Auto/Autonomous+driving+application+-+Car+detection+-+v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autonomous driving - Car detection\n",
    "\n",
    "Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (https://arxiv.org/abs/1612.08242). \n",
    "\n",
    "**You will learn to**:\n",
    "- Use object detection on a car detection dataset\n",
    "- Deal with bounding boxes\n",
    "\n",
    "Run the following cell to load the packages and dependencies that are going to be useful for your journey!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import scipy.io\n",
    "import scipy.misc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import PIL\n",
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "from keras.layers import Input, Lambda, Conv2D\n",
    "from keras.models import load_model, Model\n",
    "from yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes\n",
    "from yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important Note**: As you can see, we import Keras's backend as K. This means that to use a Keras function in this notebook, you will need to write: `K.function(...)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Problem Statement\n",
    "\n",
    "You are working on a self-driving car. As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds while you drive around. \n",
    "\n",
    "<center>\n",
    "<video width=\"400\" height=\"200\" src=\"nb_images/road_video_compressed2.mp4\" type=\"video/mp4\" controls>\n",
    "</video>\n",
    "</center>\n",
    "\n",
    "<caption><center> Pictures taken from a car-mounted camera while driving around Silicon Valley. <br> We would like to especially thank [drive.ai](https://www.drive.ai/) for providing this dataset! Drive.ai is a company building the brains of self-driving vehicles.\n",
    "</center></caption>\n",
    "\n",
    "<img src=\"nb_images/driveai.png\" style=\"width:100px;height:100;\">\n",
    "\n",
    "You've gathered all these images into a folder and have labelled them by drawing bounding boxes around every car you found. Here's an example of what your bounding boxes look like.\n",
    "\n",
    "<img src=\"nb_images/box_label.png\" style=\"width:500px;height:250;\">\n",
    "<caption><center> <u> **Figure 1** </u>: **Definition of a box**<br> </center></caption>\n",
    "\n",
    "If you have 80 classes that you want YOLO to recognize, you can represent the class label $c$ either as an integer from 1 to 80, or as an 80-dimensional vector (with 80 numbers) one component of which is 1 and the rest of which are 0. The video lectures had used the latter representation; in this notebook, we will use both representations, depending on which is more convenient for a particular step.  \n",
    "\n",
    "In this exercise, you will learn how YOLO works, then apply it to car detection. Because the YOLO model is very computationally expensive to train, we will load pre-trained weights for you to use. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - YOLO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YOLO (\"you only look once\") is a popular algoritm because it achieves high accuracy while also being able to run in real-time. This algorithm \"only looks once\" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes.\n",
    "\n",
    "### 2.1 - Model details\n",
    "\n",
    "First things to know:\n",
    "- The **input** is a batch of images of shape (m, 608, 608, 3)\n",
    "- The **output** is a list of bounding boxes along with the recognized classes. Each bounding box is represented by 6 numbers $(p_c, b_x, b_y, b_h, b_w, c)$ as explained above. If you expand $c$ into an 80-dimensional vector, each bounding box is then represented by 85 numbers. \n",
    "\n",
    "We will use 5 anchor boxes. So you can think of the YOLO architecture as the following: IMAGE (m, 608, 608, 3) -> DEEP CNN -> ENCODING (m, 19, 19, 5, 85).\n",
    "\n",
    "Lets look in greater detail at what this encoding represents. \n",
    "\n",
    "<img src=\"nb_images/architecture.png\" style=\"width:700px;height:400;\">\n",
    "<caption><center> <u> **Figure 2** </u>: **Encoding architecture for YOLO**<br> </center></caption>\n",
    "\n",
    "If the center/midpoint of an object falls into a grid cell, that grid cell is responsible for detecting that object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are using 5 anchor boxes, each of the 19 x19 cells thus encodes information about 5 boxes. Anchor boxes are defined only by their width and height.\n",
    "\n",
    "For simplicity, we will flatten the last two last dimensions of the shape (19, 19, 5, 85) encoding. So the output of the Deep CNN is (19, 19, 425).\n",
    "\n",
    "<img src=\"nb_images/flatten.png\" style=\"width:700px;height:400;\">\n",
    "<caption><center> <u> **Figure 3** </u>: **Flattening the last two last dimensions**<br> </center></caption>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, for each box (of each cell) we will compute the following elementwise product and extract a probability that the box contains a certain class.\n",
    "\n",
    "<img src=\"nb_images/probability_extraction.png\" style=\"width:700px;height:400;\">\n",
    "<caption><center> <u> **Figure 4** </u>: **Find the class detected by each box**<br> </center></caption>\n",
    "\n",
    "Here's one way to visualize what YOLO is predicting on an image:\n",
    "- For each of the 19x19 grid cells, find the maximum of the probability scores (taking a max across both the 5 anchor boxes and across different classes). \n",
    "- Color that grid cell according to what object that grid cell considers the most likely.\n",
    "\n",
    "Doing this results in this picture: \n",
    "\n",
    "<img src=\"nb_images/proba_map.png\" style=\"width:300px;height:300;\">\n",
    "<caption><center> <u> **Figure 5** </u>: Each of the 19x19 grid cells colored according to which class has the largest predicted probability in that cell.<br> </center></caption>\n",
    "\n",
    "Note that this visualization isn't a core part of the YOLO algorithm itself for making predictions; it's just a nice way of visualizing an intermediate result of the algorithm. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to visualize YOLO's output is to plot the bounding boxes that it outputs. Doing that results in a visualization like this:  \n",
    "\n",
    "<img src=\"nb_images/anchor_map.png\" style=\"width:200px;height:200;\">\n",
    "<caption><center> <u> **Figure 6** </u>: Each cell gives you 5 boxes. In total, the model predicts: 19x19x5 = 1805 boxes just by looking once at the image (one forward pass through the network)! Different colors denote different classes. <br> </center></caption>\n",
    "\n",
    "In the figure above, we plotted only boxes that the model had assigned a high probability to, but this is still too many boxes. You'd like to filter the algorithm's output down to a much smaller number of detected objects. To do so, you'll use non-max suppression. Specifically, you'll carry out these steps: \n",
    "- Get rid of boxes with a low score (meaning, the box is not very confident about detecting a class)\n",
    "- Select only one box when several boxes overlap with each other and detect the same object.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Filtering with a threshold on class scores\n",
    "\n",
    "You are going to apply a first filter by thresholding. You would like to get rid of any box for which the class \"score\" is less than a chosen threshold. \n",
    "\n",
    "The model gives you a total of 19x19x5x85 numbers, with each box described by 85 numbers. It'll be convenient to rearrange the (19,19,5,85) (or (19,19,425)) dimensional tensor into the following variables:  \n",
    "- `box_confidence`: tensor of shape $(19 \\times 19, 5, 1)$ containing $p_c$ (confidence probability that there's some object) for each of the 5 boxes predicted in each of the 19x19 cells.\n",
    "- `boxes`: tensor of shape $(19 \\times 19, 5, 4)$ containing $(b_x, b_y, b_h, b_w)$ for each of the 5 boxes per cell.\n",
    "- `box_class_probs`: tensor of shape $(19 \\times 19, 5, 80)$ containing the detection probabilities $(c_1, c_2, ... c_{80})$ for each of the 80 classes for each of the 5 boxes per cell.\n",
    "\n",
    "**Exercise**: Implement `yolo_filter_boxes()`.\n",
    "1. Compute box scores by doing the elementwise product as described in Figure 4. The following code may help you choose the right operator: \n",
    "```python\n",
    "a = np.random.randn(19*19, 5, 1)\n",
    "b = np.random.randn(19*19, 5, 80)\n",
    "c = a * b # shape of c will be (19*19, 5, 80)\n",
    "```\n",
    "2. For each box, find:\n",
    "    - the index of the class with the maximum box score ([Hint](https://keras.io/backend/#argmax)) (Be careful with what axis you choose; consider using axis=-1)\n",
    "    - the corresponding box score ([Hint](https://keras.io/backend/#max)) (Be careful with what axis you choose; consider using axis=-1)\n",
    "3. Create a mask by using a threshold. As a reminder: `([0.9, 0.3, 0.4, 0.5, 0.1] < 0.4)` returns: `[False, True, False, False, True]`. The mask should be True for the boxes you want to keep. \n",
    "4. Use TensorFlow to apply the mask to box_class_scores, boxes and box_classes to filter out the boxes we don't want. You should be left with just the subset of boxes you want to keep. ([Hint](https://www.tensorflow.org/api_docs/python/tf/boolean_mask))\n",
    "\n",
    "Reminder: to call a Keras function, you should use `K.function(...)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: yolo_filter_boxes\n",
    "\n",
    "def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):\n",
    "    \"\"\"Filters YOLO boxes by thresholding on object and class confidence.\n",
    "    \n",
    "    Arguments:\n",
    "    box_confidence -- tensor of shape (19, 19, 5, 1)\n",
    "    boxes -- tensor of shape (19, 19, 5, 4)\n",
    "    box_class_probs -- tensor of shape (19, 19, 5, 80)\n",
    "    threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n",
    "    \n",
    "    Returns:\n",
    "    scores -- tensor of shape (None,), containing the class probability score for selected boxes\n",
    "    boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes\n",
    "    classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes\n",
    "    \n",
    "    Note: \"None\" is here because you don't know the exact number of selected boxes, as it depends on the threshold. \n",
    "    For example, the actual output size of scores would be (10,) if there are 10 boxes.\n",
    "    \"\"\"\n",
    "    \n",
    "     # Step 1: Compute box scores\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    box_scores = box_confidence*box_class_probs\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score\n",
    "    ### START CODE HERE ### (≈ 2 lines)\n",
    "    box_classes = K.argmax(box_scores,-1)\n",
    "    box_class_scores = K.max(box_scores,-1)\n",
    "    #print('box_class_scores shape:'+str(box_class_scores.shape))\n",
    "    #print(box_class_scores[0,0,:].eval())\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Step 3: Create a filtering mask based on \"box_class_scores\" by using \"threshold\". The mask should have the\n",
    "    # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    filtering_mask = box_class_scores > threshold\n",
    "    #print(filtering_mask[0,0,:].eval())\n",
    "    #print('filtering_mask shape:'+str(filtering_mask.shape))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Step 4: Apply the mask to scores, boxes and classes\n",
    "    ### START CODE HERE ### (≈ 3 lines)\n",
    "    scores = tf.boolean_mask(box_class_scores,filtering_mask)\n",
    "    boxes = tf.boolean_mask(boxes,filtering_mask)\n",
    "    classes = tf.boolean_mask(box_classes,filtering_mask)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores[2] = 10.7506\n",
      "boxes[2] = [ 8.42653275  3.27136683 -0.5313437  -4.94137383]\n",
      "classes[2] = 7\n",
      "scores.shape = (?,)\n",
      "boxes.shape = (?, 4)\n",
      "classes.shape = (?,)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as test_a:\n",
    "    box_confidence = tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1)\n",
    "    boxes = tf.random_normal([19, 19, 5, 4], mean=1, stddev=4, seed = 1)\n",
    "    box_class_probs = tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1)\n",
    "    scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = 0.5)\n",
    "    print(\"scores[2] = \" + str(scores[2].eval()))\n",
    "    print(\"boxes[2] = \" + str(boxes[2].eval()))\n",
    "    print(\"classes[2] = \" + str(classes[2].eval()))\n",
    "    print(\"scores.shape = \" + str(scores.shape))\n",
    "    print(\"boxes.shape = \" + str(boxes.shape))\n",
    "    print(\"classes.shape = \" + str(classes.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **scores[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           10.7506\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           [ 8.42653275  3.27136683 -0.5313437  -4.94137383]\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           7\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            **scores.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (?,)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (?, 4)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (?,)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 - Non-max suppression ###\n",
    "\n",
    "Even after filtering by thresholding over the classes scores, you still end up a lot of overlapping boxes. A second filter for selecting the right boxes is called non-maximum suppression (NMS). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<img src=\"nb_images/non-max-suppression.png\" style=\"width:500px;height:400;\">\n",
    "<caption><center> <u> **Figure 7** </u>: In this example, the model has predicted 3 cars, but it's actually 3 predictions of the same car. Running non-max suppression (NMS) will select only the most accurate (highest probabiliy) one of the 3 boxes. <br> </center></caption>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Non-max suppression uses the very important function called **\"Intersection over Union\"**, or IoU.\n",
    "<img src=\"nb_images/iou.png\" style=\"width:500px;height:400;\">\n",
    "<caption><center> <u> **Figure 8** </u>: Definition of \"Intersection over Union\". <br> </center></caption>\n",
    "\n",
    "**Exercise**: Implement iou(). Some hints:\n",
    "- In this exercise only, we define a box using its two corners (upper left and lower right): `(x1, y1, x2, y2)` rather than the midpoint and height/width.\n",
    "- To calculate the area of a rectangle you need to multiply its height `(y2 - y1)` by its width `(x2 - x1)`.\n",
    "- You'll also need to find the coordinates `(xi1, yi1, xi2, yi2)` of the intersection of two boxes. Remember that:\n",
    "    - xi1 = maximum of the x1 coordinates of the two boxes\n",
    "    - yi1 = maximum of the y1 coordinates of the two boxes\n",
    "    - xi2 = minimum of the x2 coordinates of the two boxes\n",
    "    - yi2 = minimum of the y2 coordinates of the two boxes\n",
    "- In order to compute the intersection area, you need to make sure the height and width of the intersection are positive, otherwise the intersection area should be zero. Use `max(height, 0)` and `max(width, 0)`.\n",
    "\n",
    "In this code, we use the convention that (0,0) is the top-left corner of an image, (1,0) is the upper-right corner, and (1,1) the lower-right corner. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: iou\n",
    "\n",
    "def iou(box1, box2):\n",
    "    \"\"\"Implement the intersection over union (IoU) between box1 and box2\n",
    "    \n",
    "    Arguments:\n",
    "    box1 -- first box, list object with coordinates (x1, y1, x2, y2)\n",
    "    box2 -- second box, list object with coordinates (x1, y1, x2, y2)\n",
    "    \"\"\"\n",
    "\n",
    "    # Calculate the (y1, x1, y2, x2) coordinates of the intersection of box1 and box2. Calculate its Area.\n",
    "    ### START CODE HERE ### (≈ 5 lines)\n",
    "    xi1 = max(box1[0],box2[0])\n",
    "    yi1 = max(box1[1],box2[1])\n",
    "    xi2 = min(box1[2],box2[2])\n",
    "    yi2 = min(box1[3],box2[3])\n",
    "    inter_area = (yi2-yi1)*(xi2-xi1)\n",
    "    ### END CODE HERE ###    \n",
    "\n",
    "    # Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)\n",
    "    ### START CODE HERE ### (≈ 3 lines)\n",
    "    box1_area = (box1[3]-box1[1])*(box1[2]-box1[0])\n",
    "    box2_area = (box2[3]-box2[1])*(box2[2]-box2[0])\n",
    "    union_area = box1_area+box2_area-inter_area\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # compute the IoU\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    iou = inter_area / union_area\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iou = 0.14285714285714285\n"
     ]
    }
   ],
   "source": [
    "box1 = (2, 1, 4, 3)\n",
    "box2 = (1, 2, 3, 4) \n",
    "print(\"iou = \" + str(iou(box1, box2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **iou = **\n",
    "        </td>\n",
    "        <td>\n",
    "           0.14285714285714285\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are now ready to implement non-max suppression. The key steps are: \n",
    "1. Select the box that has the highest score.\n",
    "2. Compute its overlap with all other boxes, and remove boxes that overlap it more than `iou_threshold`.\n",
    "3. Go back to step 1 and iterate until there's no more boxes with a lower score than the current selected box.\n",
    "\n",
    "This will remove all boxes that have a large overlap with the selected boxes. Only the \"best\" boxes remain.\n",
    "\n",
    "**Exercise**: Implement yolo_non_max_suppression() using TensorFlow. TensorFlow has two built-in functions that are used to implement non-max suppression (so you don't actually need to use your `iou()` implementation):\n",
    "- [tf.image.non_max_suppression()](https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression)\n",
    "- [K.gather()](https://www.tensorflow.org/api_docs/python/tf/gather)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: yolo_non_max_suppression\n",
    "\n",
    "def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):\n",
    "    \"\"\"\n",
    "    Applies Non-max suppression (NMS) to set of boxes\n",
    "    \n",
    "    Arguments:\n",
    "    scores -- tensor of shape (None,), output of yolo_filter_boxes()\n",
    "    boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)\n",
    "    classes -- tensor of shape (None,), output of yolo_filter_boxes()\n",
    "    max_boxes -- integer, maximum number of predicted boxes you'd like\n",
    "    iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n",
    "    \n",
    "    Returns:\n",
    "    scores -- tensor of shape (, None), predicted score for each box\n",
    "    boxes -- tensor of shape (4, None), predicted box coordinates\n",
    "    classes -- tensor of shape (, None), predicted class for each box\n",
    "    \n",
    "    Note: The \"None\" dimension of the output tensors has obviously to be less than max_boxes. Note also that this\n",
    "    function will transpose the shapes of scores, boxes, classes. This is made for convenience.\n",
    "    \"\"\"\n",
    "    \n",
    "    max_boxes_tensor = K.variable(max_boxes, dtype='int32')     # tensor to be used in tf.image.non_max_suppression()\n",
    "    K.get_session().run(tf.variables_initializer([max_boxes_tensor])) # initialize variable max_boxes_tensor\n",
    "    \n",
    "    # Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    nms_indices = tf.image.non_max_suppression(boxes,scores,max_boxes,iou_threshold)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Use K.gather() to select only nms_indices from scores, boxes and classes\n",
    "    ### START CODE HERE ### (≈ 3 lines)\n",
    "    scores = K.gather(scores,nms_indices)\n",
    "    boxes = K.gather(boxes,nms_indices)\n",
    "    classes = K.gather(classes,nms_indices)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores[2] = 6.9384\n",
      "boxes[2] = [-5.299932    3.13798141  4.45036697  0.95942086]\n",
      "classes[2] = -2.24527\n",
      "scores.shape = (10,)\n",
      "boxes.shape = (10, 4)\n",
      "classes.shape = (10,)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as test_b:\n",
    "    scores = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n",
    "    boxes = tf.random_normal([54, 4], mean=1, stddev=4, seed = 1)\n",
    "    classes = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n",
    "    scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes)\n",
    "    print(\"scores[2] = \" + str(scores[2].eval()))\n",
    "    print(\"boxes[2] = \" + str(boxes[2].eval()))\n",
    "    print(\"classes[2] = \" + str(classes[2].eval()))\n",
    "    print(\"scores.shape = \" + str(scores.eval().shape))\n",
    "    print(\"boxes.shape = \" + str(boxes.eval().shape))\n",
    "    print(\"classes.shape = \" + str(classes.eval().shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **scores[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           6.9384\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           [-5.299932    3.13798141  4.45036697  0.95942086]\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           -2.24527\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            **scores.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10, 4)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Wrapping up the filtering\n",
    "\n",
    "It's time to implement a function taking the output of the deep CNN (the 19x19x5x85 dimensional encoding) and filtering through all the boxes using the functions you've just implemented. \n",
    "\n",
    "**Exercise**: Implement `yolo_eval()` which takes the output of the YOLO encoding and filters the boxes using score threshold and NMS. There's just one last implementational detail you have to know. There're a few ways of representing boxes, such as via their corners or via their midpoint and height/width. YOLO converts between a few such formats at different times, using the following functions (which we have provided): \n",
    "\n",
    "```python\n",
    "boxes = yolo_boxes_to_corners(box_xy, box_wh) \n",
    "```\n",
    "which converts the yolo box coordinates (x,y,w,h) to box corners' coordinates (x1, y1, x2, y2) to fit the input of `yolo_filter_boxes`\n",
    "```python\n",
    "boxes = scale_boxes(boxes, image_shape)\n",
    "```\n",
    "YOLO's network was trained to run on 608x608 images. If you are testing this data on a different size image--for example, the car detection dataset had 720x1280 images--this step rescales the boxes so that they can be plotted on top of the original 720x1280 image.  \n",
    "\n",
    "Don't worry about these two functions; we'll show you where they need to be called.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: yolo_eval\n",
    "\n",
    "def yolo_eval(yolo_outputs, image_shape = (720., 1280.), max_boxes=10, score_threshold=.6, iou_threshold=.5):\n",
    "    \"\"\"\n",
    "    Converts the output of YOLO encoding (a lot of boxes) to your predicted boxes along with their scores, box coordinates and classes.\n",
    "    \n",
    "    Arguments:\n",
    "    yolo_outputs -- output of the encoding model (for image_shape of (608, 608, 3)), contains 4 tensors:\n",
    "                    box_confidence: tensor of shape (None, 19, 19, 5, 1)\n",
    "                    box_xy: tensor of shape (None, 19, 19, 5, 2)\n",
    "                    box_wh: tensor of shape (None, 19, 19, 5, 2)\n",
    "                    box_class_probs: tensor of shape (None, 19, 19, 5, 80)\n",
    "    image_shape -- tensor of shape (2,) containing the input shape, in this notebook we use (608., 608.) (has to be float32 dtype)\n",
    "    max_boxes -- integer, maximum number of predicted boxes you'd like\n",
    "    score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n",
    "    iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n",
    "    \n",
    "    Returns:\n",
    "    scores -- tensor of shape (None, ), predicted score for each box\n",
    "    boxes -- tensor of shape (None, 4), predicted box coordinates\n",
    "    classes -- tensor of shape (None,), predicted class for each box\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### \n",
    "    \n",
    "    # Retrieve outputs of the YOLO model (≈1 line)\n",
    "    box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs\n",
    "\n",
    "    # Convert boxes to be ready for filtering functions \n",
    "    boxes = yolo_boxes_to_corners(box_xy, box_wh)\n",
    "\n",
    "    # Use one of the functions you've implemented to perform Score-filtering with a threshold of score_threshold (≈1 line)\n",
    "    scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = score_threshold)\n",
    "    \n",
    "    # Scale boxes back to original image shape.\n",
    "    boxes = scale_boxes(boxes, image_shape)\n",
    "\n",
    "    # Use one of the functions you've implemented to perform Non-max suppression with a threshold of iou_threshold (≈1 line)\n",
    "    scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes , iou_threshold )\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores[2] = 138.791\n",
      "boxes[2] = [ 1292.32971191  -278.52166748  3876.98925781  -835.56494141]\n",
      "classes[2] = 54\n",
      "scores.shape = (10,)\n",
      "boxes.shape = (10, 4)\n",
      "classes.shape = (10,)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as test_b:\n",
    "    yolo_outputs = (tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1),\n",
    "                    tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n",
    "                    tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n",
    "                    tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1))\n",
    "    scores, boxes, classes = yolo_eval(yolo_outputs)\n",
    "    print(\"scores[2] = \" + str(scores[2].eval()))\n",
    "    print(\"boxes[2] = \" + str(boxes[2].eval()))\n",
    "    print(\"classes[2] = \" + str(classes[2].eval()))\n",
    "    print(\"scores.shape = \" + str(scores.eval().shape))\n",
    "    print(\"boxes.shape = \" + str(boxes.eval().shape))\n",
    "    print(\"classes.shape = \" + str(classes.eval().shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **scores[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           138.791\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           [ 1292.32971191  -278.52166748  3876.98925781  -835.56494141]\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           54\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            **scores.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10, 4)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**Summary for YOLO**:\n",
    "- Input image (608, 608, 3)\n",
    "- The input image goes through a CNN, resulting in a (19,19,5,85) dimensional output. \n",
    "- After flattening the last two dimensions, the output is a volume of shape (19, 19, 425):\n",
    "    - Each cell in a 19x19 grid over the input image gives 425 numbers. \n",
    "    - 425 = 5 x 85 because each cell contains predictions for 5 boxes, corresponding to 5 anchor boxes, as seen in lecture. \n",
    "    - 85 = 5 + 80 where 5 is because $(p_c, b_x, b_y, b_h, b_w)$ has 5 numbers, and and 80 is the number of classes we'd like to detect\n",
    "- You then select only few boxes based on:\n",
    "    - Score-thresholding: throw away boxes that have detected a class with a score less than the threshold\n",
    "    - Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes\n",
    "- This gives you YOLO's final output. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Test YOLO pretrained model on images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, you are going to use a pretrained model and test it on the car detection dataset. As usual, you start by **creating a session to start your graph**. Run the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = K.get_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 - Defining classes, anchors and image shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that we are trying to detect 80 classes, and are using 5 anchor boxes. We have gathered the information about the 80 classes and 5 boxes in two files \"coco_classes.txt\" and \"yolo_anchors.txt\". Let's load these quantities into the model by running the next cell. \n",
    "\n",
    "The car detection dataset has 720x1280 images, which we've pre-processed into 608x608 images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_names = read_classes(\"model_data/coco_classes.txt\")\n",
    "anchors = read_anchors(\"model_data/yolo_anchors.txt\")\n",
    "image_shape = (720., 1280.)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Loading a pretrained model\n",
    "\n",
    "Training a YOLO model takes a very long time and requires a fairly large dataset of labelled bounding boxes for a large range of target classes. You are going to load an existing pretrained Keras YOLO model stored in \"yolo.h5\". (These weights come from the official YOLO website, and were converted using a function written by Allan Zelener. References are at the end of this notebook. Technically, these are the parameters from the \"YOLOv2\" model, but we will more simply refer to it as \"YOLO\" in this notebook.) Run the cell below to load the model from this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/keras/models.py:251: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "yolo_model = load_model(\"model_data/yolo.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This loads the weights of a trained YOLO model. Here's a summary of the layers your model contains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_1 (InputLayer)             (None, 608, 608, 3)   0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)                (None, 608, 608, 32)  864         input_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNorm (None, 608, 608, 32)  128         conv2d_1[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)        (None, 608, 608, 32)  0           batch_normalization_1[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)   (None, 304, 304, 32)  0           leaky_re_lu_1[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)                (None, 304, 304, 64)  18432       max_pooling2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNorm (None, 304, 304, 64)  256         conv2d_2[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)        (None, 304, 304, 64)  0           batch_normalization_2[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)   (None, 152, 152, 64)  0           leaky_re_lu_2[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)                (None, 152, 152, 128) 73728       max_pooling2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNorm (None, 152, 152, 128) 512         conv2d_3[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)        (None, 152, 152, 128) 0           batch_normalization_3[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)                (None, 152, 152, 64)  8192        leaky_re_lu_3[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNorm (None, 152, 152, 64)  256         conv2d_4[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)        (None, 152, 152, 64)  0           batch_normalization_4[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)                (None, 152, 152, 128) 73728       leaky_re_lu_4[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNorm (None, 152, 152, 128) 512         conv2d_5[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)        (None, 152, 152, 128) 0           batch_normalization_5[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)   (None, 76, 76, 128)   0           leaky_re_lu_5[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)                (None, 76, 76, 256)   294912      max_pooling2d_3[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNorm (None, 76, 76, 256)   1024        conv2d_6[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)        (None, 76, 76, 256)   0           batch_normalization_6[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)                (None, 76, 76, 128)   32768       leaky_re_lu_6[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNorm (None, 76, 76, 128)   512         conv2d_7[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)        (None, 76, 76, 128)   0           batch_normalization_7[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)                (None, 76, 76, 256)   294912      leaky_re_lu_7[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNorm (None, 76, 76, 256)   1024        conv2d_8[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)        (None, 76, 76, 256)   0           batch_normalization_8[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)   (None, 38, 38, 256)   0           leaky_re_lu_8[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)                (None, 38, 38, 512)   1179648     max_pooling2d_4[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNorm (None, 38, 38, 512)   2048        conv2d_9[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)        (None, 38, 38, 512)   0           batch_normalization_9[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)               (None, 38, 38, 256)   131072      leaky_re_lu_9[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNor (None, 38, 38, 256)   1024        conv2d_10[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)       (None, 38, 38, 256)   0           batch_normalization_10[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)               (None, 38, 38, 512)   1179648     leaky_re_lu_10[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNor (None, 38, 38, 512)   2048        conv2d_11[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)       (None, 38, 38, 512)   0           batch_normalization_11[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)               (None, 38, 38, 256)   131072      leaky_re_lu_11[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNor (None, 38, 38, 256)   1024        conv2d_12[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_12 (LeakyReLU)       (None, 38, 38, 256)   0           batch_normalization_12[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)               (None, 38, 38, 512)   1179648     leaky_re_lu_12[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNor (None, 38, 38, 512)   2048        conv2d_13[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_13 (LeakyReLU)       (None, 38, 38, 512)   0           batch_normalization_13[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)   (None, 19, 19, 512)   0           leaky_re_lu_13[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)               (None, 19, 19, 1024)  4718592     max_pooling2d_5[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNor (None, 19, 19, 1024)  4096        conv2d_14[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_14 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_14[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)               (None, 19, 19, 512)   524288      leaky_re_lu_14[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNor (None, 19, 19, 512)   2048        conv2d_15[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_15 (LeakyReLU)       (None, 19, 19, 512)   0           batch_normalization_15[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)               (None, 19, 19, 1024)  4718592     leaky_re_lu_15[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNor (None, 19, 19, 1024)  4096        conv2d_16[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_16 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_16[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)               (None, 19, 19, 512)   524288      leaky_re_lu_16[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNor (None, 19, 19, 512)   2048        conv2d_17[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_17 (LeakyReLU)       (None, 19, 19, 512)   0           batch_normalization_17[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)               (None, 19, 19, 1024)  4718592     leaky_re_lu_17[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNor (None, 19, 19, 1024)  4096        conv2d_18[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_18 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_18[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)               (None, 19, 19, 1024)  9437184     leaky_re_lu_18[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNor (None, 19, 19, 1024)  4096        conv2d_19[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)               (None, 38, 38, 64)    32768       leaky_re_lu_13[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_19 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_19[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNor (None, 38, 38, 64)    256         conv2d_21[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)               (None, 19, 19, 1024)  9437184     leaky_re_lu_19[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_21 (LeakyReLU)       (None, 38, 38, 64)    0           batch_normalization_21[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNor (None, 19, 19, 1024)  4096        conv2d_20[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "space_to_depth_x2 (Lambda)       (None, 19, 19, 256)   0           leaky_re_lu_21[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_20 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_20[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)      (None, 19, 19, 1280)  0           space_to_depth_x2[0][0]          \n",
      "                                                                   leaky_re_lu_20[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)               (None, 19, 19, 1024)  11796480    concatenate_1[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNor (None, 19, 19, 1024)  4096        conv2d_22[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leaky_re_lu_22 (LeakyReLU)       (None, 19, 19, 1024)  0           batch_normalization_22[0][0]     \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)               (None, 19, 19, 425)   435625      leaky_re_lu_22[0][0]             \n",
      "====================================================================================================\n",
      "Total params: 50,983,561\n",
      "Trainable params: 50,962,889\n",
      "Non-trainable params: 20,672\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "yolo_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: On some computers, you may see a warning message from Keras. Don't worry about it if you do--it is fine.\n",
    "\n",
    "**Reminder**: this model converts a preprocessed batch of input images (shape: (m, 608, 608, 3)) into a tensor of shape (m, 19, 19, 5, 85) as explained in Figure (2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 - Convert output of the model to usable bounding box tensors\n",
    "\n",
    "The output of `yolo_model` is a (m, 19, 19, 5, 85) tensor that needs to pass through non-trivial processing and conversion. The following cell does that for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You added `yolo_outputs` to your graph. This set of 4 tensors is ready to be used as input by your `yolo_eval` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 - Filtering boxes\n",
    "\n",
    "`yolo_outputs` gave you all the predicted boxes of `yolo_model` in the correct format. You're now ready to perform filtering and select only the best boxes. Lets now call `yolo_eval`, which you had previously implemented, to do this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores, boxes, classes = yolo_eval(yolo_outputs, image_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 - Run the graph on an image\n",
    "\n",
    "Let the fun begin. You have created a (`sess`) graph that can be summarized as follows:\n",
    "\n",
    "1. <font color='purple'> yolo_model.input </font> is given to `yolo_model`. The model is used to compute the output <font color='purple'> yolo_model.output </font>\n",
    "2. <font color='purple'> yolo_model.output </font> is processed by `yolo_head`. It gives you <font color='purple'> yolo_outputs </font>\n",
    "3. <font color='purple'> yolo_outputs </font> goes through a filtering function, `yolo_eval`. It outputs your predictions: <font color='purple'> scores, boxes, classes </font>\n",
    "\n",
    "**Exercise**: Implement predict() which runs the graph to test YOLO on an image.\n",
    "You will need to run a TensorFlow session, to have it compute `scores, boxes, classes`.\n",
    "\n",
    "The code below also uses the following function:\n",
    "```python\n",
    "image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n",
    "```\n",
    "which outputs:\n",
    "- image: a python (PIL) representation of your image used for drawing boxes. You won't need to use it.\n",
    "- image_data: a numpy-array representing the image. This will be the input to the CNN.\n",
    "\n",
    "**Important note**: when a model uses BatchNorm (as is the case in YOLO), you will need to pass an additional placeholder in the feed_dict {K.learning_phase(): 0}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(sess, image_file):\n",
    "    \"\"\"\n",
    "    Runs the graph stored in \"sess\" to predict boxes for \"image_file\". Prints and plots the preditions.\n",
    "    \n",
    "    Arguments:\n",
    "    sess -- your tensorflow/Keras session containing the YOLO graph\n",
    "    image_file -- name of an image stored in the \"images\" folder.\n",
    "    \n",
    "    Returns:\n",
    "    out_scores -- tensor of shape (None, ), scores of the predicted boxes\n",
    "    out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes\n",
    "    out_classes -- tensor of shape (None, ), class index of the predicted boxes\n",
    "    \n",
    "    Note: \"None\" actually represents the number of predicted boxes, it varies between 0 and max_boxes. \n",
    "    \"\"\"\n",
    "\n",
    "    # Preprocess your image\n",
    "    image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n",
    "\n",
    "    # Run the session with the correct tensors and choose the correct placeholders in the feed_dict.\n",
    "    # You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes],feed_dict={yolo_model.input:image_data,K.learning_phase(): 0})\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print predictions info\n",
    "    print('Found {} boxes for {}'.format(len(out_boxes), image_file))\n",
    "    # Generate colors for drawing bounding boxes.\n",
    "    colors = generate_colors(class_names)\n",
    "    # Draw bounding boxes on the image file\n",
    "    draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)\n",
    "    # Save the predicted bounding box on the image\n",
    "    image.save(os.path.join(\"out\", image_file), quality=90)\n",
    "    # Display the results in the notebook\n",
    "    output_image = scipy.misc.imread(os.path.join(\"out\", image_file))\n",
    "    imshow(output_image)\n",
    "    \n",
    "    return out_scores, out_boxes, out_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell on the \"test.jpg\" image to verify that your function is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1 boxes for 0015.jpg\n",
      "truck 0.72 (713, 275) (1086, 386)\n"
     ]
    },
    {
     "data": {
      "image/png": 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zMAxjjdwi8H0/uAatW6mOomMQVhc/XZVHL9oZMqozNu1g8dVpoG3jodbELwKp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tZFgXHfRhO+myO3ziSUfdKaCN+XEpJJkiBO2ucUgqEsrDPo9/vWmfXs/Q5uHPo1FkbZIrFr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b5764a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_scores, out_boxes, out_classes = predict(sess, \"0015.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **Found 7 boxes for test.jpg**\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.60 (925, 285) (1045, 374)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.66 (706, 279) (786, 350)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **bus**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.67 (5, 266) (220, 407)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.70 (947, 324) (1280, 705)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.74 (159, 303) (346, 440)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.80 (761, 282) (942, 412)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.89 (367, 300) (745, 648)\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model you've just run is actually able to detect 80 different classes listed in \"coco_classes.txt\". To test the model on your own images:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Write your image's name in the cell above code\n",
    "    4. Run the code and see the output of the algorithm!\n",
    "\n",
    "If you were to run your session in a for loop over all your images. Here's what you would get:\n",
    "\n",
    "<center>\n",
    "<video width=\"400\" height=\"200\" src=\"nb_images/pred_video_compressed2.mp4\" type=\"video/mp4\" controls>\n",
    "</video>\n",
    "</center>\n",
    "\n",
    "<caption><center> Predictions of the YOLO model on pictures taken from a camera while driving around the Silicon Valley <br> Thanks [drive.ai](https://www.drive.ai/) for providing this dataset! </center></caption>"
   ]
  },
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   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- YOLO is a state-of-the-art object detection model that is fast and accurate\n",
    "- It runs an input image through a CNN which outputs a 19x19x5x85 dimensional volume. \n",
    "- The encoding can be seen as a grid where each of the 19x19 cells contains information about 5 boxes.\n",
    "- You filter through all the boxes using non-max suppression. Specifically: \n",
    "    - Score thresholding on the probability of detecting a class to keep only accurate (high probability) boxes\n",
    "    - Intersection over Union (IoU) thresholding to eliminate overlapping boxes\n",
    "- Because training a YOLO model from randomly initialized weights is non-trivial and requires a large dataset as well as lot of computation, we used previously trained model parameters in this exercise. If you wish, you can also try fine-tuning the YOLO model with your own dataset, though this would be a fairly non-trivial exercise. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**: The ideas presented in this notebook came primarily from the two YOLO papers. The implementation here also took significant inspiration and used many components from Allan Zelener's github repository. The pretrained weights used in this exercise came from the official YOLO website. \n",
    "- Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi - [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640) (2015)\n",
    "- Joseph Redmon, Ali Farhadi - [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242) (2016)\n",
    "- Allan Zelener - [YAD2K: Yet Another Darknet 2 Keras](https://github.com/allanzelener/YAD2K)\n",
    "- The official YOLO website (https://pjreddie.com/darknet/yolo/) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Car detection dataset**:\n",
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br /><span xmlns:dct=\"http://purl.org/dc/terms/\" property=\"dct:title\">The Drive.ai Sample Dataset</span> (provided by drive.ai) is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>. We are especially grateful to Brody Huval, Chih Hu and Rahul Patel for collecting and providing this dataset. "
   ]
  }
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