diff options
| -rw-r--r-- | get_data.py | 110 | ||||
| -rw-r--r-- | gui.py | 143 | ||||
| -rw-r--r-- | main/CDIO.c | 6 |
3 files changed, 256 insertions, 3 deletions
diff --git a/get_data.py b/get_data.py new file mode 100644 index 0000000..fc36121 --- /dev/null +++ b/get_data.py @@ -0,0 +1,110 @@ +# This Python script is used to communicate with an ESP32 network and receive CSI data + +# SET THESE VARIABLES +name = "test_v2" # between ESPs (m) +category = "p" # presence or no presence or activity {"p", "n", "a"} + +path = f"{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 + +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 + +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 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 + @@ -0,0 +1,143 @@ +# 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 + diff --git a/main/CDIO.c b/main/CDIO.c index eaefdac..bc77561 100644 --- a/main/CDIO.c +++ b/main/CDIO.c @@ -43,7 +43,7 @@ void socket_transmitter_sta_loop(bool (*is_wifi_connected)()) { } printf("sending frames.\n"); - int i = 0; + int i = 0; while (1) { //double start_time = get_steady_clock_timestamp(); if (!is_wifi_connected()) { @@ -123,6 +123,8 @@ static void echo_task(void *arg) } } + + void app_main(void) { // Init LED @@ -133,10 +135,8 @@ void app_main(void) // Access point setup_softap(); - setup_csi(); - for(;;) { vTaskDelay(10); |