YCAyca

import copy
import cv2
import numpy as np
from PIL import Image, ImageFilter
from enum import Enum


class Filter_Type(Enum):
    MIN = 1
    MAX = 2

YCAyca

import copy
from nonlinear_filtering import padding, Print_Mode
import cv2
import numpy as np
from enum import Enum
import scipy.stats as st


class OPERATION_TYPE(Enum):
    Convolution = 1

YCAyca

import cv2
import numpy as np
from scipy import ndimage 

""" Erosion with OpenCV """

def Erosion_Opencv(img, kernel):
        cv2.imshow("Input Image", img)
        cv2.waitKey(0) 
        erosion = cv2.erode(img,kernel,iterations = 1)

YCAyca

import cv2
import numpy as np
import matplotlib.pyplot as plt

def show_components(labels):
     # Map component labels to hue val, 0-179 is the hue range in OpenCV
    label_hue = np.uint8(179*labels/np.max(labels))
    blank_ch = 255*np.ones_like(label_hue)
    labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])

YCAyca

""" Dataset """

v1 = [1, 1, 0, 0]
v2 = [1, 0, 0, 0]
v3 = [0, 0, 0, 1]
v4 = [0, 0, 1, 1]

inputs = [v1, v2, v3, v4]
dataset_length = len(inputs)

YCAyca

% Apply DLT using the following functions 
% World_PTS : 3D world points, GT_PTS: 2D Projected image points of these 3D world points, 
% Calibration_Points_Count : the quantity of points we use for realizing calibration

function DLT(World_PTS, GT_PTS, Calibration_Points_Count)
   A = Create_Matrix_A(Calibration_Points_Count, World_PTS, GT_PTS)
   P = Obtain_Projection_Matrix(A)
   [K,R,T] = QR_Decomposition(P)
   [Projected_X, Projected_Y] = Projection(P, World_PTS, GT_PTS);
end

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'); 

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;

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

%% 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];