{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3960a901",
   "metadata": {},
   "source": [
    "# Centre Universitaire de Mila"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4568d4a",
   "metadata": {},
   "source": [
    "# Master 1 (STIC & I2A), Matière: Traitement d'images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bedc4a",
   "metadata": {},
   "source": [
    "# Travaux pratiques N°2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c91f3f10",
   "metadata": {},
   "source": [
    "## 2.1 Objectifs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a3a70a",
   "metadata": {},
   "source": [
    "* Coder quelques opérations de base pour l'amélioration de la qualité de l'image."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57e8c63",
   "metadata": {},
   "source": [
    "## 2.2 Enoncé"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751221fd",
   "metadata": {},
   "source": [
    "Dans ce projet, après la lecture d'image \"pgm\", vous devez implémenter les quatre opérations suivantes (les images de test vous seront fournies). Chaque opération doit être implantée par une fonction à part qui prendra comme entrée le nom de l'image à traiter sous forme d'une chaine de caractère, et en sortie le(s) résultat(s) attendu(s) de l'opération:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0f7ae7",
   "metadata": {},
   "source": [
    "### 1) Négatif d'une image : (L représente le nombre de niveaux de gris)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff21564",
   "metadata": {},
   "source": [
    "$$ g(x,y) = L - 1 - f(x,y) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "506de1d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0f9b8669",
   "metadata": {},
   "source": [
    "### 2) Paramètres statistiques d’une image couleur"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56b3749",
   "metadata": {},
   "source": [
    "Calculer la moyenne, l'écart-type et la médiane de chaque bande d’une image couleur ($M \\times N$ représente la taille de l'image)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef2d7eb",
   "metadata": {},
   "source": [
    "$$  \\mu_k = \\frac{1}{MN}\\sum_{x=1}^{M}\\sum_{y=1}^{N}f(x,y,k) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56ed268",
   "metadata": {},
   "source": [
    "$$  \\sigma_k = \\sqrt{\\frac{1}{MN}\\sum_{x=1}^{M}\\sum_{y=1}^{N} \\left(f(x,y,k)-\\mu_k \\right)^2} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fca84f3",
   "metadata": {},
   "source": [
    "$$  m_k = médiane\\left( f(x,y,k) \\right) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6f921c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "acffe0b3",
   "metadata": {},
   "source": [
    "### 3) Changement de dynamique linéaire d’une image à niveaux de gris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15319988",
   "metadata": {},
   "source": [
    "Implanter l’opération suivante à une image (min et max sont le minimum et le maximum des niveaux de gris dans l’image d’entrée) :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de2c7dac",
   "metadata": {},
   "source": [
    "$$ g(x,y) = \\frac{255}{max-min} \\left( f(x,y,k) - min \\right) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1ce29ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c0f80318",
   "metadata": {},
   "source": [
    "### 4) Égalisation d’histogramme d’une image à niveaux de gris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e650433",
   "metadata": {},
   "source": [
    "Implanter l’égalisation d’histogramme pour une image à niveaux de gris."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e0d79d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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