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pose_detection.py
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pose_detection.py
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import cv2 as cv
import numpy as np
import math
from polar_point_map import generate_map
import subprocess
from time import sleep
import threading
import matplotlib.pyplot as plt
# Load calibration data (camera matrix and distortion coefficients)
camera_matrix = np.load('camera_matrix.npy')
dist_coeffs = np.load('dist_coeffs.npy')
# Initialize ArUco dictionary and parameters
aruco_dict = cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
parameters = cv.aruco.DetectorParameters()
camera_elevation = 0.35
rover_gnd_travel = 0
markerx_separation = []
markery_separation = []
markerP_seperation = []
markerO_seperation = []
highest_marker = 0
repeated = 0
round_highest_id = 0
round_highest = 0
cap = cv.VideoCapture(0)
number_colors = {
0: (255, 0, 0), # Blue
1: (0, 255, 0), # Green
2: (0, 0, 255), # Red
3: (255, 255, 0), # Yellow
4: (255, 0, 255), # Magenta
5: (0, 255, 255), # Cyan
6: (128, 0, 0), # Maroon
7: (0, 128, 0), # Green (dark)
8: (0, 0, 128), # Navy
9: (128, 0, 128) # Purple
}
points = [(0,0)]
fig = None
ax = None
current_marker_dist = 0
last_knowns = {}
last_knowns2 = {}
def polar_to_cartesian(r, theta):
x = r * math.cos(theta)
y = r * math.sin(theta)
return x, y
# Function to convert Cartesian coordinates to polar coordinates
def cartesian_to_polar(x, y):
r = math.sqrt(x**2 + y**2)
theta = math.atan2(y, x)
return r, theta
# Function to add two polar coordinates
def add_polar(r1, theta1, r2, theta2):
# Convert polar coordinates to Cartesian coordinates
x1, y1 = polar_to_cartesian(r1, theta1)
x2, y2 = polar_to_cartesian(r2, theta2)
# Add Cartesian coordinates
x_sum = x1 + x2
y_sum = y1 + y2
# Convert sum back to polar coordinates
r_sum, theta_sum = cartesian_to_polar(x_sum, y_sum)
return r_sum, theta_sum
def calculate_orientation(rotation_matrix):
# Extract angles from rotation matrix
theta_x = math.atan2(rotation_matrix[2, 1], rotation_matrix[2, 2])
theta_y = math.atan2(-rotation_matrix[2, 0], math.sqrt(rotation_matrix[2, 1] ** 2 + rotation_matrix[2, 2] ** 2))
theta_z = math.atan2(rotation_matrix[1, 0], rotation_matrix[0, 0])
# Convert angles to degrees
theta_x_deg = math.degrees(theta_x)
theta_y_deg = math.degrees(theta_y)
theta_z_deg = math.degrees(theta_z)
return theta_x_deg, theta_y_deg, theta_z_deg
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
corners, ids, rejectedImgPoints = cv.aruco.detectMarkers(gray, aruco_dict,
parameters=parameters)
rvecs, tvecs, _ = cv.aruco.estimatePoseSingleMarkers(corners, 0.05,
camera_matrix,
dist_coeffs)
cv.putText(frame, f"{rover_gnd_travel}", (20, 400),
cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)
#print("Rover gnd distance: ", rover_gnd_travel)
if ids is not None and tvecs is not None:
round_highest = 0
round_data = []
round_data2 = []
for i, marker_id in enumerate(ids):
cv.aruco.drawDetectedMarkers(frame, corners)
distance = 1.91/0.75*np.linalg.norm(tvecs[i])
gnd_distance = math.sqrt(abs(distance**2 - camera_elevation**2))
rotation_matrix, _ = cv.Rodrigues(rvecs[i])
_, _, angle_deg = calculate_orientation(rotation_matrix)
if angle_deg > 0:
angle_deg *= 1.15
else:
angle_deg *=0.85
angle_rad = math.radians(angle_deg)
marker_x = gnd_distance * math.sin(angle_rad)
marker_y = gnd_distance * math.cos(angle_rad)
cv.putText(frame, f"Marker {marker_id}: {gnd_distance:.2f}m {angle_deg:.2f} degrees", (20, 40*(i+1)),
cv.FONT_HERSHEY_SIMPLEX, 0.6, number_colors[i], 2)
cv.putText(frame,f"Coords: {marker_x:.2f}, {marker_y:.2f}",(20, 40*(i+1) + 20),cv.FONT_HERSHEY_SIMPLEX, 0.6, number_colors[i], 2)
round_data.append((marker_id, (marker_x, marker_y)))
round_data2.append((marker_id, (gnd_distance, angle_rad)))
last_knowns[int(marker_id)] = (marker_x, marker_y)
round_highest_id = int(max(round_data, key=lambda x: x[0])[0])
for pair in round_data:
if pair[0] == round_highest_id:
round_highest = pair[1]
if round_highest_id > highest_marker:
repeated += 1
past_x = 0
past_y = 0
if round_highest_id <= highest_marker:
for k in range(round_highest_id):
past_x += markerx_separation[k]
past_y += markery_separation[k]
rover_gnd_travel = math.sqrt((round_highest[0] + past_x)**2 + (round_highest[1] + past_y)**2)
if round_highest_id > highest_marker and repeated > 10:
if round_highest_id-1 in last_knowns:
print("New marker spotted!")
diffx = last_knowns[round_highest_id - 1][0] - round_highest[0]
diffy = last_knowns[round_highest_id - 1][1] - round_highest[1]
if not diffy:
diffy = 0.001
markerx_separation.append(diffx)
markery_separation.append(diffy)
markerP_seperation.append(math.sqrt(diffx**2 + diffy**2))
markerO_seperation.append(math.atan(diffx/diffy))
print('saved cart. coords', markerx_separation, markery_separation)
print('saved polar coords', markerP_seperation, markerO_seperation)
#print(marker_separation)
highest_marker += 1
repeated = 0
cv.imshow('Frame', frame)
# Exit on 'q' key press
if cv.waitKey(1) & 0xFF == ord('q'):
diffx = last_knowns[round_highest_id][0]
diffy = last_knowns[round_highest_id][1]
if not diffy:
diffy = 0.001
markerx_separation.append(diffx)
markery_separation.append(diffy)
markerP_seperation.append(math.sqrt(diffx ** 2 + diffy ** 2))
markerO_seperation.append(math.atan(diffx / diffy))
with open("points.txt", "w") as file:
file.write("0 0\n")
for i in range(len(markerx_separation)):
x_sum = sum(markerx_separation[:i + 1])
y_sum = sum(markery_separation[:i + 1])
file.write(f"{round(x_sum, 2)} {round(y_sum, 2)}\n")
with open("points2.txt", "w") as file:
file.write("0 0\n")
total_skew = 0
for i in range(len(markerP_seperation)):
total_skew += markerO_seperation[i]
file.write(f"{round(markerP_seperation[i], 2)} {round(total_skew, 2)}\n")
break
cap.release()
cv.destroyAllWindows()
generate_map()