TestCreator.ipynb

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    "colab": {
      "name": "TestCreator.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "DYaScwi3HKAB",
        "colab_type": "code",
        "outputId": "a43727e1-3cdd-4b59-a61c-d5f5415d8753",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PVvbfO6QHbKB",
        "colab_type": "code",
        "outputId": "79932a90-ff97-4cd7-b088-564180381731",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cd /content/drive/My Drive/flipchal"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/flipchal\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "56hKcj2QHi__",
        "colab_type": "code",
        "outputId": "5f991223-8d2d-4575-f938-3066eec44ab9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id_to_box   id_to_data2  id_to_size3  test1.csv     test.gsheet\n",
            "id_to_box2  id_to_data3  id_to_size4  test1.gsheet  \u001b[0m\u001b[01;34mtest_set\u001b[0m/\n",
            "id_to_box3  id_to_data4  model2.h5    test2.csv     training.csv\n",
            "id_to_box4  id_to_size   model.h5     test3.csv     \u001b[01;34mtrain_set\u001b[0m/\n",
            "id_to_data  id_to_size2  \u001b[01;34mp1\u001b[0m/          test.csv      Untitled.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wKztdQnVHl4d",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import pickle\n",
        "import random"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yWwXva0hHnum",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def Normalize(image,mean,std):\n",
        "    for channel in range(3):\n",
        "        image[:,:,channel]=(image[:,:,channel]-mean[channel])/std[channel]\n",
        "    return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KrulNA8cHrbs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('./test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nk7hwKTEIkFt",
        "colab_type": "code",
        "outputId": "c14ee316-12fd-485f-8a2b-2a1686e6f192",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_name</th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>y1</th>\n",
              "      <th>y2</th>\n",
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              "  </thead>\n",
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              "      <th>2</th>\n",
              "      <td>JPEG_20161205_135307_1000155917326.png</td>\n",
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              "      <th>4</th>\n",
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            "text/plain": [
              "                               image_name  x1  x2  y1  y2\n",
              "0               1474723840903DSC08089.png NaN NaN NaN NaN\n",
              "1        1473231475010DeeplearnS11276.png NaN NaN NaN NaN\n",
              "2  JPEG_20161205_135307_1000155917326.png NaN NaN NaN NaN\n",
              "3  JPEG_20160711_123440_1000518778437.png NaN NaN NaN NaN\n",
              "4   JPEG_20160803_115329_100034020722.png NaN NaN NaN NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "mUcWVLtxImLf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def getImgArray(id):\n",
        "  path = df.iloc[id]['image_name']\n",
        "  image=Image.open(\"./test_set/\"+path).convert('RGB')\n",
        "  image=image.resize((224,224))\n",
        "  image=np.array(image,dtype=np.float32)\n",
        "  image=image/255\n",
        "  image=Normalize(image,[0.485,0.456,0.406],[0.229,0.224,0.225])\n",
        "  inc_dim = {}\n",
        "  inc_dim[0] = image\n",
        "  inc_dim=np.array(list(inc_dim.values()))\n",
        "  return inc_dim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rfP0X_A8JpDY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def my_metric(labels,predictions):\n",
        "    threshhold=0.75\n",
        "    x=predictions[:,0]*224\n",
        "    x=tf.maximum(tf.minimum(x,224.0),0.0)\n",
        "    y=predictions[:,1]*224\n",
        "    y=tf.maximum(tf.minimum(y,224.0),0.0)\n",
        "    width=predictions[:,2]*224\n",
        "    width=tf.maximum(tf.minimum(width,224.0),0.0)\n",
        "    height=predictions[:,3]*224\n",
        "    height=tf.maximum(tf.minimum(height,224.0),0.0)\n",
        "    label_x=labels[:,0]\n",
        "    label_y=labels[:,1]\n",
        "    label_width=labels[:,2]\n",
        "    label_height=labels[:,3]\n",
        "    a1=tf.multiply(width,height)\n",
        "    a2=tf.multiply(label_width,label_height)\n",
        "    x1=tf.maximum(x,label_x)\n",
        "    y1=tf.maximum(y,label_y)\n",
        "    x2=tf.minimum(x+width,label_x+label_width)\n",
        "    y2=tf.minimum(y+height,label_y+label_height)\n",
        "    IoU=tf.abs(tf.multiply((x1-x2),(y1-y2)))/(a1+a2-tf.abs(tf.multiply((x1-x2),(y1-y2))))\n",
        "    condition=tf.less(threshhold,IoU)\n",
        "    sum=tf.where(condition,tf.ones(tf.shape(condition)),tf.zeros(tf.shape(condition)))\n",
        "    return tf.reduce_mean(sum)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xMs815sfJzKG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def smooth_l1_loss(true_box,pred_box):\n",
        "    loss=0.0\n",
        "    for i in range(4):\n",
        "        residual=tf.abs(true_box[:,i]-pred_box[:,i]*224)\n",
        "        condition=tf.less(residual,1.0)\n",
        "        small_res=0.5*tf.square(residual)\n",
        "        large_res=residual-0.5\n",
        "        loss=loss+tf.where(condition,small_res,large_res)\n",
        "    return tf.reduce_mean(loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LXMQ83yQJ0sm",
        "colab_type": "code",
        "outputId": "2cf15c60-154d-402b-d085-81a7ed96b7f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.layers import Dense, Conv2D, BatchNormalization, Activation\n",
        "from keras.layers import AveragePooling2D, MaxPooling2D, Input, Flatten\n",
        "from keras.optimizers import Adam\n",
        "from keras.regularizers import l2\n",
        "from keras import backend as K\n",
        "from keras.models import Model,load_model\n",
        "from keras.callbacks import ModelCheckpoint,LearningRateScheduler,ReduceLROnPlateau"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "E4qubN_zJ5l6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model=keras.models.load_model(\"./model2.h5\", custom_objects={'smooth_l1_loss':smooth_l1_loss, 'my_metric':my_metric})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bomn6ySqJ_di",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result=model.predict(img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZayCQ6MhLn8Y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction = result[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VC1wfpmfRM9E",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YDpMQqAQOcJN",
        "colab_type": "code",
        "outputId": "7b6f4a84-631c-43be-a18c-bf0eb13820b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        }
      },
      "cell_type": "code",
      "source": [
        "print(str(prediction[0]*640)+' '+str(prediction[1]*480)+' '+str((prediction[2]+prediction[0])*640)+' '+str((prediction[3]+prediction[1])*480)+' ')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-9849a3d71198>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m640\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m480\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m640\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m480\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'prediction' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "nF1FOF6HOlip",
        "colab_type": "code",
        "outputId": "838f4c30-d32a-46fc-fce6-5ab8a3a0e432",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12815, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "_7FIStz9RGjk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "finAns = {}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_qBq0MPzOujK",
        "colab_type": "code",
        "outputId": "8f05a699-6383-4038-912d-9c1b7a16177e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "cell_type": "code",
      "source": [
        "for i in range(12815):\n",
        "  img = getImgArray(i)\n",
        "  result = model.predict(img)\n",
        "  prediction = result[0]\n",
        "  prediction[0] = prediction[0]*640\n",
        "  prediction[1] = prediction[1]*480\n",
        "  prediction[2] = prediction[0]+prediction[2]*640\n",
        "  prediction[3] = prediction[1]+prediction[3]*480\n",
        "  finAns[i] = prediction\n",
        "  if i%500 == 0:\n",
        "    print(\"Step: \"+str(i))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Step: 0\n",
            "Step: 500\n",
            "Step: 1000\n",
            "Step: 1500\n",
            "Step: 2000\n",
            "Step: 2500\n",
            "Step: 3000\n",
            "Step: 3500\n",
            "Step: 4000\n",
            "Step: 4500\n",
            "Step: 5000\n",
            "Step: 5500\n",
            "Step: 6000\n",
            "Step: 6500\n",
            "Step: 7000\n",
            "Step: 7500\n",
            "Step: 8000\n",
            "Step: 8500\n",
            "Step: 9000\n",
            "Step: 9500\n",
            "Step: 10000\n",
            "Step: 10500\n",
            "Step: 11000\n",
            "Step: 11500\n",
            "Step: 12000\n",
            "Step: 12500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XhzK67ESRtYU",
        "colab_type": "code",
        "outputId": "8fea45a4-920f-49da-ec6d-69a103c22484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "cell_type": "code",
      "source": [
        "finAns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[240.16718 ,  81.38599 , 432.7739  , 394.47372 ],\n",
              "       [ 77.35289 , 141.3264  , 568.66516 , 347.89807 ],\n",
              "       [134.26187 ,  56.75032 , 489.63034 , 423.7465  ],\n",
              "       ...,\n",
              "       [ 33.4302  ,  26.419931, 455.29144 , 449.97208 ],\n",
              "       [219.36893 , 134.60803 , 532.5951  , 352.115   ],\n",
              "       [ 20.403927, 112.13265 , 595.7431  , 371.5594  ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "WWcUFXXuois5",
        "colab_type": "code",
        "outputId": "0edf4271-294c-4a50-c6e2-434b9e87855b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        }
      },
      "cell_type": "code",
      "source": [
        "finAns = np.array(list(finAns.values()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-9c928de17581>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfinAns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinAns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "4HKxHnoiotvt",
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        "colab": {
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          "height": 134
        }
      },
      "cell_type": "code",
      "source": [
        "finAns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[240.16718 ,  81.38599 , 432.7739  , 394.47372 ],\n",
              "       [ 77.35289 , 141.3264  , 568.66516 , 347.89807 ],\n",
              "       [134.26187 ,  56.75032 , 489.63034 , 423.7465  ],\n",
              "       ...,\n",
              "       [ 33.4302  ,  26.419931, 455.29144 , 449.97208 ],\n",
              "       [219.36893 , 134.60803 , 532.5951  , 352.115   ],\n",
              "       [ 20.403927, 112.13265 , 595.7431  , 371.5594  ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "vKpBEoUOvaxx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(12815):\n",
        "  for j in range(4):\n",
        "    if(finAns[i][j]<0):\n",
        "      finAns[i][j] = 0\n",
        "  if(finAns[i][2]>640):\n",
        "    finAns[i][2] = 640\n",
        "  if(finAns[i][3]>480):\n",
        "    finAns[i][3] = 480"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UY67Q-hNwG3Z",
        "colab_type": "code",
        "outputId": "686b155c-7bee-4599-9165-11e60102e1de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "finAns[557]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 21.96848,   0.     , 640.     , 448.35846], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "PgjkSJyAovXd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataFrame = pd.DataFrame({'x1':finAns[:,0],'y1':finAns[:,1], 'x2':finAns[:,2], 'y2':finAns[:,3]})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "cell_type": "code",
      "source": [
        "dataFrame.head()"
      ],
      "execution_count": 0,
      "outputs": [
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        "names = df['image_name']"
      ],
      "execution_count": 0,
      "outputs": []
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      "metadata": {
        "id": "yzVb9fXSs4Qo",
        "colab_type": "code",
        "colab": {}
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        "mdf = pd.DataFrame({'image_name':names})"
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              "      <th>0</th>\n",
              "      <td>1474723840903DSC08089.png</td>\n",
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              "</table>\n",
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              "                               image_name\n",
              "0               1474723840903DSC08089.png\n",
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              "2  JPEG_20161205_135307_1000155917326.png\n",
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              "4   JPEG_20160803_115329_100034020722.png"
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        "df_all_cols = pd.concat([mdf,dataFrame ], axis = 1)\n",
        "df_all_cols"
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              "      <td>141.326401</td>\n",
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              "      <th>2</th>\n",
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              "      <td>134.261871</td>\n",
              "      <td>489.630341</td>\n",
              "      <td>56.750320</td>\n",
              "      <td>423.746490</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>JPEG_20160711_123440_1000518778437.png</td>\n",
              "      <td>209.363770</td>\n",
              "      <td>445.125519</td>\n",
              "      <td>88.439995</td>\n",
              "      <td>413.595367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>JPEG_20160803_115329_100034020722.png</td>\n",
              "      <td>132.681076</td>\n",
              "      <td>484.715393</td>\n",
              "      <td>40.573833</td>\n",
              "      <td>442.267853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>147444974116511473239803010-Mast--Harbour-Men-...</td>\n",
              "      <td>176.592804</td>\n",
              "      <td>356.167053</td>\n",
              "      <td>5.840404</td>\n",
              "      <td>467.596222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>JPEG_20160622_110649_1000527459853.png</td>\n",
              "      <td>131.923569</td>\n",
              "      <td>507.590149</td>\n",
              "      <td>73.030663</td>\n",
              "      <td>360.061127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>JPEG_20160823_120737_1000784898268.png</td>\n",
              "      <td>84.360130</td>\n",
              "      <td>426.641479</td>\n",
              "      <td>118.394180</td>\n",
              "      <td>372.035767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1480965956645IMG_3094.png</td>\n",
              "      <td>197.808670</td>\n",
              "      <td>423.739960</td>\n",
              "      <td>165.883423</td>\n",
              "      <td>386.336334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14732348976417a565e40d545452688130062b267a2d2.png</td>\n",
              "      <td>26.311104</td>\n",
              "      <td>583.110107</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>480.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1474723374370DSC07852.png</td>\n",
              "      <td>285.000885</td>\n",
              "      <td>437.428802</td>\n",
              "      <td>14.606706</td>\n",
              "      <td>464.199799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>JPEG_20160820_111118_100011668802.png</td>\n",
              "      <td>252.922607</td>\n",
              "      <td>379.154846</td>\n",
              "      <td>85.632568</td>\n",
              "      <td>436.496918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>JPEG_20160706_105226_100087855419.png</td>\n",
              "      <td>144.402817</td>\n",
              "      <td>434.918304</td>\n",
              "      <td>170.278152</td>\n",
              "      <td>289.369965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>JPEG_20160621_155839_100073846506.png</td>\n",
              "      <td>204.948105</td>\n",
              "      <td>440.899078</td>\n",
              "      <td>84.120087</td>\n",
              "      <td>390.779816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1480940089861_R2A1904.png</td>\n",
              "      <td>178.168472</td>\n",
              "      <td>499.928162</td>\n",
              "      <td>162.178101</td>\n",
              "      <td>350.367950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1474637513144DSC06780.png</td>\n",
              "      <td>207.446426</td>\n",
              "      <td>424.277283</td>\n",
              "      <td>58.664482</td>\n",
              "      <td>428.790771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>JPEG_20161123_170015_1000183903221.png</td>\n",
              "      <td>277.158722</td>\n",
              "      <td>365.078888</td>\n",
              "      <td>10.602569</td>\n",
              "      <td>408.163666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>JPEG_20161205_152655_1000319785884.png</td>\n",
              "      <td>51.494011</td>\n",
              "      <td>468.109009</td>\n",
              "      <td>51.136223</td>\n",
              "      <td>401.529358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>14806550588121473324459p2-500x500.png</td>\n",
              "      <td>157.890335</td>\n",
              "      <td>504.446533</td>\n",
              "      <td>20.257343</td>\n",
              "      <td>411.139404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>JPEG_20160913_110021_1000554801748.png</td>\n",
              "      <td>129.512878</td>\n",
              "      <td>586.586914</td>\n",
              "      <td>129.056381</td>\n",
              "      <td>320.336517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>JPEG_20161028_113656_1000884495593.png</td>\n",
              "      <td>70.131348</td>\n",
              "      <td>525.681519</td>\n",
              "      <td>77.729446</td>\n",
              "      <td>440.460358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1480485006674IMG_20161129_181135.png</td>\n",
              "      <td>34.500366</td>\n",
              "      <td>618.566284</td>\n",
              "      <td>89.528534</td>\n",
              "      <td>403.586731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1468666217374JPEG_20160712_164026_100095190148...</td>\n",
              "      <td>49.111534</td>\n",
              "      <td>603.229553</td>\n",
              "      <td>203.058182</td>\n",
              "      <td>293.256317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1475159286472DSC08150.png</td>\n",
              "      <td>264.218811</td>\n",
              "      <td>399.075012</td>\n",
              "      <td>144.869461</td>\n",
              "      <td>390.782562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1473662770499DeeplearnS11593.png</td>\n",
              "      <td>4.333352</td>\n",
              "      <td>612.753906</td>\n",
              "      <td>46.211922</td>\n",
              "      <td>431.729889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>JPEG_20161121_172423_1000451802749.png</td>\n",
              "      <td>142.993866</td>\n",
              "      <td>538.526245</td>\n",
              "      <td>144.057632</td>\n",
              "      <td>406.233215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>JPEG_20160711_112635_1000386061474.png</td>\n",
              "      <td>203.130371</td>\n",
              "      <td>602.349670</td>\n",
              "      <td>192.004761</td>\n",
              "      <td>312.128235</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>JPEG_20161125_170806_1000294132098.png</td>\n",
              "      <td>164.976578</td>\n",
              "      <td>452.626312</td>\n",
              "      <td>37.530815</td>\n",
              "      <td>441.062988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1473664771684DeeplearnS11641.png</td>\n",
              "      <td>73.691780</td>\n",
              "      <td>577.040894</td>\n",
              "      <td>144.051620</td>\n",
              "      <td>327.904846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>JPEG_20161125_142000_1000881308475.png</td>\n",
              "      <td>116.869438</td>\n",
              "      <td>444.130615</td>\n",
              "      <td>47.297028</td>\n",
              "      <td>464.952240</td>\n",
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              "      <th>12785</th>\n",
              "      <td>JPEG_20160527_155707_1000436681221.png</td>\n",
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              "      <td>417.678162</td>\n",
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              "      <th>12786</th>\n",
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              "      <th>12787</th>\n",
              "      <td>1473315151219DeeplearnS11541.png</td>\n",
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              "      <th>12788</th>\n",
              "      <td>JPEG_20161114_164147_1000717591566.png</td>\n",
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              "      <th>12789</th>\n",
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              "      <th>12790</th>\n",
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              "      <td>575.263977</td>\n",
              "      <td>140.068954</td>\n",
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              "      <th>12791</th>\n",
              "      <td>1474724287220DSC08133.png</td>\n",
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              "      <td>571.581055</td>\n",
              "      <td>63.521149</td>\n",
              "      <td>411.746735</td>\n",
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              "      <th>12792</th>\n",
              "      <td>JPEG_20160924_140500_1000708658172.png</td>\n",
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              "      <td>507.788574</td>\n",
              "      <td>92.424812</td>\n",
              "      <td>393.307098</td>\n",
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              "      <th>12793</th>\n",
              "      <td>JPEG_20160920_160831_1000416032132.png</td>\n",
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              "      <td>498.643311</td>\n",
              "      <td>36.336636</td>\n",
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              "      <th>12794</th>\n",
              "      <td>1475158952049DSC06915.png</td>\n",
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              "      <td>415.455811</td>\n",
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              "      <td>433.107544</td>\n",
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              "      <th>12795</th>\n",
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              "      <td>626.894897</td>\n",
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              "      <th>12798</th>\n",
              "      <td>JPEG_20160704_123939_1000567540935.png</td>\n",
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              "      <td>516.711853</td>\n",
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              "      <th>12799</th>\n",
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              "      <th>12800</th>\n",
              "      <td>JPEG_20160629_101156_1000325247520.png</td>\n",
              "      <td>185.354218</td>\n",
              "      <td>403.966003</td>\n",
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              "      <th>12801</th>\n",
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              "      <td>105.752876</td>\n",
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              "      <th>12802</th>\n",
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              "      <td>549.079163</td>\n",
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              "      <th>12803</th>\n",
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              "      <th>12804</th>\n",
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              "      <td>99.644730</td>\n",
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              "      <td>319.972961</td>\n",
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              "      <th>12805</th>\n",
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              "      <th>12806</th>\n",
              "      <td>JPEG_20160607_135204_1000721004919.png</td>\n",
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              "      <td>590.691895</td>\n",
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              "      <th>12807</th>\n",
              "      <td>1468495348616DSC_0193.png</td>\n",
              "      <td>95.174034</td>\n",
              "      <td>512.409912</td>\n",
              "      <td>124.751274</td>\n",
              "      <td>427.770844</td>\n",
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              "      <th>12808</th>\n",
              "      <td>JPEG_20161126_141844_1000772813429.png</td>\n",
              "      <td>17.454775</td>\n",
              "      <td>629.480103</td>\n",
              "      <td>154.888092</td>\n",
              "      <td>413.472046</td>\n",
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              "      <th>12809</th>\n",
              "      <td>1473751292174IMG_0982.png</td>\n",
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              "      <td>131.561478</td>\n",
              "      <td>349.038544</td>\n",
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              "      <th>12810</th>\n",
              "      <td>JPEG_20161104_103201_1000965398606.png</td>\n",
              "      <td>104.950340</td>\n",
              "      <td>531.705688</td>\n",
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              "      <th>12811</th>\n",
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              "      <td>267.216248</td>\n",
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              "      <th>12812</th>\n",
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              "<p>12815 rows × 5 columns</p>\n",
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            "text/plain": [
              "                                              image_name          x1  \\\n",
              "0                              1474723840903DSC08089.png  240.167175   \n",
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              "...                                                  ...         ...   \n",
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              "\n",
              "               x2          y1          y2  \n",
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              "28     577.040894  144.051620  327.904846  \n",
              "29     444.130615   47.297028  464.952240  \n",
              "...           ...         ...         ...  \n",
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              "12813  532.595093  134.608032  352.114990  \n",
              "12814  595.743103  112.132652  371.559387  \n",
              "\n",
              "[12815 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "axYaC58HtyDj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_all_cols.to_csv('./test4.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EUVwxHl70qm8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}