usandoClassificador-Profundo.ipynb

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      "2023-11-24 20:42:54.897262: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-24 20:42:54.936655: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-24 20:42:54.937375: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-24 20:42:55.765230: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "execution": {
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   "source": [
    "# Carregue o modelo treinado\n",
    "model = tf.keras.models.load_model('./models/cfIsNoel-RCastilhos.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# Diretório de imagens de teste\n",
    "test_directory = './test'  # Substitua 'data/test' pelo caminho para a pasta de teste"
   ]
  },
  {
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   "execution_count": 4,
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   "source": [
    "# Obtenha a lista de arquivos na pasta de teste e ordene-a alfabeticamente\n",
    "image_files = sorted([\n",
    "    filename for filename in os.listdir(test_directory) \n",
    "    if filename.endswith((\".jpg\", \".jpeg\"))\n",
    "])\n",
    "\n",
    "erro_s = []\n",
    "erro_ns = []\n",
    "\n",
    "t = len(image_files)\n",
    "\n",
    "classe0_hit = 0\n",
    "classe0_miss = 0\n",
    "\n",
    "classe1_hit = 0\n",
    "classe1_miss = 0"
   ]
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      "\n"
     ]
    }
   ],
   "source": [
    "# Percorra todas as imagens na pasta de teste\n",
    "for filename in image_files:\n",
    "    # print(filename)\n",
    "    \n",
    "    # Caminho completo para a imagem de teste\n",
    "    image_path = os.path.join(test_directory, filename)\n",
    "\n",
    "    # Pré-processe a imagem de teste\n",
    "    img = image.load_img(image_path, target_size=(150, 150))\n",
    "    img = image.img_to_array(img)\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "    img = img / 255.0  # Normalize a imagem (igual ao pré-processamento durante o treinamento)\n",
    "\n",
    "    # Faça a previsão usando o modelo carregado\n",
    "    prediction = model.predict(img)\n",
    "\n",
    "    if prediction < 0.5:\n",
    "        # print(\"Classe 0, não é Papai Noel\", end='\\t' )  # Defina a classe correspondente\n",
    "        if 'not-a' in filename:\n",
    "            # print('ACERTOU')\n",
    "            classe0_hit += 1\n",
    "        else:\n",
    "            # print('ERROU')\n",
    "            classe0_miss +=1\n",
    "            erro_ns.append(filename)\n",
    "    else:\n",
    "        # print(\"Classe 1, é Papai Noel ... Cadê meu presente !?\", end='\\t')  # Defina a classe correspondente\n",
    "        if 'not-a' not in filename:\n",
    "            # print('ACERTOU')\n",
    "            classe1_hit += 1\n",
    "        else:\n",
    "            # print('ERROU')\n",
    "            classe1_miss += 1\n",
    "            erro_s.append(filename)\n",
    "\n",
    "    # Classifique a previsão\n",
    "    print(f'%.2f\\n' % (prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-24T23:45:05.918489Z",
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     "iopub.status.idle": "2023-11-24T23:45:05.926047Z",
     "shell.execute_reply": "2023-11-24T23:45:05.925211Z",
     "shell.execute_reply.started": "2023-11-24T23:45:05.918456Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tamanho da Amostra 616\n",
      "\n",
      "Quantidade de Acertos S  398 (64.61%)\tQuantidade de Erros   S  42 (6.82%)\n",
      "Imagens que eu disse ser Papai Noel mas errei:  ['114.not-a-santa.jpg', '115.not-a-santa.jpg', '152.not-a-santa.jpg', '17.not-a-santa.jpg', '225.not-a-santa.jpg', '226.not-a-santa.jpg', '234.not-a-santa.jpg', '236.not-a-santa.jpg', '263.not-a-santa.jpg', '275.not-a-santa.jpg', '3.not-a-santa.jpg', '31.not-a-santa.jpg', '114.not-a-santa.jpg', '115.not-a-santa.jpg', '152.not-a-santa.jpg', '17.not-a-santa.jpg', '225.not-a-santa.jpg', '226.not-a-santa.jpg', '234.not-a-santa.jpg', '236.not-a-santa.jpg', '263.not-a-santa.jpg', '275.not-a-santa.jpg', '3.not-a-santa.jpg', '31.not-a-santa.jpg', '340.not-a-santa.jpg', '41.not-a-santa.jpg', '434.not-a-santa.jpg', '435.not-a-santa.jpg', '459.not-a-santa.jpg', '466.not-a-santa.jpg', '500.not-a-santa.jpg', '509.not-a-santa.jpg', '510.not-a-santa.jpg', '52.not-a-santa.jpg', '53.not-a-santa.jpg', '537.not-a-santa.jpg', '548.not-a-santa.jpg', '57.not-a-santa.jpg', '609.not-a-santa.jpg', '67.not-a-santa.jpg', '69.not-a-santa.jpg', '78.not-a-santa.jpg']\n",
      "\n",
      "Quantidade de Acertos NS 395 (64.12%)\tQuantidade de Erros   NS 37 (6.01%)\n",
      "Imagens que eu disse não ser Papai Noel mas errei: ['107.Santa.jpg', '15.Santa.jpg', '169.Santa.jpg', '189.Santa.jpg', '195.Santa.jpg', '201.Santa.jpg', '202.Santa.jpg', '27.Santa.jpg', '281.Santa.jpg', '325.Santa.jpg', '107.Santa.jpg', '15.Santa.jpg', '169.Santa.jpg', '189.Santa.jpg', '195.Santa.jpg', '201.Santa.jpg', '202.Santa.jpg', '27.Santa.jpg', '281.Santa.jpg', '325.Santa.jpg', '376.Santa.jpg', '408.Santa.jpg', '412.Santa.jpg', '413.Santa.jpg', '416.Santa.jpg', '428.Santa.jpg', '439.Santa.jpg', '45.Santa.jpg', '476.Santa.jpg', '486.Santa.jpg', '490.Santa.jpg', '523.Santa.jpg', '539.Santa.jpg', '581.Santa.jpg', '583.Santa.jpg', '608.Santa.jpg', '80.Santa.jpg']\n"
     ]
    }
   ],
   "source": [
    "    \n",
    "print(f'Tamanho da Amostra {t}')\n",
    "print()\n",
    "print(f'Quantidade de Acertos S  {classe1_hit} ({(classe1_hit/t)*100:2.2f}%)', end='\\t')\n",
    "print(f'Quantidade de Erros   S  {classe1_miss} ({(classe1_miss/t)*100:2.2f}%)')\n",
    "print('Imagens que eu disse ser Papai Noel mas errei: ', erro_s)\n",
    "print()\n",
    "print(f'Quantidade de Acertos NS {classe0_hit} ({(classe0_hit/t)*100:2.2f}%)', end='\\t')\n",
    "print(f'Quantidade de Erros   NS {classe0_miss} ({(classe0_miss/t)*100:2.2f}%)')\n",
    "print('Imagens que eu disse não ser Papai Noel mas errei:', erro_ns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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