1893 lines
6.5 MiB
Plaintext
1893 lines
6.5 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dfb11bac",
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"metadata": {},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Widzenie komputerowe </h1>\n",
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"<h2> 01. <i>Wprowadzenie do widzenia komputerowego</i> [laboratoria]</h2> \n",
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"<h3>Andrzej Wójtowicz (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c64b51d",
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"metadata": {},
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"source": [
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"# Biblioteka OpenCV\n",
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"\n",
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"Podczas zajęć będziemy poruszali zagadnienia związane z [widzeniem komputerowym](https://en.wikipedia.org/wiki/Computer_vision) (ang. *computer vision*, CV). Tę tematykę możemy traktować jako rozwinięcie, czy też bardziej zaawansowaną formę [prztwarzania obrazów](https://en.wikipedia.org/wiki/Digital_image_processing) (ang. *image processing*, IP), gdzie tym razem będziemy starali się wyciągnąć pewną bardziej zaawansowaną wiedzę płynącą z obrazów statycznych lub wideo (cf. dyskusja na [Artificial Intelligence Stack Exchange](https://ai.stackexchange.com/a/13588)). Przedmiot ma formę laboratoryjną, zatem główną dyskusję dotyczącą zakresu obu dziedzin zostawimy w ramach dodatkowej literatury uzupełniającej.\n",
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"\n",
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"Standardem dla algorytmów z dziedzin IP/CV jest biblioteka [OpenCV](https://opencv.org/), która implementuje wiele z tych algorytmów oraz jest aktywnie rozwijana przez społeczność. Sama biblioteka posiada interfejsy do wielu języków programowania, natomiast my skupimy się na języku [Python](https://www.python.org/), który będzie dla nas idealny na potrzeby intensywnego prototypowania. Dokumentację online będzie głównie prowadziła do języka C++, ponieważ nie ma dedykowanej online dla Pythona, ale argumenty funkcji i metod są analogiczne.\n",
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"\n",
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"Początkowe zajęcia będą głównie dotyczyły zagadnień IP, tak aby zapoznać się z biblioteką OpenCV, a dalsze zajęcia będą już związane z CV.\n",
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"\n",
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"## Instalacja\n",
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"\n",
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"Materiały do zajęć Jupyter Notebook są tworzone na serwerze JupyterHub z kernelem Python 3. Pominiemy tutaj tworzenie wirtualnego środowiska, jednak należy mieć na uwadze, że poniższe polecenia mogą być też przydatne podczas próby uruchomienia notebooków lub programów na własnym komputerze.\n",
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"\n",
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"Poniższe polecenie wyświetla używaną wersję Pythona:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8fda1098",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.9.2\n"
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]
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}
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],
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"source": [
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"import platform\n",
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"print(platform.python_version())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c8e7b1e3",
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"metadata": {},
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"source": [
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"Niezbędne moduły zainstalujemy poprzez menadżer `pip`. Sama biblioteka OpenCV, abstrahując od np. paczek debianowych, posiada [4 możliwe opcje instalacji](https://pypi.org/project/opencv-contrib-python/). My zainstalujemy pełną wersję tej biblioteki, a dodatkowo doinstalujemy pakiety związane m.in. wyświetlaniem grafiki oraz uczeniem maszynowym."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "78e42f58",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: opencv-contrib-python==4.5.3.56 in /home/students/s444380/.local/lib/python3.9/site-packages (4.5.3.56)\n",
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"Requirement already satisfied: numpy in /usr/lib/python3/dist-packages (1.19.5)\n",
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"Requirement already satisfied: scipy in /usr/lib/python3/dist-packages (1.6.0)\n",
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"Requirement already satisfied: matplotlib in /usr/lib/python3/dist-packages (3.3.4)\n",
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"Requirement already satisfied: scikit-learn in /usr/lib/python3/dist-packages (0.23.2)\n"
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]
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}
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],
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"source": [
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"!pip3 install --user --disable-pip-version-check opencv-contrib-python==4.5.3.56 numpy scipy matplotlib scikit-learn"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1d241431",
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"metadata": {},
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"source": [
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"Sama biblioteka posiada też własne moduły związane z wyświetlaniem grafiki (moduł *HighGUI*) oraz z uczeniem maszynowym (moduł *ml*), jednak my raczej nie będziemy z nich korzystać podczas zajęć (aczkolwiek mogą być przydatne podczas realizacji projektu).\n",
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"\n",
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"Wszystkie moduły, algorytmy i zmienne są dostępne z poziomu modułu `cv2`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8eca2db5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2 as cv"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82701dba",
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"metadata": {},
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"source": [
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"Poniższym poleceniem możemy również sprawdzić wersję zainstalowanej biblioteki:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f34f551a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4.5.3\n"
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]
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}
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],
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"source": [
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"print(cv.__version__)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a39875c",
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"metadata": {},
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"source": [
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"## Obrazy jako tablice\n",
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"\n",
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"Obraz możemy traktować jako standardową tablicę [NumPy](https://www.numpy.org/) zawierającą dane dotyczące danych pikseli. Im większa liczba pikseli w obrazie, tym większa jest jego rozdzielczość. Intuicyjnie można przyjąć, że piksele są niewielkimi blokami informacji ułożonymi w postaci siatki dwuwymiarowej, a głębokość piksela odnosi się do informacji o kolorze. Aby obraz mógł być przetworzony przez komputer, to taki obraz musi zostać przekonwertowany na postać binarną. Kolor obrazu można obliczyć w następujący sposób:\n",
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"\n",
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"Liczba kolorów (odcieni) = 2<sup>*bpp*</sup>, gdzie *bpp* oznacza bity na piksel.\n",
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"\n",
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"Większa liczba bitów na piksel daje więcej możliwych kolorów na obrazach.\n",
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"\n",
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"| Bity na piksel | Liczba kolorów |\n",
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"| :------------: | :------------: |\n",
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"| 1 | 2<sup>1</sup> = 2 |\n",
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"| 2 | 2<sup>2</sup> = 4 |\n",
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"| 3 | 2<sup>3</sup> = 8 |\n",
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"| 4 | 2<sup>4</sup> = 16 |\n",
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"| 8 | 2<sup>8</sup> = 256 |\n",
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"| 16 | 2<sup>16</sup> = 65536 |\n",
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"\n",
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"Spójrzmy na trzy typowe reprezentacje obrazów.\n",
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"\n",
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"### Obraz czarno-biały\n",
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"\n",
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"Obraz binarny składa się z 1 bita na piksel, a więc może mieć tylko dwa możliwe kolory, tj. czarny lub biały. Kolor czarny jest reprezentowany przez wartość 0, a 1 oznacza biel (czasem w użyciu są reprezentacje, które mają odwrotne wartości czerni i bieli).\n",
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"\n",
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"\n",
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"\n",
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"### Obraz w skali odcieni szarości\n",
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"\n",
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"Obraz w skali szarości składa się z 8 bitów na piksel. Oznacza to, że może mieć 256 różnych odcieni, przy czym 0 pikseli będzie reprezentować kolor czarny, a 255 oznacza biel.\n",
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"\n",
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"\n",
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"\n",
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"### Obraz kolorowy\n",
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"\n",
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"Kolorowe obrazy w standardowej formie są reprezentowane jako połączenie barwy czerwonej, niebieskiej i zielonej - wszystkie inne kolory można uzyskać, mieszając te podstawowe kolory we właściwych proporcjach.\n",
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"\n",
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"\n",
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"\n",
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"Kolorowy obraz składa się również z 8 bitów na piksel, z tym że na taki obraz składają się 3 kanały (czerwony, zielony i niebieski). W rezultacie 256 różnych natężeń danego koloru podstawowego można przedstawić za pomocą 0 oznaczającego najmniej intensywny i 255 najbardziej intensywny. Dla poniższego obrazu pawiana:\n",
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"\n",
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"\n",
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"\n",
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"podstawowe parametry dotyczące wymiarów prezentują się następująco:\n",
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"\n",
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"```plaintext\n",
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"Shape\n",
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"(288, 289, 3)\n",
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"288: Pixel height (wysokość w pikselach)\n",
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"289: Pixel width (szerokość w pikselach)\n",
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"3: color channel (liczba kanałów)\n",
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"```\n",
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"\n",
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"Taki obraz możemy reprezentować w postaci trójwymiarowej tablicy:\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"id": "650cae81",
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"metadata": {},
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"source": [
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"## Wczytywanie obrazów\n",
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"\n",
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"Przy użyciu funkcji [`cv.imread()`](https://docs.opencv.org/4.5.3/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) możemy odczytać obraz. Pierwszy argument to lokalizacja pliku. Obraz powinien znajdować się w katalogu roboczym lub powinna zostać podana ścieżka bezwzględna do pliku.\n",
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"\n",
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"Drugi argument (opcjonalny) jest flagą oznaczającą w jaki sposób obraz powinien zostać wczytany:\n",
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"\n",
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"* [`cv.IMREAD_COLOR`](https://docs.opencv.org/4.5.3/d4/da8/group__imgcodecs.html#gga61d9b0126a3e57d9277ac48327799c80af660544735200cbe942eea09232eb822) - wczytuje kolorowy obraz z pominięciem przezroczystości; flaga domyślna,\n",
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"* [`cv.IMREAD_GRAYSCALE`](https://docs.opencv.org/4.5.3/d4/da8/group__imgcodecs.html#gga61d9b0126a3e57d9277ac48327799c80ae29981cfc153d3b0cef5c0daeedd2125) : wczytuje obraz w skali odcieni szarości,\n",
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"* [`cv.IMREAD_UNCHANGED`](https://docs.opencv.org/4.5.3/d4/da8/group__imgcodecs.html#gga61d9b0126a3e57d9277ac48327799c80aeddd67043ed0df14f9d9a4e66d2b0708) : wczytuje obraz razem z kanałem alfa (przezroczystość).\n",
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"\n",
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"Zamiast trzech powyższych flag można alternatywnie przekazać odpowiednio 1, 0 lub -1."
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "d7fccc7d",
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"metadata": {},
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"outputs": [],
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"source": [
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"image = cv.imread(\"img/baboon.png\", cv.IMREAD_COLOR)"
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]
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},
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{
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||
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"cell_type": "markdown",
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||
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"id": "d9024a2d",
|
||
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"metadata": {},
|
||
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"source": [
|
||
|
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"Sprawdźmy typ zmiennej:"
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]
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},
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{
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"cell_type": "code",
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||
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"execution_count": 6,
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||
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"id": "78c727fe",
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||
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"metadata": {},
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||
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||
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"<class 'numpy.ndarray'>\n"
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||
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]
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}
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],
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"source": [
|
||
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"print(type(image))"
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]
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||
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},
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||
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{
|
||
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"cell_type": "markdown",
|
||
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"id": "7c488a2d",
|
||
|
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"metadata": {},
|
||
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"source": [
|
||
|
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"Sprawdźmy kształt tablicy:"
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||
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]
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||
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},
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||
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{
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||
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"cell_type": "code",
|
||
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"execution_count": 7,
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||
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"id": "67bafe94",
|
||
|
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"metadata": {},
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||
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"outputs": [
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{
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||
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||
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"(288, 289, 3)\n"
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]
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}
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||
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],
|
||
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"source": [
|
||
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"print(image.shape)"
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]
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||
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},
|
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{
|
||
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"cell_type": "markdown",
|
||
|
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"id": "68d3db9e",
|
||
|
|
"metadata": {},
|
||
|
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"source": [
|
||
|
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"[Typ danych](https://numpy.org/doc/stable/user/basics.types.html) tablicy:"
|
||
|
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]
|
||
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},
|
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|
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{
|
||
|
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"cell_type": "code",
|
||
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"execution_count": 8,
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||
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"id": "8463a5df",
|
||
|
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"metadata": {},
|
||
|
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"outputs": [
|
||
|
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{
|
||
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"name": "stdout",
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"output_type": "stream",
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||
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"text": [
|
||
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"uint8\n"
|
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]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(image.dtype)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "243e1fd8",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Jak widać, poszczególne piksele na kanałach są 8-bitowymi liczbami całkowitymi bez znaku."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "185a63ad",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Wyświetlanie obrazów przy pomocy Matplotlib\n",
|
||
|
|
"\n",
|
||
|
|
"W notebooku Jupyter najwygodniej wyświetla się obrazy przy pomocy biblioteki [Matplotlib](https://matplotlib.org/), a dokładniej modułu `pyplot`:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"id": "c4516873",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"import matplotlib\n",
|
||
|
|
"%matplotlib inline"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "7e320379",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Przed wyświetleniem obrazu musimy dokonać drobnej konwersji obrazu. OpenCV domyślnie wczytuje obraz w formacie BGR, natomiast Matplotlib pracuje na obrazie w formacie RGB. Do konwersji użyjemy funkcji [`cv.cvtColor()`](https://docs.opencv.org/4.5.3/d8/d01/group__imgproc__color__conversions.html#ga397ae87e1288a81d2363b61574eb8cab), której pierwszym argumentem jest konwertowany obraz, a drugim sposób konwersji (w tym wypadku definiowany przez stałą [`cv.COLOR_BGR2RGB`](https://docs.opencv.org/4.5.3/d8/d01/group__imgproc__color__conversions.html#gga4e0972be5de079fed4e3a10e24ef5ef0ad3db9ff253b87d02efe4887b2f5d77ee)). *Nota bene*, czasami lepiej jest operować w przestrzeni barw [HSV](https://pl.wikipedia.org/wiki/HSV_(grafika)) niż RGB."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"id": "bd083db2",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"image2 = cv.cvtColor(image, cv.COLOR_BGR2RGB)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "f8b4ae77",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Po przekonwertowaniu obrazu możemy wyświetlić go przy pomocy `pyplot`. Użyjemy do tego funkcji [`matplotlib.pyplot.imshow()`](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"id": "e39ea012",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ee395eb0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(image2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "fc1c34db",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Zwróćmy uwagę, że piksel o współrzędnych `(0, 0)` znajduje się w lewym górnym rogu.\n",
|
||
|
|
"\n",
|
||
|
|
"Można też obejść się bez konwersji i po prostu odwrócić w locie kanały w tablicy `numpy`:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"id": "aa374723",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ee3067c0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(image[..., ::-1])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "232c3da7",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Sprawdźmy kolejny plik:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"id": "bccd478c",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"x_small = cv.imread(\"img/x_small.jpg\", cv.IMREAD_GRAYSCALE)\n",
|
||
|
|
"plt.imshow(x_small, cmap = 'gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "2ba47521",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Wczytaliśmy obraz w skali odcieni szarości. Podczas wyświetlania obrazu możemy ustawić mapę kolorów (parametr `cmap`). Dodatkowo, jeśli na końcu polecenia damy średnik (`;`), to w wynikowej komórce notebooka nie będzie się wyświetlał zwracany typ.\n",
|
||
|
|
"\n",
|
||
|
|
"Czasami warto również wyświetlić pasek z informacją o tym jaka wartość odpowiada za dany odcień:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"id": "066b4778",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(x_small, cmap = 'gray');\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "c561f1ea",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Proste operacje\n",
|
||
|
|
"\n",
|
||
|
|
"Możemy sprawdzić, że obraz jest w istocie tablicą NumPy o wartościach w zakresie od 0 do 255:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"id": "fd384995",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[ 0 0 0 0 0 0 1 10 6 1]\n",
|
||
|
|
" [ 0 9 71 39 4 2 33 100 54 8]\n",
|
||
|
|
" [ 1 70 198 173 71 58 158 202 133 25]\n",
|
||
|
|
" [ 1 25 150 208 201 203 221 172 57 3]\n",
|
||
|
|
" [ 0 2 43 186 243 245 187 53 4 0]\n",
|
||
|
|
" [ 0 4 90 207 231 236 189 87 9 0]\n",
|
||
|
|
" [ 0 37 160 210 137 156 209 195 63 2]\n",
|
||
|
|
" [ 0 28 112 114 23 28 128 185 71 3]\n",
|
||
|
|
" [ 0 4 25 15 2 1 13 58 10 0]\n",
|
||
|
|
" [ 0 0 0 0 0 0 0 0 0 0]]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(x_small)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "794d3cb6",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Wracając do samej biblioteki Matplotlib, możemy również np. sterować rozmiarem wyświetlanego obrazu oraz nadać mu tytuł:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"id": "5998b09d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAIYCAYAAACbuB7uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAiMElEQVR4nO3df3DU9Z3H8XcIkCCGKCgUFPlx6qBSUMBSBVt7Vc7fx0zHqtWq2FapiCKjVaoeaoUM6nXsacULFsYfRblr9cBO6ZXTFhHLKEHwV0/HSiWnIurYJIKNknzvD4dMU4iQJcmXfHw8ZnambL67+yJTfPJddjdFWZZlAQB0al3yHgAA7D5BB4AECDoAJEDQASABgg4ACRB0AEiAoANAAgQdABIg6ACQAEEHCnLhhRdGUVFRDB48eIdfHzx4cBQVFcWFF17Yobvg80rQoY1s3rw5Kisr49RTT40DDzwwSktLY++9946hQ4fGMcccE5MnT46HH3443n777byn7vEaGhriS1/6UhQVFUVpaWn88Y9/3OltfvrTn0ZRUVEUFRXFtdde2wErYc/SNe8BkIJnnnkmzjrrrPjzn//c7Pr6+vpYv359rF+/PlatWhX//u//Hv369YuNGzfmM7STKC4ujnvvvTfGjBkT9fX18b3vfS9WrFgRRUVFOzy+uro6ZsyYERERhxxySNx4440duBb2DM7QYTe99tprceKJJzbF/Iwzzoj7778/Vq1aFWvWrInf/va3cdttt8WECROiW7du+Y7tREaMGBE/+MEPIiJi5cqVcffdd7d47CWXXBJ1dXVRVFQU8+bNi9LS0o6aCXsMZ+iwm6677rqora2NiIj58+fHpEmTtjvmxBNPjKuuuirefffd+I//+I+Onthp3XDDDfHLX/4y/vd//zdmzJgRZ5xxRgwcOLDZMQ888EAsXbo0IiIuvvji+OpXv5rHVMidM3TYDQ0NDfGrX/0qIiLGjBmzw5j/rf333z+mTJnSEdOSUFJSEvPmzYuioqKoq6uLyZMnN/v6pk2b4sorr4yIiAMOOCBuvfXWPGbCHkHQYTe8++67sWXLloiIOPjgg3frvv7+VeFr1qyJc889NwYOHBg9evSIgw8+OKZPnx7vvfdes9s9/fTTceaZZ8ZBBx0UpaWl8Q//8A9xzTXXRF1dXYuP1djYGE888URcddVVMW7cuNhvv/2iW7dusc8++8SRRx4ZV111VWzYsGG3fj9tZfz48fH9738/IiJ+/etfx8KFC5u+NnXq1Hj//fcjImLu3LnRq1evXDbCHiEDCvb+++9nEZFFRDZy5Mjduq9BgwZlEZFdcMEF2f33359179696b7/9nLooYdmb7/9dpZlWXbbbbdlRUVFOzxu1KhRWV1d3Q4fa+bMmTu8zd9e9tprr+yRRx5pce8FF1yQRUQ2aNCgnf5+dldtbW02cODALCKy/fbbL3v33XezxYsXN209++yzd/sxoLNzhg67oXfv3jFo0KCIiFi3bl3MmTMnGhsbd+s+161bF9/97nfj4IMPjvnz58ezzz4bTzzxRJx33nkREfHqq6/GVVddFY8++mhcffXVMXbs2Pj5z38eq1evjt/85jdxyimnRMSnZ/i33HLLDh9j69at0b9//7j00kvjgQceiJUrV0ZVVVX813/9V/zgBz+IvffeO7Zs2RLf+ta3duktY+2trKws5s6dGxER7733XlxyySVNZ+19+vSJn/zkJ3nOgz1D3n+jgM7u9ttvb3ZmO2jQoOyyyy7Lfv7zn2evvfbaLt/PtjPaiMiOPfbYbPPmzdsdc+aZZ2YRkRUXF2e9e/fOvvGNb2Rbt25tdszWrVuzL3/5y1lEZH369Mk++eST7e5n/fr12ccff9zilurq6uyAAw7IIiI777zzdnhMR56hb3POOeds90zCAw880Gb3D52ZM3TYTVdeeWVcdNFFTb9+44034q677opzzz03Dj744PjCF74QZ599djz22GORZdku3ee9994be+2113bXX3rppRHx6Yvx/vrXv0ZlZWUUFxc3O6a4uDguvvjiiIh4//334+WXX97ufgYPHvyZb6E78MAD4+qrr46IiCVLluzy7vb2k5/8JMrKypp+fdJJJzU9cwGfd4IOu6lLly7xs5/9LJYuXRonnnhidOnS/I/VO++8E4sWLYozzjgjvvSlL8Wf/vSnz7y/kSNHxmGHHbbDr40YMaLpf5944onRu3fvFu9jm9dff32nv4fa2tpYv359vPTSS/Hiiy/Giy++2PQXim1f2xOsW7eu2Yv9BgwYkOMa2LN4Hzq0kZNOOilOOumk+OCDD2LlypWxevXqqKqqihUrVkRNTU1ERKxevTqOO+64qKqqiv79++/wfg499NAWH2OfffZp9XEtvdr9jTfeiNtvvz0ee+yxeOONNz7jd/bpv1sPHTr0M49pb5s3b2565mGbBQsWxPnnn++95xDO0KHN7bvvvnHaaafFjTfeGI899li88847MX/+/Nh3330jIuLtt9+OG264ocXb7+ip9m3+9ux/V49raGjY7utLly6Nww8/PO66666dxjwi4qOPPtrpMe3t+uuvb3qm4JZbbomePXtGlmVx8cUXR319fc7rIH+CDu2spKQkJk2aFA899FDTdY888shuvxq+UO+//35861vfii1btsTee+8dN954Y/zhD3+ITZs2RX19fWRZFlmWxeOPP950m7z/Df2ZZ56Jf/u3f4uIT58Jue666+JHP/pRRHz6qv9t/xs+zwQdOsg//dM/NX1s6QcffND0gSgd7T//8z/jL3/5S0R8+heLmTNnxpe//OXYf//9o3v37k3HffDBB7ns+3uffPJJfOc734nGxsbo2bNn3HPPPRERcfnll8eYMWMiIuLWW2+NF198Mc+ZkDtBhw70ty/i+vsXz3WUl156KSI+fQ/9iSee2OJxq1ev7qhJn2n27NlNsZ41a1bT+/63/US2rl27xieffBLf+973cnvWA/YEgg4dZMuWLU1vIevVq1eLr1Bvb1u3bo2IT3+0a0sB3LJlS9x///0dOWuHXn755Zg9e3ZERIwdOzamTp3a7OsjR46Mq666KiIiVq1a1fThM/B5JOiwGz788MMYO3Zs/OpXv/rMs8PGxsaYOnVq0yvOzzjjjBZ/tnd7O+SQQyLi01eN/+IXv9ju6w0NDfHd73433nrrrY6e1kxjY2N85zvfiY8//ji6desW99577w6f1Zg5c2bT5+jPmDEj/u///q+jp8IewdvWYDc988wzcfrpp8cBBxwQEydOjGOOOSYGDRoUZWVl8Ze//CWee+65mD9/frzwwgsREVFeXp7ri7i++c1vxg9/+MOor6+PCy+8MNauXRsnnHBC9OrVK1566aW48847o6qqKsaNGxcrV67Mbeedd94Zq1atioiIa6+9NoYPH77D40pLS6OysjL+8R//Merq6mLKlCmxePHijpwKe4Y8P6YOOruPPvoo+8IXvrDTH3Sy7XLIIYdkq1ev3uF97epHpW67r5kzZ7Z4zPr165uOW7BgwXZfnz9/ftalS5cWd5511lnZ//zP/zT9+ne/+91299GeH/365z//OevZs2cWEdmwYcOyv/71rzu9zUUXXdS09xe/+EWrHxM6O0+5w24oLS2NN998M1auXBk33XRTnHzyyTF06NDo2bNnFBcXR69evWLYsGFx1llnxcKFC+PFF1+M0aNH5z07Jk2aFCtWrIiJEyfG/vvvH926dYv+/fvHSSedFIsWLYqHH354u4+U7UiXXHJJbN68OYqKiuLee++NkpKSnd7m9ttvj379+kXEpz9WdduH+cDnRVGW7SEf0gwAFMwZOgAkQNABIAGCDgAJEHQASICgA0ACBB0AEtDhnxTX2NgYb731VpSVleX20ZcA0FlkWRZ1dXUxYMCAz/yhTh0e9LfeeqvpR0gCALumuro6DjzwwBa/3uFBLysr6+iHBNgj9ejRI+8JBdn2E/s6o08++STvCQXbWT87POieZgfaWmf974rdHa8zbt/2ga472+5FcQCQAEEHgAQIOgAkQNABIAGCDgAJEHQASICgA0ACBB0
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize = [6, 6])\n",
|
||
|
|
"plt.imshow(x_small, cmap = 'gray')\n",
|
||
|
|
"plt.title('Small X', fontsize = 20);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "059710ee",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Niektóre parametry Matlplotlib (np. `figsize`) możemy ustawić na domyślne przy pomocy [`matplotlib.rcParams`](https://matplotlib.org/stable/tutorials/introductory/customizing.html#matplotlib-rcparams).\n",
|
||
|
|
"\n",
|
||
|
|
"Podczas wczytywania obrazów zawierających przezroczystość należy zwrócić uwagę na sposób wczytywania obrazu:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"id": "77861dde",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"img_lfl = cv.imread(\"img/linux_foundation.png\", cv.IMREAD_COLOR)\n",
|
||
|
|
"plt.imshow(img_lfl);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "46deb78b",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Mamy tutaj obraz 4-kanałowy, przez co musimy go wczytać jako `cv.IMREAD_UNCHANGED`, no i dodatkowo odpowiednio go przekonwertować:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"id": "ce0a9061",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"img_lfl = cv.imread(\"img/linux_foundation.png\", cv.IMREAD_UNCHANGED)\n",
|
||
|
|
"plt.imshow(cv.cvtColor(img_lfl, cv.COLOR_BGRA2RGBA));"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "9f0f1db3",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Możemy również podzielić obraz na poszczególne kanały i ew. ponownie je połączyć, odpowiednio przy pomocy funkcji [`cv.split()`](https://docs.opencv.org/4.5.3/d2/de8/group__core__array.html#ga0547c7fed86152d7e9d0096029c8518a) i [`cv.merge()`](https://docs.opencv.org/4.5.3/d2/de8/group__core__array.html#ga7d7b4d6c6ee504b30a20b1680029c7b4):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"id": "60ab993b",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"b, g, r = cv.split(image)\n",
|
||
|
|
"plt.imshow(b, cmap = 'gray')\n",
|
||
|
|
"plt.title('Blue channel', fontsize = 20);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"id": "f8418716",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAGiCAYAAABZHumlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9Sa9tW5bfh/1muapdneIW774XVWZSJA0KlpkCBBlgV4DckeSOWuwT2SKyRYItspPfgIT5Ddh0h51sEiBgyYJkIE2KQUZG8cp7yl2satZqzB1ByRTEgC0zIsA7gIOHe7DfOfusvdYcc45/JUophU/1qT7Vp/pUn+q3uORv+g18qk/1qT7Vp/pU/7b61Kw+1af6VJ/qU/3W16dm9ak+1af6VJ/qt74+NatP9ak+1af6VL/19alZfapP9ak+1af6ra9PzepTfapP9ak+1W99fWpWn+pTfapP9al+6+tTs/pUn+pTfapP9Vtfn5rVp/pUn+pTfarf+vrUrD7Vp/pUn+pT/dbXb7RZ/f2///f50Y9+RNu2/OEf/iH/5J/8k9/k2/lUn+pTfapP9Vtav7Fm9Y/+0T/ib/7Nv8nf+Tt/h//+v//v+Wt/7a/xn//n/zm/+MUvflNv6VN9qk/1qT7Vb2mJ35SR7X/yn/wn/NW/+lf5B//gH/zqe3/5L/9l/sv/8r/kT/7kT34Tb+lTfapP9ak+1W9p6d/EL/Xe89/9d/8df+tv/a3/xff/s//sP+Of/tN/+m+83jmHc+5X/8458/Lywt3dHUKI/7+/30/1qT7Vp/pU//tXKYXL5cKHDx+Q8n970PcbaVZPT0+klHj37t3/4vvv3r3ju++++zde/yd/8if83b/7d/9dvb1P9ak+1af6VP8O68svv+SLL77433zNb6RZ/bL+P09FpZT/1ZPS3/7bf5s//uM//tW/T6cT3//+9/m//l/+ApRELjBeMtv+BikhZs+8jqzrSsyw3e7Yb/ccTyeOp1dyDmw2LT/6vS/4P/+1v8rT40e+/fY7np9e2e1v+OLzD0AipkhMAaTg5fmZbrC8fXvH7//B95n9idPxyOl04fVl5PHjidNxxoeC1ob3n73lP/hLP2S/2xJT4OHpiZfjmcPugJs90zhTkNwc7piXBecDmczhfkCoSIieXAptu8XN4FbFNDpOxxNtH/nRj37IZrejSMV3D0+sq6NtWlprsdailebh4zO7w4HNZss8O376s5/z+3/wF9jf3KCU5vV05PbmACXhlpXX1xcgI9DYpqfttyA0QmmQknGa+PM//ymdHbiczggEbdvR9T1FSkJKrCmyxkA/9EzjSHSO6COX45E//I/+T0ilCTGyLivffvsVUgg2fc/NbsfNfk+KnufHB8ZpRBT40Q9/yJ//9Kd0Xcvh9hYXMsfzkXl1pFzQpuHNu7fc3NwipSLnAlmgteV4OnE6njhfRj57/4HT+YQ2ir7v2W23nC8XLpczblkIKQCCtunQWmOMpW17fvbnP0VKKDkTY6SxDX03kCmEGPE+sDiHFIphu2G33/Mv/+W/4nA48P79O5q24cuvv0IrQdM0aKNQSiEETKczOScabbi7uWUJK10/oIwh58Lj0wun8UxKmZwBJO8//4KCwpqGfthyc3vH68uJaVpJqdB1LV988TlPTx9x60TOEWMNXdcjpEBKgVaKyzjy7bffMo0XYozcHg5opbm9vUEKwcvjE8E7dpstPgSmy4XxdKYxmr5rkUIQQ2AcJ4ahY9huaLuWcRwJ0V8/i4wLjhACMUYQAoSg5EzTdbx584a+71FS4Z1nmVeC9zjvOR9fefv2LcZoUo641RFTZLyMLG4BBHf3b/jhD3/Esjgen154eHzk/v07Dvs7QFAQWGNp+57b23vapkUIWJcFqzVSQIye0+sL/81/+//g937wQ774/ANv394zTSOX04mXlydeX49cLme224H3n71jt9vTdpZpmgh+wTtHCI6cAz/44nO++vpLnp4eWOaZ3/+9P+Du7g3aWIRQpFInRSkmUgyUnNjtduw2LS6sCFHYDi2X6Qwi4f2CW2dyjjSmI0dBTgKSpG06bg4bbFMorIzTC421jPPMMi8sq6NrG7SFUgIpeXIK5FRIQSKKwdqeD++/z3k+sqwLqUSGXYsLkZwFYBGyYVky0+hIMROjZ11GPnt/R9cpUliZxjOUhCwCITWlCJx3XC5HfFhIKYMQaNUyzSslg7Etm+6e/9vf/7+z3W7/rf3iN9Ks7u/vUUr9G6eoh4eHf+O0BdA0DU3T/Bvf79sGSiClTLKFvhMoJSmiYRgkLvS8vl4oOeGDo+sN3mtSTmgT8e4EeWW3aZk3PdNpJC4Ov040raRtBFo3bPY3eDcjFUgJxkoOfYMQBqUs2mxQyqD1xDJHBIrtZsvtfsvd/YFSEkpmlmnELzNCaLq+53QeeXx+QhmDbixdY9nud0gdmeYJ5z1d37HpB4KXtM0MJZLLhcNhYH+7R2iDbjTzvEARUOrCuq4ziMzQ99zf31OK4jIu7HZ7dts9Ummenl5xa0BIiAWKVHRNh7UtKQsW59kfBvrdjtV5XEzc3L8h+YwNAaMNQ78hxsTqPUlAEpCFQGhNAtYQWaaJcZp5eHxi2O1oupbd3Q2jm4jO0W8G9rc3fPbuPeP5yPH1lZILUire3r/Dh8Q8L6xrZHdzw+QC23aLui5Eh5sb+mEgxEjwAWMtJUEzDKhlZX564vn4Ss6RmCUpJwqZFAM5BwoZIcBaS0qBnBMU2O92XMYLXWNp24a+3yKEoghYV8fiHDFlhFQIY9Fti+06toc9w3ZLETBOF16eHvne9z5w2G/o2gYpJY+PD4zjkRgjUgou8wllNG+M4GZzS9cPPB+faFuN95EQMzklcvTYpqNrLZu+o2sscdPT2IbgI6tb8X5hvhzJJWKtYpovtH1L1/UYaxFScp4nigTdGNq+Ydh2jKczsEVJhRCZN2/uMVqzLAvRexZjGLYDtzcHWmuJPvDw+EihkEvBeU9MCakkAvDBcTy+UnJiu9vRDwPGWOZlxrnA5XJmWWZyynjvWecVay3GGNq+o+lalJIYLE3bsTqPj4kkJAiJ7Qak7Xh9fOU0rwjb8f77P2K/van3QoiArJu4fscw9DTW4p2Dkmkag6DQDxv+4Hjie59/4OawQ1vN+DDhgucyTVzGE6tb2O16KJmSIykIUnTE4NntNyi1YxrPCCmJMWJNw3C/4d37D2y3O4SUSKVpuw5jLOfzmWm8EILn/bu3WC2ADCRSCjwdn7CNxLaWptNYo2m0ZV0cfgkEFxl6wXarSGlmns645cK7t9/D+5m5eHJeWVwguQBElILNsGFod5zPCzkqjB14OV74+PKRpjXsDls2ux1yXVnXQkqSlBUpF1IulJKRArQWjOcjVu9pbEN3c0M3KHbbuh5M08LDwxPeC6y1IGoTQ2h8WLG24e72Dd/74i8A/+bB5X+tfiPNylrLH/7hH/Knf/qn/Ff/1X/1q+//6Z/+Kf/Ff/Ff/No/Z9PtaBpYlpHoL9zeNEgNqQTWkBlki24gxoIQGdsaTLvFmC1aF7SEdZnJMUFKkAOnyytNk7i939MPLSlFvJ/5/PN7xunM8fSRf/WTCWUDxihyAaULn3/xlnfvDNPoGC8z3i/89Kc/Zp7v2e429J3GysLX33xku7tnu7shlZnj8YJQlr4fuLnb0PdvkaawuifW5ZnpfOTzDweMsWy3BiklpfSEGJmXmXbY8Nn7d8zTwvl0YRpHpvPIt998B0UiiyaHwrv3n9O3G7775pFljrx7/xmH3R0ICN7hlkRYC0PboETLPE+8vF7Ybd7QNVu8v2B0x/c+/wGP3z2zTgHvIsFPFAQueHyKJAqqsXTtljxA9jCnhegzX375DffvIjf3d9iu5+b2nrCuDF3HMGzZ7Q5oodjtXpnHhcv5zHffPfG9Dz/i6eWZb777jvHiUKpjt9uz2e3Z7vcoY1jdyryuLOtCj2C32XG+TNi240e/9/t8+bOf4/xMihFKRgqBup4yBFAKrHnEGotSGmky6/mC8LGeNZuWfT+QCpzHkWWeWZ3DNi1v7+8xTYM
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"restored_image = cv.merge((r,g,b))\n",
|
||
|
|
"plt.imshow(restored_image);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "9ba6f17e",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Dostęp do poszczególnych pikseli odbywa się tak jak w macierzy, czyli podając wiersz i kolumnę. Poniżej możemy zobaczyć zmianę piksela na współrzędnych `(1, 1)`:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"id": "7a018550",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(x_small, cmap = 'gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"id": "3aebbb19",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"x_small[1, 1] = 255\n",
|
||
|
|
"plt.imshow(x_small, cmap = 'gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "14278fd3",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Przycinanie odbywa się przez znany w Pythonie tzw. *slicing*, czyli wycinki:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"id": "52b49c59",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(x_small[3:8, 2:], cmap = 'gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "e191d0e8",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Możemy również ustawić kolor dla kilku kanałów jednocześnie dla zadanego regionu. Wykonamy to na kopii obrazu:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"id": "94db5076",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"image_edited = image.copy()\n",
|
||
|
|
"image_edited[100:200, 150:250] = (128, 0, 128)\n",
|
||
|
|
"plt.imshow(image_edited[..., ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "f340e7ec",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Przeskalowanie obrazu odbywa się przez [`cv.resize()`](https://docs.opencv.org/4.5.3/da/d54/group__imgproc__transform.html#ga47a974309e9102f5f08231edc7e7529d), w której albo podajemy dokładne docelowe wymiary, albo podajemy współczynniki skalowania na osiach *x* i *y*; możemy też uwzględnić odpowiednią metodę interpolacji. Matplotlib nie wyświetli nam wprost powiększonych obrazków, ale będziemy mogli zauważyć zmianę poprzez np. poprzez zmianę zakresów skali osi *x* i *y*:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"id": "fda0546a",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"x_small_resized_1 = cv.resize(x_small, (40, 40))\n",
|
||
|
|
"plt.imshow(x_small_resized_1, cmap='gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"id": "e85d632e",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"x_small_resized_2 = cv.resize(x_small, None, fx=3, fy=4, interpolation=cv.INTER_CUBIC)\n",
|
||
|
|
"plt.imshow(x_small_resized_2, cmap='gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "09a01036",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Obrót obrazu dokonywany jest przez funkcję [`cv.flip()`](https://docs.opencv.org/4.5.3/d2/de8/group__core__array.html#gaca7be533e3dac7feb70fc60635adf441), w której podajemy według której osi ma nastąpić obrót (`0`: *x*, `1`: *y*, `-1`: obie)."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"id": "fd673085",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"image_flipped = cv.flip(image, 0)\n",
|
||
|
|
"plt.imshow(image_flipped[..., ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "e4c0f0d8",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Może pojawić się potrzeba utworzenia nowych obrazów, więc najczęściej używa się do tego biblioteki NumPy:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"id": "fc86f294",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "bc67f439",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Poniższe wywołania pokazują kilka wariantów. Możemy utworzyć pustą trójwymiarową macierz wypełnioną zerami przy pomocy [`numpy.zeros()`](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"id": "a8cb1c58",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"image_empty = np.zeros((50, 100, 3), dtype='uint8')\n",
|
||
|
|
"plt.imshow(image_empty);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "dde3f2b0",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Jeżeli chcemy ustawić jakąś początkową wartość, to możemy przeskalować tablicę jedynek uzyskaną z [`numpy.ones()`](https://numpy.org/doc/stable/reference/generated/numpy.ones.html):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"id": "7190f1df",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec78b490>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"image_empty = 128*np.ones((50, 100, 3), dtype='uint8')\n",
|
||
|
|
"plt.imshow(image_empty)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "4f785c86",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Jeżeli nowy obraz powinien mieć takie same wymiary jak inny obraz, to możemy do tej operacji użyć [`numpy.ones_like()`](https://numpy.org/doc/stable/reference/generated/numpy.ones_like.html):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"id": "0f99dda7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"empty_from_other = 255*np.ones_like(image)\n",
|
||
|
|
"plt.imshow(empty_from_other);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "ac02a038",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Czasami może być przydatne utworzenie maski zawierającej informację o tym czy np. dany piksel zawiera wartość w danym zakresie. Przy pomocy [`cv.inRange()`](https://docs.opencv.org/4.5.3/d2/de8/group__core__array.html#ga48af0ab51e36436c5d04340e036ce981) możemy sprawdzić zakresy dla trzech kanałów; przy okazji możemy zobaczyć jak zgrupować kilka obrazów jednocześnie (por. [`matplotlib.pyplot.subplot()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html)):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"id": "d536f6e6",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x1000 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"mask = cv.inRange(image, (0,100,150), (100,150,200))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=[10,10])\n",
|
||
|
|
"plt.subplot(121)\n",
|
||
|
|
"plt.imshow(image[..., ::-1])\n",
|
||
|
|
"plt.title(\"Baboon\")\n",
|
||
|
|
"plt.subplot(122)\n",
|
||
|
|
"plt.imshow(mask, cmap='gray')\n",
|
||
|
|
"plt.title(\"Mask\");"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "622a0f0d",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Standardowo operujemy w zakresie liczb całkowitych `[0; 255]`, przez co jeśli w wyniku jakiejś operacji mielibyśmy wykroczyć poza zakres, to skończy się to albo operacją modulo albo np. uzyskaniem wartości ujemnych, co ostatecznie przełoży się na błędny wynik.\n",
|
||
|
|
"\n",
|
||
|
|
"Załóżmy, że chcemy podbić kontrast o 50%. Przemnożenie obrazu o wartość 1.5 zwróci nam tablicę o wartościach liczb rzeczywistych:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"id": "d48cf055",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"float64\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_image = image * 1.5\n",
|
||
|
|
"print(new_image.dtype)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "b2dbeacc",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"W takim wypadku możemy naiwnie spróbować przekonwertować to ponownie do liczb całkowitych, ale wartości powyżej 255 zostaną obliczone według operacji modulo, przez co wynik będzie niezadowalający:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"id": "411dabb9",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_image = np.uint8(new_image)\n",
|
||
|
|
"plt.imshow(new_image[..., ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "1fea50c7",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Rozwiązaniem jest przycięcie wartości przy użyciu [`numpy.clip()`](https://numpy.org/doc/stable/reference/generated/numpy.clip.html) do zakresu `[0; 255]` jeśli operujemy na liczbach całkowitych, z ew. znormalizowaniem wartości do zakresu `[0; 1]` jeśli chcemy pracować na liczbach rzeczywistych:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"id": "eac38489",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_image = image * 1.5\n",
|
||
|
|
"clipped_new_image = np.clip(new_image, 0, 255)\n",
|
||
|
|
"clipped_new_image = np.uint8(clipped_new_image)\n",
|
||
|
|
"plt.imshow(clipped_new_image[..., ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"id": "bc2cb3df",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_image = (image * 1.5)/255\n",
|
||
|
|
"clipped_new_image = np.clip(new_image, 0, 1)\n",
|
||
|
|
"plt.imshow(clipped_new_image[..., ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "a2f8c4a5",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Jeśli zwiększymy jasność i potencjalnie wyjdziemy poza zakres `[0; 255]`, to ponownie wartości zostaną przekręcone:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"id": "ce823b52",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_image = image + 50\n",
|
||
|
|
"plt.imshow(new_image[..., ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "4be595c9",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Z drugiej strony należy mieć też na uwadze pewne subtelne konwersje, które mogą odbywać się niejawnie. Poniżej mamy operację, która powoduje użycie danych typu `int16`, przez co następuje niejawna konwersja i przycięcie (jest o tym informacja w ostrzeżeniu):"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"id": "c93528a2",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAGiCAYAAABZHumlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9Sa+lW5rXCf5W+3a7O4011+693kREJhklUpVFICFKxRSJETBilHMUIxQjECOYxDcAiW/AF2DCLFGiUlUhSCkKCic8vLmdmZ1uN2+3+hqs7U5lUcp0VVHp7sIe6ch0jo7ts/e797uetZ5/J0ophU/1qT7Vp/pUn+o3uOSv+wl8qk/1qT7Vp/pU/2v1qVl9qk/1qT7Vp/qNr0/N6lN9qk/1qT7Vb3x9alaf6lN9qk/1qX7j61Oz+lSf6lN9qk/1G1+fmtWn+lSf6lN9qt/4+tSsPtWn+lSf6lP9xtenZvWpPtWn+lSf6je+PjWrT/WpPtWn+lS/8fWpWX2qT/WpPtWn+o2vX2uz+kf/6B/xwx/+kLZt+YM/+AP+xb/4F7/Op/OpPtWn+lSf6je0fm3N6p/+03/K3/k7f4e///f/Pv/6X/9r/spf+Sv8tb/21/j5z3/+63pKn+pTfapP9al+Q0v8uoxs/9Jf+kv8hb/wF/jH//gf//Jnv//7v8/f+Bt/gz/+4z/+dTylT/WpPtWn+lS/oaV/HX/Ue8+/+lf/ir/7d//u/+znf/Wv/lX+5b/8l//J7zvncM798vucM8/Pz9zd3SGE+P/78/1Un+pTfapP9Z+/SilcLhfevXuHlP/Lg75fS7N6fHwkpcSbN2/+Zz9/8+YN79+//09+/4//+I/5B//gH/xv9fQ+1af6VJ/qU/1vWF999RVffPHF/+Lv/Fqa1S/q//1UVEr5/3hS+nt/7+/xR3/0R7/8/nQ68b3vfY//6nuSFDOlwHiBbS8RspByYV5h9RAjbLeK3dZwOq0cz5ALbAbBD3/Y8n/8P/15Hh4/8v67Z54eL+x2DV988Q5IxBSJKYAUPD890fUNr97c8ru/9z0Wd+J0PHI6XXh5nnj4mDgdwQfQGt58Zvhz/82X7Hc7Ygp8fHjk+XjmsD+wzo55XChIbg53zPOCC4FM5nA/IGTAp0Aphbbdss7gV8U0Oo7HE10X+eEPv8+w31OE5MPDE+vqaJuWxliaxqKV5uOHJ3b7A5vtlnle+clPf87v/t5/xf7mBqU0L6cjtzcHKIl1WTi+vAAZgcI2A023BakRSoOUTNPEn/3ZT2htz3i6IBC0bUfX9xQpCSmxxsASI8OmZxpHonNEF7gcj/zBf/d/QGpNiJFlcbz/9mukEGyGnpvdjpv9jhQCj48fmcYRUeCHP/gBf/aTn9D1LYfbW1xIHM8n5sWRckFZy6s3b7i5uUVJRc6FkgVGG46nM6fjifN55LPPPuN0PqO1ohs6dtsdl8uZy+XMuqzEGABB07YYbTDa0nYdP/2znyAllJyJMdHYhq4byKUQY8SHwLI6hJRsNlt2hz3/4T/8KYfDgbdv3mC7hq+//hqtBLa1GKNRUiEETOczOSWsMdzd3LL6lXbo0caSc+Hj4xPn8UJMmZIBJG/ffU5BYW1L12+5ub3j5fnEPC+kVOi6ji++eMfj40fcOpJzQhtN3w8IKZBSoKRknCa+++47xsuZlBI3hwNGKm5vb5BC8Pz4hPeO/WaL957pMnI5n2mNpmsblBCEGJnGkX7o2Ww3NG3LNI344FFKknLBB0cInhBjvbeFIOdM2/W8un9FP/QoqXDOsc6O4D3OOU6nF968foMxmpQj6+pIMTKOI4tbAMHd3St+8MMfMi8rj4/PfPz4yP1nbzjs7wBBQWCMpet6bu/uaZsGBLh5xRqNEJCi5/T8zL/6v/6f+cEPfsDnn3/O69f3jOOF8Xzi6emR4/HI5XJmu9nw9t0bdrsdbdswzRPeLQTvCMGRkuf7X37BN9/8nMeHB5Zl5nd/5/e4u3uFMhYhJLmAc54UEzF4Sknsd3t2mxbnV4QobDYt43QGkXB+wa8TOSca05GjIEcBSdK0LTeHLU1TKKyM4zO2sUzzxDKvLOtK17YoC6V4cgr1KxZSlIhisKbn3Wff4zwfmdeFXCLDtsHFRMoCMAjZsi6ZcVzJMRNDYFlH3r25pe01OaxM4xlKQhaBkIpSBKvzjOMR7xdSSiAkWrdM80rOBWs79pvX/I//w5+x3W7/V/vFr6VZ3d/fo5T6T05RHz9+/E9OWwBN09A0zX/y81YrkiikVLAKGgtKCYqAti24AC8vEENiXRO2gcZAyiBEYZkdKc5sOsOmM5wluMmxTBeaVmC0oGs1m/0NyzwhJVASShW2O00uEoRCqBaEuz5mQSAYupb9ZuDudkspCUpgvJxYxhFQ2KbhdB55//EDyhi0tbRNS7/ZInWEecJ5j20sXTMQvMTomRQduVzY7jr2NxuENkgjmecFioBSF9ZpvpByoG0bbm5uOBwUx9PEMGwY+g1SaT58eGSeVoSEEDOxQNe0WNuSsmBaFvaHG/rdjtV5pPPsbm5JPqMai9GGvt8QY2JZVpKAQCaWTBYCnxLL6ljGifky8t2Hjwy7HU3Xsjns6Ocd0bn6/X7H6zdvGc9Hnp4eiTGipOLu9p7FeeZ5YZocu5sbzrOj3/UoY2n7nsPNDd0wEGIk+4DpLDmBblvQE5dlQj09kXNEBomPnpQiKYbrIhPIJWFtQ/COGALJJDabgePpRNdY2rZhGHqEUKScWNaVxTliygipUEojrEFZS7/d0PY9sSTW85GHj+/58st37DY9XdsgpeTh4SOn0xMxRqQUnC7PKKN5pV5z092y6Td8fPqAMYJSCiFncooEv2KbDmsUfWtpjGLoG4zWBB9Z3cqyTFyOT+QSsVZxGSdsa+nbHmMtQkrieCGWhDQK2xq63jKezqQ8IJUi58Dd7Q1GaxAFt2qElLR9x83NgdZaog98FIJCqdd+XXDeI2Qh58y6LrwcXyg5sd3t6IcBYyzzMuOc43h6YZwu5JTx3rPOK9ZajDHYpkFbg1QSiUIby+o8i/eoXEBIVNNSlOXp9MjzZSZry6vPv8d+e0OIkRAiIDFNi2kG2qGnsRbfOSiZpjEICk3b8b3f/a/5/PN33Bx2CKU4Xy6s68rpcuF4emFdF4ahJcVADB4vCt7NeLey2Q4oNTCNZ0opOOdRSnNzc8f9qzdstzuElEilabsOYyzn85lpvBCC5/WbV1gtgA2QSCkwPy3YRqKNwtgN1mgabVkXh18CwUW6tjD0kNLCMh2ZpyP391+yLhdiXAhhIWVHmgMQUQo2w4btdsf5vJCjQpqWh6cjH56/pWkNu8OWbrOhrCvrWkhJkgP4kAkhUVKilIQUmdPxGSX3NFZzs9vRDYrdtq4H07Tw8eMj65pRSoHQCKlBaJZ1pm0b7m5v+cH3f5f/8X/4s18Jzvm1NCtrLX/wB3/AP//n/5y/+Tf/5i9//s//+T/nr//1v/4rP86mt2itWZaF6OH2xiJ1IZXEGiKDBN3U05UQYFswLRhTTz9aFtZlJsdUO1iG0wWa5pHb+55+aEgp4v3M55/fMk5njqcP/OmPJ5QNGKPIBZQufP7Fa968MUyjY7zMeL/wk5/8iHm+Z7vb0HcaKwvffDuy3e3Y7gZSmTkeHUIF+r5wc7eh718jTWF1j6zLE9P5yOfvDhhj2W4NUkpK6QkxMi8z7bDhs7dvmKeF8+nCNI5M55Hvvn0PRSKLJofCm7ef07cb3n/7wDJH3rz9jMPuDgQE73BLIqyFoW1QomWeJ55fLuw2r+iaLd5fMLrjy8+/z8P7J9Yp4F0k+ImCwAWPT5FEQTWWrt2SB8gelrSAz3z11bfcv4nc3N9hu56b23vCujJ0HcOwZbc7oIVit3thHhcu5zPv3z/y5bsf8vj8xLfv3zNeHEp17HZ7Nrs92/0eZQyrW5nXlWVd6BHsNjvOlwnbdvzwd36Xr376M5yfSTFCyUghUFKglUIApcCaR6yxKKWRJrOeLwgfyQh007LvB1KB8ziyzDOrc9im5fX9PaZpMMZiBLx784qcC24eWeY
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_image = image + (-50)\n",
|
||
|
|
"plt.imshow(new_image[..., ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "6b3a8f53",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Rozwiązaniem powyższych problemów może być w przypadku zmiany jasności użycie funkcji [`cv.add()`](https://docs.opencv.org/4.5.3/d2/de8/group__core__array.html#ga10ac1bfb180e2cfda1701d06c24fdbd6) i [`cv.substract()`](https://docs.opencv.org/4.5.3/d2/de8/group__core__array.html#gaa0f00d98b4b5edeaeb7b8333b2de353b), a w przypadku zmiany kontrastu użycie [`cv.multiply()`](https://docs.opencv.org/4.5.3/d2/de8/group__core__array.html#ga979d898a58d7f61c53003e162e7ad89f), konwersja do typu np. `float64` i powrót do `uint8` z przycięciem wartości."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"id": "fba240f9",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"matrix = np.ones(image.shape, dtype='uint8') * 50\n",
|
||
|
|
"\n",
|
||
|
|
"image_brighter = cv.add(image, matrix)\n",
|
||
|
|
"plt.imshow(image_brighter[:, :, ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 40,
|
||
|
|
"id": "79b54cc1",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"image_darker = cv.subtract(image, matrix)\n",
|
||
|
|
"plt.imshow(image_darker[:, :, ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"id": "2fe3e9d5",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"matrix = np.ones(image.shape, dtype = 'float64')\n",
|
||
|
|
"\n",
|
||
|
|
"image_lower = np.uint8(cv.multiply(np.float64(image), matrix, scale = 0.8))\n",
|
||
|
|
"plt.imshow(image_lower[:, :, ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 42,
|
||
|
|
"id": "65da736b",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"image_higher = np.uint8(np.clip(cv.multiply(np.float64(image), matrix, scale = 1.2) , 0, 255))\n",
|
||
|
|
"plt.imshow(image_higher[:, :, ::-1]);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "54e22dd0",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Zapisywanie obrazów\n",
|
||
|
|
"\n",
|
||
|
|
"Funkcja [`cv.imwrite()`](https://docs.opencv.org/4.5.3/d4/da8/group__imgcodecs.html#gabbc7ef1aa2edfaa87772f1202d67e0ce) zapisuje obraz do pliku. Pierwszy argument to nazwa pliku, drugi argument to obraz, który chcemy zapisać. Np. poniższe polecenie spowodowałoby zapisanie obrazu w formacie PNG w katalogu roboczym:\n",
|
||
|
|
"\n",
|
||
|
|
"```python\n",
|
||
|
|
"cv.imwrite('pawian.png', image)\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"## Anotowanie\n",
|
||
|
|
"\n",
|
||
|
|
"Sprawdźmy w jaki sposób do pustego obrazu dodać kilka obiektów geometrycznych. Użyjemy do tego funkcji [`cv.line()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2), [`cv.circle()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#gaf10604b069374903dbd0f0488cb43670) , [`cv.rectangle()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga07d2f74cadcf8e305e810ce8eed13bc9), [`cv.ellipse()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga28b2267d35786f5f890ca167236cbc69), [`cv.polylines()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#gaa3c25f9fb764b6bef791bf034f6e26f5) i [`cv.putText()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga5126f47f883d730f633d74f07456c576). Funkcje te będą nadpisywały wejściowy obraz.\n",
|
||
|
|
"\n",
|
||
|
|
"W powyższych funkcjach pojawiają się wspólne argumenty:\n",
|
||
|
|
"\n",
|
||
|
|
"* `img` - obraz, na którym będziemy umieszczać obiekty,\n",
|
||
|
|
"* `color` - kolor obiektu podany w formacie jako krotka (*tuple*), np. `(255, 0, 0)`; w przypadku obrazów w skali odcieni szarości wystarczy podać wartość skalarną,\n",
|
||
|
|
"* `thickness` - grubość linii, okręgu, itp.; w przypadku wartości `-1` w przypadku zamkniętych figur jak okrąg, obiekt zostanie wypełniony wewnątrz wskazanym kolorem; domyślna wartość to `1`,\n",
|
||
|
|
"* `lineType` : rodzaj linii, np. 8-sąsiedztwo, antyaliasing, itp.; domyślnie jest 8-sąsiedztwo, natomiast wartość [`cv.LINE_AA`](https://docs.opencv.org/4.5.3/d0/de1/group__core.html#ggaf076ef45de481ac96e0ab3dc2c29a777a85fdabe5335c9e6656563dfd7c94fb4f) włącza antyaliasing.\n",
|
||
|
|
"\n",
|
||
|
|
"Na początku zaimportujemy bibliotekę NumPy i przy pomocy funkcji [`numpy.zeros()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html) utworzymy pusty 3-warstwowy czarny obraz o rozmiarze 512x512."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"id": "efd40bcf",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec97aca0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 43,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np\n",
|
||
|
|
"\n",
|
||
|
|
"img = np.zeros((512,512,3), np.uint8)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "2b23ae8a",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"W dalszej części operowali na obrazie w formacie RGB.\n",
|
||
|
|
"\n",
|
||
|
|
"### Linie\n",
|
||
|
|
"\n",
|
||
|
|
"Aby narysować linię, musimy podać początkowe i końcowe współrzędne linii. Przy pomocy [`cv.line()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2) narysujemy na obrazie czerwoną linię od lewego górnego rogu do prawego dolnego rogu o grubości 5 pikseli."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"id": "eecb08e6",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec9d03d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 44,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"cv.line(img, (0,0), (511,511), (255,0,0), 5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "bb1a0101",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Prostokąty\n",
|
||
|
|
"\n",
|
||
|
|
"Aby narysować prostokąt, potrzebujemy lewego górnego rogu i prawego dolnego rogu prostokąta. Tym razem przy pomocy [`cv.rectangle()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga07d2f74cadcf8e305e810ce8eed13bc9) narysujemy zielony prostokąt w prawym górnym rogu obrazu."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"id": "9d3cc9ad",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ecaa2460>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"cv.rectangle(img, (384,0), (510,128), (0,255,0), 3)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "cdcb4b70",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Okręgi\n",
|
||
|
|
"\n",
|
||
|
|
"Aby narysować okrąg, potrzebujemy jego współrzędnych środka i promienia. Przy pomocy [`cv.circle()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#gaf10604b069374903dbd0f0488cb43670) narysujemy okrąg wewnątrz prostokąta narysowanego powyżej."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 46,
|
||
|
|
"id": "48207862",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec8de790>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 46,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"cv.circle(img, (447,63), 63, (0,0,255), -1)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "60678ca6",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Elipsy\n",
|
||
|
|
"\n",
|
||
|
|
"Aby narysować elipsę, musimy przekazać kilka argumentów. Jednym argumentem jest położenie środka `(x, y)`. Następnym argumentem jest długość dwóch osi (długość głównej osi i mniejsza długość osi). Kolejny parametr to kąt obrotu elipsy w kierunku przeciwnym do ruchu wskazówek zegara. Kolejne dwa argumenty oznaczają początek i koniec łuku elipsy mierzonego zgodnie z ruchem wskazówek zegara od głównej osi. tj. podanie wartości `0` i `360` daje pełną elipsę. Aby uzyskać więcej informacji, sprawdź dokumentację [`cv.ellipse()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga28b2267d35786f5f890ca167236cbc69). Poniższy kod rysuje pół elipsy na środku obrazu."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 47,
|
||
|
|
"id": "4423e4b0",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec85ef10>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 47,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"cv.ellipse(img, (256,256), (100,50), 0, 0, 180, 255, -1)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "1219997d",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Poligony\n",
|
||
|
|
"\n",
|
||
|
|
"Aby narysować wielokąt, najpierw potrzebujemy współrzędnych wierzchołków. Ustawmy te punkty w tablicy o wymiarach `liczba_wierszy x 1 x 2`, gdzie `liczba_wierszy` to liczba wierzchołków i powinna być typu `int32`. Tutaj narysujemy mały wielokąt z czterema wierzchołkami w kolorze jasnoniebieskim."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"id": "60223ef1",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec74a5e0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 48,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pts = np.array([[10,5], [20,30], [70,20], [50,10]], np.int32)\n",
|
||
|
|
"pts = pts.reshape((-1, 1, 2))\n",
|
||
|
|
"cv.polylines(img, [pts], True, (0,255,255))\n",
|
||
|
|
"\n",
|
||
|
|
"plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "cdba71d3",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Wielokąt może wydawać się przerywany, ale jeśli powiększylibyśmy wynikowy obraz, to byśmy zobaczyli, że jest on zamknięty.\n",
|
||
|
|
"\n",
|
||
|
|
"Jeśli trzecim argumentem [`cv.polylines()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#gaa3c25f9fb764b6bef791bf034f6e26f5) jest `False`, otrzymamy niedomknięty poligon.\n",
|
||
|
|
"\n",
|
||
|
|
"Funkcja `cv.polylines()` może być używana do rysowania wielu linii. Wystarczy utworzyć listę wszystkich linii, które chcemy narysować i przekazać je do funkcji. Wszystkie linie zostaną narysowane osobno. Jest to znacznie szybszy sposób na narysowanie grupy linii niż wywołanie osobno [`cv.line()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2) dla każdej linii.\n",
|
||
|
|
"\n",
|
||
|
|
"### Tekst\n",
|
||
|
|
"\n",
|
||
|
|
"Aby umieścić tekst na obrazie, musimy określić następujące rzeczy:\n",
|
||
|
|
"\n",
|
||
|
|
"* dane tekstowe, które chcemy napisać,\n",
|
||
|
|
"* współrzędne miejsca, w którym chcemy umieścić tekst (np. lewy dolny róg, w którym zacznie się tekst),\n",
|
||
|
|
"* typ czcionki (sprawdź dokumentację [`cv.putText()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga5126f47f883d730f633d74f07456c576) dla obsługiwanych czcionek),\n",
|
||
|
|
"* rozmiar czcionki,\n",
|
||
|
|
"* pozostałe rzeczy takie jak kolor, grubość, typ linii, itp.; dla uzyskania lepszego wynikowego wyglądu zaleca się `lineType = `[`cv.LINE_AA`](https://docs.opencv.org/4.5.3/d0/de1/group__core.html#ggaf076ef45de481ac96e0ab3dc2c29a777a85fdabe5335c9e6656563dfd7c94fb4f).\n",
|
||
|
|
"\n",
|
||
|
|
"Na naszym obrazie umieścimy tekst w kolorze białym."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 49,
|
||
|
|
"id": "2de2bcf6",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec84c850>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 49,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"cv.putText(img, 'OpenCV', (10,500), cv.FONT_HERSHEY_SIMPLEX, 4, (255,255,255), 2, cv.LINE_AA)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.imshow(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "326c28ac",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Czasami potrzebujemy jednak dokładnych informacji o wynikowym tekście, tak aby np. odpowiednio go umiejscowić lub umieścić pod nim kontrastowe tło. Do uzyskania tych informacji pomocne są funkcje [`cv.getTextSize()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga3d2abfcb995fd2db908c8288199dba82) i [`cv.getFontScaleFromHeight()`](https://docs.opencv.org/4.5.3/d6/d6e/group__imgproc__draw.html#ga442ff925c1a957794a1309e0ed3ba2c3)."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "eec58cd3",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Wyświetlanie obrazów poza Jupyterem\n",
|
||
|
|
"\n",
|
||
|
|
"### HighGUI\n",
|
||
|
|
"\n",
|
||
|
|
"W OpenCV obraz standardowo wyświetla się przy pomocy modułu HighGUI. Przy użyciu funkcji [`cv.imshow()`](https://docs.opencv.org/4.5.3/d7/dfc/group__highgui.html#ga453d42fe4cb60e5723281a89973ee563) możemy wyświetlić obraz w oknie. Okno automatycznie dopasowuje się do rozmiaru obrazu.\n",
|
||
|
|
"\n",
|
||
|
|
"Pierwszy argument to nazwa okna. Drugi argument to nasz obraz. Możemy utworzyć dowolną liczbę okien, ale z różnymi nazwami okien.\n",
|
||
|
|
"\n",
|
||
|
|
"```python\n",
|
||
|
|
"cv.imshow('image', image)\n",
|
||
|
|
"cv.waitKey(0)\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"Pod Windowsem wyświetli nam się wskazany przez nas obraz, natomiast pod Linuxem mechanizm HighGUI przy wyświetlaniu obrazu udostępnia trochę więcej opcji i informacji o wyświetlanym obrazie.\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"Zwróćmy uwagę, że obraz pojawia się dopiero po poleceniu `cv.waitKey(0)` oraz będzie oczekiwał na wciśnięcie klawisza.\n",
|
||
|
|
"\n",
|
||
|
|
"[`cv.waitKey()`](https://docs.opencv.org/4.5.3/d7/dfc/group__highgui.html#ga5628525ad33f52eab17feebcfba38bd7) jest funkcją związaną z klawiaturą. Jej argumentem jest czas w milisekundach. Funkcja czeka przez określony czas na każde zdarzenie klawiatury. Jeśli w tym czasie zostanie naciśnięty dowolny klawisz, program przejdzie do dalszych operacji. Jeśli jako argument zostanie przekazane 0, to funkcja czeka bez końca na uderzenie klawisza klawiatury. Można również ustawić wykrywanie konkretnych klawiszy, takich jak naciśnięcie klawisza `<A>`, itp. Poza przetwarzaniem zdarzeń klawiatury, ta funkcja przetwarza również wiele innych zdarzeń GUI, więc **musisz** użyć jej przy wyświetleniu obrazu.\n",
|
||
|
|
"\n",
|
||
|
|
"Klikając na obraz i wciskając `<Enter>`, konsola zwróci informację liczbową o wciśniętym klawiszu, a ponadto obsługa programu wróci z powrotem do konsoli. Wydając poniższe polecenie usuniemy wszystkie okna:\n",
|
||
|
|
"\n",
|
||
|
|
"```python\n",
|
||
|
|
"cv.destroyAllWindows()\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"[`cv.destroyAllWindows()`](https://docs.opencv.org/4.5.3/d7/dfc/group__highgui.html#ga6b7fc1c1a8960438156912027b38f481) usuwa wszystkie utworzone przez nas okna, a jeśli chcemy usunąć konkretne okno, należy użyć funkcji [`cv.destroyWindow()`](https://docs.opencv.org/4.5.3/d7/dfc/group__highgui.html#ga851ccdd6961022d1d5b4c4f255dbab34), w której jako argument podajemy dokładną nazwę okna.\n",
|
||
|
|
"\n",
|
||
|
|
"Istnieje specjalny przypadek, w którym możemy utworzyć okno i później załadować do niego obraz. W takim przypadku określamy czy rozmiar okna jest zmienny, czy nie. Robi się to za pomocą funkcji [`cv.namedWindow()`](https://docs.opencv.org/4.5.3/d7/dfc/group__highgui.html#ga5afdf8410934fd099df85c75b2e0888b). Domyślna flaga to [`cv.WINDOW_AUTOSIZE`](https://docs.opencv.org/4.5.3/d7/dfc/group__highgui.html#ggabf7d2c5625bc59ac130287f925557ac3acf621ace7a54954cbac01df27e47228f), ale jeśli określimy flagę jako [`cv.WINDOW_NORMAL`](https://docs.opencv.org/4.5.3/d7/dfc/group__highgui.html#ggabf7d2c5625bc59ac130287f925557ac3a29e45c5af696f73ce5e153601e5ca0f1), to możemy zmienić rozmiar okna. Będzie to pomocne, gdy obraz jest zbyt duży:\n",
|
||
|
|
"\n",
|
||
|
|
"```python\n",
|
||
|
|
"cv.namedWindow('image', cv.WINDOW_NORMAL)\n",
|
||
|
|
"cv.imshow('image', image)\n",
|
||
|
|
"cv.waitKey(0)\n",
|
||
|
|
"cv.destroyAllWindows()\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"Interfejs HighGUI czasami pomaga w debugowaniu, ale trzeba mieć też świadomość, że nie jest on specjalnie rozbudowany i zasadniczo nie służy do budowania zaawansowanych interfejsów graficznych. HighGUI obsługuje sygnały idące nie tylko z klawiatury, ale również myszy, a ponadto pozawala na umieszczenie np. sliderów. Krótkie przykłady znajdują się w samouczkach opisujących [`cv.setMouseCallback()`](https://docs.opencv.org/4.5.3/db/d5b/tutorial_py_mouse_handling.html) oraz [`cv.getTrackbarPos()` i `cv.createTrackbar()`](https://docs.opencv.org/4.5.3/d9/dc8/tutorial_py_trackbar.html).\n",
|
||
|
|
"\n",
|
||
|
|
"### Matplotlib\n",
|
||
|
|
"\n",
|
||
|
|
"Po przekonwertowaniu obrazu do formatu RGB wyświetlamy go przy pomocy `pyplot`. Używamy przy tym funkcji [`matplotlib.pyplot.imshow()`](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow) oraz [`matplotlib.pyplot.show()`](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show):\n",
|
||
|
|
"\n",
|
||
|
|
"```python\n",
|
||
|
|
"plt.imshow(image2)\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"Funkcja [`matplotlib.pyplot.show()`](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show) posiada opcjonalny argument `block` (domyślna wartość `True`), która kontroluje czy funkcja jest blokująca:\n",
|
||
|
|
"\n",
|
||
|
|
"```python\n",
|
||
|
|
"plt.imshow(image2)\n",
|
||
|
|
"plt.show(False)\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"lub krócej:\n",
|
||
|
|
"\n",
|
||
|
|
"```python\n",
|
||
|
|
"plt.imshow(image2)\n",
|
||
|
|
"plt.show(0)\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"**Uwaga**: jeżeli w skrypcie ustawimy argument `block` na `False`, to po uruchomieniu skryptu najprawdopodobniej nie zobaczymy wynikowego obrazu/diagramu, ponieważ skrypt zacznie wykonywać dalsze polecenia i np. zakończy działanie programu. Ustawienie argumentu `block` na `False` najlepiej sprawdza się podczas pracy/eksperymentowania w konsoli interpretera Pythona.\n",
|
||
|
|
"\n",
|
||
|
|
"## Moduły\n",
|
||
|
|
"\n",
|
||
|
|
"[Lista modułów OpenCV](https://docs.opencv.org/4.5.3/) jest dość długa i zawiera pakiety algorytmów m.in. dla przetwarzania obrazów, uczenia maszynowego, głębokich sieci neuronowych, fotografii obliczeniowej, dedykowane rozwiązania dla CUDA, przepływ optyczny, operacje dla obrazów w logice rozmytej, przetwarzania RGB-D i wiele, wiele innych. Z poziomu Pythona dostęp do poszczególnych algorytmów odbywa się bezpośrednio przez moduł `cv2` (u nas alias `cv`). Część klas i funkcji ma przedrostek oznaczający moduł, np. `cv.bgsegm_BackgroundSubtractorCNT(...)` oznacza klasę [BackgroundSubtractorCNT](https://docs.opencv.org/4.5.3/de/dca/classcv_1_1bgsegm_1_1BackgroundSubtractorCNT.html) z modułu [bgsegm](https://docs.opencv.org/4.5.3/df/d5d/namespacecv_1_1bgsegm.html) zawierającym udoskonalone metody segmentacji pomiędzy tłem a pierwszym planem.\n",
|
||
|
|
"\n",
|
||
|
|
"## Ćwiczenie 1\n",
|
||
|
|
"\n",
|
||
|
|
"Wczytaj plik [`img/soyjaks.jpg`](https://knowyourmeme.com/memes/two-soyjaks-pointing) i spróbuj odtworzyć poniższy obrazek."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 50,
|
||
|
|
"id": "ff692a8e",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0ec8bb670>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 50,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAGiCAYAAADnSLH4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d7xt11nfC3/HGLOtuuvpTdJROeqyZVtyt3EBAzY2EAwEAiQXAiYEBy5gSm4gIcANNySEFlJuIHQwfnEvso2rZMtW7/30c3Zvq805R3n/GGOutXY5ksj7ua9fvZxHn6O991qzjDHmmE/5PU045xwX6SJdpIt0kf7ek/xaD+AiXaSLdJEu0v9v0EWBcJEu0kW6SBcJuCgQLtJFukgX6SIFuigQLtJFukgX6SIBFwXCRbpIF+kiXaRAFwXCRbpIF+kiXSTgokC4SBfpIl2kixTookC4SBfpIl2kiwRcFAgX6SJdpIt0kQJdFAgX6SJdpIt0kYCvsUD43d/9XS699FKyLOPmm2/m85///NdyOBfpIl2ki/T3mr5mAuEv/uIvePe7383P//zPc8899/DqV7+at7zlLZw8efJrNaSLdJEu0kX6e03ia1Xc7pZbbuHFL34xv/d7vzf87Oqrr+btb387v/qrv/q1GNJFukgX6SL9vaboa3HToii46667eM973rPp8ze/+c3cfvvt247P85w8z4d/W2tZXl5mZmYGIcT/4+O9SBfpIl2kFzI559jY2GD//v1IeWFg6GsiEBYXFzHGsGfPnk2f79mzh/Pnz287/ld/9Vf5pV/6pf9vDe8iXaSLdJH+/5JOnTrFwYMHL/j910QgVLRVu3fO7ajx/+zP/iw/8RM/Mfx7bW2Nw4cP8wPf9w72Hr6EwSCn19mg39sgcgOuuuIgjURw+tRp8rwPCLR29AY52oI2BiEF2hiMNggEIHDW4ZwIYwGHBCfD35Y0lRw5sJt6KyOJIiKVUBQFp0+dBme5+qorefjBx5k/vUSjnjEx2SKOJAgLgDUGi0fonLXgQGtDv2/o9gf0ewNqaUK9UUMISZxEqCgGqYizOo1Wm9bUFLt2zdKaaNOcbJFlKULAM888wb33f5UkgjRLKcsSY6AoNb1ujrUgBCAcQsDExDQqTjl+8hzzC6solXLu9Gmuu+5Krr36SqTL6SzPUxZdIunIsphmLSFKBEoKlBJYo2k1I17z2utpT8RIAUICDhAghQL8M63+WStZmu/ziffdyeMPnOfIVZdw88tfTG5y1pZXiZKILM2YX1rm7Ol5ev0BrXbG7t2zrKyssji/SqNRJ4ojBBbpHNicfQcavOwVx4hTgzMG48KaD3eNotdzfPGLj7O6btFG4BAU1lJoRy93dDfWOXbpIV586SGWjx/HliXT+/ay6+BeyqJk/vQZ9h88SHfQp8gLpndPI6ylt77Oeq/H9IEDfP6ue1lc76JRWKA0DqM1IEFIoigiSzKsk6ytd1lZWadWUxy7bD/1WDPdluBKv0eEwKGZnE55zWtvIIkBa8MCE36GxXYOZ0YfCeXC7w4Bfm8bh7MSXUoefeQEK6tdpIxQSiKEv06sJLVaRhzHCATGQWmhm+d0BwXaOKyNWFrOefTJ8ySR5EXXHuTYZXV2TcckicA5C86ibIk0JcJp4jiCsP8cGiEdCIM1JQKLYGxaIwYBSvifjjBP4dcSN3q4m3iGqJgJOOt5yqZjBM5JbCmZX+zQ7Ru0iekWKedWHE+ctMS1I3Q2SlbOnGHl/Bls3iNSEZFSSOmIE8n0gTq3fN217D0wiXMFYBFCDKcgRcVHHBUqL4TwvwuBtRZtDcaWWKtxOBBu7FwZ+JJDOIsU0j9jHGL4u5+XkxLnQGuHNo6i1DgioMHtd57k4598kPWNmNNnHqXVavFs9DURCLOzsyiltlkD8/Pz26wGgDRNSdN02+dRrcHErr00rCNdW2XWFtRUyfRUyjNPPsLpM3NYYzHWAgKU8otp8YuIQBAFRiZw0oH1D8ViCUuOEBKBY++uNjffdBXNtsJhUEIAiiOHprj33ofpD9a5+thl9Fc7JFFEogRJqnBCIRxYLXHC4fAbXCJARDTaCY0S4ihldmaGvbtn2bV7ltldM0xMT5DWUxoTLZJaghAgpcQKsNaFFwRuvPF6Xvzi67jn7i+yurZMv5dTFhatHUpI+nnht5gUOBxSQJomTLRaLC13yLKUKE3o5wVJlpIIhc1r9CmIpSWNFWkWkcaSKFbEsQSnSVNIE2i3U4SwQ4FeCXcpJUIIlJQIFM4qKCSzEy3OJcu0s4yZySYFCZgBcRJRrzcY5H3mlaCepignwGic1tTrCUkcUaun/hkAzjiUhEY9pVZ3OFvinMMvTyWIoJZJZmYm6PTWkFGEkIrIWFShPdMwDZRKmJmdQayvsnD2NJPTddqtGkVPsCEEE7UMdEFaSjJtsFoT5yWx1Uy0GrRaTdaKEoEEK5G2pMALfyUEcaRIYolBEqcRIpI4Y4ijiDSV1OoVIyAwD0m9ltJopNRrkVckrAmsAjyD85JAWOuZpCMwQC8dvECwGOMAhckl7WaKHpQIFXkhjkVKiNOELEuII4VEYB1oJxAROBxFYbBOUcti4kRhjfXPrJbQqMUkyiGkQAiDNI7IWoRQSCURjISZkAYnLQ6JcGJsuIHZi6CkyUogVKxWDNfHHxuOrxj/GFWMeLOiKXBWkA8sjUwgRYS1MXGs6BUwMS04fN3VRM29NKI6j37lLr7yt59mde4coiiR0tKUKXES0ahlNGoRxlrsUDp5QWysDWpmNXSBcyCFxGEhAmnBOYkjDlMcKU8o6dfRAdYhHEgZ1sRZnHWgwDr/TmMlpbbkpUbFElxEfyBZXOgQiRqTzTqn2a6Eb+Opz/rt/0OUJAk333wzt912G+94xzuGn9922218y7d8y/O+jnCShfPniNOUoiiIncUpRz4oWJhbQDjL7MwUSZyQJAlZPaPT7XHm7BzGWaSMcM6hpN+Q1jmssAgECol1nuk6Z5FSUJYlRZmjVMNvSuvFxvR0i8svv4QnHn+ay45cTr1Vp7PRJS5iVBQTBYE20W6T1mo0mpM0mxM0Wi3qExPUmnVqjTrNRoMsTYljhVISFQmcMMgoPHS8wLLWoa1DCoE11QuouOSSy1ECvvzFT+NKjdGGSEjSKMYai7NefhjrNScpIYolcaxI05QkScjznKLUxKlARTFSgcBbFSK8pNX/hYywVpPnxSYrANgkDPwHQVvCkWUprWYNjEbnA6zRxJkgq8UURY5zGWka4zD+5SoN6ysbmMLQqNdRSg15hECgVIQxYUyVMukckVQgJNY5pJJYK0niGAto55DWv1DWSjxDFPTzHlZB1qihIkWWZVgcVghKHAbH+vo6tp9jBn02VtfI0hQb+zlHUTS0khzWKxNC4qxDSLzygfPWonFIISmKPDBeLwDG1EycA2MM4QGAlGF+fo6e6Un8wwXrTHg+W3BiKZHCgfXviFIWKT2DrhgWQnlN05ZERBi8RmusxYY9Y63BOjBWU5YaU2pKbSlKjdYCBYHBG0TQYqUDp23QXSxgkLKybhgyQ5xABOZeCTVRTXao+wSLCEdlIgw/2aqJM9LQNzFCISiMwziIogjrJMpKRCQ4efoEH7/j94hqk1x2xTFe/rJX8Irmm/niBz6M7XQRyhInEhVHoASltcPnVN3L4oKV5Py740RYZ4fnLv6dVUDpvGWwLbQnKKau2geAUN7KstrhhPPvFM4LB+f5l7HWn+qgLA1FYZmZ3U2STPLgY3c+F0v92kFGP/ETP8H3fu/38pKXvISXv/zl/Jf/8l84efIkP/zDP/y8r7G4uMDK2gpxVsM5QRZLGoemWF/bIO8POHrZYY4c3keWKoRwqDhida1gcWmJTq8Ea5FKYP3T9MJBKZRSOLyGKZzCWYExBWVZorVGl9ZrUCLCGUskYg4dOML6WklWn+Kml74crGByYoo9e/fSnmpTbzWo1WqoKEbFKSqOiVKJFAIlQSm8Fu0cOjBO/3J7xiqDOWqFxdgCXebkecH6xgZSRkxOzJJlGfsOHGLfgcP0nniENNIUxmv31ia
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# miejsce na eksperymenty\n",
|
||
|
|
"img_soy = cv.imread(\"img/soyjaks.jpg\", cv.IMREAD_COLOR)\n",
|
||
|
|
"img_soy = cv.cvtColor(img_soy, cv.COLOR_BGR2RGB)\n",
|
||
|
|
"img_soy = img_soy[0:500, 50:500]\n",
|
||
|
|
"cv.putText(img_soy, 'OMG not chikenz!', (10,80), cv.FONT_HERSHEY_PLAIN , 3, (255,0,0), 5, cv.FILLED)\n",
|
||
|
|
"cv.circle(img_soy, (275,225), 50, (255,0,255), 10)\n",
|
||
|
|
"plt.imshow(img_soy)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "279f1a5d",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
""
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "288aa117",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Ćwiczenie 2\n",
|
||
|
|
"\n",
|
||
|
|
"Załaduj obrazy `img/pipe.png` oraz `img/man-without-pipe.png` i wykonaj operacje tak, aby uzyskać poniższy obraz."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 93,
|
||
|
|
"id": "2cb83db1",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[ 76 56 64 255]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.image.AxesImage at 0x7fe0e5f52700>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 93,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# miejsce na eksperymenty\n",
|
||
|
|
"img_pipe = cv.cvtColor(cv.imread(\"img/pipe.png\", cv.IMREAD_UNCHANGED), cv.COLOR_BGRA2RGBA)\n",
|
||
|
|
"img_man_no_pipe = cv.cvtColor(cv.imread(\"img/man-without-pipe.png\", cv.IMREAD_UNCHANGED), cv.COLOR_BGRA2RGBA)\n",
|
||
|
|
"img_man_pipe = img_man_no_pipe\n",
|
||
|
|
"img_pipe = cv.flip(cv.resize(img_pipe, None, fx=0.55, fy=0.55, interpolation=cv.INTER_AREA), 1)\n",
|
||
|
|
"rows,cols,channels = img_pipe.shape\n",
|
||
|
|
"print(img_pipe[50,50])\n",
|
||
|
|
"for r in range(rows):\n",
|
||
|
|
" for c in range(cols):\n",
|
||
|
|
" if img_pipe[r,c,3] > 150:\n",
|
||
|
|
" img_man_pipe[278+r, 65+c] = img_pipe[r,c]\n",
|
||
|
|
"#img_man_pipe[275:275+rows,70:70+cols] = img_pipe\n",
|
||
|
|
"plt.imshow(img_man_pipe)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "7baba75b",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
""
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "f0b793fc",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Co dalej?\n",
|
||
|
|
"\n",
|
||
|
|
"Przygotuj środowisko pracy instalując wygodne dla Ciebie IDE, np. [PyCharm](https://www.jetbrains.com/pycharm/) (są darmowe studenckie licencje), [Visual Studio](https://visualstudio.microsoft.com) z dodatkiem [Python Tools](https://microsoft.github.io/PTVS/) lub [Visual Studio Code](https://code.visualstudio.com/). Sprawdź czy możesz zaimportować OpenCV, ew. stwórz wirtualne środowisko i doinstaluj niezbędne paczki."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "4a713f31",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"----\n",
|
||
|
|
"\n",
|
||
|
|
"Źródło:\n",
|
||
|
|
"\n",
|
||
|
|
"* OpenCV, [Getting Started with Images](https://docs.opencv.org/4.5.3/dc/d2e/tutorial_py_image_display.html).\n",
|
||
|
|
"* OpenCV, [Drawing Functions in OpenCV](https://docs.opencv.org/4.5.3/dc/da5/tutorial_py_drawing_functions.html).\n",
|
||
|
|
"* P. Pandey, [Face Detection with Python using OpenCV](https://www.datacamp.com/community/tutorials/face-detection-python-opencv)."
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"author": "Andrzej Wójtowicz",
|
||
|
|
"email": "andre@amu.edu.pl",
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"lang": "pl",
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.2"
|
||
|
|
},
|
||
|
|
"subtitle": "01. Wprowadzenie do widzenia komputerowego [laboratoria]",
|
||
|
|
"title": "Widzenie komputerowe",
|
||
|
|
"year": "2021"
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|