printf("ho_tari\n");

#include extern void show_camera(); extern void show_movie(); extern void show_video_record(); extern void show_draw_lines(); extern void show_draw_polygons(); extern void show_draw_text(); extern void keyboard_event(); extern void mouse_event(); extern void trackbar_event(); extern void mask_setTo(); extern void mask_copyTo(); extern void time_inverse(); extern void useful_function(); int main(..

#include extern void show_images(); // show_images()가 외부에 있다는 의미 extern void show_code_3(); // show_code_3()가 외부에 있다는 의미 extern void show_code_3_8(); // show_code_3_8()가 외부에 있다는 의미 extern void show_code_3_9(); // show_code_3_9()가 외부에 있다는 의미 extern void show_code_3_10(); // show_code_3_10()가 외부에 있다는 의미 extern void show_code_3_11(); // show_code_3_11()가 외부에 있다는 의미 extern void show_code_3_12(); // ..

#pragma once // 헤더파일의 충돌을 막기 위해서 헤더파일이 두개가 있으면 하나만 사용하라는 의미 #include void show_images() { cv::Mat img = cv::imread("lenna.bmp", cv::IMREAD_GRAYSCALE); // 파일 이름이 lenna.bmp인 이미지를 matrix형태로 읽어옴 (IMREAD_COLOR : 색깔 이미지, IMREAD_GRAYSCALE : 회색 이미지) cv::Mat img2 = cv::imread("dog.bmp", cv::IMREAD_COLOR); if (img.empty() or img2.empty()) // image file이 없다면 다음 문장 출력 { std::cout

Daily COVID-19 confirmed cases prediction Let’s predict the daily number of confirmed cases of COVID-19 in Spain through recurrent neural network (RNN). Predicting the daily number of confirmed cases of COVID-19 is a regression problem. Dataset • Daily incidence of the 52 Spanish provinces for 450 days from January 1st , 2020 to March 27th, 2021 Q1 Let’s load the dataset using “pickle” python mo..

Chest X-ray (Pneumonia 폐렴) Classification problem ◦ input variable: images ◦ 1 binary output variable (pneumonia or normal)  5863 x-ray images ◦ Already split into train, validation and test. Q1 Data preprocessing: The image sizes all vary. Thus, resizing is essential. ◦ When loading images, resize the image into [128, 128] ◦ flow_from_directory(train_dir, target_size=(128,128), batch_size=20,c..

#include #include "rational.h" using iot::Rational; int main() { Rational r1; // 0/1 Rational r2 = 1; // 1/1 //Rational r2(1); Rational r3(3, 4); // 3/4 Rational r4 = r3; //r1.setNum(r3.getNum()); //r1.setNum(r3.getNum()); r1 = r3; if (r1 == r3) { std::cout

#include #include "complex.h" using iot::Complex; int main() { Complex c1(3.0, 4.0); // (3.0, 4.0i) Complex c2(3.0); // (3.0, 0.0i) //Complex c2 = 3.0; // implicit conversion good! Complex c3; // (0.0, 0.0i) const Complex c4 = c1; c2 = c1; c2.real(c1.real()); c2.imag(c1.imag()); std::cout