website and live video added

Histogram_Matching/.ipynb_checkpoints/Histogram-Matching-Generation-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Histogram_Matching/.ipynb_checkpoints/Histogram_matching-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Histogram_Matching/Histogram-Matching-Generation.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "67bf3a0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import os.path\n",
+    "from scipy import stats\n",
+    "import random\n",
+    "import PIL.ImageDraw as ImageDraw\n",
+    "import PIL.Image as Image\n",
+    "import math\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0506c3b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_images():\n",
+    "    img_path = './imgs2/'\n",
+    "    imgs = []\n",
+    "    for file in tqdm(os.listdir(img_path)):\n",
+    "        imgs.append(np.array(Image.open(img_path + file)))\n",
+    "    return imgs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "98f561ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 200.56it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "img = load_images()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "dae45f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_histogram(img):\n",
+    "    \"\"\"\n",
+    "    @params: img: can be a grayscale or color image. We calculate the Normalized histogram of this image.\n",
+    "    @params: do_print: if or not print the result histogram\n",
+    "    @return: will return both histogram and the grayscale image \n",
+    "    \"\"\"\n",
+    "    if len(img.shape) == 3: # img is colorful, so we convert it to grayscale\n",
+    "        gr_img = np.mean(img, axis=-1)\n",
+    "    else:\n",
+    "        gr_img = img\n",
+    "    '''now we calc grayscale histogram'''\n",
+    "    gr_hist = np.zeros([256])\n",
+    "\n",
+    "    for x_pixel in range(gr_img.shape[0]):\n",
+    "        for y_pixel in range(gr_img.shape[1]):\n",
+    "            pixel_value = int(gr_img[x_pixel, y_pixel])\n",
+    "            gr_hist[pixel_value] += 1\n",
+    "            \n",
+    "    '''normalizing the Histogram'''\n",
+    "    ##gr_hist /= (gr_img.shape[0] * gr_img.shape[1])\n",
+    "    return gr_hist, gr_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "877261d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "256"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(generate_histogram(img)[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "22e3f908",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixel_count1 = generate_histogram(img)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3aa0d772",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intensity = list(range(0,256))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "740b5dc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17745c58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(pixel_count1, columns = ['pixel_count1'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9421f9cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixel_count2 = generate_histogram(img)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d71124af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixel_count2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8190c2dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(pixel_count2, columns = ['pixel_count2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa177a8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixel_count1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63fa4d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generate_histogram(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "cdfd36d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = np.corrcoef(pixel_count1, pixel_count2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "60574196",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.        , -0.07814372],\n",
+       "       [-0.07814372,  1.        ]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb1ddb65",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}