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
|
# This Python script is used to communicate with an ESP32 network and receive CSI data
# SET THESE VARIABLES
name = "alone" # between ESPs (m)
category = "n" # presence or no presence or activity {"p", "n", "a"}
path = f"../datasets/{name}/{category}/"
import os
# Create base and label subfolders
os.makedirs(path, exist_ok=True)
import serial, re
import numpy as np
import matplotlib.pyplot as plt
import collections
import datetime
import sys, select, tty, termios
# Is there data on stdin?
def isData():
return select.select([sys.stdin], [], [], 0) == ([sys.stdin], [], [])
amplitude = collections.deque(maxlen=50)
phase = collections.deque(maxlen=50)
# Check operating system
if os.name == "nt":
esp_serial = serial.Serial(port='COM9', baudrate=921600)
else:
esp_serial = serial.Serial(port='/dev/ttyUSB0', baudrate=921600)
data = ""
monitor_dpi = 192
plt.ion()
fig = plt.figure()
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
fig.canvas.draw()
plt.show(block=False)
img_i = 0
i = 0
while 1:
data = esp_serial.readline().decode(errors='ignore')
if 'CSI_DATA' in data:
data = re.findall(r"\[(.*?)\]", data)
data = data[0].split()
csi_size = len(data)
# print(csi_size)
if csi_size == 384:
amplitudes = []
phases = []
real = []
imag = []
buf_i = i
for i in range(int(csi_size/2)):
real.append(int(data[i * 2]))
imag.append(int(data[(i * 2) + 1]))
#if (i > 65 and i < 123):
# Non-logarithmic
amplitudes.append(np.sqrt(real[i] ** 2 + imag[i] ** 2))
phases.append(np.atan2(imag[i], real[i]))
i = buf_i
amplitude.append(amplitudes)
phase.append(phases)
plt.clf()
# df has shape (50, 58) -> (samples, freqs)
df = np.clip(np.asarray(amplitude, dtype=np.float32) * (255/35), 0, 255) # Get max 255, min 0
plt.pcolormesh(np.transpose(df), cmap='gray', vmin=0, vmax=255)
plt.title(f"Gathering data ({category})\nImage #{img_i}", fontsize=30)
plt.axis('off')
fig.canvas.flush_events()
plt.show()
date = datetime.datetime.now().strftime("%Y-%m-%d %H%M%S")
i += 1
if i == 50:
if img_i > 0:
df = np.clip(np.asarray(amplitude, dtype=np.float32) * (255/35), 0, 255) # Get max 255, min 0
# Save a 58X50 pixel image (freq x samples), matching the live plot data
img = np.transpose(df) # shape (58, 50) -> 58 px high, 50 px wide
# img_norm = np.clip(img / 35.0, 0, 1) # normalize like vmin=0, vmax=35
plt.imsave(f"{path}{date}.png", img, cmap='gray')
print(f"Image: {img_i}")
# if img_i == 55:
# exit()
i = 0
img_i += 1
|
