notebook/Autonomous driving application Car detection.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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "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": 3,
   "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,axis = -1)\n",
    "    box_class_scores = K.max(box_scores,axis = -1,keepdims=False)\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",
    "    ### 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": 4,
   "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",
    "    \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": 5,
   "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 = (xi2-xi1) * (yi2-yi1) \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[2] -box1[0]) * (box1[3] -box1[1])\n",
    "    box2_area = (box2[2] -box2[0]) * (box2[3] -box2[1])\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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "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 = 10, iou_threshold = iou_threshold)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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(yolo_eval(yolo_outputs, image_shape),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": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10 boxes for car.jpg\n",
      "car 0.64 (1014, 214) (1024, 252)\n",
      "car 0.66 (507, 247) (563, 314)\n",
      "car 0.66 (1064, 413) (1024, 519)\n",
      "car 0.69 (688, 284) (781, 374)\n",
      "car 0.69 (193, 458) (321, 576)\n",
      "car 0.71 (574, 339) (654, 453)\n",
      "car 0.72 (886, 284) (991, 353)\n",
      "car 0.75 (433, 406) (528, 548)\n",
      "car 0.75 (362, 292) (431, 370)\n",
      "car 0.76 (618, 514) (755, 576)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAADfCAYAAAD1GGg+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmY4Q+v8B4117VisC2sMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9d363c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_scores, out_boxes, out_classes = predict(sess, \"car.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>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "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. "
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "convolutional-neural-networks",
   "graded_item_id": "OMdut",
   "launcher_item_id": "bbBOL"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}