practice&test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = np.array([[[ 32. , 46.]],\n",
    "\n",
    " [[ 38. , 34.]],\n",
    "\n",
    " [[ 27. , 34.]],\n",
    "\n",
    " [[ 22. , 46.]]],dtype = int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.array([[[ 31.9966259 ,  45.99843597]],\n",
    "\n",
    " [[ 37.99458313 , 33.98783493]],\n",
    "\n",
    " [[ 27.00013351 , 33.99293518]],\n",
    "\n",
    " [[ 21.98985291 , 45.99660492]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 31.9966259   45.99843597]]\n",
      "\n",
      " [[ 37.99458313  33.98783493]]\n",
      "\n",
      " [[ 27.00013351  33.99293518]]\n",
      "\n",
      " [[ 21.98985291  45.99660492]]]\n",
      "[[[32 46]]\n",
      "\n",
      " [[38 34]]\n",
      "\n",
      " [[27 34]]\n",
      "\n",
      " [[22 46]]]\n"
     ]
    }
   ],
   "source": [
    "print(n)\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 31.9966259   45.99843597]]\n",
      "\n",
      " [[ 37.99458313  33.98783493]]\n",
      "\n",
      " [[ 27.00013351  33.99293518]]\n",
      "\n",
      " [[ 21.98985291  45.99660492]]\n",
      "\n",
      " [[ 32.          46.        ]]\n",
      "\n",
      " [[ 38.          34.        ]]\n",
      "\n",
      " [[ 27.          34.        ]]\n",
      "\n",
      " [[ 22.          46.        ]]]\n"
     ]
    }
   ],
   "source": [
    "#for ele in k:\n",
    "#    n=np.concatenate(n,n.size,ele)\n",
    "n = np.concatenate((n,k))\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8.  6.]\n",
      "[[ 1.  0.]\n",
      " [ 0.  1.]]\n",
      "0\n",
      "[ 1.  0.]\n"
     ]
    }
   ],
   "source": [
    "mat =  np.array([[8,0],[0,6]])\n",
    "u,v =np.linalg.eig(mat)\n",
    "print(u)\n",
    "print(v)\n",
    "\n",
    "max_arg = np.argmax(u)\n",
    "print(max_arg)\n",
    "\n",
    "print(v[max_arg])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import time\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the frame directory:C:\\Users\\Legion\\Desktop\\Internship_IIT\\UCSD_Anomaly_Dataset.v1p2\\UCSDped2\\Test\\Test001\n",
      "(240, 360, 3)\n",
      "180\n",
      "[ 4.9771018   4.5463748   3.80341554 ...,  4.48253345  4.57645464\n",
      "  4.67544794]\n",
      "[ 5.37375832  5.54646111  5.66971064 ...,  0.85003209  0.90585673\n",
      "  1.0990237 ]\n",
      "[ 1.81339431  1.37959814  1.25224245 ...,  5.66287422  5.83265066\n",
      "  5.72672987]\n",
      "[ 2.5675025   2.41069412  2.26212811 ...,  3.03550339  3.03215361\n",
      "  3.03168416]\n",
      "[ 0.67987496  0.77672964  0.90080738 ...,  4.42814875  4.3196702\n",
      "  4.24855328]\n",
      "[ 1.93120646  2.09948659  2.0331254  ...,  0.12246127  0.14464737\n",
      "  0.18828939]\n",
      "[ 1.15176439  1.19681418  1.14942586 ...,  5.92141294  5.69921303\n",
      "  5.21157503]\n",
      "[ 1.35910845  1.41963768  1.46743476 ...,  4.14925337  3.86189437\n",
      "  3.66477942]\n",
      "[ 2.15825343  2.02852225  1.9872992  ...,  6.0103035   5.71953249\n",
      "  4.6452775 ]\n",
      "[ 0.13624264  0.04960204  5.8998313  ...,  5.81841564  5.73648262\n",
      "  3.66286445]\n",
      "[ 1.27009797  1.36359656  1.37288249 ...,  0.06754085  0.0261941\n",
      "  6.18864822]\n",
      "[ 1.19606555  1.21056032  1.18948281 ...,  3.38865805  3.34067917\n",
      "  3.28771353]\n",
      "[ 0.64816934  1.01974332  1.39680898 ...,  3.15003395  3.15910959\n",
      "  3.1833334 ]\n",
      "[ 3.29452109  3.09227467  2.76215053 ...,  4.00349188  3.91383123\n",
      "  3.83822751]\n",
      "[ 0.46417817  0.49582076  0.37545171 ...,  0.61015612  0.58731061\n",
      "  0.76158738]\n",
      "[ 5.11415339  5.2489624   5.38826656 ...,  5.8389883   3.18745995\n",
      "  3.19159532]\n",
      "[ 0.5529834   0.72712708  1.02569723 ...,  3.22814465  3.23466825\n",
      "  3.24775386]\n",
      "[ 0.79817343  0.87271273  0.92253745 ...,  6.07407665  5.85002232\n",
      "  5.16089439]\n",
      "[ 1.40680277  1.40656114  1.42269111 ...,  5.30536604  5.29827261\n",
      "  4.90532494]\n",
      "[ 5.97321939  5.77773333  4.93277884 ...,  5.99107265  5.78076696\n",
      "  5.2899251 ]\n",
      "[ 3.0166893   2.76516819  2.51157856 ...,  0.33059007  1.01041579\n",
      "  3.09460807]\n",
      "[ 4.74816179  4.8523488   5.07520962 ...,  5.27716494  5.21875954\n",
      "  5.04971457]\n",
      "[ 1.13091743  1.21245396  1.34792411 ...,  6.26733923  6.11042738\n",
      "  5.97587013]\n",
      "[ 0.74466628  0.8086859   0.89158976 ...,  0.88896054  1.12602413\n",
      "  1.94272673]\n",
      "[ 3.61036587  3.41486454  3.09999251 ...,  6.09447432  5.44406223\n",
      "  4.7022891 ]\n",
      "[ 3.64454365  3.53016782  3.35085416 ...,  1.53881931  1.49453259\n",
      "  1.48084271]\n",
      "[ 0.55376077  0.61028397  0.58912653 ...,  0.61956859  0.85220909\n",
      "  1.37769651]\n",
      "[ 6.13579369  6.04900265  6.02321768 ...,  4.10328436  4.09994984\n",
      "  4.09409332]\n",
      "[ 2.73573542  2.79680562  0.53750759 ...,  6.05276251  5.9549017\n",
      "  5.81326246]\n",
      "[ 2.17410684  2.69207978  2.92275238 ...,  4.48878241  4.26933241\n",
      "  4.13924217]\n",
      "[ 1.09473479  1.12837017  1.13873649 ...,  5.5926981   4.78717566\n",
      "  3.83303022]\n",
      "[ 0.43996796  0.40003011  0.38149557 ...,  5.87486458  5.46990585\n",
      "  4.85778284]\n",
      "[ 1.32704353  1.67663372  2.18676233 ...,  5.32237959  4.8705163\n",
      "  4.42959023]\n",
      "[ 6.27764606  0.21169782  0.67822808 ...,  0.97616833  0.99709129\n",
      "  1.07694662]\n",
      "[ 0.70315218  0.67063904  0.68271452 ...,  0.46784216  0.6173144\n",
      "  2.54975295]\n",
      "[ 0.14539622  0.45325357  5.80140686 ...,  6.22233057  2.13639736\n",
      "  3.02532887]\n",
      "[ 1.40114498  1.43259895  1.49016571 ...,  5.50428724  4.96401596\n",
      "  4.18720198]\n",
      "[ 1.24409366  1.24403787  1.25077355 ...,  4.95351362  4.77315044\n",
      "  4.4823823 ]\n",
      "[ 4.45716858  0.48496112  0.43277311 ...,  4.96326208  4.72121716\n",
      "  4.65810537]\n",
      "[ 0.55904377  0.62273252  0.68567467 ...,  5.72268105  5.2523036\n",
      "  4.70160437]\n",
      "[ 1.27070153  1.66536164  2.85420275 ...,  4.19222498  3.71222901\n",
      "  3.38992047]\n",
      "[ 1.48566055  1.48871791  1.49054813 ...,  5.80638027  4.84515715\n",
      "  3.88099885]\n",
      "[ 1.16513884  1.11895132  1.08211184 ...,  3.68903899  3.42518067\n",
      "  3.35968876]\n",
      "[ 0.84414089  0.84497368  0.81913382 ...,  3.52586555  3.51054835\n",
      "  3.48464489]\n",
      "[ 4.17162323  4.2379837   4.17781448 ...,  0.62021661  0.87396431\n",
      "  3.01119852]\n",
      "[ 3.16270852  2.95055032  2.76847363 ...,  5.31950521  5.00124502\n",
      "  4.61431503]\n",
      "[ 2.09461212  2.26118326  2.35097456 ...,  3.29683137  3.25335407\n",
      "  3.22564697]\n",
      "[ 2.55131674  1.6268332   0.62642157 ...,  5.02400732  3.30866075\n",
      "  3.21754098]\n",
      "[ 1.26167011  1.33802402  1.41787791 ...,  4.35701561  4.49464893\n",
      "  4.36080408]\n",
      "[ 5.02997637  0.69927686  0.86063737 ...,  5.50307512  5.13695288  4.650805  ]\n",
      "[ 0.98305476  1.0582422   1.06325078 ...,  4.78964853  4.39806414\n",
      "  4.00628328]\n",
      "[ 1.19767094  1.33976352  1.62600029 ...,  6.0697794   5.70565414\n",
      "  4.4529686 ]\n",
      "[ 1.32190812  1.2893914   1.14035928 ...,  5.48536825  4.93348265\n",
      "  4.19633865]\n",
      "[ 1.32190824  1.28939164  1.14036    ...,  5.42902184  4.87812614\n",
      "  4.16689062]\n",
      "[ 1.32190788  1.28939104  1.14035857 ...,  5.56864834  5.32941532\n",
      "  5.11177969]\n",
      "[ 2.45643044  2.19963527  2.00879145 ...,  0.05296576  0.01135953\n",
      "  6.14387512]\n",
      "[ 1.61845326  0.94694704  0.89191246 ...,  3.36414266  3.21700025\n",
      "  3.18554926]\n",
      "[ 1.29693365  1.27420712  1.14733386 ...,  5.61033678  5.12755728\n",
      "  4.47359324]\n",
      "[ 1.62208796  0.94528419  0.89146006 ...,  3.38134336  3.24255991\n",
      "  3.21621585]\n",
      "[ 5.1738081   5.20662355  5.10493803 ...,  5.48307323  5.43962479\n",
      "  5.24988747]\n",
      "[ 0.09367798  0.08096241  0.06418768 ...,  3.12290812  3.12853622\n",
      "  3.13269401]\n",
      "[ 1.46643531  1.23152888  1.05097067 ...,  3.22084618  3.20086765\n",
      "  3.1817553 ]\n",
      "[ 1.40038407  1.66820264  1.85410655 ...,  3.12860632  3.15964389\n",
      "  3.1923449 ]\n",
      "[ 2.88052535  2.12699556  1.72890389 ...,  3.23762989  3.16598845\n",
      "  3.15871191]\n",
      "[ 1.78618801  1.7581538   1.71961331 ...,  5.4766016   5.30137253\n",
      "  4.88276005]\n",
      "[ 0.8265844   0.94152552  1.04876077 ...,  5.85895681  3.4105885\n",
      "  3.24713182]\n",
      "(240, 360)\n"
     ]
    }
   ],
   "source": [
    "#calculating the dense optical flow using farneback algorithm\n",
    "\n",
    "frameset_path = input(\"Enter the frame directory:\")\n",
    "\n",
    "list_frames_name = os.listdir(frameset_path)\n",
    "\n",
    "if '._.DS_Store' in list_frames_name:\n",
    "    list_frames_name.remove('._.DS_Store')\n",
    "    \n",
    "if '.DS_Store' in list_frames_name:\n",
    "    list_frames_name.remove('.DS_Store')\n",
    "    \n",
    "    \n",
    "ref_frame_path = os.path.join(frameset_path,list_frames_name[0])\n",
    "ref_frame = cv2.imread(ref_frame_path,0)\n",
    "\n",
    "hsv = np.zeros((ref_frame.shape[0], ref_frame.shape[1], 3))\n",
    "hsv[...,1] = 255\n",
    "print(hsv.shape)\n",
    "\n",
    "print(len(list_frames_name))\n",
    "\n",
    "for frame_name in list_frames_name[1:]:\n",
    "    frame_path = os.path.join(frameset_path, frame_name)\n",
    "    \n",
    "    frame = cv2.imread(frame_path, 0)\n",
    "    #print(frame.shape)\n",
    "    flow = cv2.calcOpticalFlowFarneback(ref_frame, frame, None, 0.5, 5, 15, 3, 5, 1.1, cv2.OPTFLOW_FARNEBACK_GAUSSIAN)\n",
    "    \n",
    "    mag, direction = cv2.cartToPolar(flow[...,0],flow[...,1])\n",
    "    #print(direction.flatten())\n",
    "    hsv[..., 0] = direction * 180/np.pi\n",
    "    hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n",
    "    hsv = np.uint8(hsv)\n",
    "    bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n",
    "    \n",
    "    cv2.imshow('mask ', bgr)\n",
    "    cv2.imshow('frame ', frame)\n",
    "    \n",
    "    ref_frame = frame\n",
    "    \n",
    "    if cv2.waitKey(1) & 0xff == ord('q'):\n",
    "        break\n",
    "        \n",
    "cv2.destroyAllWindows()\n",
    "print(mag.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.  0.  0.]\n",
      "  [ 0.  0.  0.]]] (1, 2, 3)\n"
     ]
    }
   ],
   "source": [
    "k=np.zeros((1,2,3))\n",
    "print(k,k.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    }
   ],
   "source": [
    "l= cv2.imread(r'C:\\Users\\Legion\\Documents\\standard_test_images\\mandril_color.tif',1)\n",
    "print(l.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  0.]\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-26905ae06bcc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmax_arg\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marctan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "k = v[max_arg]\n",
    "print(k)\n",
    "np.arctan(1/0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arctan(np.tan(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.linalg import norm, svd\n",
    "\n",
    "def inexact_augmented_lagrange_multiplier(X, lmbda=.01, tol=1e-4,\n",
    "                                          maxiter=100, verbose=True):\n",
    "    \"\"\"\n",
    "    Inexact Augmented Lagrange Multiplier\n",
    "    \"\"\"\n",
    "    Y = X\n",
    "    norm_two = norm(Y.ravel(), 2)\n",
    "    norm_inf = norm(Y.ravel(), np.inf) / lmbda\n",
    "    dual_norm = np.max([norm_two, norm_inf])\n",
    "    Y = Y / dual_norm\n",
    "    A = np.zeros(Y.shape)\n",
    "    E = np.zeros(Y.shape)\n",
    "    dnorm = norm(X, 'fro')\n",
    "    mu = 1.25 / norm_two\n",
    "    rho = 1.5\n",
    "    sv = 10.\n",
    "    n = Y.shape[0]\n",
    "    itr = 0\n",
    "    while True:\n",
    "        Eraw = X - A + (1 / mu) * Y\n",
    "        Eupdate = np.maximum(Eraw - lmbda / mu, 0) + np.minimum(Eraw + lmbda / mu, 0)\n",
    "        U, S, V = svd(X - Eupdate + (1 / mu) * Y, full_matrices=False)\n",
    "        svp = (S > 1 / mu).shape[0]\n",
    "        if svp < sv:\n",
    "            sv = np.min([svp + 1, n])\n",
    "        else:\n",
    "            sv = np.min([svp + round(.05 * n), n])\n",
    "        Aupdate = np.dot(np.dot(U[:, :svp], np.diag(S[:svp] - 1 / mu)), V[:svp, :])\n",
    "        A = Aupdate\n",
    "        E = Eupdate\n",
    "        Z = X - A - E\n",
    "        Y = Y + mu * Z\n",
    "        mu = np.min([mu * rho, mu * 1e7])\n",
    "        itr += 1\n",
    "        if ((norm(Z, 'fro') / dnorm) < tol) or (itr >= maxiter):\n",
    "            break\n",
    "    if verbose:\n",
    "        print(\"Finished at iteration %d\" % (itr))  \n",
    "    return A, E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished at iteration 26\n",
      "[[ 0.00299179 -0.00036024 -0.00105244]\n",
      " [-0.00138035 -0.00158685 -0.00246985]\n",
      " [-0.00076284 -0.00240055 -0.00115055]]\n",
      "[[  1.00636919   1.99781983   2.99730402]\n",
      " [ 66.00256475  87.99667444  21.0068771 ]\n",
      " [ 99.00057883  57.00797386  38.99535132]]\n"
     ]
    }
   ],
   "source": [
    "l = np.array([[1,2,3],[66,88,21],[99,57,39]])\n",
    "\n",
    "import cv2\n",
    "image  = cv2.imread(r'C:\\Users\\Legion\\Desktop\\Internship_IIT\\UCSD_Anomaly_Dataset.v1p2\\UCSDped2\\Test\\Test001\\112.tif',0)\n",
    "A,S = inexact_augmented_lagrange_multiplier(l)\n",
    "\n",
    "cv2.imshow('A',A)\n",
    "cv2.imshow('S',S)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "print(A)\n",
    "print(S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GODEC algorithm\n",
    "\n",
    "import numpy as np\n",
    "from numpy.linalg import norm\n",
    "from scipy.linalg import qr\n",
    "\n",
    "def wthresh(a, thresh):\n",
    "    #Soft wavelet threshold\n",
    "    res = np.abs(a) - thresh\n",
    "    return np.sign(a) * ((res > 0) * res)\n",
    "\n",
    "#Default threshold of .03 is assumed to be for input in the range 0-1...\n",
    "#original matlab had 8 out of 255, which is about .03 scaled to 0-1 range\n",
    "def go_dec(X, thresh=.03, rank=2, power=0, tol=1e-3,\n",
    "           max_iter=100, random_seed=0, verbose=True):\n",
    "    m, n = X.shape\n",
    "    if m < n:\n",
    "        X = X.T\n",
    "    m, n = X.shape\n",
    "    L = X\n",
    "    S = np.zeros(L.shape)\n",
    "    itr = 0\n",
    "    random_state = np.random.RandomState(random_seed)    \n",
    "    while True:\n",
    "        Y2 = random_state.randn(n, rank)\n",
    "        for i in range(power + 1):\n",
    "            Y1 = np.dot(L, Y2)\n",
    "            Y2 = np.dot(L.T, Y1);\n",
    "        Q, R = qr(Y2, mode='economic')\n",
    "        L_new = np.dot(np.dot(L, Q), Q.T)\n",
    "        T = L - L_new + S\n",
    "        L = L_new\n",
    "        S = wthresh(T, thresh)\n",
    "        T -= S\n",
    "        err = norm(T.ravel(), 2)\n",
    "        if (err < tol) or (itr >= max_iter):\n",
    "            break \n",
    "        L += T\n",
    "        itr += 1\n",
    "    #Is this even useful in soft GoDec? May be a display issue...\n",
    "    G = X - L - S\n",
    "    if m < n:\n",
    "        L = L.T\n",
    "        S = S.T\n",
    "        G = G.T\n",
    "    if verbose:\n",
    "        print(\"Finished at iteration %d\" % (itr))\n",
    "    return L, S, G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished at iteration 100\n",
      "[[ 25.64567809  25.64885922  26.0278235  ...,  40.57203365  43.12379339\n",
      "   43.48600438]\n",
      " [ 26.17249152  26.17090144  26.56003239 ...,  41.40828684  43.97175358\n",
      "   44.33602963]\n",
      " [ 26.99766361  27.10669868  27.31611915 ...,  39.03166451  41.40494749\n",
      "   41.77413992]\n",
      " ..., \n",
      " [ 29.3809928   27.5343201   19.94549721 ...,  37.45228847  42.20872145\n",
      "   45.52213717]\n",
      " [ 28.67532384  27.85161181  21.22314611 ...,  38.65725669  40.45656214\n",
      "   43.77116607]\n",
      " [ 28.08194415  28.25489176  21.63373559 ...,  39.01354754  38.80437866\n",
      "   41.1173561 ]]\n",
      "a: [[  75.67567809   75.67885922   76.0578235  ...,  116.60203365\n",
      "   117.15379339  117.51600438]\n",
      " [  76.20249152   76.20090144   76.59003239 ...,  117.43828684\n",
      "   118.00175358  118.36602963]\n",
      " [  75.02766361   75.13669868   75.34611915 ...,  115.06166451\n",
      "   115.43494749  115.80413992]\n",
      " ..., \n",
      " [  68.5890072    68.4356799    69.02450279 ...,  106.48228847\n",
      "   107.23872145  107.55213717]\n",
      " [  69.29467616   69.11838819   69.74685389 ...,  107.68725669\n",
      "   108.48656214  108.80116607]\n",
      " [  68.88805585   68.71510824   69.33626441 ...,  107.04354754\n",
      "   107.83437866  108.1473561 ]]\n",
      "s: [[-25.64567809 -25.64885922 -26.0278235  ..., -40.57203365 -43.12379339\n",
      "  -43.48600438]\n",
      " [-26.17249152 -26.17090144 -26.56003239 ..., -41.40828684 -43.97175358\n",
      "  -44.33602963]\n",
      " [-26.99766361 -27.10669868 -27.31611915 ..., -39.03166451 -41.40494749\n",
      "  -41.77413992]\n",
      " ..., \n",
      " [ 29.3809928   27.5343201   19.94549721 ..., -37.45228847 -42.20872145\n",
      "  -45.52213717]\n",
      " [ 28.67532384  27.85161181  21.22314611 ..., -38.65725669 -40.45656214\n",
      "  -43.77116607]\n",
      " [ 28.08194415  28.25489176  21.63373559 ..., -39.01354754 -38.80437866\n",
      "  -41.1173561 ]]\n",
      "-------- -25.6456780898\n",
      "g: [[-0.03 -0.03 -0.03 ..., -0.03 -0.03 -0.03]\n",
      " [-0.03 -0.03 -0.03 ..., -0.03 -0.03 -0.03]\n",
      " [-0.03 -0.03 -0.03 ..., -0.03 -0.03 -0.03]\n",
      " ..., \n",
      " [ 0.03  0.03  0.03 ..., -0.03 -0.03 -0.03]\n",
      " [ 0.03  0.03  0.03 ..., -0.03 -0.03 -0.03]\n",
      " [ 0.03  0.03  0.03 ..., -0.03 -0.03 -0.03]]\n",
      "[[  0.           0.           0.         ...,   0.           0.           0.        ]\n",
      " [  0.           0.           0.         ...,   0.           0.           0.        ]\n",
      " [  0.           0.           0.         ...,   0.           0.           0.        ]\n",
      " ..., \n",
      " [ 29.3809928   27.5343201   19.94549721 ...,   0.           0.           0.        ]\n",
      " [ 28.67532384  27.85161181  21.22314611 ...,   0.           0.           0.        ]\n",
      " [ 28.08194415  28.25489176  21.63373559 ...,   0.           0.           0.        ]]\n"
     ]
    }
   ],
   "source": [
    "l = np.array([[1,2,3],[66,88,21],[99,57,39]])\n",
    "\n",
    "import cv2\n",
    "image  = cv2.imread(r'C:\\Users\\Legion\\Desktop\\Internship_IIT\\UCSD_Anomaly_Dataset.v1p2\\UCSDped2\\Test\\Test001\\112.tif',0)\n",
    "A,S,G = go_dec(image)\n",
    "\n",
    "mnop = S\n",
    "m2 = S\n",
    "print(np.absolute(m2))\n",
    "print('a:',A)\n",
    "print('s:',mnop)\n",
    "print('--------',mnop[0][0])\n",
    "print('g:',G)\n",
    "\n",
    "indices = mnop<0\n",
    "mnop[indices] = 0\n",
    "cv2.imshow('A',A)\n",
    "cv2.imshow('S',mnop.T)\n",
    "cv2.imshow('G', G)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "print(mnop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.           0.           0.         ...,  29.3809928   28.67532384\n",
      "   28.08194415]\n",
      " [  0.           0.           0.         ...,  27.5343201   27.85161181\n",
      "   28.25489176]\n",
      " [  0.           0.           0.         ...,  19.94549721  21.22314611\n",
      "   21.63373559]\n",
      " ..., \n",
      " [  0.           0.           0.         ...,   0.           0.           0.        ]\n",
      " [  0.           0.           0.         ...,   0.           0.           0.        ]\n",
      " [  0.           0.           0.         ...,   0.           0.           0.        ]]\n",
      "\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "indices = S<0\n",
    "S[indices] = 0\n",
    "print(S.T)\n",
    "print()\n",
    "print(mnop[0][0])\n",
    "cv2.imshow('s' ,np.absolute(s_))\n",
    "cv2.imshow('S', S.T)\n",
    "cv2.imshow('image',image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   -1   -22   -45]\n",
      " [    2     4     5]\n",
      " [-1299    22    45]]\n",
      "[[   1   22   45]\n",
      " [   2    4    5]\n",
      " [1299   22   45]]\n"
     ]
    }
   ],
   "source": [
    "l=np.array([[-1,-22,-45],[2,4,5],[-1299,22,45]])\n",
    "la = np.absolute(l)\n",
    "print(l)\n",
    "print(la)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished at iteration 100\n",
      "[[  1.00003283   1.91360005   3.01900508]\n",
      " [ 21.99999861  45.00126659  70.99919763]\n",
      " [ 90.99999997   2.00003163   2.99997997]]\n",
      "[[ 0.          0.05639995  0.        ]\n",
      " [-0.          0.         -0.        ]\n",
      " [-0.          0.         -0.        ]]\n"
     ]
    }
   ],
   "source": [
    "l= np.array([[1,2,3],[22,45,71],[91,2,3]])\n",
    "a,s,g = go_dec(l)\n",
    "print(a)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2\n",
      "0.5\n",
      "0.05\n"
     ]
    }
   ],
   "source": [
    " x = np.array([[0,0.2,0.5],[0.05,0.01,0]])\n",
    "    \n",
    "ind = np.argwhere(x>0.01)\n",
    "for i in ind:\n",
    "    a,b = i\n",
    "    print(x[a,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12%4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-174.51712391   93.97547818  -97.50100453 ..., -209.57144204\n",
      "    92.89096447  -78.38601862]\n",
      " [ -70.74677896  -78.87206124 -142.07088027 ...,  -11.22983627\n",
      "   117.04547108  120.60777665]\n",
      " [ -78.8079064   114.54743502   79.33839689 ...,   -3.41308031  143.9053749\n",
      "     6.74537942]\n",
      " ..., \n",
      " [  -9.23632501  -23.82495173  109.98854713 ...,   97.02097098\n",
      "    33.84902512  -34.92292784]\n",
      " [ -66.02708926 -154.36752821  -65.50450619 ...,  109.98158796\n",
      "   129.29339367   56.98854853]\n",
      " [  32.21950228  -16.73211041  -16.12064276 ...,   32.69921232\n",
      "   -58.90704281  -76.59829472]]\n",
      "[[2 3]\n",
      " [8 9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "k = np.array(([[2,3,4],[8,9,6],[10,88,3]]))\n",
    "l = np.random.randn(100,100)*100\n",
    "print(l)\n",
    "print(k[0:2,0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished at iteration 31\n",
      "-0.00876882004025\n",
      "-9815.54760963\n",
      "[[-174.51712391   93.97547818  -97.50100453 ..., -209.57144204\n",
      "    92.89096447  -78.38601862]\n",
      " [ -70.74677896  -78.87206124 -142.07088027 ...,  -11.22983627\n",
      "   117.04547108  120.60777665]\n",
      " [ -78.8079064   114.54743502   79.33839689 ...,   -3.41308031  143.9053749\n",
      "     6.74537942]\n",
      " ..., \n",
      " [  -9.23632501  -23.82495173  109.98854713 ...,   97.02097098\n",
      "    33.84902512  -34.92292784]\n",
      " [ -66.02708926 -154.36752821  -65.50450619 ...,  109.98158796\n",
      "   129.29339367   56.98854853]\n",
      " [  32.21950228  -16.73211041  -16.12064276 ...,   32.69921232\n",
      "   -58.90704281  -76.59829472]]\n",
      "[[-174.51782984   93.97052687  -97.5077192  ..., -209.58607751\n",
      "    92.88867675  -78.38758889]\n",
      " [ -70.74603212  -78.88353961 -142.07520691 ...,  -11.22455011  117.0516736\n",
      "   120.60646953]\n",
      " [ -78.79909418  114.55566891   79.33612136 ...,   -3.41797421\n",
      "   143.90098745    6.74731736]\n",
      " ..., \n",
      " [  -9.23464159  -23.81867513  109.98881828 ...,   97.03375725   33.8366325\n",
      "   -34.92864938]\n",
      " [ -66.01905796 -154.36820459  -65.51321445 ...,  109.99489848\n",
      "   129.29683049   56.9994882 ]\n",
      " [  32.22010452  -16.74704092  -16.11797108 ...,   32.71126711\n",
      "   -58.90001664  -76.60406307]]\n",
      "[[ -2.70110536e-03  -1.44113986e-03  -4.19462338e-04 ...,  -3.27761849e-04\n",
      "    3.01967231e-03   4.81653035e-05]\n",
      " [  2.60992467e-03   1.48871698e-04  -1.33799750e-03 ...,   5.05858620e-03\n",
      "   -1.51879010e-03  -3.00926387e-03]\n",
      " [  2.20433763e-03   2.75525374e-03   4.76248360e-04 ...,  -1.64238891e-03\n",
      "    7.95846928e-04   3.52920909e-03]\n",
      " ..., \n",
      " [  1.60521328e-03   3.93472989e-03  -2.84211004e-04 ...,   3.45497421e-03\n",
      "   -2.29156153e-03  -2.23940676e-03]\n",
      " [ -2.47410445e-04   2.98644617e-03   1.63424862e-03 ...,   1.93975508e-03\n",
      "    1.28798199e-03   2.55723219e-04]\n",
      " [  1.52705424e-03  -2.65687253e-03  -8.03566006e-04 ...,  -1.21487175e-03\n",
      "    3.92980829e-03  -1.74768575e-03]]\n",
      "[[-174.51512873   93.97196801  -97.50729974 ..., -209.58574974\n",
      "    92.88565708  -78.38763705]\n",
      " [ -70.74864205  -78.88368848 -142.07386891 ...,  -11.22960869\n",
      "   117.05319239  120.60947879]\n",
      " [ -78.80129852  114.55291365   79.33564511 ...,   -3.41633182  143.9001916\n",
      "     6.74378815]\n",
      " ..., \n",
      " [  -9.23624681  -23.82260986  109.98910249 ...,   97.03030228\n",
      "    33.83892406  -34.92640998]\n",
      " [ -66.01881055 -154.37119104  -65.5148487  ...,  109.99295872  129.2955425\n",
      "    56.99923247]\n",
      " [  32.21857746  -16.74438405  -16.11716752 ...,   32.71248198\n",
      "   -58.90394645  -76.60231538]]\n"
     ]
    }
   ],
   "source": [
    "a,s= inexact_augmented_lagrange_multiplier(l)\n",
    "print(np.sum(a))\n",
    "print(np.sum(s))\n",
    "print(l)\n",
    "print(a+s)\n",
    "print(a)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  7.17137166e-01   5.97614305e-01   3.58568583e-01]\n",
      " [ -5.34522484e-01   8.01783726e-01  -2.67261242e-01]\n",
      " [  4.47213595e-01   6.31845431e-17  -8.94427191e-01]]\n",
      "[[ -3.98409536e+00  -1.60356745e+00  -6.66133815e-16]\n",
      " [ -9.96023841e-01   2.40535118e+00  -3.33066907e-16]\n",
      " [  4.98011921e+00  -8.01783726e-01   1.11022302e-15]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data = np.array([[-2,-4,2],[-2,1,2],[4,2,5]])\n",
    "'''\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(data)\n",
    "data = scaler.transform(data)\n",
    "print(data)\n",
    "'''\n",
    "pca = PCA(svd_solver='auto')\n",
    "pca.fit(data)\n",
    "print(pca.components_)\n",
    "print(pca.transform(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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