = np.array([[0,1,1],[0,1,1],[0,1,1]], dtype=np.uint8)\n",
    "component2 = np.array([[0,1,0],[1,1,0],[1,1,0]], dtype=np.uint8)\n",
    "def iou_metric(scores, output):  \n",
    "    scores = scores.astype(np.bool)\n",
    "    output = output.astype(np.bool)\n",
    "    overlap = scores*output\n",
    "    union = scores+output # Logical OR\n",
    "    IOU = (overlap.sum())/(float(union.sum())+1e-10) \n",
    "    return IOU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[0,1,3],[0,2,1],[0,0,0]], dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_masks(x):\n",
    "    n_classes = 5\n",
    "    mask = np.zeros((n_classes, x.shape[0], x.shape[1]))\n",
    "    for c in range(n_classes):\n",
    "        mask[c] = x == c\n",
    "    print(mask)\n",
    "    return mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[[1. 0. 0.]\n",
      "  [1. 0. 0.]\n",
      "  [1. 1. 1.]]\n",
      "\n",
      " [[0. 1. 0.]\n",
      "  [0. 0. 1.]\n",
      "  [0. 0. 0.]]\n",
      "\n",
      " [[0. 0. 0.]\n",
      "  [0. 1. 0.]\n",
      "  [0. 0. 0.]]\n",
      "\n",
      " [[0. 0. 1.]\n",
      "  [0. 0. 0.]\n",
      "  [0. 0. 0.]]\n",
      "\n",
      " [[0. 0. 0.]\n",
      "  [0. 0. 0.]\n",
      "  [0. 0. 0.]]]\n"
     ]
    }
   ],
   "source": [
    "x_mask = create_masks(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "truth = np.array([[0,1,1],[1,2,3],[2,1,2]], dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1. 0. 0.]\n",
      "  [0. 0. 0.]\n",
      "  [0. 0. 0.]]\n",
      "\n",
      " [[0. 1. 1.]\n",
      "  [1. 0. 0.]\n",
      "  [0. 1. 0.]]\n",
      "\n",
      " [[0. 0. 0.]\n",
      "  [0. 1. 0.]\n",
      "  [1. 0. 1.]]\n",
      "\n",
      " [[0. 0. 0.]\n",
      "  [0. 0. 1.]\n",
      "  [0. 0. 0.]]\n",
      "\n",
      " [[0. 0. 0.]\n",
      "  [0. 0. 0.]\n",
      "  [0. 0. 0.]]]\n"
     ]
    }
   ],
   "source": [
    "t_mask = create_masks(truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ True False False]\n",
      " [False False False]\n",
      " [False False False]]\n",
      "[[ True False False]\n",
      " [ True False False]\n",
      " [ True  True  True]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iou_metric(x_mask[0], t_mask[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0]\n",
      "[0.199999999996, 0.199999999996, 0.3333333333222222, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "n_classes = 5\n",
    "iou = [0]*n_classes\n",
    "print(iou)\n",
    "for c in range(n_classes):\n",
    "    iou[c] = iou_metric(x_mask[c], t_mask[c])\n",
    "print(iou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.146"
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     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "(.2+.2+.33)/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.zeros((64, 5))\n",
    "out = np.mean(z, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "array([0., 0., 0., 0., 0.])"
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     "execution_count": 41,
     "metadata": {},
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    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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