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# This Python script is used to communicate with an ESP32 network and receive CSI data
# SET THESE VARIABLES
# name = "test" # between ESPs (m)
# category = "a" # presence or no presence {"p", "n"}
#
# path = f"{name}/{category}/"
#
import os
# Create base and label subfolders
# true_path = os.path.join(base_path, "True")
# false_path = os.path.join(base_path, "False")
# os.makedirs(path, exist_ok=True)
import serial, re
import numpy as np
import matplotlib.pyplot as plt
import collections
import datetime
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)
i = 0
img_i = 0
# Load ML model
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import tensorflow as tf
model = load_model('model.h5')
model.summary()
#class_names = ["Activity", "No presence", "Presence"]
class_names = ["n", "p"]
while 1:
data = esp_serial.readline().decode(errors='ignore')
if 'CSI DATA' in data:
data = re.findall(r"\(.*?\)", data)
csi_size = len(data)
# print(csi_size)
if csi_size == 192:
amplitudes = []
phases = []
iteration = 0
for tup in data:
tup = re.sub(r'[()\ ]', '', tup)
ints = tup.split(",")
a = 0
b = 0
if ints[0].isdigit() or (ints[0].startswith('-') and ints[0][1:].isdigit()):
a = int(ints[0])
if ints[1].isdigit() or (ints[1].startswith('-') and ints[1][1:].isdigit()):
b = int(ints[1])
# (iteration > 5 and iteration < 32) or (iteration > 32 and iteration < 59)
# or (iteration > 65 and iteration < 123) or (iteration > 133 and iteration < 191):
if (iteration > 65 and iteration < 123):
# Non-logarithmic
amplitudes.append(np.sqrt(a ** 2 + b ** 2))
phases.append(np.atan2(b, a))
iteration += 1
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')
plt.axis('off')
fig.canvas.flush_events()
plt.show()
date = datetime.datetime.now().strftime("%Y-%m-%d %H%M%S")
i += 1
if img_i > 1:
# img = image.load_img(
# "pred.png",
# target_size=(57, 50),
# color_mode="grayscale"
# )
# img_array = image.img_to_array(img)
# img_array = tf.expand_dims(img_array, 0)
df = tf.expand_dims(np.transpose(df), 0)
prediction = model.predict(np.array(df))
prediction = prediction.argmax(axis=-1)[0]
print(f"{class_names[prediction - 1]}")
if class_names[prediction - 1] == "n":
esp_serial.write(b"red")
else:
esp_serial.write(b"green")
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("pred.png", img, cmap='gray')
print(f"Image: {img_i}")
if img_i == 55:
exit()
i = 0
img_i += 1
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