{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61468bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import BytesIO\n",
    "\n",
    "import cv2\n",
    "import IPython.display\n",
    "import numpy as np\n",
    "import PIL.Image\n",
    "from ipywidgets import widgets\n",
    "\n",
    "from hsv_utils import identify_object, resize_img\n",
    "\n",
    "img_hsv = None  # Initialize global variable\n",
    "img_rgb = None  # Initialize global variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hourly-navigator",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def hsv_select(**kwargs) -> None:\n",
    "    hsv_min = tuple([int(low) for low, high in kwargs.values()])\n",
    "    hsv_max = tuple([int(high) for low, high in kwargs.values()])\n",
    "    hsv_min_new = (hsv_min[0]/2, hsv_min[1], hsv_min[2])\n",
    "    hsv_max_new = (hsv_max[0]/2, hsv_max[1], hsv_max[2])\n",
    "    mask = cv2.inRange(img_hsv, hsv_min_new, hsv_max_new)\n",
    "    img_masked = cv2.bitwise_and(img_rgb, img_rgb, mask = mask)\n",
    "    f = BytesIO()\n",
    "    PIL.Image.fromarray(img_masked).save(f, \"jpeg\")\n",
    "    img_jpeg = IPython.display.Image(data=f.getvalue())\n",
    "    IPython.display.display_jpeg(img_jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "external-purple",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 画像を読み込む。\n",
    "img = cv2.imread(\"./picture/red_blue_cone.jpg\")\n",
    "img = resize_img(img)\n",
    "\n",
    "img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "\n",
    "# hsvを設定するスライダー\n",
    "hsv = {}\n",
    "hsv[\"h\"] = widgets.IntRangeSlider(\n",
    "    value=[0, 360], min=0, max=360, description=\"Hue:\", continuous_update=True, layout=widgets.Layout(width=\"100%\"),\n",
    ")\n",
    "hsv[\"s\"] = widgets.IntRangeSlider(\n",
    "    value=[0, 255],\n",
    "    min=0,\n",
    "    max=255,\n",
    "    description=\"Saturation:\",\n",
    "    continuous_update=True,\n",
    "    layout=widgets.Layout(width=\"100%\"),\n",
    ")\n",
    "hsv[\"v\"] = widgets.IntRangeSlider(\n",
    "    value=[0, 255], min=0, max=255, description=\"Value:\", continuous_update=True, layout=widgets.Layout(width=\"100%\"),\n",
    ")\n",
    "\n",
    "\n",
    "# マスクされた画像を表示\n",
    "widgets.interactive(hsv_select, **hsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "toxic-possession",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# HSVの下限と上限を設定\n",
    "HSV_LOWER = np.array([0, 0, 0])\n",
    "HSV_UPPER = np.array([360, 255, 255])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "seasonal-cigarette",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 物体とみなす最小サイズ\n",
    "EXIST_SIZE = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "friendly-stewart",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 検出された最も大きな物体に輪郭の線を表示する\n",
    "identify_object(img, HSV_LOWER, HSV_UPPER, EXIST_SIZE)"
   ]
  }
 ],
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