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# -*- coding: utf-8 -*-
"""Funcional WLAN_design_gradio.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1MIfY3UkK4eSXOiPx3gtMSPoZMtnyJxux
"""
# from google.colab import drive
# drive.mount('/content/drive')
# Commented out IPython magic to ensure Python compatibility.
# %%capture
# !pip install gradio
import gradio as gr
from PIL import Image
import os
from tensorflow import keras
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from io import BytesIO
# Images path
path_main = 'Data/'
images_file = path_main + 'Scennarios init/Scennarios W'
# Load DL models
modelo_1ap = load_model(path_main + 'Models/modelo_1ap_app.keras')
modelo_2ap = load_model(path_main + 'Models/modelo_2ap_app.keras')
plt.rc('font', family='Times New Roman')
fontsize_t = 15
def coordinates_process(texto):
coordinates = texto.split("), ")
resultado = []
for coord in coordinates:
try:
coord = coord.replace("(", "").replace(")", "")
x, y = map(int, coord.split(","))
# Validate range
if 0 <= x <= 255 and 0 <= y <= 255:
resultado.append((x, y))
else:
return False
except ValueError:
return False
while len(resultado) < 3:
resultado.append((0, 0))
return resultado
# plan images path
def plan_images_list():
return [file_ for file_ in os.listdir(images_file) if file_.endswith((".JPG", ".jpg", ".jpeg", ".png"))]
# Valdate inputs
def validate_input(value):
if value == "" or value is None:
return 0
elif value >= 0 or value <= 2:
return value
# MAIN FUNCTION ****************************************************************
def main_function(plan_name, apch1, apch6, apch11, coord1, coord6, coord11):
image_plan_path = os.path.join(images_file, plan_name)
imagen1 = Image.open(image_plan_path)
# No negative number as input
if not (0 <= apch1 <= 2):
return False
if not (0 <= apch6 <= 2):
return False
if not (0 <= apch11 <= 2):
return False
# Some variables init
deep_count = 0
deep_coverage = []
channels = [1, 6, 11]
num_APs = np.zeros(len(channels), dtype=int)
num_APs[0] = apch1
num_APs[1] = apch6
num_APs[2] = apch11
dimension = 256
aps_chs = np.zeros((dimension, dimension, len(channels)))
# Load plan
numero = plan_name[:1]
plan_in = np.array(Image.open(f"{path_main}Scennarios init/Scennarios B/{numero}.png")) / 255
coords = [coord1, coord6, coord11]
for att, channel in enumerate(channels):
if num_APs[att] > 0:
coordinates = coordinates_process(coords[att])
for x, y in coordinates:
if x != 0 and y != 0:
aps_chs[int(y), int(x), att] = 1
# Coverage process
deep_coverage = []
ap_images = []
layer_indices = []
imagencober = {}
for k in range(len(channels)):
capa = aps_chs[:, :, k]
filas, columnas = np.where(capa == 1)
if len(filas) == 2:
# For 2 AP
deep_count += 1
layer_1 = np.zeros_like(capa)
layer_2 = np.zeros_like(capa)
layer_1[filas[0], columnas[0]] = 1
layer_2[filas[1], columnas[1]] = 1
datos_entrada = np.stack([plan_in, layer_1, layer_2], axis=-1)
prediction = modelo_2ap.predict(datos_entrada[np.newaxis, ...])[0]
elif len(filas) == 1:
# For 1 AP
deep_count += 1
layer_1 = np.zeros_like(capa)
layer_1[filas[0], columnas[0]] = 1
datos_entrada = np.stack([plan_in, layer_1], axis=-1)
prediction = modelo_1ap.predict(datos_entrada[np.newaxis, ...])[0]
else:
# Whitout AP
prediction = np.zeros((dimension,dimension,1))
# print(prediction.shape)
deep_coverage.append(prediction)
prediction_rgb = np.squeeze((Normalize()(prediction)))
ap_images.append(prediction_rgb) # Guardar la imagen de cobertura del AP
if np.all(prediction == 0):
plt.imshow(prediction_rgb)
plt.title('No coverage', fontsize=fontsize_t + 2, family='Times New Roman')
plt.axis("off")
else:
plt.imshow(prediction_rgb, cmap='jet')
cbar = plt.colorbar(ticks=np.linspace(0, 1, num=6),)
cbar.set_label('SINR [dB]', fontsize=fontsize_t, fontname='Times New Roman')
cbar.set_ticklabels(['-3.01', '20.29', '43.60', '66.90', '90.20', '113.51'])
cbar.ax.tick_params(labelsize=fontsize_t, labelfontfamily = 'Times New Roman')
plt.axis("off")
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
imagencober[k] = Image.open(buf)
# Cell map estimation
layer_indices.append(np.argmax(prediction, axis=0))
# Final coverage
if deep_coverage:
deep_coverage = np.array(deep_coverage)
nor_matrix = np.max(deep_coverage, axis=0)
celdas = np.argmax(deep_coverage, axis=0)
resultado_rgb = np.squeeze((Normalize()(nor_matrix)))
plt.imshow(resultado_rgb, cmap='jet')
cbar = plt.colorbar(ticks=np.linspace(0, 1, num=6))
cbar.set_label('SINR [dB]', fontsize=fontsize_t, fontname='Times New Roman')
cbar.set_ticklabels(['-3.01', '20.29', '43.60', '66.90', '90.20', '113.51'])
cbar.ax.tick_params(labelsize=fontsize_t, labelfontfamily = 'Times New Roman')
plt.axis("off")
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
imagen3 = Image.open(buf)
if num_APs[0] > 0 and num_APs[1] > 0 and num_APs[2] > 0:
cmap = plt.cm.colors.ListedColormap(['blue', 'red', 'green'])
plt.imshow(celdas, cmap=cmap)
cbar = plt.colorbar()
cbar.set_ticks([0, 1, 2])
cbar.set_ticklabels(['1', '6', '11'])
cbar.set_label('Cell ID', fontsize=fontsize_t, fontname='Times New Roman')
cbar.ax.tick_params(labelsize=fontsize_t, labelfontfamily = 'Times New Roman')
plt.axis("off")
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
imagen4 = Image.open(buf)
elif num_APs[0] > 0 and num_APs[1] > 0:
cmap = plt.cm.colors.ListedColormap(['blue', 'red'])
plt.imshow(celdas, cmap=cmap)
cbar = plt.colorbar()
cbar.set_ticks([0, 1])
cbar.set_ticklabels(['1', '6'])
cbar.set_label('Cell ID', fontsize=fontsize_t, fontname='Times New Roman')
cbar.ax.tick_params(labelsize=fontsize_t, labelfontfamily = 'Times New Roman')
plt.axis("off")
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
imagen4 = Image.open(buf)
elif num_APs[0] > 0 and num_APs[2] > 0:
cmap = plt.cm.colors.ListedColormap(['blue', 'red'])
plt.imshow(celdas, cmap=cmap)
cbar = plt.colorbar()
cbar.set_ticks([0, 1])
cbar.set_ticklabels(['1', '11'])
cbar.set_label('Cell ID', fontsize=fontsize_t, fontname='Times New Roman')
cbar.ax.tick_params(labelsize=fontsize_t, labelfontfamily = 'Times New Roman')
plt.axis("off")
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
imagen4 = Image.open(buf)
elif num_APs[1] > 0 and num_APs[2] > 0:
cmap = plt.cm.colors.ListedColormap(['blue', 'red'])
plt.imshow(celdas, cmap=cmap)
cbar = plt.colorbar()
cbar.set_ticks([0, 1])
cbar.set_ticklabels(['6', '11'])
cbar.set_label('Cell ID', fontsize=fontsize_t, fontname='Times New Roman')
cbar.ax.tick_params(labelsize=fontsize_t, labelfontfamily = 'Times New Roman')
plt.axis("off")
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
imagen4 = Image.open(buf)
else:
cmap = plt.cm.colors.ListedColormap(['blue'])
plt.imshow(celdas, cmap=cmap)
cbar = plt.colorbar()
cbar.set_ticks([0])
cbar.set_ticklabels(['1'])
cbar.set_label('Cell ID', fontsize=fontsize_t, fontname='Times New Roman')
cbar.ax.tick_params(labelsize=fontsize_t, labelfontfamily = 'Times New Roman')
plt.axis("off")
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
imagen4 = Image.open(buf)
return [imagencober[0], imagencober[1], imagencober[2], imagen3, imagen4]
# plan visualization
def load_plan_vi(mapa_seleccionado):
image_plan_path1 = os.path.join(images_file, mapa_seleccionado)
plan_image = Image.open(image_plan_path1)
plan_n = np.array(plan_image.convert('RGB'))
plt.figure(figsize=(3, 3))
plt.imshow(plan_n)
plt.xticks(np.arange(0, 256, 50))
plt.yticks(np.arange(0, 256, 50))
# Save the plot to a buffer
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
# Convert buffer to an image
plan_im = Image.open(buf)
return plan_im
with gr.Blocks() as demo:
gr.Markdown("""
## Fast Radio Propagation Prediction in WLANs Using Deep Learning
This app use deep learning models to radio map estimation (RME). RME entails estimating the received RF power based on spatial information maps.
Instructions for use:
- A predefined list of indoor floor plans is available for use.
- A maximum of (255,255) pixels is allowed for image size.
- Negative numbers are not allowed.
- The established format for the coordinates of each access point (AP) must be maintained.
- A maximum of 2 APs are allowed per channel.
""")
with gr.Row():
# Input left panel
with gr.Column(scale=1): # Scale to resize
map_dropdown = gr.Dropdown(choices=plan_images_list(), label="Select indoor plan")
ch1_input = gr.Number(label="APs CH 1")
ch6_input = gr.Number(label="APs CH 6")
ch11_input = gr.Number(label="APs CH 11")
coords_ch1_input = gr.Textbox(label="Coordinate CH 1", placeholder="Format: (x1, y1), (x2, y2)")
coords_ch6_input = gr.Textbox(label="Coordinate CH 6", placeholder="Format: (x1, y1), (x2, y2)")
coords_ch11_input = gr.Textbox(label="Coordinate CH 11", placeholder="Format: (x1, y1), (x2, y2)")
button1 = gr.Button("Load plan")
button2 = gr.Button("Predict coverage")
# Rigth panel
a_images = 320 # Size putput images
with gr.Column(scale=3):
first_image_output = gr.Image(label="plan image", height=a_images, width=a_images)
# with gr.Row():
with gr.Row():
image_ch1 = gr.Image(label="CH 1 coverage", height=a_images, width=a_images)
image_ch6 = gr.Image(label="CH 6 coverage", height=a_images, width=a_images)
image_ch11 = gr.Image(label="CH 11 coverage", height=a_images, width=a_images)
with gr.Row():
image_cover_final = gr.Image(label="Final coverage", height=a_images, width=a_images)
image_cells = gr.Image(label="Cells coverage", height=a_images, width=a_images)
# Buttons
button1.click(load_plan_vi, inputs=[map_dropdown], outputs=first_image_output)
button2.click(
lambda map_dropdown, ch1, ch6, ch11, coords1, coords6, coords11: main_function(
map_dropdown,
validate_input(ch1),
validate_input(ch6),
validate_input(ch11),
coords1,
coords6,
coords11,
),
inputs=[map_dropdown, ch1_input, ch6_input, ch11_input, coords_ch1_input, coords_ch6_input, coords_ch11_input],
outputs=[image_ch1, image_ch6, image_ch11, image_cover_final, image_cells]
)
demo.launch() |