exercises/1_4_Feature_Vectors/3_2. HOG Examples.ipynb

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    "# Examples\n",
    "\n",
    "Below you will find various examples for you to experiment with HOG. For each image, you can modify the `cell_size`, `num_cells_per_block`, and `num_bins` (the number of angular bins in your histograms), to see how those parameters affect the resulting HOG descriptor. These examples, will help you get some intuition for what each parameter does and how they can be *tuned* to pick out the amount of detail required. Below is a list of the available images that you can load:\n",
    "\n",
    "* cat.jpeg\n",
    "* jeep1.jpeg\n",
    "* jeep2.jpeg\n",
    "* jeep3.jpeg\n",
    "* man.jpeg\n",
    "* pedestrian_bike.jpeg\n",
    "* roundabout.jpeg\n",
    "* scrabble.jpeg\n",
    "* shuttle.jpeg\n",
    "* triangle_tile.jpeg\n",
    "* watch.jpeg\n",
    "* woman.jpeg\n",
    "\n",
    "\n",
    "**NOTE**: If you are running this notebook in the Udacity workspace, there is around a 2 second lag in the interactive plot. This means that if you click in the image to zoom in, it will take about 2 seconds for the plot to refresh. "
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      "\n",
      "The gray scale image has shape:  (424, 640)\n",
      "\n",
      "HOG Descriptor Parameters:\n",
      "\n",
      "Window Size: (600, 400)\n",
      "Cell Size: (50, 50)\n",
      "Block Size: (100, 100)\n",
      "Block Stride: (50, 50)\n",
      "Number of Bins: 9\n",
      "\n"
     ]
    },
    {
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       "\n",
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       "    this.ws.send(JSON.stringify(properties));\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
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       "    fig.ondownload(fig, format);\n",
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       "\n",
       "\n",
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       "    var size = msg['size'];\n",
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       "        fig._resize_canvas(size[0], size[1]);\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
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       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
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       "    case 0:\n",
       "        cursor = 'pointer';\n",
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       "        break;\n",
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       "\n",
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       "    fig.message.textContent = msg['message'];\n",
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       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
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       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
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       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
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       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
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       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"980\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import cv2\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# Set the default figure size\n",
    "plt.rcParams['figure.figsize'] = [9.8, 9]\n",
    "\n",
    "\n",
    "# -------------------------- Select the Image and Specify the parameters for our HOG descriptor --------------------------\n",
    "\n",
    "# Load the image \n",
    "image = cv2.imread('./images/jeep2.jpeg')\n",
    "\n",
    "# Cell Size in pixels (width, height). Must be smaller than the size of the detection window\n",
    "# and must be chosen so that the resulting Block Size is smaller than the detection window.\n",
    "cell_size = (50, 50)\n",
    "\n",
    "# Number of cells per block in each direction (x, y). Must be chosen so that the resulting\n",
    "# Block Size is smaller than the detection window\n",
    "num_cells_per_block = (2, 2)\n",
    "\n",
    "# Number of gradient orientation bins\n",
    "num_bins = 9\n",
    "\n",
    "# -------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "# Convert the original image to RGB\n",
    "original_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Convert the original image to gray scale\n",
    "gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "\n",
    "# Block Size in pixels (width, height). Must be an integer multiple of Cell Size.\n",
    "# The Block Size must be smaller than the detection window\n",
    "block_size = (num_cells_per_block[0] * cell_size[0],\n",
    "              num_cells_per_block[1] * cell_size[1])\n",
    "\n",
    "# Calculate the number of cells that fit in our image in the x and y directions\n",
    "x_cells = gray_image.shape[1] // cell_size[0]\n",
    "y_cells = gray_image.shape[0] // cell_size[1]\n",
    "\n",
    "# Horizontal distance between blocks in units of Cell Size. Must be an integer and it must\n",
    "# be set such that (x_cells - num_cells_per_block[0]) / h_stride = integer.\n",
    "h_stride = 1\n",
    "\n",
    "# Vertical distance between blocks in units of Cell Size. Must be an integer and it must\n",
    "# be set such that (y_cells - num_cells_per_block[1]) / v_stride = integer.\n",
    "v_stride = 1\n",
    "\n",
    "# Block Stride in pixels (horizantal, vertical). Must be an integer multiple of Cell Size\n",
    "block_stride = (cell_size[0] * h_stride, cell_size[1] * v_stride)\n",
    "\n",
    "      \n",
    "# Specify the size of the detection window (Region of Interest) in pixels (width, height).\n",
    "# It must be an integer multiple of Cell Size and it must cover the entire image. Because\n",
    "# the detection window must be an integer multiple of cell size, depending on the size of\n",
    "# your cells, the resulting detection window might be slightly smaller than the image.\n",
    "# This is perfectly ok.\n",
    "win_size = (x_cells * cell_size[0] , y_cells * cell_size[1])\n",
    "\n",
    "# Print the shape of the gray scale image for reference\n",
    "print('\\nThe gray scale image has shape: ', gray_image.shape)\n",
    "print()\n",
    "\n",
    "# Print the parameters of our HOG descriptor\n",
    "print('HOG Descriptor Parameters:\\n')\n",
    "print('Window Size:', win_size)\n",
    "print('Cell Size:', cell_size)\n",
    "print('Block Size:', block_size)\n",
    "print('Block Stride:', block_stride)\n",
    "print('Number of Bins:', num_bins)\n",
    "print()\n",
    "\n",
    "# Set the parameters of the HOG descriptor using the variables defined above\n",
    "hog = cv2.HOGDescriptor(win_size, block_size, block_stride, cell_size, num_bins)\n",
    "\n",
    "# Compute the HOG Descriptor for the gray scale image\n",
    "hog_descriptor = hog.compute(gray_image)\n",
    "\n",
    "# Calculate the total number of blocks along the width of the detection window\n",
    "tot_bx = np.uint32(((x_cells - num_cells_per_block[0]) / h_stride) + 1)\n",
    "\n",
    "# Calculate the total number of blocks along the height of the detection window\n",
    "tot_by = np.uint32(((y_cells - num_cells_per_block[1]) / v_stride) + 1)\n",
    "\n",
    "# Calculate the total number of elements in the feature vector\n",
    "tot_els = (tot_bx) * (tot_by) * num_cells_per_block[0] * num_cells_per_block[1] * num_bins\n",
    "\n",
    "\n",
    "# Reshape the feature vector to [blocks_y, blocks_x, num_cells_per_block_x, num_cells_per_block_y, num_bins].\n",
    "# The blocks_x and blocks_y will be transposed so that the first index (blocks_y) referes to the row number\n",
    "# and the second index to the column number. This will be useful later when we plot the feature vector, so\n",
    "# that the feature vector indexing matches the image indexing.\n",
    "hog_descriptor_reshaped = hog_descriptor.reshape(tot_bx,\n",
    "                                                 tot_by,\n",
    "                                                 num_cells_per_block[0],\n",
    "                                                 num_cells_per_block[1],\n",
    "                                                 num_bins).transpose((1, 0, 2, 3, 4))\n",
    "\n",
    "# Create an array that will hold the average gradients for each cell\n",
    "ave_grad = np.zeros((y_cells, x_cells, num_bins))\n",
    "\n",
    "# Create an array that will count the number of histograms per cell\n",
    "hist_counter = np.zeros((y_cells, x_cells, 1))\n",
    "\n",
    "# Add up all the histograms for each cell and count the number of histograms per cell\n",
    "for i in range (num_cells_per_block[0]):\n",
    "    for j in range(num_cells_per_block[1]):\n",
    "        ave_grad[i:tot_by + i,\n",
    "                 j:tot_bx + j] += hog_descriptor_reshaped[:, :, i, j, :]\n",
    "        \n",
    "        hist_counter[i:tot_by + i,\n",
    "                     j:tot_bx + j] += 1\n",
    "\n",
    "# Calculate the average gradient for each cell\n",
    "ave_grad /= hist_counter\n",
    "   \n",
    "# Calculate the total number of vectors we have in all the cells.\n",
    "len_vecs = ave_grad.shape[0] * ave_grad.shape[1] * ave_grad.shape[2]\n",
    "\n",
    "# Create an array that has num_bins equally spaced between 0 and 180 degress in radians.\n",
    "deg = np.linspace(0, np.pi, num_bins, endpoint = False)\n",
    "\n",
    "# Each cell will have a histogram with num_bins. For each cell, plot each bin as a vector (with its magnitude\n",
    "# equal to the height of the bin in the histogram, and its angle corresponding to the bin in the histogram). \n",
    "# To do this, create rank 1 arrays that will hold the (x,y)-coordinate of all the vectors in all the cells in the\n",
    "# image. Also, create the rank 1 arrays that will hold all the (U,V)-components of all the vectors in all the\n",
    "# cells in the image. Create the arrays that will hold all the vector positons and components.\n",
    "U = np.zeros((len_vecs))\n",
    "V = np.zeros((len_vecs))\n",
    "X = np.zeros((len_vecs))\n",
    "Y = np.zeros((len_vecs))\n",
    "\n",
    "# Set the counter to zero\n",
    "counter = 0\n",
    "\n",
    "# Use the cosine and sine functions to calculate the vector components (U,V) from their maginitudes. Remember the \n",
    "# cosine and sine functions take angles in radians. Calculate the vector positions and magnitudes from the\n",
    "# average gradient array\n",
    "for i in range(ave_grad.shape[0]):\n",
    "    for j in range(ave_grad.shape[1]):\n",
    "        for k in range(ave_grad.shape[2]):\n",
    "            U[counter] = ave_grad[i,j,k] * np.cos(deg[k])\n",
    "            V[counter] = ave_grad[i,j,k] * np.sin(deg[k])\n",
    "        \n",
    "            X[counter] = (cell_size[0] / 2) + (cell_size[0] * i)\n",
    "            Y[counter] = (cell_size[1] / 2) + (cell_size[1] * j)\n",
    "        \n",
    "            counter = counter + 1\n",
    "\n",
    "# Create the bins in degress to plot our histogram. \n",
    "angle_axis = np.linspace(0, 180, num_bins, endpoint = False)\n",
    "angle_axis += ((angle_axis[1] - angle_axis[0]) / 2)\n",
    "\n",
    "# Create a figure with 4 subplots arranged in 2 x 2\n",
    "fig, ((a,b),(c,d)) = plt.subplots(2,2)\n",
    "\n",
    "# Set the title of each subplot\n",
    "a.set(title = 'Gray Scale Image\\n(Click to Zoom)')\n",
    "b.set(title = 'HOG Descriptor\\n(Click to Zoom)')\n",
    "c.set(title = 'Zoom Window', xlim = (0, 18), ylim = (0, 18), autoscale_on = False)\n",
    "d.set(title = 'Histogram of Gradients')\n",
    "\n",
    "# Plot the gray scale image\n",
    "a.imshow(gray_image, cmap = 'gray')\n",
    "a.set_aspect(aspect = 1)\n",
    "\n",
    "# Plot the feature vector (HOG Descriptor)\n",
    "b.quiver(Y, X, U, V, color = 'white', headwidth = 0, headlength = 0, scale_units = 'inches', scale = 5)\n",
    "b.invert_yaxis()\n",
    "b.set_aspect(aspect = 1)\n",
    "b.set_facecolor('black')\n",
    "\n",
    "# Define function for interactive zoom\n",
    "def onpress(event):\n",
    "    \n",
    "    #Unless the left mouse button is pressed do nothing\n",
    "    if event.button != 1:\n",
    "        return\n",
    "    \n",
    "    # Only accept clicks for subplots a and b\n",
    "    if event.inaxes in [a, b]:\n",
    "        \n",
    "        # Get mouse click coordinates\n",
    "        x, y = event.xdata, event.ydata\n",
    "        \n",
    "        # Select the cell closest to the mouse click coordinates\n",
    "        cell_num_x = np.uint32(x / cell_size[0])\n",
    "        cell_num_y = np.uint32(y / cell_size[1])\n",
    "        \n",
    "        # Set the edge coordinates of the rectangle patch\n",
    "        edgex = x - (x % cell_size[0])\n",
    "        edgey = y - (y % cell_size[1])\n",
    "        \n",
    "        # Create a rectangle patch that matches the the cell selected above        \n",
    "        rect = patches.Rectangle((edgex, edgey),\n",
    "                                  cell_size[0], cell_size[1],\n",
    "                                  linewidth = 1,\n",
    "                                  edgecolor = 'magenta',\n",
    "                                  facecolor='none')\n",
    "        \n",
    "        # A single patch can only be used in a single plot. Create copies\n",
    "        # of the patch to use in the other subplots\n",
    "        rect2 = copy.copy(rect)\n",
    "        rect3 = copy.copy(rect)\n",
    "        \n",
    "        # Update all subplots\n",
    "        a.clear()\n",
    "        a.set(title = 'Gray Scale Image\\n(Click to Zoom)')\n",
    "        a.imshow(gray_image, cmap = 'gray')\n",
    "        a.set_aspect(aspect = 1)\n",
    "        a.add_patch(rect)\n",
    "\n",
    "        b.clear()\n",
    "        b.set(title = 'HOG Descriptor\\n(Click to Zoom)')\n",
    "        b.quiver(Y, X, U, V, color = 'white', headwidth = 0, headlength = 0, scale_units = 'inches', scale = 5)\n",
    "        b.invert_yaxis()\n",
    "        b.set_aspect(aspect = 1)\n",
    "        b.set_facecolor('black')\n",
    "        b.add_patch(rect2)\n",
    "\n",
    "        c.clear()\n",
    "        c.set(title = 'Zoom Window')\n",
    "        c.quiver(Y, X, U, V, color = 'white', headwidth = 0, headlength = 0, scale_units = 'inches', scale = 1)\n",
    "        c.set_xlim(edgex - cell_size[0], edgex + (2 * cell_size[0]))\n",
    "        c.set_ylim(edgey - cell_size[1], edgey + (2 * cell_size[1]))\n",
    "        c.invert_yaxis()\n",
    "        c.set_aspect(aspect = 1)\n",
    "        c.set_facecolor('black')\n",
    "        c.add_patch(rect3)\n",
    "\n",
    "        d.clear()\n",
    "        d.set(title = 'Histogram of Gradients')\n",
    "        d.grid()\n",
    "        d.set_xlim(0, 180)\n",
    "        d.set_xticks(angle_axis)\n",
    "        d.set_xlabel('Angle')\n",
    "        d.bar(angle_axis,\n",
    "              ave_grad[cell_num_y, cell_num_x, :],\n",
    "              180 // num_bins,\n",
    "              align = 'center',\n",
    "              alpha = 0.5,\n",
    "              linewidth = 1.2,\n",
    "              edgecolor = 'k')\n",
    "\n",
    "        fig.canvas.draw()\n",
    "\n",
    "# Create a connection between the figure and the mouse click\n",
    "fig.canvas.mpl_connect('button_press_event', onpress)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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