import numpy as np
import pandas as pd
import cv2
import sklearn
import matplotlib
from modules import LidarFunctions
lf = LidarFunctions()
data_frame = pd.read_csv("data_set.csv")
data_set1 = np.array([data_frame["0"], data_frame["1"]], dtype=np.uint8)
data_set2 = np.array([data_frame["2"], data_frame["3"]], dtype=np.uint8)
print(data_set1)
image1 = lf.draw_point_cloud(data_set1.T, (500, 500))
image2 = lf.draw_point_cloud(data_set2.T, (500, 500))
images = np.hstack((image1, image2))
print(images.shape)
cv2.imshow("Image", cv2.resize(images, (1000, 500)))
cv2.waitKey(0)
cv2.destroyAllWindows()
exit()
try:
get_lidar_data.start()
while True:
if not lidar_data.empty():
print("Scan-time:", time.time() - start_time)
start_time = time.time()
i, data = lidar_data.get()
pre_data = lf.prepare_data(data, position)
mainMap = lf.draw_main_map(pre_data, position, size,
np.zeros(size, np.uint8), 0)
ImgQueue.put(mainMap)
Image = ImgQueue.get_image()
print(pre_data.shape)
pointcloud = lf.draw_point_cloud(pre_data, size)
raw_mask = np.array(Motion(last_point_cloud,
pointcloud).motion_detection(100, 0),
dtype=np.uint8)
bmask = cv2.resize(raw_mask, (5000, 5000))
assert pointcloud.shape == bmask.shape
mask_img = np.copy(pointcloud)
np.putmask(mask_img, bmask == False, 0)
mask = cv2.resize(
cv2.threshold(raw_mask, 0, 255, cv2.THRESH_BINARY)[1],
(5000, 5000))
print("Mask:", mask.shape)
Image = np.vstack((np.hstack(
(pointcloud, last_point_cloud)), np.hstack((mask, Image))))
new_scan_event.set()
cv2.imshow(