codes/course4/Autonomous driving application - Car detection - v1.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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aduan\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:493: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\Aduan\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:494: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\Aduan\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:495: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\Aduan\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:496: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\Aduan\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:497: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\Aduan\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\dtypes.py:502: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\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": 32,
   "metadata": {},
   "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#(19, 19, 5, 80)\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)#(19, 19, 5)\n",
    "    box_class_scores = K.max(box_scores, axis=-1)#(19, 19, 5)\n",
    "    #print(str(box_class_scores.eval()))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Step 3: Create a filtering mask based on \"box_class_scores\" by using \"threshold\". The mask should have the\n",
    "    # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    filtering_mask = (box_class_scores >= threshold)#(19, 19, 5)\n",
    "    #print(str(filtering_mask.eval()))\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",
    "    #print(str(scores.eval()))\n",
    "    #print(str(boxes.eval()))\n",
    "    #print(str(classes.eval()))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores[2] = 10.750582\n",
      "boxes[2] = [ 8.426533   3.2713668 -0.5313436 -4.9413733]\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",
    "    <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",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (?,)\n",
    "        </td>\n",
    "    </tr>\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": 34,
   "metadata": {},
   "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 = np.maximum(box1[0], box2[0])\n",
    "    yi1 = np.maximum(box1[1], box2[1])\n",
    "    xi2 = np.minimum(box1[2], box2[2])\n",
    "    yi2 = np.minimum(box1[3], box2[3])\n",
    "    inter_area = (xi1-xi2)*(yi1-yi2)\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": 35,
   "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": 36,
   "metadata": {},
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores[2] = 6.938395\n",
      "boxes[2] = [-5.299932    3.1379814   4.450367    0.95942086]\n",
      "classes[2] = -2.2452729\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",
    "    <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",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\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": 38,
   "metadata": {},
   "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, score_threshold)\n",
    "    \n",
    "    # Scale boxes back to original image shape.\n",
    "    boxes = scale_boxes(boxes, image_shape)\n",
    "\n",
    "    # Use one of the functions you've implemented to perform Non-max suppression with a threshold of iou_threshold (≈1 line)\n",
    "    scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes, iou_threshold)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores[2] = 138.79124\n",
      "boxes[2] = [1292.3297  -278.52167 3876.9893  -835.56494]\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",
    "    <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",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\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": 40,
   "metadata": {},
   "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": 41,
   "metadata": {},
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aduan\\anaconda3\\envs\\python36\\lib\\site-packages\\keras\\models.py:255: 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": 43,
   "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 (BatchNor (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 (BatchNor (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 (BatchNor (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 (BatchNor (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 (BatchNor (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 (BatchNor (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 (BatchNor (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 (BatchNor (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 (BatchNor (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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 (BatchNo (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": 44,
   "metadata": {},
   "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": 45,
   "metadata": {},
   "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": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(sess, image_file):\n",
    "    \"\"\"\n",
    "    Runs the graph stored in \"sess\" to predict boxes for \"image_file\". Prints and plots the preditions.\n",
    "    \n",
    "    Arguments:\n",
    "    sess -- your tensorflow/Keras session containing the YOLO graph\n",
    "    image_file -- name of an image stored in the \"images\" folder.\n",
    "    \n",
    "    Returns:\n",
    "    out_scores -- tensor of shape (None, ), scores of the predicted boxes\n",
    "    out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes\n",
    "    out_classes -- tensor of shape (None, ), class index of the predicted boxes\n",
    "    \n",
    "    Note: \"None\" actually represents the number of predicted boxes, it varies between 0 and max_boxes. \n",
    "    \"\"\"\n",
    "\n",
    "    # Preprocess your image\n",
    "    image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n",
    "\n",
    "    # Run the session with the correct tensors and choose the correct placeholders in the feed_dict.\n",
    "    # You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes], feed_dict = {yolo_model.input:image_data, K.learning_phase(): 0})\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print predictions info\n",
    "    print('Found {} boxes for {}'.format(len(out_boxes), image_file))\n",
    "    # Generate colors for drawing bounding boxes.\n",
    "    colors = generate_colors(class_names)\n",
    "    # Draw bounding boxes on the image file\n",
    "    draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)\n",
    "    # Save the predicted bounding box on the image\n",
    "    image.save(os.path.join(\"out\", image_file), quality=90)\n",
    "    # Display the results in the notebook\n",
    "    output_image = scipy.misc.imread(os.path.join(\"out\", image_file))\n",
    "    imshow(output_image)\n",
    "    \n",
    "    return out_scores, out_boxes, out_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell on the \"test.jpg\" image to verify that your function is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aduan\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipykernel_launcher.py:35: DeprecationWarning:     `imread` is deprecated!\n",
      "    `imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "    Use ``imageio.imread`` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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>"
   ]
  },
  {
   "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. "
   ]
  }
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