From d64ce2dc70a07285bb3222e69463e11385fc9e27 Mon Sep 17 00:00:00 2001
From: Edvin <edj7467@gmail.com>
Date: Sun, 7 Dec 2025 15:41:35 +0100
Subject: Added GUI and data gathering to repo

---
 gui.py | 143 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 143 insertions(+)
 create mode 100644 gui.py

(limited to 'gui.py')

diff --git a/gui.py b/gui.py
new file mode 100644
index 0000000..d7641af
--- /dev/null
+++ b/gui.py
@@ -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
+
-- 
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