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% 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 |
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""" 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) |
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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]) |
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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) |
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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 |
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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 |
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