Low_Contrast_Contour_Faint_Object.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7eb3b497",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08b050a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.util import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f6b5c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_low_contrast(obj_image, fraction_threshold, lower_percentile,\n",
    "                    upper_percentile, method='linear'):\n",
    "    \n",
    "    dlimits = dtype_limits(obj_image, clip_negative=False)\n",
    "    limits = numpy.percentile(obj_image, [lower_percentile, upper_percentile])\n",
    "    \n",
    "    ratio = (limits[1] - limits[0]) / (dlimits[1] - dlimits[0])\n",
    "\n",
    "    return ratio < fraction_threshold, ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52f17804",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_low_contrast_image(obj_image, fraction_threshold=0.2, lower_percentile=10, upper_percentile=90):\n",
    "    low_contrast = False\n",
    "\n",
    "    low_contrast = is_low_contrast(obj_image, \n",
    "                                   fraction_threshold=fraction_threshold, \n",
    "                                   lower_percentile=lower_percentile, \n",
    "                                   upper_percentile=upper_percentile)\n",
    "    return low_contrast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8eb1fa22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_contrast_slope_ratios(obj_image):\n",
    "    obj_flat = obj_image.flatten()\n",
    "    ratio = numpy.diff(obj_flat)\n",
    "\n",
    "    ratio = numpy.unique(ratio)\n",
    "\n",
    "    obj_flat_norm = (ratio[1:] - ratio[1:].min()) / (ratio[1:].max() - ratio[1:].min())\n",
    "\n",
    "    return obj_flat_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa8e607",
   "metadata": {},
   "source": [
    "<h3>環境設置</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d01aae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F000234-00001.jpg',\n",
       " 'F000235-00001.jpg',\n",
       " 'F000236-00000.jpg',\n",
       " 'F000237-00001.jpg',\n",
       " 'F000238-00001.jpg']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_folder = 'D:/Projects/ComputerVision/AutoEncoder/Fish/images/objects/faint'\n",
    "image_files = [ f[len(image_folder) + 1:] for f in sorted(glob('%s/F*.jpg' % (image_folder)))]\n",
    "\n",
    "image_files[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f550210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F000234-00001'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'F000234-00001.jpg'[:-4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521350de",
   "metadata": {},
   "source": [
    "<h3>轉換資料格式</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb637646",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_images = dict()\n",
    "\n",
    "for image_file in image_files:\n",
    "    _image_file = '%s/%s' % (image_folder, image_file)\n",
    "\n",
    "    my_image = cv2.imread(_image_file, cv2.IMREAD_GRAYSCALE)\n",
    "    _image = cv2.resize(my_image.copy(), (120, 120), interpolation=cv2.INTER_CUBIC)\n",
    "\n",
    "    obj_images[image_file[:-4]] = _image\n",
    "    \n",
    "my_image_list = numpy.array(list(obj_images.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28618eeb",
   "metadata": {},
   "source": [
    "<h3>邊界直方圖均衡化處裡</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9af4b9a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_countour_list = []\n",
    "my_contour_image_list = []\n",
    "\n",
    "img_blank = numpy.ones(my_image_list[0].shape, numpy.uint8)\n",
    "\n",
    "for obj_name in obj_images:\n",
    "    my_image = obj_images[obj_name]    \n",
    "    my_image = cv2.equalizeHist(my_image.copy())\n",
    "\n",
    "    ret, thresh = cv2.threshold(my_image, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    my_countour_list.append(contours)\n",
    "    \n",
    "    _image = img_blank.copy()\n",
    "\n",
    "    for contour in contours:\n",
    "        cv2.drawContours(_image, contour, -1, (0, 0, 255), 1)\n",
    "\n",
    "    my_contour_image_list.append(_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09b9266f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x960 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120, figsize=(8, 8), nrows=2, ncols=2)\n",
    "\n",
    "for row in range(2):\n",
    "    for col in range(2):\n",
    "        my_image = my_contour_image_list[numpy.random.randint(0, len(my_contour_image_list) - 1)] \n",
    "        _image = my_image.copy()\n",
    "      \n",
    "        ax[row][col].imshow(_image, cmap='gray')\n",
    "        ax[row][col].axis('off')\n",
    "        \n",
    "        my_contour_image_list.append(_image)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "98d0cc22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x960 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = numpy.random.randint(0, len(my_contour_image_list) - 1)\n",
    "source_image = my_image_list[pos]\n",
    "equal_image = cv2.equalizeHist(source_image.copy())\n",
    "\n",
    "ret, thresh = cv2.threshold(equal_image, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "contour_image = img_blank.copy()\n",
    "\n",
    "contrast_slope_ratios = get_contrast_slope_ratios(my_image_list[pos])\n",
    "\n",
    "for contour in contours:\n",
    "    cv2.drawContours(contour_image, contour, -1, (0, 0, 255), 1)\n",
    "\n",
    "fig, ax = plt.subplots(dpi=120, figsize=(8, 8), nrows=2, ncols=2)\n",
    "\n",
    "ax[0][0].imshow(source_image, cmap='gray')\n",
    "ax[0][0].axis('off')\n",
    "\n",
    "ax[0][1].imshow(equal_image, cmap='gray')\n",
    "ax[0][1].axis('off')\n",
    "\n",
    "ax[1][0].imshow(contour_image, cmap='gray')\n",
    "ax[1][0].axis('off')\n",
    "\n",
    "ax[1][1].scatter(numpy.arange(len(contrast_slope_ratios)), contrast_slope_ratios)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f3f6a2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 0.2901960784313726)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_low_contrast_image(source_image, fraction_threshold=0.3)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "8e2a8f86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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