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change_detection_bb_YOLO.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jan 31 20:00:48 2021
@author: Admin
"""
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 10 15:58:20 2021
@author: Admin
"""
import cv2
import numpy as np
from XYZ_WFK import camera_pos, calcWFKfromPhotoMatrix, calcPhotogrammetryMatrixFromSolvePnp
import time
from collections import namedtuple
width = 5.37 ## mm
height = 4.04 ## mm
Xpixel= width/1920
Ypixel= height/1080
pixel = (Xpixel + Ypixel) / 2
class Detects:
diction = {}
def __init__(self,
num,
box_list,
camera_matrix,
rotation_matrix,
projection_matrix,
tvecs,
dist,
frame):
self.x = box_list[0]
self.y = box_list[1]
self.w = box_list[2]
self.h = box_list[3]
self.list = box_list
self.dist = dist
self.frame = frame
self.id = num
# exterior openCV orientation
self.rotation_matrix = rotation_matrix
self.projection_matrix = projection_matrix
self.tvecs = tvecs
self.down_mid_x = (self.x + self.w / 2.0)
self.down_mid_y = (self.y + self.h)
self.camera_matrix = camera_matrix
# interior orientation
self.xo = (self.camera_matrix[0, 2] - 1920.0 / 2.0)
self.yo = (-self.camera_matrix[1, 2] + 1080.0 / 2.0)
self.f = ((self.camera_matrix[0, 0] + self.camera_matrix[1, 1]) / 2.0)
# exterior photogrammetric orientation
self.gXo, self.gYo, self.gZo = camera_pos(self.rotation_matrix, self.tvecs)
# self.omega, self.phi, self.kappa = calcWFKfromPhotoMatrix(calcPhotogrammetryMatrixFromSolvePnp(self.rotation_matrix))
self.photogram_rot_mat = calcPhotogrammetryMatrixFromSolvePnp(self.rotation_matrix)
# ground coordinate system
self.X = None
self.Y = None
self.down_mid_x_corrected = (self.down_mid_x - 1920.0 / 2.0)
self.down_mid_y_corrected = (1080.0 / 2.0 - self.down_mid_y )
# print(self.down_mid_x_corrected, self.down_mid_y_corrected)
# self.x_corrected = self.x - 1920 / 2
# self.y_corrected = 1080/2 - self.y
def triangulation_z(self, z = 0.006):
X = self.gXo + (z - self.gZo) * ((self.photogram_rot_mat[0, 0]*(self.down_mid_x_corrected - self.xo) + \
self.photogram_rot_mat[1, 0]*(self.down_mid_y_corrected - self.yo) - \
self.photogram_rot_mat[2, 0]*self.f) / (self.photogram_rot_mat[0, 2]*(self.down_mid_x_corrected - self.xo)+ \
self.photogram_rot_mat[1, 2]*(self.down_mid_y_corrected - self.yo)- \
self.photogram_rot_mat[2, 2]*self.f))
Y = self.gYo + (z - self.gZo)* ((self.photogram_rot_mat[0, 1]*(self.down_mid_x_corrected - self.xo) + \
self.photogram_rot_mat[1, 1]*(self.down_mid_y_corrected - self.yo) - \
self.photogram_rot_mat[2, 1]*self.f) / (self.photogram_rot_mat[0, 2]*(self.down_mid_x_corrected - self.xo)+ \
self.photogram_rot_mat[1, 2]*(self.down_mid_y_corrected - self.yo)- \
self.photogram_rot_mat[2, 2]*self.f))
self.X = X
self.Y = Y
# print(self.X, self.Y)
coordinates = np.float32(np.array([self.X, self.Y, z]))
mid_down = np.int32(cv2.projectPoints(coordinates.reshape(1, -1), cv2.Rodrigues(self.rotation_matrix)[0], self.tvecs, self.camera_matrix, np.zeros((1,5)) )[0])
cv2.circle(self.frame, (mid_down[0][0][0], mid_down[0][0][1]), 10, (0, 0, 0), -1)
# print(mid_down)
return(self)
def correlation_distance(self, detection):
dist = np.sqrt((self.X - detection.X)**2 + (self.Y - detection.Y)**2)
Detects.diction[self.id , detection.id] = dist