supermachine-tobacco/01_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 彩色图像读取"
   ],
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    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ],
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     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "img_path = r\"data/tobacco/Image_2022_0716_1409_08_547-001482.bmp\"\n",
    "label_path = r\"data/yangeng.bmp\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "img = cv2.imread(img_path)\n",
    "label = cv2.imread(label_path)\n",
    "# img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def trans_color(pixel: np.ndarray) -> int:\n",
    "    color_dict = {(0, 0, 255): 1, (255, 255, 255): 0, (255, 0, 0): 2}\n",
    "    if (pixel[0], pixel[1], pixel[2]) in color_dict.keys():\n",
    "        return color_dict[(pixel[0], pixel[1], pixel[2])]\n",
    "    else:\n",
    "        return -1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "img = img.reshape(1024*4096,3)\n",
    "label = label.reshape(1024*4096,3)"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "x_list = []\n",
    "for i in range(len(label)):\n",
    "    y = trans_color(label[i])\n",
    "    x_list.append(y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "y_list = []\n",
    "for i in range(len(x_list)):\n",
    "    if (x_list[i] == 1):\n",
    "        y_list.append(img[i])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "y = np.array(y_list)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "b = y[:,0]\n",
    "g = y[:,1]\n",
    "r = y[:,2]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
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