YCAyca

# -*- coding: utf-8 -*-
"""
Created on Mon Jan  3 14:34:44 2022

@author: aktas
"""

# import necessary layers  
from tensorflow.keras.layers import Input, Conv2D , Dropout, MaxPool2D, Flatten, Dense,  Activation, BatchNormalization 
from tensorflow.keras import Model

YCAyca

# import necessary layers  
from tensorflow.keras.layers import Input, Conv2D , Dropout, MaxPool2D, Flatten, Dense 
from tensorflow.keras import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.regularizers import l2
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import os
import matplotlib.pyplot as plt
import sys

YCAyca

# -*- coding: utf-8 -*-
"""
Created on Wed Dec 29 20:56:04 2021

@author: aktas
"""

# import necessary layers  
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.regularizers import l2

YCAyca

# import necessary layers  
from tensorflow.keras.layers import Input, Conv2D , Dropout, MaxPool2D, Flatten, Dense 
from tensorflow.keras import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.regularizers import l2
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import os
import matplotlib.pyplot as plt
import sys

YCAyca

import numpy as np


def loss_function(prediction, ground_truth): #mean square error with batch size = 1
    cost = (ground_truth-prediction)**2
    return cost


def prediction(x,current_weights,current_bias): 
    y_predicted = np.sum((current_weights * x)) + current_bias

YCAyca

% reference : https://medium.com/data-breach/introduction-to-harris-corner-detector-32a88850b3f6
clear all; 
close all; 
clc;


% Take the grayscale of the original image

im=imread('corner2.png');
figure

YCAyca

%% Obtain 2 cameras seeing the same scene

clear all;
close all;
clc;

PTS= load('pts3D.txt');
count_3d_points = length(PTS);
tmp = ones(count_3d_points,1);
PTS_homogenous = [PTS, tmp];

YCAyca

clear all;
close all;
clc;

PTS1 = load('pts2D_1.txt'); % 300 x 2 matrix
PTS2 = load('pts2D_2.txt'); % 300 x 2 matrix

tmp = ones(length(PTS1),1);

Homogenous_PTS1 = [PTS1, tmp];

YCAyca

clear all;
close all;
clc;

%We have simulated a regular checkerboard pattern, comprising 10x10 points, and a camera observing it. 
%The checkerboard is moved around the camera and 10 images of it are acquired in the process. 

%PTS_world = load('ptsXY.txt'); % Cartesian coordinates of the checkerboard points,
%Pixel coordinates of the 2D points in the 10 images, given in pts2D_i.txt, i = 1, 2, ..., 10.

YCAyca

clear all;
close all;
clc;

format short

%% DLT

% Loading 3D World Points and 2D projected points (Ground Truth Points for projection)coming from previous lab
World_PTS = load('pts3D.txt');