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"cell_type": "markdown", + "metadata": {}, + "source": [ + "INF8770 Technologies multimédias\n", + "\n", + "Polytechnique Montréal\n", + "\n", + "Exemple du calcul de la corrélation croisée" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.io import wavfile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Soit les trois vecteurs suivants:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "A = [1,3,3,6,3,4,7,5,4,3,2,6,3,2,4]\n", + "B = [3,4,7,5]\n", + "C = [10,10,11,10]\n", + "\n", + "plt.plot(range(len(A)), A)\n", + "plt.plot(range(len(B)), B)\n", + "plt.plot(range(len(C)), C)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On voit que B est plus ressemblant à A que C. Un vecteur est semblable ou fait partie d'un autre vecteur si la corrélation est suffisamment grande. Corrélation entre A et B:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[66, 78, 74, 93, 99, 95, 84, 62, 68, 74, 61, 64, 45, 22, 12]\n", + "[66, 40, 22, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 22, 40, 66, 78, 74, 93, 99, 95, 84, 62, 68, 74, 61, 64, 45, 22, 12]\n" + ] + } + ], + "source": [ + "# On ajoute des 0 pour que les signaux soient de même taille. Ça facilite les caculs.\n", + "B = np.pad(B, (0, len(A)-len(B)), 'constant')\n", + "C = np.pad(C, (0, len(A)-len(C)), 'constant')\n", + "Rxy=[]\n", + "#Rxy Décalage de A vers la gauche, ou B vers la droite\n", + "for i in range(0,len(A)):\n", + " Somme = 0;\n", + " for j in range(0, len(A)-i):\n", + " Somme += A[i+j] * B[j]\n", + " Rxy += [Somme]\n", + "print(Rxy)\n", + " \n", + "Ryx=[]\n", + "#Rxy Décalage de A vers la droite, ou B vers la gauche\n", + "for i in range(0,len(A)):\n", + " Somme = 0;\n", + " for j in range(0, len(A)-i):\n", + " Somme += B[i+j] * A[j]\n", + " Ryx += [Somme]\n", + "print(Ryx)\n", + "\n", + "CorrCroisee = Ryx[::-1] +Rxy[1:] \n", + "print(CorrCroisee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affichage du résultat de la correlation entre A et B" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Le meilleur alignement est: 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(range(-len(A)+1,len(A)), CorrCroisee)\n", + "Decalage = CorrCroisee.index(max(CorrCroisee))-len(A)+1\n", + "print('Le meilleur alignement est:', Decalage)\n", + "plt.axvline(Decalage, color='k', ls='--')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vérifions maintenant la correlation croisée entre A et C avec la fonction correlate de numpy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 0 0 0 0 0 0 0 0 0 0 10 41 73 133 156 163 204\n", + " 197 205 194 143 152 146 133 152 94 60 40]\n" + ] + } + ], + "source": [ + "CorrCroisee2 = np.correlate(A, C, \"full\")\n", + "print(CorrCroisee2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affichage du résultat de la correlation. Notez que la meilleure correlation entre A et B donne 99, alors que le résultat est 205 entre A et C. Ce résulat est causé par les amplitudes différentes des signaux B et C. Un signal de plus grande amplitude aura nécessairement une meilleure corrélation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Le meilleur alignement est: 5\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(range(-len(A)+1,len(A)), CorrCroisee2)\n", + "Decalage2 = np.argmax(CorrCroisee2)-len(C)+1\n", + "print('Le meilleur alignement est:', Decalage2)\n", + "plt.axvline(Decalage2, color='k', ls='--')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voici le résultat de l'alignement des signaux pour maximiser leur corrélation. L'alignement entre A et B et parfait. Ce n'est pas le cas pour C." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(range(len(A)), A)\n", + "B = B[:4]\n", + "C = C[:4]\n", + "plt.plot(range(Decalage, Decalage+len(B)), B)\n", + "plt.plot(range(Decalage2, Decalage2+len(C)), C)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maintenant, un exemple d'application pour rechercher un extrait audio. On normalize les signaux pour obtenir des résultats indépendant de l'amplitude. Lecture d'un fichier wav d'une personne disant zéro, un, deux, cinq." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "[fs1, signal1] = wavfile.read('0125.wav')\n", + "moy = np.mean(signal1)\n", + "ecart = np.std(signal1)\n", + "signal1 = (signal1 - moy)/ecart #normalisation.\n", + "plt.figure(figsize = (10,5))\n", + "plt.plot(range(len(signal1)), signal1)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Une personne disant zéro." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "[fs2, signal2] = wavfile.read('0.wav')\n", + "moy = np.mean(signal2)\n", + "ecart = np.std(signal2)\n", + "signal2 = (signal2 - moy)/ecart\n", + "plt.figure(figsize = (10,5))\n", + "plt.plot(range(len(signal2)), signal2)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Est-ce que 0 est inclut dans 0125, et à quel endroit. Pour l'inclusion, il faudrait établir un seuil de correlation minimum. Dans ce cas-ci, on cherche seulement à localiser le 0." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Le meilleur alignement est: 625 avec une correlation de: 1554.0852650066836\n" + ] + } + ], + "source": [ + "CorrCroisee3 = np.correlate(signal1, signal2, \"full\")\n", + "Decalage3 = np.argmax(CorrCroisee3)-len(signal2)+1\n", + "Maxcor = np.max(CorrCroisee3)\n", + "print('Le meilleur alignement est:', Decalage3, 'avec une correlation de: ', Maxcor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affichage de l'alignement trouvé." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10,5))\n", + "plt.plot(range(len(signal1)), signal1)\n", + "plt.plot(range(Decalage3, Decalage3+len(signal2)), signal2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}