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": 1,
   "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": 2,
   "metadata": {
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   "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",
    "                   -- confidence Pc of object for each of 5 boxes in each of the 19x19 cells.\n",
    "                   \n",
    "    boxes -- tensor of shape (19, 19, 5, 4)\n",
    "          --  containing  (bx,by,bh,bw)(bx,by,bh,bw)  for each of the 5 boxes per cell.\n",
    "          \n",
    "    box_class_probs -- tensor of shape (19, 19, 5, 80)\n",
    "                    -- detection Probabilities (c1,c2,...c80) for each of80 classes for each of 5 boxes per cell\n",
    "                    \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",
    "    \n",
    "    # Step 1: Compute box scores\n",
    "    # Element wise Multiply box_confidence with box_class_prob -> ie \n",
    "    box_scores = box_confidence * box_class_probs # Pc * C\n",
    "    # Box Scores is bow of Shape 19 X 19 X 5 X (Classes = 80)\n",
    "    \n",
    "    print(\"Scores Shape = \" + str(box_scores.shape))\n",
    "    \n",
    "    # Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score\n",
    "    # Find Index of Class with Max Score for Each Box ie the index of Max Value in the last Dimension\n",
    "    # use K.argmax -> Gives index of Max Value across a given Axis , for axis = -1 -> last dimension\n",
    "    box_classes = K.argmax(box_scores, axis=-1) # 19x19x5x1 (1 class idx)\n",
    "    \n",
    "    ## box_classes stores the Index ie which class\n",
    "    print(\"Classes Shape = \"  +str(box_classes.shape))\n",
    "\n",
    "    # Also need Max Values, Keras.backend.max(tensor, axis) -> Gives max Value across a Axis\n",
    "    box_class_scores = K.max(box_scores, axis=-1) # 19x19x5x1 (1 class score)\n",
    "    \n",
    "    print(\"Class Scores Shape = \" + str(box_class_scores.shape))\n",
    "    \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",
    "    filtering_mask = box_class_scores >= threshold\n",
    "    \n",
    "    print(\"Filter shape = \" +  str(filtering_mask.shape))\n",
    "    \n",
    "    # Step 4: Apply the mask to scores, boxes and classes\n",
    "    # Apply a the Mask on box_class_scores , only keep Boxes with Probability >= Threshold\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",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores Shape = (19, 19, 5, 80)\n",
      "Classes Shape = (19, 19, 5)\n",
      "Class Scores Shape = (19, 19, 5)\n",
      "Filter shape = (19, 19, 5)\n",
      "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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 4,
   "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",
    "    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 = max(xi2 - xi1, 0) * max(yi2 - yi1, 0)\n",
    "\n",
    "    # Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)\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",
    "    \n",
    "    # compute the IoU\n",
    "    return inter_area / union_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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",
    "    nms_indices = tf.image.non_max_suppression(boxes, scores, max_boxes, iou_threshold=0.5)\n",
    "    \n",
    "    # Use K.gather() to select only nms_indices from scores, boxes and classes\n",
    "    scores = K.gather(scores, nms_indices)\n",
    "    boxes = K.gather(boxes, nms_indices)\n",
    "    classes = K.gather(classes, nms_indices)\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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\n",
    "    # remove boxes with scores less than threshold\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=max_boxes, iou_threshold=iou_threshold)\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores Shape = (19, 19, 5, 80)\n",
      "Classes Shape = (19, 19, 5)\n",
      "Class Scores Shape = (19, 19, 5)\n",
      "Filter shape = (19, 19, 5)\n",
      "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": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<tf.Tensor 'Sigmoid:0' shape=(?, ?, ?, 5, 1) dtype=float32>, <tf.Tensor 'truediv_2:0' shape=(?, ?, ?, 5, 2) dtype=float32>, <tf.Tensor 'truediv_3:0' shape=(?, ?, ?, 5, 2) dtype=float32>, <tf.Tensor 'Reshape_7:0' shape=(?, ?, ?, 5, 80) dtype=float32>)\n"
     ]
    }
   ],
   "source": [
    "yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))\n",
    "print(yolo_outputs)"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores Shape = (?, ?, ?, 5, 80)\n",
      "Classes Shape = (?, ?, ?, 5)\n",
      "Class Scores Shape = (?, ?, ?, 5)\n",
      "Filter shape = (?, ?, ?, 5)\n"
     ]
    }
   ],
   "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": 16,
   "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 Size needs to be 608 X 608 as the model and weights we loaded was trained on this size\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",
    "    out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes], feed_dict={\n",
    "        yolo_model.input: image_data, K.learning_phase():0\n",
    "    })\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": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 7 boxes for test.jpg\n",
      "car 0.60 (925, 285) (1045, 374)\n",
      "car 0.66 (706, 279) (786, 350)\n",
      "bus 0.67 (5, 266) (220, 407)\n",
      "car 0.70 (947, 324) (1280, 705)\n",
      "car 0.74 (159, 303) (346, 440)\n",
      "car 0.80 (761, 282) (942, 412)\n",
      "car 0.89 (367, 300) (745, 648)\n"
     ]
    },
    {
     "data": {
      "image/png": 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z1VZ6T04QX5jhY88/RSJ+ndhImIzUGBNJKhZSNLVu5MJb19i+62E6airJqICh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8tFQv4M9U6FTHKEymGDTslE90WoVI0HM69h4TZdzp0YZbG4S5IEEW2f+3/859ysl7m7uMSx/nEi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FhD0SRVFnV00BOxGG2MUC733vf+JDH/0Y41t28vgTD3HuDRcxfXiKsRhyqSRxO5geYHJ+iZx2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JPAHcA/f6PvqjyLGqecoUvC1JuXUqIzMDZhkk9oN9rMFxFpZ5t0DHt0wk+/fI3U79NWhr/8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\ndap1RpNBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f810296ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_scores, out_boxes, out_classes = predict(sess, \"test.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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