simple_yolo.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "simple_yolo.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/codeakash/vision.ai/blob/master/simple_yolo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "G2piQECfaSPU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**\n",
        "This is a simple YOLO V1 implementation from scratch in tensorflow to detect drones in images trained on a very small dataset of labelled drone images with bounding boxes** "
      ]
    },
    {
      "metadata": {
        "id": "pEqWnwcH4sKE",
        "colab_type": "code",
        "outputId": "1f8f260e-1649-42d4-8cac-65df68ebb52a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-gpu\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow-gpu in /usr/local/lib/python3.6/dist-packages (1.13.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.9)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.15.0)\n",
            "Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.7.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.14.6)\n",
            "Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.13.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.33.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.7)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.7.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.7.1)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.2.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.11.0)\n",
            "Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.13.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu) (0.14.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu) (3.0.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu) (2.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu) (40.8.0)\n",
            "Requirement already satisfied: mock>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow-gpu) (2.0.0)\n",
            "Requirement already satisfied: pbr>=0.11 in /usr/local/lib/python3.6/dist-packages (from mock>=2.0.0->tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow-gpu) (5.1.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "g-YwdBmwa7Ch",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "connecting and mounting google drive to get data and save models "
      ]
    },
    {
      "metadata": {
        "id": "Gze_oXM_oUjU",
        "colab_type": "code",
        "outputId": "5e67d22b-9e62-419f-eff8-ff02ec8afb73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZcP9zFKKzybc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!cd \"/content/drive/My Drive/yolo_drone\"\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8jusolk6A5ge",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ]
    },
    {
      "metadata": {
        "id": "gaGZxzZF5H6p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#my own implementation of yolo - for faces or other custom objects\n",
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "77p0mh5g4urO",
        "colab_type": "code",
        "outputId": "da33bbe8-e3e3-40f2-d1e1-e2a54917245d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        }
      },
      "cell_type": "code",
      "source": [
        "Xinput=tf.placeholder(shape=[None,500,500,3],dtype=tf.float32,name='input')\n",
        "ypc=tf.placeholder(shape=[None,10,10,1],dtype=tf.float32,name='object_prob')\n",
        "ywd=tf.placeholder(shape=[None,10,10,1],dtype=tf.float32,name='object_rel_width')\n",
        "yht=tf.placeholder(shape=[None,10,10,1],dtype=tf.float32,name='object_rel_height')\n",
        "ycx=tf.placeholder(shape=[None,10,10,1],dtype=tf.float32,name='object_rel_xcenter')\n",
        "ycy=tf.placeholder(shape=[None,10,10,1],dtype=tf.float32,name='object_rel_ycenter')\n",
        "yflat=tf.placeholder(shape=[None,500],dtype=tf.float32,name='object_all_flat')\n",
        "\n",
        "scaling_filter = tf.constant(1/255.0,shape=[1,1,3])\n",
        "#input_Scaled=tf.nn.conv2d(Xinput,scaling_filter,strides=[1,1,1,1],padding='SAME')\n",
        "\n",
        "input_Scaled = tf.multiply(scaling_filter,Xinput)\n",
        "\n",
        "\n",
        "w0=tf.get_variable('w0', shape=(3,3,3,32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b0=tf.get_variable('b0', shape=(32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l0 = tf.add(tf.nn.conv2d(input_Scaled, w0,strides=[1,1,1,1],padding='SAME'),b0)\n",
        "l0 = tf.nn.relu(l0) \n",
        "\n",
        "w1=tf.get_variable('w1', shape=(3,3,32,128), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b1=tf.get_variable('b1', shape=(128), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l1 = tf.add(tf.nn.conv2d(l0, w1,strides=[1,1,1,1],padding='VALID'),b1)\n",
        "l1 = tf.nn.leaky_relu(l1) \n",
        "\n",
        "w2=tf.get_variable('w2', shape=(3,3,128,128), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b2=tf.get_variable('b2', shape=(128), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l2 = tf.add(tf.nn.conv2d(l1, w2,strides=[1,2,2,1],padding='VALID'),b2)\n",
        "l2 = tf.nn.leaky_relu(l2) \n",
        "\n",
        "w3=tf.get_variable('w3', shape=(3,3,128,128), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b3=tf.get_variable('b3', shape=(128), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l3 = tf.add(tf.nn.conv2d(l2, w3,strides=[1,2,2,1],padding='VALID'),b3)\n",
        "l3 = tf.nn.leaky_relu(l3) \n",
        "\n",
        "w4=tf.get_variable('w4', shape=(3,3,128,32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b4=tf.get_variable('b4', shape=(32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l4 = tf.add(tf.nn.conv2d(l3, w4,strides=[1,2,2,1],padding='VALID'),b4)\n",
        "l4 = tf.nn.leaky_relu(l4) \n",
        "\n",
        "w5=tf.get_variable('w5', shape=(3,3,32,32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b5=tf.get_variable('b5', shape=(32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l5 = tf.add(tf.nn.conv2d(l4, w5,strides=[1,2,2,1],padding='VALID'),b5)\n",
        "l5 = tf.nn.leaky_relu(l5) \n",
        "\n",
        "w6=tf.get_variable('w6', shape=(3,3,32,32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b6=tf.get_variable('b6', shape=(32), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l6 = tf.add(tf.nn.conv2d(l5, w6,strides=[1,2,2,1],padding='VALID'),b6)\n",
        "l6 = tf.nn.leaky_relu(l6) #14,14,128\n",
        " \n",
        "w7=tf.get_variable('w7', shape=(3,3,32,16), initializer=tf.contrib.layers.xavier_initializer())\n",
        "b7=tf.get_variable('b7', shape=(16), initializer=tf.contrib.layers.xavier_initializer())\n",
        "l7 = tf.add(tf.nn.conv2d(l6, w7,strides=[1,1,1,1],padding='VALID'),b7)\n",
        "l7 = tf.nn.leaky_relu(l7)#12,12,128\n",
        "#####################################################################################################\n",
        "wpc=tf.get_variable('wpc', shape=(3,3,16,1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "bpc=tf.get_variable('bpc', shape=(1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "lpc = tf.add(tf.nn.conv2d(l7, wpc,strides=[1,1,1,1],padding='VALID'),bpc)\n",
        "lpc = tf.nn.sigmoid(lpc)#10,10,1\n",
        "    \n",
        "wwd=tf.get_variable('wwd', shape=(3,3,16,1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "bwd=tf.get_variable('bwd', shape=(1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "lwd = tf.add(tf.nn.conv2d(l7, wwd,strides=[1,1,1,1],padding='VALID'),bwd)\n",
        "lwd = tf.nn.sigmoid(lwd)#10,10,1\n",
        "\n",
        "wht=tf.get_variable('wht', shape=(3,3,16,1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "bht=tf.get_variable('bht', shape=(1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "lht = tf.add(tf.nn.conv2d(l7, wht,strides=[1,1,1,1],padding='VALID'),bht)\n",
        "lht = tf.nn.sigmoid(lht)#10,10,1\n",
        "\n",
        "\n",
        "wcx=tf.get_variable('wcx', shape=(3,3,16,1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "bcx=tf.get_variable('bcx', shape=(1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "lcx = tf.add(tf.nn.conv2d(l7, wcx,strides=[1,1,1,1],padding='VALID'),bcx)\n",
        "lcx = tf.nn.sigmoid(lcx)#10,10,1\n",
        "\n",
        "wcy=tf.get_variable('wcy', shape=(3,3,16,1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "bcy=tf.get_variable('bcy', shape=(1), initializer=tf.contrib.layers.xavier_initializer())\n",
        "lcy = tf.add(tf.nn.conv2d(l7, wcy,strides=[1,1,1,1],padding='VALID'),bcy)\n",
        "lcy = tf.nn.sigmoid(lcy)#10,10,1\n",
        "####################################################################\n",
        "flat=tf.reshape(l7,[-1,12*12*16]) #flat1=tf.reshape(l11a,[-1,120*200*10])\n",
        "wf=tf.get_variable('wf', shape=(12*12*16,500), initializer=tf.contrib.layers.xavier_initializer())\n",
        "bf=tf.get_variable('bf', shape=(500), initializer=tf.contrib.layers.xavier_initializer())\n",
        "lf = tf.add(tf.matmul(flat,wf),bf)\n",
        "lf = tf.nn.sigmoid(lf)#none,500\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jUSuevxx4tHv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#loss func\n",
        "\n",
        "# masking unecessary stuff\n",
        "lpcobj = tf.multiply(ypc,lpc)#masking\n",
        "bcgrnd=tf.ones([10,10,1]) - ypc # we need to 5 times the loss where object is and 0.05 times the loss where object is not\n",
        "#masking other outputs\n",
        "lpcbcgrnd= tf.multiply(bcgrnd,lpc)\n",
        "lwdobj= tf.multiply(lwd,ypc)\n",
        "lhtobj= tf.multiply(lht,ypc)\n",
        "lcxobj= tf.multiply(lcx,ypc)\n",
        "lcyobj= tf.multiply(lcy,ypc)\n",
        "cplx_loss= tf.reduce_sum(5*tf.square(ypc-lpcobj)+0.5*tf.square(lpcbcgrnd) + 0.5*tf.square(lwdobj- ywd)+ 0.5*tf.square(lhtobj- yht) + 0.5*tf.square(lcxobj- ycx) + 0.5*tf.square(lcyobj- ycy))\n",
        "# this loss function will make prob ojb more important and then boxes with high actual obj will be taken for loss for size and locationof center rest everythig is ignored \n",
        "#i.e. we are only imporving predictions where object is and and then imporving size and loc prediction in places having objects #i dont know if it will be better \n",
        "simpl_loss= tf.reduce_sum(tf.square(lf - yflat))\n",
        "optimiser=tf.train.AdamOptimizer(learning_rate=0.001,name='Adam')\n",
        "complex_train_op=optimiser.minimize(cplx_loss)\n",
        "simple_train_op = optimiser.minimize(simpl_loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QY9rMZlO4thz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "input_img=np.load('/content/drive/My Drive/yolo_drone/x_yolo.npy')\n",
        "y_pc=np.load('/content/drive/My Drive/yolo_drone/y_pc.npy')\n",
        "y_wd=np.load('/content/drive/My Drive/yolo_drone/y_wd.npy')\n",
        "y_ht=np.load('/content/drive/My Drive/yolo_drone/y_ht.npy')\n",
        "y_cx=np.load('/content/drive/My Drive/yolo_drone/y_cx.npy')\n",
        "y_cy=np.load('/content/drive/My Drive/yolo_drone/y_cy.npy')\n",
        "y_flat=np.load('/content/drive/My Drive/yolo_drone/y_flat.npy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MZX5Amdfg3-R",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#saver"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1ffi0yxZbqRP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Trained it to over fit just to check that if the model is deep enough or not"
      ]
    },
    {
      "metadata": {
        "id": "0kTSptZp6EmY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "saver = tf.train.Saver()\n",
        "with tf.Session() as sess:\n",
        "\t#sess.run(tf.global_variables_initializer())\n",
        "\tsavepath= \"/content/drive/My Drive/yolo_drone/simplemodel/dronenet.ckpt\"\n",
        "\t\n",
        "\t#restore dont run initializer\n",
        "\tsaver.restore(sess,savepath)\n",
        "\tmini_batch=8\n",
        "\tbatch=len(input_img)\n",
        "\tepochs=20\n",
        "\tfor j in range (epochs):\n",
        "\t\tfor x in range (0,batch,mini_batch):\n",
        "\t\t\tif x<(int(batch/mini_batch)*mini_batch):\n",
        "\t\t\t\timage= input_img[x:mini_batch+x]\t\n",
        "\t\t\t\tpc= y_pc[x:mini_batch+x]\n",
        "\n",
        "\t\t\t\twd = y_wd[x:mini_batch+x]\t\n",
        "\t\t\t\tht= y_ht[x:mini_batch+x]\n",
        "\t\t\t\tcx= y_cx[x:mini_batch+x]\n",
        "\t\t\t\tcy = y_cy[x:mini_batch+x]\n",
        "\t\t\t\tflat = y_flat[x:mini_batch+x]\n",
        "\t\t\tif x==(int(batch/mini_batch)*mini_batch):\n",
        "\t\t\t\timage= input_img[x:batch]\t\n",
        "\t\t\t\tpc= y_pc[x:batch]\n",
        "\n",
        "\t\t\t\twd = y_wd[x:batch]\t\n",
        "\t\t\t\tht= y_ht[x:batch]\n",
        "\t\t\t\tcx= y_cx[x:batch]\n",
        "\t\t\t\tcy = y_cy[x:batch]\n",
        "\t\t\t\tflat = y_flat[x:batch]\t\n",
        "\t\t\tprint(\"epoch:\",j+1)\n",
        "\t\t\tprint(\"batch \"+str(x)+\"/\"+str(batch))\n",
        "\t\t\t\n",
        "\t\t\t\t\n",
        "\n",
        "\t\t\t#INFERENCE=sess.run([lpcobj,lpcbcgrnd,lwdobj,lhtobj,lcxobj,lcyobj,cplx_loss],feed_dict={Xinput:[image],ypc:[pc],ywd:[wd],yht:[ht],ycx:[cx],ycy:[cy],yflat:[flat]})\n",
        "\t\t\tstraining = sess.run(simple_train_op,feed_dict={Xinput:image,ypc:pc,ywd:wd,yht:ht,ycx:cx,ycy:cy,yflat:flat})\n",
        "\n",
        "\t\t\t#loss = sess.run(cplx_loss,feed_dict={Xinput:image,ypc:pc,ywd:wd,yht:ht,ycx:cx,ycy:cy,yflat:flat})\n",
        "\t\t\tloss = sess.run(simpl_loss,feed_dict={Xinput:image,ypc:pc,ywd:wd,yht:ht,ycx:cx,ycy:cy,yflat:flat})\n",
        "\n",
        "\t\t\t#ctraining = sess.run(complex_train_op,feed_dict={Xinput:image,ypc:pc,ywd:wd,yht:ht,ycx:cx,ycy:cy,yflat:flat})\n",
        "\t\t\t#inpt =sess.run(input_Scaled,feed_dict={Xinput:image,ypc:pc,ywd:wd,yht:ht,ycx:cx,ycy:cy,yflat:flat})\n",
        "\t\t\t#print(ctraining)\n",
        "      \n",
        "      \n",
        "      \n",
        "      \n",
        "\t\t\t\n",
        "\t\t\tprint(loss)\n",
        "\tsaver_path=saver.save(sess,savepath)      \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_o17nWyzn8q7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ]
    },
    {
      "metadata": {
        "id": "_mb9_-5mDG0r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.patches import Rectangle\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sTNPubrtcHP-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Testing the model on Training data Just to  see the degreeof  overfit and **also cos i Dont have testing data**"
      ]
    },
    {
      "metadata": {
        "id": "DC-ENdExoCC0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "55b116af-9b85-4ce6-dc03-2bc2347fc2c8"
      },
      "cell_type": "code",
      "source": [
        "saver = tf.train.Saver()\n",
        "with tf.Session() as sess:\n",
        "  savepath= \"/content/drive/My Drive/yolo_drone/models/dronenet.ckpt\"\n",
        "\t#restore dont run initializer\n",
        "  saver.restore(sess,savepath)\n",
        "  example=50\n",
        "  image= input_img[example]\n",
        "  pred=sess.run([lpc,lwd,lht,lcx,lcy],feed_dict={Xinput:[image]})\n",
        "  INFERENCE=pred\n",
        "        \n",
        "  \n",
        "  pc=INFERENCE[0].ravel()\n",
        "  wd=INFERENCE[1].ravel()\n",
        "  ht=INFERENCE[2].ravel()\n",
        "  cx=INFERENCE[3].ravel()\n",
        "  cy=INFERENCE[4].ravel()\n",
        "  data=y_pc[example].ravel()\n",
        "  pred_box_c=[]\n",
        "  pred_box_w=[]\n",
        "  pred_box_h=[]\n",
        "  for i in range(0,len(pc)):\n",
        "    if pc[i]>0.5:\n",
        "      print(i)\n",
        "      xp=(i%10+cx[i])*50 - wd[i]*250\n",
        "      yp=(i/10+cy[i])*50 - ht[i]*250\n",
        "      print(wd[i])\n",
        "      pred_box_c.append((xp,yp))\n",
        "      pred_box_w.append(wd[i]*500)\n",
        "      pred_box_h.append(ht[i]*500 )\n",
        "  \n",
        "  for i in range(len(pred_box_c)):\n",
        "    print(pred_box_c[i], pred_box_w[i],pred_box_h[i])\n",
        "    rect = Rectangle(pred_box_c[i], pred_box_w[i],pred_box_h[i],linewidth=2,edgecolor='r',facecolor='none')\n",
        "    #rect_test = Rectangle((0, 0), pred_box_w[i],pred_box_h[i],linewidth=2,edgecolor='b',facecolor='none')\n",
        "    plt.gca().add_patch(rect)\n",
        "  plt.imshow(image)\n",
        "  plt.show()\n",
        "      \n",
        "  \n",
        "  "
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from /content/drive/My Drive/yolo_drone/models/dronenet.ckpt\n",
            "58\n",
            "0.21281844\n",
            "(353.43579798936844, 218.09077948331833) 106.4092218875885 191.28018617630005\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ThLqHMTFRGLAgBLHWtLc6eMpjVZ9jcXGFwFso9aXMkZS1pkYM2EGNGX1Sl9KJqh+nhqy9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\nkmgBSvj+8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mVYjdplsh6zkywtLXP16lW2t7cRQrCyskLfDTCmYjga8dBDD3lR6YO9+rsy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\nLWtZyyWUtZJdy1rWspZLKP8f+PyWgKh4JsMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DJmpvgMFeMbD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "the model is not working properly but can be imporved a lot (i forgot to mention that  i am too  lazy and maybe i will update it after a few months ; -) ) "
      ]
    },
    {
      "metadata": {
        "id": "EPanES7ldJ7j",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "link to my project on google drive "
      ]
    },
    {
      "metadata": {
        "id": "cjyZlTDdaPq9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "https://drive.google.com/drive/folders/1xbSZ-x6NNzsYISkaMuBBY0fTqMcDUf40?usp=sharing"
      ]
    },
    {
      "metadata": {
        "id": "-KHfrneoRK90",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}