-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathradar_image_maker.py
134 lines (112 loc) · 5.86 KB
/
radar_image_maker.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
import sys
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageOps
import time
from pathlib import Path
import shutil
from argparse import ArgumentParser
import settings
import pdb
sys.path.insert(-1, "/workspace/code/corelibs/src/tools-python")
sys.path.insert(-1, "/workspace/code/corelibs/build/datatypes")
sys.path.insert(-1, "/workspace/code/radar-utilities/build/radarutilities_datatypes_python")
sys.path.insert(-1, "/workspace/code/corelibs/src/tools-python/mrg/adaptors")
from mrg.logging.indexed_monolithic import IndexedMonolithic
from mrg.adaptors.radar import pbNavtechRawConfigToPython, pbNavtechRawScanToPython
def get_radar_image(params, radar_mono, config, split_data_path, idx):
subset_start_index = params.subset_start_index
image_dimension = params.image_dimension
sensor_rotation = params.rotation_angle
output_file_extension = params.output_file_extension
intensity_multiplier = 2
scan_index = subset_start_index + idx
pb_raw_scan, name_scan, _ = radar_mono[scan_index]
radar_sweep = pbNavtechRawScanToPython(pb_raw_scan, config)
width, height, res = (image_dimension,
image_dimension,
config.bin_size_or_resolution)
cart_img = radar_sweep.GetCartesian(pixel_width=width, pixel_height=height, resolution=res,
method='cv2', verbose=False)
img = Image.fromarray(cart_img.astype(np.uint8) * intensity_multiplier, 'L')
img = img.rotate(sensor_rotation)
img = ImageOps.mirror(img)
img_as_np_array = np.array(img)
img.save("%s%s%i%s" % (split_data_path, "/", scan_index, output_file_extension))
img.close()
print("Generated samples up to index:", scan_index, "with dim =", image_dimension,
"and written to:", split_data_path)
# return img_as_np_array
return cart_img
def overlay_landmarks_onto_radar_image(params, radar_img, config, output_path, idx):
landmarks_csv_path = params.landmarks_path + "landmarks_" + str(idx)
print(landmarks_csv_path)
landmarks = np.genfromtxt(landmarks_csv_path + ".csv", delimiter=",")
landmarks[:, 0], landmarks[:, 1] = np.array(landmarks[:, 1]), np.array(landmarks[:, 0])
# print(landmarks)
# th = 0.9 + np.pi # TODO - get this from platform config
# rotation_matrix = np.array([[np.cos(th), -np.sin(th)], [np.sin(th), np.cos(th)]])
# rotate
# landmarks = landmarks @ rotation_matrix
# mirror (z-axis is down for robot)
# landmarks[:, 0] = -landmarks[:, 0]
# translate (platform config is accounted for in RO, so need to move landmarks back to radar frame)
# print(metric_scale_factor)
# landmarks[:, 0] = landmarks[:, 0] - 0.05 * metric_scale_factor
# landmarks[:, 1] = landmarks[:, 1] - 0.0526 * metric_scale_factor
# landmarks[:, 0] = landmarks[:, 0] - 0.8
# landmarks[:, 1] = landmarks[:, 1] - 1.2
# plt.figure(figsize=(20, 20))
# dim = 200
# plt.xlim(-dim, dim)
# plt.ylim(-dim, dim)
# plt.plot(landmarks[:, 0], landmarks[:, 1], "*")
# plt.grid()
# plt.savefig("%s%s%i%s" % (output_path, "/only_landmarks_", idx, ".png"))
# plt.close()
plt.figure(figsize=(15, 15))
plt.imshow(radar_img, cmap='gray')
metric_scale_factor = 1 / config.bin_size_or_resolution
image_landmarks = np.array(landmarks) * metric_scale_factor
image_landmarks[:, 0], image_landmarks[:, 1] = image_landmarks[:, 0] + settings.RADAR_IMAGE_DIMENSION / 2, \
image_landmarks[:, 1] + settings.RADAR_IMAGE_DIMENSION / 2
plt.scatter(image_landmarks[:, 0], image_landmarks[:, 1], marker='^', s=40, facecolors='none', edgecolors='r')
dim = settings.RADAR_IMAGE_DIMENSION
plt.xlim(0, dim)
plt.ylim(0, dim)
plt.savefig("%s%s%i%s" % (output_path, "/landmarks_", idx, ".jpg"))
def main():
# Define a main loop to run and show some example data if this script is run as main
parser = ArgumentParser(add_help=False)
parser.add_argument('--subset_start_index', type=int, default=0, help='Scan index from which to begin processing')
parser.add_argument('--num_samples', type=int, default=1, help='Number of samples for processing')
parser.add_argument('--image_dimension', type=int, default=settings.RADAR_IMAGE_DIMENSION,
help='Exported image dimension')
parser.add_argument('--rotation_angle', type=int, default=0, help='Account for sensor offset angle')
parser.add_argument('--output_file_extension', type=str, default=".jpg", help='File extension for output images')
parser.add_argument('--landmarks_path', type=str, default="",
help='Path to landmarks that were exported for processing')
params = parser.parse_args()
print("Starting dataset generation...")
start_time = time.time()
print("Generating data, size =", params.num_samples)
split_data_path = Path(settings.RADAR_IMAGE_DIR)
if split_data_path.exists() and split_data_path.is_dir():
shutil.rmtree(split_data_path)
split_data_path.mkdir(parents=True)
radar_config_mono = IndexedMonolithic(settings.RADAR_CONFIG)
config_pb, name, timestamp = radar_config_mono[0]
config = pbNavtechRawConfigToPython(config_pb)
radar_mono = IndexedMonolithic(settings.RAW_SCAN_MONOLITHIC)
# landmarks_path = params.landmarks_path
# figure_path = landmarks_path + "figs/"
# output_path = Path(figure_path)
# if output_path.exists() and output_path.is_dir():
# shutil.rmtree(output_path)
# output_path.mkdir(parents=True)
for i in range(params.num_samples):
radar_img = get_radar_image(params, radar_mono, config, split_data_path, i)
# overlay_landmarks_onto_radar_image(params, radar_img, config, output_path, i)
print("--- Radar image generation execution time: %s seconds ---" % (time.time() - start_time))
if __name__ == "__main__":
main()