Denis Sergienko "Pip install driven deep learning"
•
0 likes•98 views
Machine learning is still a relatively new and rapidly growing field of computer science. Every day we see new methods for solving increasingly complex problems. But at the same time, using machine learning in your project may look like a challenging task. In this talk, I want to show that a lot of practical problems can be solved with the help of ready to go pip packages. We will look into the most popular tasks of computer vision and natural language processing.
Report
Share
Report
Share
Download to read offline
Recommended
Primer vistazo al computer vision | 4Sessions Feb17
Lo cognitivo está de moda, y en el Cloud empresas como Microsoft y Google, con Cloud Vision y Cognitive Services, se están encargando de atraer a muchos desarrolladores para que las cámaras de nuestros dispositivos empiecen a comprender un poquito el mundo que les rodea.
¿Pero estamos hablando realmente de algo nuevo, o es otra excusa para engancharnos a la nube?
En esta sesión hemos analizado las opciones que nos expone el mercado, más allá de las grandes compañías y hemos visto otras con mucha relevancia e historia a sus espaldas en el mercado como OpenCV (Open Source Computer Vision Library).
Qué, con quién y cómo hacer que tus desarrollos vean y comprendan el mundo.
The Ring programming language version 1.5.4 book - Part 59 of 185
The Ring programming language version 1.5.4 book - Part 59 of 185
Computer vision Nebraska (Nebraska Code)
A beginners guide to computer vision as shared at Nebraska Code
The Ring programming language version 1.7 book - Part 63 of 196
The Ring programming language version 1.7 book - Part 63 of 196
apidays LIVE Australia 2020 - Building distributed systems on the shoulders o...
apidays LIVE Australia 2020 - Building Business Ecosystems
Building distributed systems on the shoulders of giants
Dasith Wijesiriwardena, Telstra Purple (Readify)
The Ring programming language version 1.5.1 book - Part 57 of 180
The Ring programming language version 1.5.1 book - Part 57 of 180
The Ring programming language version 1.5.3 book - Part 70 of 184
The Ring programming language version 1.5.3 book - Part 70 of 184
29-kashyap-mask-detaction.pptx
i want to create mask detection using ai and i want explain all workings in tutorial of youtube and also i will make ppt and blog for refrences
Recommended
Primer vistazo al computer vision | 4Sessions Feb17
Lo cognitivo está de moda, y en el Cloud empresas como Microsoft y Google, con Cloud Vision y Cognitive Services, se están encargando de atraer a muchos desarrolladores para que las cámaras de nuestros dispositivos empiecen a comprender un poquito el mundo que les rodea.
¿Pero estamos hablando realmente de algo nuevo, o es otra excusa para engancharnos a la nube?
En esta sesión hemos analizado las opciones que nos expone el mercado, más allá de las grandes compañías y hemos visto otras con mucha relevancia e historia a sus espaldas en el mercado como OpenCV (Open Source Computer Vision Library).
Qué, con quién y cómo hacer que tus desarrollos vean y comprendan el mundo.
The Ring programming language version 1.5.4 book - Part 59 of 185
The Ring programming language version 1.5.4 book - Part 59 of 185
Computer vision Nebraska (Nebraska Code)
A beginners guide to computer vision as shared at Nebraska Code
The Ring programming language version 1.7 book - Part 63 of 196
The Ring programming language version 1.7 book - Part 63 of 196
apidays LIVE Australia 2020 - Building distributed systems on the shoulders o...
apidays LIVE Australia 2020 - Building Business Ecosystems
Building distributed systems on the shoulders of giants
Dasith Wijesiriwardena, Telstra Purple (Readify)
The Ring programming language version 1.5.1 book - Part 57 of 180
The Ring programming language version 1.5.1 book - Part 57 of 180
The Ring programming language version 1.5.3 book - Part 70 of 184
The Ring programming language version 1.5.3 book - Part 70 of 184
29-kashyap-mask-detaction.pptx
i want to create mask detection using ai and i want explain all workings in tutorial of youtube and also i will make ppt and blog for refrences
This face recognition attendance system code for face recogn.pdf
This face recognition attendance system code for face recognition:
# import re
from sys import path
from tkinter import*
from tkinter import ttk
from PIL import Image,ImageTk
import os
import mysql.connector
import cv2
import numpy as np
from tkinter import messagebox
from time import strftime
from datetime import datetime
class Face_Recognition:
def __init__(self,root):
self.root=root
self.root.geometry("1366x768+0+0")
self.root.title("Face Recognition Pannel")
# This part is image labels setting start
# first header image
img=Image.open(r"C:UserselhanDocumentsPython_Test_ProjectsPython_Test_ProjectsImages_
GUIbanner.jpg")
img=img.resize((1366,130),Image.ANTIALIAS)
self.photoimg=ImageTk.PhotoImage(img)
# set image as lable
f_lb1 = Label(self.root,image=self.photoimg)
f_lb1.place(x=0,y=0,width=1366,height=130)
# backgorund image
bg1=Image.open(r"C:UserselhanDocumentsPython_Test_ProjectsPython_Test_ProjectsImages_
GUIbg2.jpg")
bg1=bg1.resize((1366,768),Image.ANTIALIAS)
self.photobg1=ImageTk.PhotoImage(bg1)
# set image as lable
bg_img = Label(self.root,image=self.photobg1)
bg_img.place(x=0,y=130,width=1366,height=768)
#title section
title_lb1 = Label(bg_img,text="Welcome to Face Recognition
Pannel",font=("verdana",30,"bold"),bg="white",fg="navyblue")
title_lb1.place(x=0,y=0,width=1366,height=45)
# Create buttons below the section
# -------------------------------------------------------------------------------------------------------------------
# Training button 1
std_img_btn=Image.open(r"C:UserselhanDocumentsPython_Test_ProjectsPython_Test_ProjectsI
mages_GUIf_det.jpg")
std_img_btn=std_img_btn.resize((180,180),Image.ANTIALIAS)
self.std_img1=ImageTk.PhotoImage(std_img_btn)
std_b1 = Button(bg_img,command=self.face_recog,image=self.std_img1,cursor="hand2")
std_b1.place(x=600,y=170,width=180,height=180)
std_b1_1 = Button(bg_img,command=self.face_recog,text="Face
Detector",cursor="hand2",font=("tahoma",15,"bold"),bg="white",fg="navyblue")
std_b1_1.place(x=600,y=350,width=180,height=45)
#=====================Attendance===================
def mark_attendance(self,i,r,n):
with open("attendance.csv","r+",newline="n") as f:
myDatalist=f.readlines()
name_list=[]
for line in myDatalist:
entry=line.split((","))
name_list.append(entry[0])
if((i not in name_list)) and ((r not in name_list)) and ((n not in name_list)):
now=datetime.now()
d1=now.strftime("%d/%m/%Y")
dtString=now.strftime("%H:%M:%S")
f.writelines(f"n{i}, {r}, {n}, {dtString}, {d1}, Present")
#================face recognition==================
def face_recog(self):
def draw_boundray(img,classifier,scaleFactor,minNeighbors,color,text,clf):
gray_image=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
featuers=classifier.detectMultiScale(gray_image,scaleFactor,minNeighbors)
coord=[]
for (x,y,w,h) in featuers:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),3)
id,predict=clf.predict(gray_image[y:y+h,x:x+w])
confidence=int((100*(1-predict/300)))
conn = mysql.connector.connect(username='root',
password='root',host='l.
Can someone please explain what the code below is doing and comment on.pdf
Can someone please explain what the code below is doing and comment on its network
performance? Please and thank you.
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * generated_images + 0.5
fig, axs = plt.subplots(r, c)
count = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(ge.
Python program to build deep learning algorithm using a CNNs model to.docx
Python program to build deep learning algorithm using a CNNs model to detect malware attacks.
The code is working, but I faced issues with the last part of the code, and I needed to know precisely where the problem was.
I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes through the filters, and it shows me the output.
this is the code
import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from os import listdir from PIL import Image import os.path import numpy as np path = '/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256 #height of image w = 256 #width of image # be careful with using this function, it will consume memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE'): with open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr) == h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And you can save them into png files # count = 0 # for img in images: # png = Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') # png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)): img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list /= 255 # ################################################################ #approca 1 failed go back from scrtuch # implement the architecture of the CNN model = Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25), padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25, 25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2))) #Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu')) model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy']) model.summary()
''' FINALLY If you have a set of executable files from both types (benign and malicious) you can try to fit the model. You should prepare a list of images for malwares and a list for benign and here how you fit the model. ''' #Change the parameters to whatever suits you
I already have to files that contain benign files and malicious images, but it shows me syntax error!
batch_size = 512 epochs = 100 labels = [0 for _ in benign_images ] + [1 for _ in malicious_images ] model.fit( benign_images + maliciou.
Using the code below- I need help with the following 3 things- 1) Writ.pdf
Using the code below, I need help with the following 3 things:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
3) Comment on the network performance.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a .
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning Service
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Need helping adding to the code below to plot the images from the firs.pdf
Need helping adding to the code below to plot the images from the first epoch. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
Using the code below- I need help with creating code for the following.pdf
Using the code below, I need help with creating code for the following:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display th.
Hello- I hope you are doing well- I am doing my project- which is Rans (1).pdf
Hello, I hope you are doing well,
I am doing my project, which is Ransomware attack detection using Deep learning CNNs
I got a result of 0.93, which is not good enough, I have tried to improve my accuracy, but I gave
up. Could you modify the code feature selection or pre-processing to get a higher result? Even
for that result, I have to wait a long time to get it because I increased the epoch, and could you
explain the added part?
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
from keras.preprocessing.image import ImageDataGenerator #, load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation,
BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=128 # maybe 256
IMAGE_HEIGHT=128 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work df['category'].value_counts().plot.bar()
# See sample image
# sample = random.choice(filenames)
# image = load_img("D:\Ransomware_Detection_using _CNN\MixImages"+sample)
# plt.imshow(image)
# in collab it will work df['category'].value_counts().plot.bar()
# Build Model
#===============================================================================================================
# Testing
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='re.
Eugene Khvedchenya. State of the art Image Augmentations with Albumentations.
Eugene Khvedchenya. State of the art Image Augmentations with Albumentations.
http://aiconf.com.ua/
What is the UML Class diagram for accident detection using CNN- i have.pdf
What is the UML Class diagram for accident detection using CNN.
i have make the class diagram which is not sufficient to my guide.
Class diagram: .
SYSTEM ARCHITECTURE:
Code for the accident detection:-
from tkinter import messagebox
from tkinter import *
from tkinter import simpledialog
import tkinter
from tkinter import filedialog
from tkinter.filedialog import askopenfilename
import time
import cv2
import tensorflow as tf
from collections import namedtuple
import numpy as np
import winsound
main = tkinter.Tk()
main.title("Accident Detection")
main.geometry("1300x1200")
net =
cv2.dnn.readNetFromCaffe("model/MobileNetSSD_deploy.prototxt.txt","model/MobileNetSSD_deploy.caffemodel")
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
global filename
global detectionGraph
global msg
def loadModel():
global detectionGraph
detectionGraph = tf.Graph()
with detectionGraph.as_default():
od_graphDef = tf.compat.v1.GraphDef()
with tf.compat.v2.io.gfile.GFile('model/frozen_inference_graph.pb', 'rb') as file:
serializedGraph = file.read()
od_graphDef.ParseFromString(serializedGraph)
tf.import_graph_def(od_graphDef, name='')
messagebox.showinfo("Training model loaded","Training model loaded")
def beep():
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
def uploadVideo():
global filename
filename = filedialog.askopenfilename(initialdir="videos")
pathlabel.config(text=filename)
text.delete('1.0', END)
text.insert(END,filename+" loaded\n");
def calculateCollision(boxes,classes,scores,image_np):
global msg
#cv2.putText(image_np, "NORMAL!", (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,
255, 255), 2, cv2.LINE_AA)
for i, b in enumerate(boxes[0]):
if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
if scores[0][i] > 0.5:
for j, c in enumerate(boxes[0]):
if (i != j) and (classes[0][j] == 3 or classes[0][j] == 6 or classes[0][j] == 8) and scores[0][j]> 0.5:
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
ra = Rectangle(boxes[0][i][3], boxes[0][i][2], boxes[0][i][1], boxes[0][i][3])
rb = Rectangle(boxes[0][j][3], boxes[0][j][2], boxes[0][j][1], boxes[0][j][3])
ar = rectArea(boxes[0][i][3], boxes[0][i][1],boxes[0][i][2],boxes[0][i][3])
col_threshold = 0.6*np.sqrt(ar)
area(ra, rb)
if (area(ra,rb)<col_threshold) :
print('accident')
msg = 'ACCIDENT!'
beep()
return True
else:
return False
def rectArea(xmax, ymax, xmin, ymin):
x = np.abs(xmax-xmin)
y = np.abs(ymax-ymin)
return x*y
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
def area(a, b): # returns None if rectangles don't intersect
dx = min(a.xmax, b.xmax) - max(a.xmin, .
Python OpenCV Real Time projects
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez (which was later acquired by Intel). The library is cross-platform and free for use under the open-source BSD license.
I have tried running this code below- and it is working- but the accur.pdf
I have tried running this code below, and it is working, but the accuracy is less than expected.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reduction
learning_rate_reduction = ReduceLROnPlateau(mon.
You are task to add a yawning detection to the programme below;i.pdf
You are task to add a yawning detection to the programme below;
import numpy as np
from pygame import mixer
import time
import cv2
from tkinter import *
import tkinter.messagebox
import mysql.connector
from datetime import datetime
root = Tk()
root.geometry('500x570')
frame = Frame(root, relief=RIDGE, borderwidth=2)
frame.pack(fill=BOTH, expand=1)
root.title('Drive Main')
frame.config(background='#74B0D6')
label = Label(frame, text="Drive Care", bg='#74B0D6', font='Garcedo 25 bold')
label.pack(side=TOP)
filename = PhotoImage(file="picco.png")
background_label = Label(frame, image=filename)
background_label.pack(side=TOP)
from mysql.connector import Error
def connect():
""" Connect to MySQL database """
conn = None
try:
conn = mysql.connector.connect(host='localhost',
database='drive_main',
user='root',
password='')
mySql_insert_query = """INSERT INTO tbl_drivers(id, name, event_time)
VALUES (%s, %s, %s) """
cursor = conn.cursor()
current_Date = datetime.now()
# convert date in the format you want
formatted_date = current_Date.strftime('%Y-%m-%d %H:%M:%S')
insert_tuple = (5, 'meleda ulett', current_Date)
result = cursor.execute(mySql_insert_query, insert_tuple)
conn.commit()
print("Date Record inserted successfully")
if conn.is_connected():
print('Connected to MySQL database')
except Error as e:
print(e)
except mysql.connector.Error as error:
conn.rollback()
print("Failed to insert into MySQL table {}".format(error))
finally:
if conn is not None and conn.is_connected():
conn.close()
connect()
def hel_doc():
help(cv2)
def Contri():
tkinter.messagebox.showinfo("Contributors", "\n1.Romairo Reid\n2. Kayla-Marie Sooman \n3.
Bradly Walcott \n4. Lee Hinds \n")
def anotherWin():
tkinter.messagebox.showinfo("About",
'Drive Care version 01.1\n Made Using\n-OpenCV\np\n-Tkinter\n In Python
3')
menu = Menu(root)
root.config(menu=menu)
subm1 = Menu(menu)
menu.add_cascade(label="Tools", menu=subm1)
subm1.add_command(label="Open CV Docs", command=hel_doc)
subm2 = Menu(menu)
menu.add_cascade(label="About", menu=subm2)
subm2.add_command(label="Driver Cam", command=anotherWin)
subm2.add_command(label="Contributors", command=Contri)
def exitt():
exit()
def web():
capture = cv2.VideoCapture(0)
while True:
ret, frame = capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('e'):
break
capture.release()
cv2.destroyAllWindows()
def webrec():
capture = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
op = cv2.VideoWriter('Sample1.avi', fourcc, 11.0, (640, 480))
while True:
ret, frame = capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame', frame)
op.write(frame)
if cv2.waitKey(1) & 0xFF == ord('e'):
break
op.release()
capture.release()
cv2.destroyAllWindows()
def webdet():
capture = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier('lbpcascade_frontalface.xml')
eye_glass = cv2.CascadeClassifier('haarcascade_eye_tree_eyeglasses.xml')
while True:
ret, frame = .
Pydata DC 2018 (Skorch - A Union of Scikit-learn and PyTorch)
Github repo: https://github.com/thomasjpfan/pydata2018_dc_skorch
Learning Predictive Modeling with TSA and Kaggle
Ever wanted to do a challenging data science project but feel like you don’t have enough experience? Just go for it! Diving into a 3 TB, 3D image dataset has been my best learning experience. I want to share this deep learning project with you, and tips for overcoming challenges along the way.
Need an detailed analysis of what this code-model is doing- Thanks #St.pdf
Need an detailed analysis of what this code/model is doing. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
# Generate a batch of random noise
noise = np.random.normal(0, 1, (16, latent_dimensions))
# Generate images from the noise
generated_images = generator.predict(noise)
# Rescale the images to 0.
"What I learned through reverse engineering", Yuri Artiukh
In recent years, I have gained most of my knowledge through reverse engineering, how I did it and what I learned during this period, I decided to share. All this concerns graphic programming, performance, best practices in the frontend.
"Impact of front-end architecture on development cost", Viktor Turskyi
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
More Related Content
Similar to Denis Sergienko "Pip install driven deep learning"
This face recognition attendance system code for face recogn.pdf
This face recognition attendance system code for face recognition:
# import re
from sys import path
from tkinter import*
from tkinter import ttk
from PIL import Image,ImageTk
import os
import mysql.connector
import cv2
import numpy as np
from tkinter import messagebox
from time import strftime
from datetime import datetime
class Face_Recognition:
def __init__(self,root):
self.root=root
self.root.geometry("1366x768+0+0")
self.root.title("Face Recognition Pannel")
# This part is image labels setting start
# first header image
img=Image.open(r"C:UserselhanDocumentsPython_Test_ProjectsPython_Test_ProjectsImages_
GUIbanner.jpg")
img=img.resize((1366,130),Image.ANTIALIAS)
self.photoimg=ImageTk.PhotoImage(img)
# set image as lable
f_lb1 = Label(self.root,image=self.photoimg)
f_lb1.place(x=0,y=0,width=1366,height=130)
# backgorund image
bg1=Image.open(r"C:UserselhanDocumentsPython_Test_ProjectsPython_Test_ProjectsImages_
GUIbg2.jpg")
bg1=bg1.resize((1366,768),Image.ANTIALIAS)
self.photobg1=ImageTk.PhotoImage(bg1)
# set image as lable
bg_img = Label(self.root,image=self.photobg1)
bg_img.place(x=0,y=130,width=1366,height=768)
#title section
title_lb1 = Label(bg_img,text="Welcome to Face Recognition
Pannel",font=("verdana",30,"bold"),bg="white",fg="navyblue")
title_lb1.place(x=0,y=0,width=1366,height=45)
# Create buttons below the section
# -------------------------------------------------------------------------------------------------------------------
# Training button 1
std_img_btn=Image.open(r"C:UserselhanDocumentsPython_Test_ProjectsPython_Test_ProjectsI
mages_GUIf_det.jpg")
std_img_btn=std_img_btn.resize((180,180),Image.ANTIALIAS)
self.std_img1=ImageTk.PhotoImage(std_img_btn)
std_b1 = Button(bg_img,command=self.face_recog,image=self.std_img1,cursor="hand2")
std_b1.place(x=600,y=170,width=180,height=180)
std_b1_1 = Button(bg_img,command=self.face_recog,text="Face
Detector",cursor="hand2",font=("tahoma",15,"bold"),bg="white",fg="navyblue")
std_b1_1.place(x=600,y=350,width=180,height=45)
#=====================Attendance===================
def mark_attendance(self,i,r,n):
with open("attendance.csv","r+",newline="n") as f:
myDatalist=f.readlines()
name_list=[]
for line in myDatalist:
entry=line.split((","))
name_list.append(entry[0])
if((i not in name_list)) and ((r not in name_list)) and ((n not in name_list)):
now=datetime.now()
d1=now.strftime("%d/%m/%Y")
dtString=now.strftime("%H:%M:%S")
f.writelines(f"n{i}, {r}, {n}, {dtString}, {d1}, Present")
#================face recognition==================
def face_recog(self):
def draw_boundray(img,classifier,scaleFactor,minNeighbors,color,text,clf):
gray_image=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
featuers=classifier.detectMultiScale(gray_image,scaleFactor,minNeighbors)
coord=[]
for (x,y,w,h) in featuers:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),3)
id,predict=clf.predict(gray_image[y:y+h,x:x+w])
confidence=int((100*(1-predict/300)))
conn = mysql.connector.connect(username='root',
password='root',host='l.
Can someone please explain what the code below is doing and comment on.pdf
Can someone please explain what the code below is doing and comment on its network
performance? Please and thank you.
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * generated_images + 0.5
fig, axs = plt.subplots(r, c)
count = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(ge.
Python program to build deep learning algorithm using a CNNs model to.docx
Python program to build deep learning algorithm using a CNNs model to detect malware attacks.
The code is working, but I faced issues with the last part of the code, and I needed to know precisely where the problem was.
I have the dataset of malware and benign in exe, I convert them to grayscale images, and it goes through the filters, and it shows me the output.
this is the code
import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D import numpy as np from os import listdir from PIL import Image import os.path import numpy as np path = '/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE' h = 256 #height of image w = 256 #width of image # be careful with using this function, it will consume memory, access to disk and time images = [] for f in listdir('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE'): with open(os.path.join('/media/malkuwari/New Volume/Ransomware_Detection_using _CNN/mixEXE', f), 'rb') as img_set: img_arr = img_set.read(h*w) while img_arr: if len(img_arr) == h*w and img_arr not in images: images.append(img_arr) img_arr = img_set.read(h*w) #And you can save them into png files # count = 0 # for img in images: # png = Image.fromarray(np.reshape(list(img), (h,w)).astype('float32'), mode='L') # png.save('image_l%d.png'%count) # count += 1 #reshape images to fit into the CNN model img_list = np.zeros(shape = (len(images), h,w,1), dtype = np.uint8) for j in range(len(images)): img_list[j,:,:,0] = np.reshape(list(images[j]), (h,w)) img_list = img_list.astype('float32') img_list /= 255 # ################################################################ #approca 1 failed go back from scrtuch # implement the architecture of the CNN model = Sequential() #Conv2D Layers model.add(Conv2D(12, (25, 25), padding='same',input_shape=img_list.shape[1:], activation = 'relu')) model.add(Conv2D(12, (25, 25), activation = 'relu')) #Max Pooling Layer model.add(MaxPooling2D(pool_size=(2, 2))) #Conv2D Layer model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu')) model.add(Conv2D(12, (13, 13), activation = 'relu')) #Max Pooling model.add(MaxPooling2D(pool_size=(2, 2))) #Flattening Layer model.add(Flatten()) #Dense Layer model.add(Dense(1024, activation = 'relu')) model.add(Dense(1, activation = 'sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy']) model.summary()
''' FINALLY If you have a set of executable files from both types (benign and malicious) you can try to fit the model. You should prepare a list of images for malwares and a list for benign and here how you fit the model. ''' #Change the parameters to whatever suits you
I already have to files that contain benign files and malicious images, but it shows me syntax error!
batch_size = 512 epochs = 100 labels = [0 for _ in benign_images ] + [1 for _ in malicious_images ] model.fit( benign_images + maliciou.
Using the code below- I need help with the following 3 things- 1) Writ.pdf
Using the code below, I need help with the following 3 things:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
3) Comment on the network performance.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a .
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning Service
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Need helping adding to the code below to plot the images from the firs.pdf
Need helping adding to the code below to plot the images from the first epoch. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
Using the code below- I need help with creating code for the following.pdf
Using the code below, I need help with creating code for the following:
1) Write Python code to plot the images from the first epoch. Take a screenshot of the images
from the first epoch.
2) Write Python code to plot the images from the last epoch. Take a screenshot of the images
from the last epoch.
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display th.
Hello- I hope you are doing well- I am doing my project- which is Rans (1).pdf
Hello, I hope you are doing well,
I am doing my project, which is Ransomware attack detection using Deep learning CNNs
I got a result of 0.93, which is not good enough, I have tried to improve my accuracy, but I gave
up. Could you modify the code feature selection or pre-processing to get a higher result? Even
for that result, I have to wait a long time to get it because I increased the epoch, and could you
explain the added part?
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
from keras.preprocessing.image import ImageDataGenerator #, load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation,
BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=128 # maybe 256
IMAGE_HEIGHT=128 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work df['category'].value_counts().plot.bar()
# See sample image
# sample = random.choice(filenames)
# image = load_img("D:\Ransomware_Detection_using _CNN\MixImages"+sample)
# plt.imshow(image)
# in collab it will work df['category'].value_counts().plot.bar()
# Build Model
#===============================================================================================================
# Testing
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='re.
Eugene Khvedchenya. State of the art Image Augmentations with Albumentations.
Eugene Khvedchenya. State of the art Image Augmentations with Albumentations.
http://aiconf.com.ua/
What is the UML Class diagram for accident detection using CNN- i have.pdf
What is the UML Class diagram for accident detection using CNN.
i have make the class diagram which is not sufficient to my guide.
Class diagram: .
SYSTEM ARCHITECTURE:
Code for the accident detection:-
from tkinter import messagebox
from tkinter import *
from tkinter import simpledialog
import tkinter
from tkinter import filedialog
from tkinter.filedialog import askopenfilename
import time
import cv2
import tensorflow as tf
from collections import namedtuple
import numpy as np
import winsound
main = tkinter.Tk()
main.title("Accident Detection")
main.geometry("1300x1200")
net =
cv2.dnn.readNetFromCaffe("model/MobileNetSSD_deploy.prototxt.txt","model/MobileNetSSD_deploy.caffemodel")
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
global filename
global detectionGraph
global msg
def loadModel():
global detectionGraph
detectionGraph = tf.Graph()
with detectionGraph.as_default():
od_graphDef = tf.compat.v1.GraphDef()
with tf.compat.v2.io.gfile.GFile('model/frozen_inference_graph.pb', 'rb') as file:
serializedGraph = file.read()
od_graphDef.ParseFromString(serializedGraph)
tf.import_graph_def(od_graphDef, name='')
messagebox.showinfo("Training model loaded","Training model loaded")
def beep():
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
def uploadVideo():
global filename
filename = filedialog.askopenfilename(initialdir="videos")
pathlabel.config(text=filename)
text.delete('1.0', END)
text.insert(END,filename+" loaded\n");
def calculateCollision(boxes,classes,scores,image_np):
global msg
#cv2.putText(image_np, "NORMAL!", (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,
255, 255), 2, cv2.LINE_AA)
for i, b in enumerate(boxes[0]):
if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
if scores[0][i] > 0.5:
for j, c in enumerate(boxes[0]):
if (i != j) and (classes[0][j] == 3 or classes[0][j] == 6 or classes[0][j] == 8) and scores[0][j]> 0.5:
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
ra = Rectangle(boxes[0][i][3], boxes[0][i][2], boxes[0][i][1], boxes[0][i][3])
rb = Rectangle(boxes[0][j][3], boxes[0][j][2], boxes[0][j][1], boxes[0][j][3])
ar = rectArea(boxes[0][i][3], boxes[0][i][1],boxes[0][i][2],boxes[0][i][3])
col_threshold = 0.6*np.sqrt(ar)
area(ra, rb)
if (area(ra,rb)<col_threshold) :
print('accident')
msg = 'ACCIDENT!'
beep()
return True
else:
return False
def rectArea(xmax, ymax, xmin, ymin):
x = np.abs(xmax-xmin)
y = np.abs(ymax-ymin)
return x*y
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
def area(a, b): # returns None if rectangles don't intersect
dx = min(a.xmax, b.xmax) - max(a.xmin, .
Python OpenCV Real Time projects
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez (which was later acquired by Intel). The library is cross-platform and free for use under the open-source BSD license.
I have tried running this code below- and it is working- but the accur.pdf
I have tried running this code below, and it is working, but the accuracy is less than expected.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_loss value
not decreased
earlystop = EarlyStopping(patience=10)
# Learning Rate Reduction
learning_rate_reduction = ReduceLROnPlateau(mon.
You are task to add a yawning detection to the programme below;i.pdf
You are task to add a yawning detection to the programme below;
import numpy as np
from pygame import mixer
import time
import cv2
from tkinter import *
import tkinter.messagebox
import mysql.connector
from datetime import datetime
root = Tk()
root.geometry('500x570')
frame = Frame(root, relief=RIDGE, borderwidth=2)
frame.pack(fill=BOTH, expand=1)
root.title('Drive Main')
frame.config(background='#74B0D6')
label = Label(frame, text="Drive Care", bg='#74B0D6', font='Garcedo 25 bold')
label.pack(side=TOP)
filename = PhotoImage(file="picco.png")
background_label = Label(frame, image=filename)
background_label.pack(side=TOP)
from mysql.connector import Error
def connect():
""" Connect to MySQL database """
conn = None
try:
conn = mysql.connector.connect(host='localhost',
database='drive_main',
user='root',
password='')
mySql_insert_query = """INSERT INTO tbl_drivers(id, name, event_time)
VALUES (%s, %s, %s) """
cursor = conn.cursor()
current_Date = datetime.now()
# convert date in the format you want
formatted_date = current_Date.strftime('%Y-%m-%d %H:%M:%S')
insert_tuple = (5, 'meleda ulett', current_Date)
result = cursor.execute(mySql_insert_query, insert_tuple)
conn.commit()
print("Date Record inserted successfully")
if conn.is_connected():
print('Connected to MySQL database')
except Error as e:
print(e)
except mysql.connector.Error as error:
conn.rollback()
print("Failed to insert into MySQL table {}".format(error))
finally:
if conn is not None and conn.is_connected():
conn.close()
connect()
def hel_doc():
help(cv2)
def Contri():
tkinter.messagebox.showinfo("Contributors", "\n1.Romairo Reid\n2. Kayla-Marie Sooman \n3.
Bradly Walcott \n4. Lee Hinds \n")
def anotherWin():
tkinter.messagebox.showinfo("About",
'Drive Care version 01.1\n Made Using\n-OpenCV\np\n-Tkinter\n In Python
3')
menu = Menu(root)
root.config(menu=menu)
subm1 = Menu(menu)
menu.add_cascade(label="Tools", menu=subm1)
subm1.add_command(label="Open CV Docs", command=hel_doc)
subm2 = Menu(menu)
menu.add_cascade(label="About", menu=subm2)
subm2.add_command(label="Driver Cam", command=anotherWin)
subm2.add_command(label="Contributors", command=Contri)
def exitt():
exit()
def web():
capture = cv2.VideoCapture(0)
while True:
ret, frame = capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('e'):
break
capture.release()
cv2.destroyAllWindows()
def webrec():
capture = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
op = cv2.VideoWriter('Sample1.avi', fourcc, 11.0, (640, 480))
while True:
ret, frame = capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame', frame)
op.write(frame)
if cv2.waitKey(1) & 0xFF == ord('e'):
break
op.release()
capture.release()
cv2.destroyAllWindows()
def webdet():
capture = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier('lbpcascade_frontalface.xml')
eye_glass = cv2.CascadeClassifier('haarcascade_eye_tree_eyeglasses.xml')
while True:
ret, frame = .
Pydata DC 2018 (Skorch - A Union of Scikit-learn and PyTorch)
Github repo: https://github.com/thomasjpfan/pydata2018_dc_skorch
Learning Predictive Modeling with TSA and Kaggle
Ever wanted to do a challenging data science project but feel like you don’t have enough experience? Just go for it! Diving into a 3 TB, 3D image dataset has been my best learning experience. I want to share this deep learning project with you, and tips for overcoming challenges along the way.
Need an detailed analysis of what this code-model is doing- Thanks #St.pdf
Need an detailed analysis of what this code/model is doing. Thanks
#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
X = X[y.flatten() == 8]
#Step 3: Define parameters to be used in later processes.
#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
#Step 4: Define a utility function to build the generator.
def build_generator():
model = Sequential()
#Building the input layer
model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
# Generate a batch of random noise
noise = np.random.normal(0, 1, (16, latent_dimensions))
# Generate images from the noise
generated_images = generator.predict(noise)
# Rescale the images to 0.
Similar to Denis Sergienko "Pip install driven deep learning" (20)
This face recognition attendance system code for face recogn.pdf
This face recognition attendance system code for face recogn.pdf
Can someone please explain what the code below is doing and comment on.pdf
Can someone please explain what the code below is doing and comment on.pdf
Python program to build deep learning algorithm using a CNNs model to.docx
Python program to build deep learning algorithm using a CNNs model to.docx
Using the code below- I need help with the following 3 things- 1) Writ.pdf
Using the code below- I need help with the following 3 things- 1) Writ.pdf
Need helping adding to the code below to plot the images from the firs.pdf
Need helping adding to the code below to plot the images from the firs.pdf
Using the code below- I need help with creating code for the following.pdf
Using the code below- I need help with creating code for the following.pdf
Hello- I hope you are doing well- I am doing my project- which is Rans (1).pdf
Hello- I hope you are doing well- I am doing my project- which is Rans (1).pdf
Eugene Khvedchenya. State of the art Image Augmentations with Albumentations.
Eugene Khvedchenya. State of the art Image Augmentations with Albumentations.
What is the UML Class diagram for accident detection using CNN- i have.pdf
What is the UML Class diagram for accident detection using CNN- i have.pdf
A basic introduction to open cv for image processing
A basic introduction to open cv for image processing
Viktor Tsykunov "Microsoft AI platform for every Developer"
Viktor Tsykunov "Microsoft AI platform for every Developer"
I have tried running this code below- and it is working- but the accur.pdf
I have tried running this code below- and it is working- but the accur.pdf
You are task to add a yawning detection to the programme below;i.pdf
You are task to add a yawning detection to the programme below;i.pdf
Pydata DC 2018 (Skorch - A Union of Scikit-learn and PyTorch)
Pydata DC 2018 (Skorch - A Union of Scikit-learn and PyTorch)
Need an detailed analysis of what this code-model is doing- Thanks #St.pdf
Need an detailed analysis of what this code-model is doing- Thanks #St.pdf
More from Fwdays
"What I learned through reverse engineering", Yuri Artiukh
In recent years, I have gained most of my knowledge through reverse engineering, how I did it and what I learned during this period, I decided to share. All this concerns graphic programming, performance, best practices in the frontend.
"Impact of front-end architecture on development cost", Viktor Turskyi
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
"Micro frontends: Unbelievably true life story", Dmytro Pavlov
A real life story about the experience of using Micro frontends in an existing Enterprise product. Problems and their solutions on the way from the integration of a separate component to an extensible No-code platform.
"Objects validation and comparison using runtime types (io-ts)", Oleksandr Suhak
A common task in modern JS is parsing, validating and then comparing JSON objects. In this talk I will quickly go through most common ways to parse/validate and compare objects we use today and then focus more on how runtime types (based on io-ts) can help make such tasks easier and quicker to implement.
"JavaScript. Standard evolution, when nobody cares", Roman Savitskyi
Should we take a look at JavaScript when everyone is writing in TypeScript? What happens to the standard? What did we get last year? What new features can we expect this and next year? And most importantly, when will Observer be standardized?
Let's try to answer all these questions and even a little more, dream about the future, and enjoy that Observer is alive (or not).
"How Preply reduced ML model development time from 1 month to 1 day",Yevhen Y...
Case study of how small team in Preply started with inheriting an existing ranking model to being able to produce a model per day. In this talk we'll cover steps to take if you find yourself in a similar situation: what kind of technology and processes can you introduce in order to achieve a great speedup in a development speed.
"GenAI Apps: Our Journey from Ideas to Production Excellence",Danil Topchii
In my talk, I will tell about the world of GenAI services beyond GPT-wrappers and how we developed and scaled GenAI-centric applications. I'll share personal experiences about the obstacles, lessons, and strategic tools and methodologies that were key in taking GenAI applications from 0 to 1. I'll talk about the challenges we faced when launching LLM-based and image generative applications and delivering them to end users, and what conclusions and solutions were made.
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
Python engineers are introduced to the transformative potential of Large Language Models (LLMs) in the realm of advanced data analysis and the application of Semantic Kernel techniques. We will talk about how LLMs like ChatGPT can be integrated into Python environments to automate data processing, enhance predictive modeling, and unlock deeper insights from complex datasets. The session will delve into practical strategies for embedding Semantic Kernel methods within Python projects, illustrating how these advanced techniques can refine the accuracy of machine learning models by embedding domain-specific knowledge directly into the analysis process. Attendees will leave with a clear roadmap for leveraging the combined power of LLMs and Semantic Kernels, equipped with actionable knowledge to drive innovation in their data analysis projects and beyond, marking a significant leap forward in the evolution of Python engineering practices.
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
Federated learning. Algorithmic solution to the problem of privacy preserving ML. Pieces involved to support the training with NVIDIA Flare as example. How newest legislation affects federated learning.
"What is a RAG system and how to build it",Dmytro Spodarets
Today, large language models are becoming an integral part of almost every IT solution. However, their use is often accompanied by certain limitations, such as the relevance of information or its depth and specificity. One of the ways to overcome these limitations is the method of working with LLMs - RAG (Retrieval Augmented Generation).
"Debugging python applications inside k8s environment", Andrii Soldatenko
In an ideal world, you would write Python code and then it would work perfectly. But unfortunately, it doesn't work in this manner. In my talk, I'll cover how to efficiently debug your programs, especially in cloud environments or inside Kubernetes.
"ML in Production",Oleksandr Bagan
MLOps (Machine Learning Operations) is a recent buzzword, that trends a lot. Let's figure out together how maintaining applications with machine learning components is significantly different from maintaining applications without them.
We will look into MLOps best practices and typical problems and their implementations/solutions in real world production.
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
Ever seen a code base where understanding a simple method meant jumping through tangled class hierarchies? We all have! And while "Favor composition over inheritance!" is almost as old as object-oriented programming, strictly avoiding all types of subclassing leads to verbose, un-Pythonic code. So, what to do?
The discussion on composition vs. inheritance is so frustrating because far-reaching design decisions like this can only be made with the ecosystem in mind – and because there's more than one type of subclassing!
Let's take a dogma-free stroll through the types of subclassing through a Pythonic lens and untangle some patterns and trade-offs together. By the end, you'll be more confident in deciding when subclassing will make your code more Pythonic and when composition will improve its clarity.
"Distributed graphs and microservices in Prom.ua", Maksym Kindritskyi
The current architecture of Prom.ua is built on microservices and GraphQL API, but it was not always like that. In this talk, I'll tell you how far we've come and how we've made using graphs in a microservice architecture convenient and simple. I will talk about the problems we faced and how we overcame them, made our development process more accessible, deployments faster, and the remains of the monolith less loaded.
"Rethinking the existing data loading and processing process as an ETL exampl...
ETL stands for extract, transform, load. It's a process that combines data from different sources into a single repository for further processing, analysis, and utilization.
This talk provides an example of how pandas can be used to solve ETL tasks as a stage in the evolution of the data intake component. This involves preliminary validation, filtering, and conversion of data according to a set of business rules and internal representation, with intermediate combination with other sources.
"How Ukrainian IT specialist can go on vacation abroad without crossing the T...
I’m confident that many IT professionals are currently facing the same situation I was in a few months ago. Mobilization, uncertainty. How can I be maximally beneficial to the country with my experience and continue professional development in such circumstances? Since the onset of the full-scale invasion, I've been actively volunteering and assisting the army. Mobilization became the next logical step.
I want to share:
My journey in IT, volunteering, and the beginning of my service in the Armed Forces
Impressions from the first few months
Which Soft Skills are helpful in this context
I aim to dispel myths about the mobilization process and projects of the Armed Forces. Address your questions
And yes, military personnel can travel abroad during their leave.
"The Strength of Being Vulnerable: the experience from CIA, Tesla and Uber", ...
The leader must be strong all the time. The leader cannot afford to make mistakes, let alone fail in front of their team. Is that really true? Nick Gicinto, a cybersecurity leader with over 25 years of experience, who has worked for the CIA and has built security systems from scratch at Tesla and Uber, fully hiring teams for these projects, will talk about the importance of being vulnerable to build trust within a team.
"[QUICK TALK] Radical candor: how to achieve results faster thanks to a cultu...
Sharing open feedback can be difficult because it equals much work on yourself. However, feedback needs attention and a special place in the corporate culture. It helps to grow dynamically, build a team of like-minded people and achieve powerful results.
In the presentation, I will talk about:
The ability to work with feedback as a soft, solid skill in developing technical specialists.
A list of difficulties that prevent quality work with feedback.
The 4A Framework is a tool for successful giving and receiving feedback.
I will also help specialists learn the following:
Form constructive feedback and understand how and when to give it.
Work analytically with the received feedback.
Feel free to share your thoughts and be heard.
"[QUICK TALK] PDP Plan, the only one door to raise your salary and boost care...
Will discuss:
Current communication challenges, including mishaps and toxic versus productive interactions.
Ever wondered about PDP? It’s likely because its relevance to career planning, even outside your current company, hasn’t been fully spotlighted.
Exploring how PDP functions within career planning, applicable even if you’re eyeing an exit.
“Who do I aspire to become?”
Summarizing key points with a reference to a practical form you can download to use.
"4 horsemen of the apocalypse of working relationships (+ antidotes to them)"...
This talk will reveal four destructive communication patterns that can undermine team spirit, reduce productivity and cause conflict, and offer effective strategies for neutralizing them.
Let's start with exciting storytelling about a fictional team of developers working on Scrum. You will learn about situations that their team member noticed during team meetings.
Next, we will analyze "The Gottman Four Horsemen" model, which describes the four "horsemen of the apocalypse" of work relationships: criticism, defensiveness, contempt, and stonewalling. For each of these patterns, specific "antidotes" will be offered that allow you to build healthier and more productive relationships in the team.
Finally, we'll look at why this topic is critical to team productivity, drawing on Google's "Project Aristotle" research. Special attention will be paid to the concept of psychological safety, which is a key factor in the success of high-performance teams.
This talk will not only provide valuable insights and tools for improving communication and management in Tech teams, but will also help each member better understand their own contribution to the overall success of the team.
More from Fwdays (20)
"What I learned through reverse engineering", Yuri Artiukh
"What I learned through reverse engineering", Yuri Artiukh
"Impact of front-end architecture on development cost", Viktor Turskyi
"Impact of front-end architecture on development cost", Viktor Turskyi
"Micro frontends: Unbelievably true life story", Dmytro Pavlov
"Micro frontends: Unbelievably true life story", Dmytro Pavlov
"Objects validation and comparison using runtime types (io-ts)", Oleksandr Suhak
"Objects validation and comparison using runtime types (io-ts)", Oleksandr Suhak
"JavaScript. Standard evolution, when nobody cares", Roman Savitskyi
"JavaScript. Standard evolution, when nobody cares", Roman Savitskyi
"How Preply reduced ML model development time from 1 month to 1 day",Yevhen Y...
"How Preply reduced ML model development time from 1 month to 1 day",Yevhen Y...
"GenAI Apps: Our Journey from Ideas to Production Excellence",Danil Topchii
"GenAI Apps: Our Journey from Ideas to Production Excellence",Danil Topchii
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"What is a RAG system and how to build it",Dmytro Spodarets
"What is a RAG system and how to build it",Dmytro Spodarets
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Distributed graphs and microservices in Prom.ua", Maksym Kindritskyi
"Distributed graphs and microservices in Prom.ua", Maksym Kindritskyi
"Rethinking the existing data loading and processing process as an ETL exampl...
"Rethinking the existing data loading and processing process as an ETL exampl...
"How Ukrainian IT specialist can go on vacation abroad without crossing the T...
"How Ukrainian IT specialist can go on vacation abroad without crossing the T...
"The Strength of Being Vulnerable: the experience from CIA, Tesla and Uber", ...
"The Strength of Being Vulnerable: the experience from CIA, Tesla and Uber", ...
"[QUICK TALK] Radical candor: how to achieve results faster thanks to a cultu...
"[QUICK TALK] Radical candor: how to achieve results faster thanks to a cultu...
"[QUICK TALK] PDP Plan, the only one door to raise your salary and boost care...
"[QUICK TALK] PDP Plan, the only one door to raise your salary and boost care...
"4 horsemen of the apocalypse of working relationships (+ antidotes to them)"...
"4 horsemen of the apocalypse of working relationships (+ antidotes to them)"...
Recently uploaded
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Epistemic Interaction - tuning interfaces to provide information for AI support
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
UiPath Test Automation using UiPath Test Suite series, part 4
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
PHP Frameworks: I want to break free (IPC Berlin 2024)
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Elevating Tactical DDD Patterns Through Object Calisthenics
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Recently uploaded (20)
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
PHP Frameworks: I want to break free (IPC Berlin 2024)
PHP Frameworks: I want to break free (IPC Berlin 2024)
Elevating Tactical DDD Patterns Through Object Calisthenics
Elevating Tactical DDD Patterns Through Object Calisthenics
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Denis Sergienko "Pip install driven deep learning"
- 1. Pip install driven deep learning Denis Sergienko Rails Reactor
- 2. ML includes ML Engineer Data Scientist Training data Training infrastructure Data preparation Models training Experiments Evaluation Deployment
- 7. Face detection Photo from Open Images dataset
- 8. Face detection with OpenCV import cv2 face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') img = cv2.imread("test_image.jpg") img = cv2.resize(img, (1024, 768)) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) faces = face_cascade.detectMultiScale(gray, 1.1, 4) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 255, 255), 5) pip install opencv-python
- 9. Face detection with OpenCV ~230ms for 1024x768 image on Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz
- 10. Face processing with face_recognition https://github.com/ageitgey/face_recognition
- 11. Face processing with face_recognition import face_recognition img = face_recognition.load_image_file("test_image.jpg") face_locations = face_recognition.face_locations(img) face_encodings = face_recognition.face_encodings(img, face_locations) print(face_encodings[0]) for (y1, x1, y2, x2) in face_locations: cv2.rectangle(img, (x1, y1), (x2, y2), (255, 255, 255), 5)
- 12. Face processing with face_recognition ~500ms for 1024x768 image on Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz
- 13. face_recognition face encodings [-1.53406277e-01, 7.34197125e-02, 6.22343011e-02, 2.74792314e-03, -1.57757625e-01, -3.44386743e-03, -3.86646464e-02, 3.81346559e-03, 1.34139627e-01, -2.37786956e-02, 1.50458574e-01, -1.82383899e-02, -2.00692132e-01, 2.54428945e-02, -8.35503787e-02, 8.91055465e-02, -1.34999692e-01, -1.38403967e-01, -1.94509141e-03, -2.76396908e-02, 7.65011981e-02, 5.99545725e-02, -2.67361160e-02, 8.64139497e-02, -1.05263099e-01, -2.57028311e-01, -6.59355745e-02, -6.60505146e-02, 2.00792030e-03, -9.47101340e-02, -4.30351272e-02, 1.61789767e-02, -1.93842828e-01, -1.88902766e-03, -5.59503213e-03, 5.93306050e-02, 3.08027603e-02, 4.00841236e-06, 1.81151792e-01, 4.04844396e-02, -1.94502324e-01, 4.00878489e-02, 3.23042385e-02, 3.15126538e-01, 1.87466532e-01, 2.70547774e-02, -2.12351028e-02, -5.77141233e-02, 1.13720685e-01, -2.84361243e-01, 6.61392957e-02, 2.05567792e-01, 1.19581595e-01, 5.21309301e-03, 3.98933841e-03, -1.06219955e-01, -4.64719236e-02, 1.17458284e-01, -1.06732622e-01, 8.52644444e-02, 5.99832870e-02, 2.44516134e-03, -1.12115061e-02, -1.13738239e-01, 1.81140810e-01, 7.71739632e-02, -8.50351304e-02, -1.69618860e-01, 9.19920430e-02, -1.70908883e-01, -9.62008089e-02, 1.39822483e-01, -1.24618910e-01, -1.88907489e-01, -2.39977300e-01, 9.80893075e-02, 4.42225605e-01, 1.22307248e-01, -1.32055923e-01, 7.27334693e-02, -8.66499916e-02, -1.79484040e-02, 3.45968269e-02, 2.60131024e-02, -8.96709561e-02, 3.88366021e-02, -1.12767063e-01, 1.01631664e-01, 2.66246736e-01, -3.26854028e-02, -5.24140056e-03, 1.77416772e-01, 4.87578101e-02, 4.56356853e-02, 8.57064780e-03, 3.35841253e-02, 2.69737486e-02, 2.23426335e-02, -1.39297083e-01, 2.52203364e-02, 1.91986673e-02, -5.72529212e-02, 9.17259324e-03, 7.66231045e-02, -2.18871102e-01, 8.93882960e-02, 3.31270974e-04, -5.46076559e-02, -5.42774051e-02, 1.48810530e-02, -1.42202020e-01, 2.89437734e-02, 1.72604963e-01, -2.54900217e-01, 1.95765272e-01, 1.25853226e-01, 5.95723726e-02, 9.47328135e-02, 4.61505465e-02, 3.43371816e-02, 3.79003584e-04, -4.46776785e-02, -9.75686088e-02, -4.58939746e-02, 1.02416836e-01, -5.21236807e-02, 6.40562549e-02, 3.29996943e-02]
- 15. Face distance import face_recognition def encoding(photo): img = face_recognition.load_image_file(photo) face_locations = face_recognition.face_locations(img) return face_recognition.face_encodings(img, face_locations)[0] print(face_recognition.face_distance([encoding("keanu_1.jpg")], encoding("keanu_2.jpg"))) print(face_recognition.face_distance([encoding("keanu_2.jpg")], encoding("adam_driver.jpeg"))) 0.373 0.658
- 16. Object detection with Keras https://keras.io/applications/
- 17. Object detection with Keras from keras.applications.resnet50 import ResNet50 from keras.preprocessing import image from keras.applications.resnet50 import preprocess_input, decode_predictions import numpy as np model = ResNet50(weights="imagenet") img = image.load_img("mycat.jpg", target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) predictions = model.predict(x) decoded_predictions = decode_predictions(predictions, top=5)[0] for label_id, label_name, probability in decoded_predictions: print(label_id, label_name, probability)
- 18. Object detection with Keras n02124075 Egyptian_cat 0.28732845 n02123159 tiger_cat 0.23341466 n02123045 tabby 0.08286285 n02971356 carton 0.04206756 n02808304 bath_towel 0.028219415
- 22. Spacy pip install spacy python -m spacy download en_core_web_lg https://spacy.io/
- 23. Spacy import spacy import en_core_web_lg nlp = en_core_web_lg.load() document = nlp("Kyiv is the capital and most populous city of Ukraine.") pprint([(token.text, token.pos_, token.dep_, token.head.text) for token in document]) [('Kyiv', 'PROPN', 'nsubj', 'is'), ('is', 'AUX', 'ROOT', 'is'), ('the', 'DET', 'det', 'capital'), ('capital', 'NOUN', 'attr', 'is'), ('and', 'CCONJ', 'cc', 'capital'), ('most', 'ADV', 'advmod', 'populous'), ('populous', 'ADJ', 'amod', 'city'), ('city', 'NOUN', 'conj', 'capital'), ('of', 'ADP', 'prep', 'city'), ('Ukraine', 'PROPN', 'pobj', 'of'), ('.', 'PUNCT', 'punct', 'is')]
- 24. Spacy test_text = "Kyiv is the capital and most populous city of Ukraine. It is in north-central Ukraine along the Dnieper River. Its population in July 2015 was 2,887,974, making Kiev the 7th most populous city in Europe." document = nlp(test_text) pprint([(ent.text, ent.label_) for ent in document.ents]) [('Kyiv', 'GPE'), ('Ukraine', 'GPE'), ('Ukraine', 'GPE'), ('the Dnieper River', 'LOC'), ('July 2015', 'DATE'), ('2,887,974', 'CARDINAL'), ('Kiev', 'GPE'), ('7th', 'ORDINAL'), ('Europe', 'LOC')] spacy.explain("GPE") 'Countries, cities, states'
- 25. Spacy vector = nlp("hello").vector vector[:10] array([0.25233, 0.10176, -0.67485, 0.21117, 0.43492, 0.16542, 0.48261, -0.81222, 0.041321, 0.78502], dtype=float32) nlp("I like pizza").similarity(nlp("I like burgers")) # 0.924 nlp("I like pizza").similarity(nlp("Kyiv is the capital of Ukraine")) # 0.27 nlp("cat").similarity(nlp("dog")) # 0.80 nlp("cat").similarity(nlp("car")) # 0.31
- 26. Thank you! @zxftr45Twitter: Company: RailsReactor