Ricampionare un segnale con SciPy

Un sistema rapido per ricampionare dati con spaziatura non omogenea  usando una curva di interpolazione

 

Dati originali

 

import matplotlib.pyplot as plt
from scipy import interpolate
import numpy as np

x = np.array([1, 2, 5, 10])
y = np.array([1, 4, 25, 100])
fcubic = interpolate.interp1d(x, y,kind='cubic')

xnew = np.arange(1, 10, 0.25)
ynew = fcubic(xnew)
plt.plot(x, y, 'X', xnew, ynew,'bo')
plt.show()
print(x)
print(y)
print(xnew)
print(ynew)
 

da osservare che l'interpolazione avviene tramite una spline cubica per cui su cuspidi non e' detto che il risultato sia accettabile. SciPy offre altre opzioni per la curva di interpolazione

 

Riparazione volante Logitech G920

Con il tempo la pedaliera del Logitech G920 ha cominciato a fare i capricci in particolare per quanto riguarda l'acceleratore

Ho provato a smontarla per rimediare

Dopo avere tolto le brugole dai pedali si rovescia la pedaliera e si tolgono tutte le viti con testa argentata. Attenzione che due sono poco visibili perche' nascoste sotto al blocco zigrinato per il tappeto

Una volta tolte le viti si separa il guscio della base...c'e' da fare attenzione al blocco del cavo 

La componente elettrica e' molto semplice, un potenziometro per ogni pedale.

Una volta tolta tutta la polvere accumulata e spruzzato il pulisci contatti sui potenziometri la pedaliera e' tornata a funzionare

Si deve fare molta attenzione a richiudere perche' e' molto semplice rompere i fili stringendoli tra i due gusci (ovviamente cosa che ho fatto)

 

 

 

 

 

Eth0 su LuckFox Pico Mini A

 Le schede Luckfox Pico Mini A (a differenza delle sorelle maggiori) non hanno un connettore RJ45 e nonostante i pin da saldare non sembrano poter disporre di connessione di rete Ethernet.

In realta' basta saldare un cavo ethernet come da foto e modificare il kernel per abilitare la rete

I colori del cavo ethernet da collegare sono

 

TXP : verde bianco

TXN : verde

RXP : arancio bianco

RXN : arancio

Per compilare il kernel sono preferito partire dall'SDK 

Ho clonato il repository https://github.com/LuckfoxTECH/luckfox-pico in /home/luca/luckfox ed ho installato il docker

sudo docker run -it --name luckfox -v /home/luca/luckfox:/home luckfoxtech/luckfox_pico:1.0 /bin/bash

A questo punto

cd tools/linux/toolchain/arm-rockchip830-linux-uclibcgnueabihf/
source env_install_toolchain.sh

Per abilitare la ethernet in Pico Mini A si modifica il file rv1103g-luckfox-pico-mini-a.dts che si trova in 

 /home/luca/luckfox-pico/sysdrv/source/kernel/arch/arm/boot/dts

 sostituendo sotto la stringa &gmac lo status in okay

/**********ETH**********/
&gmac {
    status = "okay";
};

/**********USB**********/

 si compila il kernel con ./build.sh lunch (opzione 1) e ./build.sh

Attenzione: per l'immagine Ubuntu non funziona la camera

I file si troveranno nella cartella IMAGE  

Tramite il tool https://files.luckfox.com/wiki/Luckfox-Pico/Software/SocToolKit.zip

ATTENZIONE: VirusTotal segnala presenza di virus (aprire in sandbox)

si crea la scheda SD con i file appena generati

 Tutto funziona camera compresa...attenzione a non usare VLC. Io ho usato il player rtsp integrato in Firefox

 

 

 

 

 

 

Perche' investire su Unix

 Un libro trovato nel libero scambio alla Coop su cio' che poteva essere e non e' stato...interessante la storia su Unix del primo capitolo

Arcobaleno

 Si vede poco ma era un doppio arcobaleno

Frane con rete DeepLabV3

Aggiornamento

Per usare la GPU T4 sono migrato su Google Colab

Per avere Keras 3 sono state fatte le seguenti modifiche

!pip install keras --upgrade --quiet

!pip install keras-preprocessing==1.0.6

import keras

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

inoltre su Colab non sono riuscito a salvare il modello in .keras o in .hd5.Per questo motivo ho usato il formato dati legacy

tf.saved_model.save(model,"/content/drive/MyDrive/UAV/salvataggio/")

Su Colab sono riuscito a portare il training fino a 25 epochs. Si nota come la T4 sia nettamente superiore al M1

------------------------------------------------------------------------

Nel post precedente avevo trovato che il letteratura i risultati migliori per la segmentazione delle frane e' stati ottenuti con la rete DeepLabV3. 

Ho usato l'esempio a questo link per provare con lo stesso dataset del post precedente. Attenzione, e' necessario utilizzare Keras 3

questo e' il codice leggermente riadattato per puntare alle immagini delle frane

#!/usr/bin/env python

# coding: utf-8

# In[1]:

import keras

from keras import layers

from keras import ops

import os

import numpy as np

from glob import glob

import cv2

from scipy.io import loadmat

import matplotlib.pyplot as plt

# For data preprocessing

get_ipython().system('pip install tensorflow')

from tensorflow import image as tf_image

from tensorflow import data as tf_data

from tensorflow import io as tf_io

# In[2]:

IMAGE_SIZE = 512

BATCH_SIZE = 4

NUM_CLASSES = 2

#DATA_DIR = "./1/instance-level_human_parsing/instance-level_human_parsing/Training"

DATA_DIR = "./UAV"

NUM_TRAIN_IMAGES = 900

NUM_VAL_IMAGES = 100

train_images = sorted(glob(os.path.join(DATA_DIR, "img/*")))[:NUM_TRAIN_IMAGES]

train_masks = sorted(glob(os.path.join(DATA_DIR, "mask/*")))[:NUM_TRAIN_IMAGES]

val_images = sorted(glob(os.path.join(DATA_DIR, "img/*")))[

NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES

]

val_masks = sorted(glob(os.path.join(DATA_DIR, "mask/*")))[

NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES

]

def read_image(image_path, mask=False):

image = tf_io.read_file(image_path)

if mask:

image = tf_image.decode_png(image, channels=1)

image.set_shape([None, None, 1])

image = tf_image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])

else:

image = tf_image.decode_png(image, channels=3)

image.set_shape([None, None, 3])

image = tf_image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])

return image

def load_data(image_list, mask_list):

image = read_image(image_list)

mask = read_image(mask_list, mask=True)

return image, mask

def data_generator(image_list, mask_list):

dataset = tf_data.Dataset.from_tensor_slices((image_list, mask_list))

dataset = dataset.map(load_data, num_parallel_calls=tf_data.AUTOTUNE)

dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)

return dataset

train_dataset = data_generator(train_images, train_masks)

val_dataset = data_generator(val_images, val_masks)

print("Train Dataset:", train_dataset)

print("Val Dataset:", val_dataset)

# In[3]:

def convolution_block(

block_input,

num_filters=256,

kernel_size=3,

dilation_rate=1,

use_bias=False,

):

x = layers.Conv2D(

num_filters,

kernel_size=kernel_size,

dilation_rate=dilation_rate,

padding="same",

use_bias=use_bias,

kernel_initializer=keras.initializers.HeNormal(),

)(block_input)

x = layers.BatchNormalization()(x)

return ops.nn.relu(x)

def DilatedSpatialPyramidPooling(dspp_input):

dims = dspp_input.shape

x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)

x = convolution_block(x, kernel_size=1, use_bias=True)

out_pool = layers.UpSampling2D(

size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]),

interpolation="bilinear",

)(x)

out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)

out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)

out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)

out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)

x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])

output = convolution_block(x, kernel_size=1)

return output

# In[4]:

def DeeplabV3Plus(image_size, num_classes):

model_input = keras.Input(shape=(image_size, image_size, 3))

preprocessed = keras.applications.resnet50.preprocess_input(model_input)

resnet50 = keras.applications.ResNet50(

weights="imagenet", include_top=False, input_tensor=preprocessed

)

x = resnet50.get_layer("conv4_block6_2_relu").output

x = DilatedSpatialPyramidPooling(x)

input_a = layers.UpSampling2D(

size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),

interpolation="bilinear",

)(x)

input_b = resnet50.get_layer("conv2_block3_2_relu").output

input_b = convolution_block(input_b, num_filters=48, kernel_size=1)

x = layers.Concatenate(axis=-1)([input_a, input_b])

x = convolution_block(x)

x = convolution_block(x)

x = layers.UpSampling2D(

size=(image_size // x.shape[1], image_size // x.shape[2]),

interpolation="bilinear",

)(x)

model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)

return keras.Model(inputs=model_input, outputs=model_output)

model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)

model.summary()

# In[5]:

loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)

model.compile(

optimizer=keras.optimizers.Adam(learning_rate=0.001),

loss=loss,

metrics=["accuracy"],

)

history = model.fit(train_dataset, validation_data=val_dataset, epochs=10)

plt.plot(history.history["loss"])

plt.title("Training Loss")

plt.ylabel("loss")

plt.xlabel("epoch")

plt.show()

plt.plot(history.history["accuracy"])

plt.title("Training Accuracy")

plt.ylabel("accuracy")

plt.xlabel("epoch")

plt.show()

plt.plot(history.history["val_loss"])

plt.title("Validation Loss")

plt.ylabel("val_loss")

plt.xlabel("epoch")

plt.show()

plt.plot(history.history["val_accuracy"])

plt.title("Validation Accuracy")

plt.ylabel("val_accuracy")

plt.xlabel("epoch")

plt.show()

# In[6]:

# Loading the Colormap

colormap = loadmat(

"./1/instance-level_human_parsing/instance-level_human_parsing/human_colormap.mat"

)["colormap"]

colormap = colormap * 100

colormap = colormap.astype(np.uint8)

def infer(model, image_tensor):

predictions = model.predict(np.expand_dims((image_tensor), axis=0))

predictions = np.squeeze(predictions)

predictions = np.argmax(predictions, axis=2)

return predictions

def decode_segmentation_masks(mask, colormap, n_classes):

r = np.zeros_like(mask).astype(np.uint8)

g = np.zeros_like(mask).astype(np.uint8)

b = np.zeros_like(mask).astype(np.uint8)

for l in range(0, n_classes):

idx = mask == l

r[idx] = colormap[l, 0]

g[idx] = colormap[l, 1]

b[idx] = colormap[l, 2]

rgb = np.stack([r, g, b], axis=2)

return rgb

def get_overlay(image, colored_mask):

image = keras.utils.array_to_img(image)

image = np.array(image).astype(np.uint8)

overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0)

return overlay

def plot_samples_matplotlib(display_list, figsize=(5, 3)):

_, axes = plt.subplots(nrows=1, ncols=len(display_list), figsize=figsize)

for i in range(len(display_list)):

if display_list[i].shape[-1] == 3:

axes[i].imshow(keras.utils.array_to_img(display_list[i]))

else:

axes[i].imshow(display_list[i])

plt.show()

def plot_predictions(images_list, colormap, model):

for image_file in images_list:

image_tensor = read_image(image_file)

prediction_mask = infer(image_tensor=image_tensor, model=model)

prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)

overlay = get_overlay(image_tensor, prediction_colormap)

plot_samples_matplotlib(

[image_tensor, overlay, prediction_colormap], figsize=(18, 14)

)

# In[7]:

plot_predictions(train_images[:4], colormap, model=model)

# ### Inference on Validation Images

#

# You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/deeplabv3p-resnet50)

# and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/Human-Part-Segmentation).

# In[8]:

plot_predictions(val_images[:4], colormap, model=model)

# In[12]:

get_ipython().system('pip install ipython')

get_ipython().system('mkdir -p saved_model_2')

model.save('saved_model_2/landslide.keras')

Anche su M1 il calcolo e' molto lento....sarebbe stati necessari piu' di 10 epochs per trovare l'asintoto della Loss ma ogni epoch ha impiegato ogni 25 minuti per circa 1000 immagini

in conclusione si vede che le accuracy e' nettamente superiore rispetto a Unet a conferma di quanto riportato in letteratura

Frane da drone con rete UNET

Alla ricerca di immagini di training gia' pronte per reti neurali mi sono imbattuto nel CAS Landslide Database (scaricabile da https://zenodo.org/records/10294997). Sono oltre Gb di immagini tif con immagini di frane sia da satellite che da drone con gia' pronta la maschera della verita' a terra. (avevo gia' provato in questo post)

Il database e' stato oggetto di un articolo di Nature

A questo link viene riportata anche una tabella comparativa di diversi metodi sviluppati sul medesimo dataset

 

Per una prova ho abbondato Colab per installare Tensorflow e Jupyter Notebook su Mac Air M1 e vedere se poteva essere una base di sviluppo. 

conda config --set auto_activate_base false

conda create --name mlp python=3.8 

conda activate mlp   

conda install -c apple tensorflow-deps    

pip install tensorflow-macos   

pip install tensorflow-metal

conda install jupyter pandas numpy matplotlib scikit-learn

jupyter notebook        

Le immagini del dataset Moxitaidi (UAV-0.6m) sono state trasformate in jpg ( magick mogrify -format jpg *.tif) e ridotte 256x256 (mogrify -resize 256x256 *.jpg) tramite imagemagick e sono state rinominate le maschere tramite il comando bash (for f in *.png; do mv "$f" "${f//mask/image}"; done ). Rispetto a quanto provato qui il numero delle epochs e' stato ridotto a 40. M1 si e' comportato egregiamente come velocita' (circa 70 secondi per ogni epoch)

#!/usr/bin/env python

# coding: utf-8

# In[1]:

from functools import partial

import os

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import tensorflow as tf

from tensorflow import keras

# In[2]:

images_dir = './UAVjpg256/img/'

masks_dir = './UAVjpg256/mask/'

dirname, _, filenames = next(os.walk(images_dir))

@tf.function

def load_img_with_mask(image_path, images_dir: str = 'img', masks_dir: str = 'label',images_extension: str = 'jpg', masks_extension: str = 'jpg') -> dict:

image = tf.io.read_file(image_path)

image = tf.image.decode_jpeg(image, channels=3)

mask_filename = tf.strings.regex_replace(image_path, images_dir, masks_dir)

mask_filename = tf.strings.regex_replace(mask_filename, images_extension, masks_extension)

mask = tf.io.read_file(mask_filename)

mask = tf.image.decode_image(mask, channels=1, expand_animations = False)

return (image, mask)

n_examples = 3

examples = [load_img_with_mask(os.path.join(images_dir, filenames[i])) for i in range(n_examples)]

fig, axs = plt.subplots(n_examples, 2, figsize=(14, n_examples*7), constrained_layout=True)

for ax, (image, mask) in zip(axs, examples):

ax[0].imshow(image)

ax[1].imshow(mask)

plt.show()

# In[3]:

@tf.function

def resize_images(images, masks, max_image_size=1500):

shape = tf.shape(images)

scale = (tf.reduce_max(shape) // max_image_size) + 1

target_height, target_width = shape[-3] // scale, shape[-2] // scale

images = tf.cast(images, tf.float32)

masks = tf.cast(masks, tf.float32)

if scale != 1:

images = tf.image.resize(images, (target_height, target_width), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

masks = tf.image.resize(masks, (target_height, target_width), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

return (images, masks)

@tf.function

def scale_values(images, masks, mask_split_threshold = 128):

images = tf.math.divide(images, 255)

masks = tf.where(masks > mask_split_threshold, 1, 0)

return (images, masks)

@tf.function

def pad_images(images, masks, pad_mul=16, offset=0):

shape = tf.shape(images)

height, width = shape[-3], shape[-2]

target_height = height + tf.math.floormod(tf.math.negative(height), pad_mul)

target_width = width + tf.math.floormod(tf.math.negative(width), pad_mul)

images = tf.image.pad_to_bounding_box(images, offset, offset, target_height, target_width)

masks = tf.cast(tf.image.pad_to_bounding_box(masks, offset, offset, target_height, target_width), tf.uint8)

return (images, masks)

batch_size = 32

test_set_size = 200

validation_set_size = 150

# In[4]:

dataset = tf.data.Dataset.list_files(images_dir + '*.jpg', seed=42)

test_dataset = dataset.take(test_set_size)

dataset = dataset.skip(test_set_size)

test_dataset = test_dataset.map(load_img_with_mask)

test_dataset = test_dataset.map(scale_values)

test_dataset = test_dataset.shuffle(20)

test_dataset = test_dataset.map(lambda img, mask: resize_images(img, mask, max_image_size=2500))

test_dataset = test_dataset.map(pad_images)

test_dataset = test_dataset.batch(1).prefetch(5)

validation_dataset = dataset.take(validation_set_size)

train_dataset = dataset.skip(validation_set_size)

validation_dataset = validation_dataset.map(load_img_with_mask)

validation_dataset = validation_dataset.map(scale_values)

validation_dataset = validation_dataset.shuffle(20)

validation_dataset = validation_dataset.map(resize_images)

validation_dataset = validation_dataset.map(pad_images)

validation_dataset = validation_dataset.batch(1).prefetch(5)

train_dataset = train_dataset.map(load_img_with_mask)

train_dataset = train_dataset.map(scale_values)

train_dataset = train_dataset.shuffle(20)

train_dataset = train_dataset.map(resize_images)

train_dataset = train_dataset.map(pad_images)

train_dataset = train_dataset.batch(1).prefetch(5)

# In[5]:

def get_unet(hidden_activation='relu', initializer='he_normal', output_activation='sigmoid'):

PartialConv = partial(keras.layers.Conv2D,

activation=hidden_activation,

kernel_initializer=initializer,

padding='same')

# Encoder

model_input = keras.layers.Input(shape=(None, None, 3))

enc_cov_1 = PartialConv(32, 3)(model_input)

enc_cov_1 = PartialConv(32, 3)(enc_cov_1)

enc_pool_1 = keras.layers.MaxPooling2D(pool_size=(2, 2))(enc_cov_1)

enc_cov_2 = PartialConv(64, 3)(enc_pool_1)

enc_cov_2 = PartialConv(64, 3)(enc_cov_2)

enc_pool_2 = keras.layers.MaxPooling2D(pool_size=(2, 2))(enc_cov_2)

enc_cov_3 = PartialConv(128, 3)(enc_pool_2)

enc_cov_3 = PartialConv(128, 3)(enc_cov_3)

enc_pool_3 = keras.layers.MaxPooling2D(pool_size=(2, 2))(enc_cov_3)

# Center

center_cov = PartialConv(256, 3)(enc_pool_3)

center_cov = PartialConv(256, 3)(center_cov)

# Decoder

upsampling1 = keras.layers.UpSampling2D(size=(2, 2))(center_cov)

dec_up_conv_1 = PartialConv(128, 2)(upsampling1)

dec_merged_1 = tf.keras.layers.Concatenate(axis=3)([enc_cov_3, dec_up_conv_1])

dec_conv_1 = PartialConv(128, 3)(dec_merged_1)

dec_conv_1 = PartialConv(128, 3)(dec_conv_1)

upsampling2 = keras.layers.UpSampling2D(size=(2, 2))(dec_conv_1)

dec_up_conv_2 = PartialConv(64, 2)(upsampling2)

dec_merged_2 = tf.keras.layers.Concatenate(axis=3)([enc_cov_2, dec_up_conv_2])

dec_conv_2 = PartialConv(64, 3)(dec_merged_2)

dec_conv_2 = PartialConv(64, 3)(dec_conv_2)

upsampling3 = keras.layers.UpSampling2D(size=(2, 2))(dec_conv_2)

dec_up_conv_3 = PartialConv(32, 2)(upsampling3)

dec_merged_3 = tf.keras.layers.Concatenate(axis=3)([enc_cov_1, dec_up_conv_3])

dec_conv_3 = PartialConv(32, 3)(dec_merged_3)

dec_conv_3 = PartialConv(32, 3)(dec_conv_3)

output = keras.layers.Conv2D(1, 1, activation=output_activation)(dec_conv_3)

return tf.keras.Model(inputs=model_input, outputs=output)

model = get_unet()

optimizer = tf.keras.optimizers.Nadam()

model.compile(loss='binary_crossentropy', optimizer=optimizer)

model.summary()

# In[6]:

early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)

lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=3, verbose=1)

epochs = 40

history = model.fit(train_dataset, validation_data=validation_dataset, epochs=epochs, callbacks=[early_stopping, lr_reduce])

# In[7]:

get_ipython().system('mkdir -p saved_model')

model.save('saved_model/landslide_drone')

# In[8]:

converter = tf.lite.TFLiteConverter.from_saved_model('saved_model/landslide_drone')

tflite_model = converter.convert()

with open('model.tflite', 'wb') as f:

f.write(tflite_model)

# In[9]:

n_examples = 10

fig, axs = plt.subplots(n_examples, 3, figsize=(14, n_examples*7), constrained_layout=True)

for ax, ele in zip(axs, test_dataset.take(n_examples)):

image, y_true = ele

prediction = model.predict(image)[0]

prediction = tf.where(prediction > 0.6, 255, 0)

ax[0].set_title('Original image')

ax[0].imshow(image[0])

ax[1].set_title('Original mask')

ax[1].imshow(y_true[0])

ax[2].set_title('Predicted area')

ax[2].imshow(prediction)

plt.show()

# In[10]:

meanIoU = tf.keras.metrics.MeanIoU(num_classes=2)

for ele in test_dataset.take(test_set_size):

image, y_true = ele

prediction = model.predict(image)[0]

prediction = tf.where(prediction > 0.5, 1, 0)

meanIoU.update_state(y_true[0], prediction)

print(meanIoU.result().numpy())

Questi sono due esempi dei risulati