diff options
Diffstat (limited to 'gui.py')
| -rw-r--r-- | gui.py | 154 |
1 files changed, 75 insertions, 79 deletions
@@ -1,18 +1,10 @@ # 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") +model_name = "alone" -# os.makedirs(path, exist_ok=True) +import os import serial, re import numpy as np @@ -49,95 +41,99 @@ from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing import image import tensorflow as tf -model = load_model('model.h5') +model = load_model(f"{model_name}.keras") model.summary() -#class_names = ["Activity", "No presence", "Presence"] +activity_model = load_model(f"{model_name}_activities.keras") + +class_names = ["No presence", "Presence"] -class_names = ["n", "p"] +#class_names_activity = ["Run", "Sit", "Stand", "Walk"] +class_names_activity = ["Sit", "Stand", "Walk"] + +confidence = 0 while 1: data = esp_serial.readline().decode(errors='ignore') + # print(data) - if 'CSI DATA' in data: - data = re.findall(r"\(.*?\)", data) + if 'CSI_DATA' in data: + data = re.findall(r"\[(.*?)\]", data) - csi_size = len(data) + print(len(data)) + print(type(data)) - # print(csi_size) + if len(data) > 0: + data = data[0].split() - if csi_size == 192: - amplitudes = [] - phases = [] + csi_size = len(data) - iteration = 0 - for tup in data: - tup = re.sub(r'[()\ ]', '', tup) - ints = tup.split(",") - a = 0 - b = 0 + # print(csi_size) - 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]) + if csi_size == 384: + amplitudes = [] + phases = [] - # (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)) + 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])) - iteration += 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])) - amplitude.append(amplitudes) - phase.append(phases) + i = buf_i + + amplitude.append(amplitudes) + phase.append(phases) - plt.clf() + 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') + # df has shape (50, 58) -> (samples, frequencies) + 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.axis('off') - fig.canvas.flush_events() - plt.show() + i += 1 - 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 + if img_i > 1: + df = tf.expand_dims(np.transpose(df), 0) + prediction = model.predict(np.array(df), verbose=0) + prediction = prediction.argmax(axis=-1)[0] + # print(f"{prediction}") + + if class_names[prediction] == "Presence": + if confidence < 10: + confidence += 1 + else: + if confidence > -10: + confidence -= 1 + + # print(f"Confidence: {confidence}") + if confidence <= 0: + prediction = 0 + esp_serial.write(b"green") + plt.title(f"{class_names[prediction]}", fontsize=50) + else: + prediction = 1 + activity_prediction = activity_model.predict(np.array(df), verbose=0) + activity_prediction = activity_prediction.argmax(axis=-1)[0] + esp_serial.write(b"red") + + plt.title(f"{class_names[prediction]}: {class_names_activity[activity_prediction]}", fontsize=50) - plt.imsave("pred.png", img, cmap='gray') - - print(f"Image: {img_i}") - if img_i == 55: - exit() - i = 0 - img_i += 1 + + + if i == 50: + i = 0 + img_i += 1 + fig.canvas.flush_events() + plt.show() |