Python OpenCV Real Time projects
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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.
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![Python programming with Open CV Page 3
Step2: Source Code
import numpy as np
import cv2 #Import Open Source Computer Vision Package
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
cap = cv2.VideoCapture(0) #Capture video from front camera of our laptop
while 1:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #By default in CV the image is read in BGR format
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2) #Rectangular box size initialization
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
cv2.imshow('img', img)
k = cv2.waitKey(30) & 0xff # cv2.waitKey() will show the image for assigned time duration
if k == 27: #If user presses Esc Key then video capturing will be terminated
break
cap.release() #Its mandatory to release video capture
cv2.destroyAllWindows()#Once the task completes then we will close all the opened windows
Step3: Run the code
Result:
1. Showing my face and eyes detected and enclosed in a box](https://image.slidesharecdn.com/opencvprojects-200531075224/85/Python-OpenCV-Real-Time-projects-3-320.jpg)
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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, .
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Cypherock Assessment (1).pdfPARNIKA GUPTA Title: Embedded Security Analysis Task: Side Channel Analysis and Fault Injection
Objective:
The objective of this task is to assess your ability to work with Python tools, C programming,
Computer Architecture, and apply side channel analysis and fault injection techniques to
uncover a hidden flag embedded within an ELF file compiled for an STM32 processor. You will
use the Lascar and Rainbow tools from Ledger's repository to analyze the binary and retrieve
the hidden flag.
Requirements:
1. Proficiency in Python programming.
2. Basic understanding of side channel analysis and fault injection concepts.
3. Familiarity with ELF file format and embedded systems.
Task Description:
Setup and Familiarization:
1. Clone the Ledger's repository containing Lascar and Rainbow tools.
2. Install the necessary dependencies and set up the environment as per the provided
documentation.
3. Review the documentation and examples to understand how Lascar and Rainbow tools
are used for side channel analysis and fault injection.
Binary Analysis:
1. You will be provided with an ELF file compiled for an STM32 processor.
2. Study the provided stubbed source code for the binary to understand its functionality and
potential vulnerabilities.
Side Channel Analysis/ Fault Injection:
1. Choose a specific side channel analysis technique or fault injection technique based on
your analysis of the binary.
2. Implement the chosen technique using Lascar or Rainbow tools to extract information
from the binary.
3. Document your approach, code snippets, and any findings from the side channel
analysis.
4. Provide a detailed explanation of your fault injection methodology, along with relevant
code snippets and observations.
Flag Retrieval:
1. Apply the insights gained from side channel analysis and fault injection to uncover the
hidden secret embedded within the binary.
2. Document the process you followed to successfully retrieve the secret.
3. Provide the extracted secret as proof of completion.
Evaluation Criteria:
You will be evaluated based on the following criteria:
1. Understanding of side channel analysis and fault injection concepts.
2. Proficiency in understanding the assignment and applying conceptual knowledge in
practice.
3. Explanation of the approach taken.
4. Successful retrieval of the hidden flag.
Submit all scripts, analysis, images, documentation in form of a zip file directly to Cypherock.
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.pdfacteleshoppe 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.
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I have to understand this code and i have to explain the each line of.pdf
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Can you plz add comment to each line of code which would be helpful me to understand the
code
Code for Accident detection using CNN:
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.xma.
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ISO 4020-6.1- Filter Cleanliness Test Rig Catalogue.pdf FILTRATION ENGINEERING & CUNSULTANT ISO 4020-6.1 – Filter Cleanliness Test Rig: Precision Testing for Fuel Filter Integrity
Explore the design, functionality, and standards compliance of our advanced Filter Cleanliness Test Rig developed according to ISO 4020-6.1. This rig is engineered to evaluate fuel filter cleanliness levels with high accuracy and repeatability—critical for ensuring the performance and durability of fuel systems.
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Overview of ISO 4020-6.1 testing protocols
Rig components and schematic layout
Test methodology and data acquisition
Applications in automotive and industrial filtration
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Perfect for R&D engineers, quality assurance teams, and lab technicians focused on filtration performance and standard compliance.
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UNIT-4-PPT UNIT COMMITMENT AND ECONOMIC DISPATCH
UNIT-4-PPT UNIT COMMITMENT AND ECONOMIC DISPATCHSridhar191373 Statement of unit commitment problem-constraints: spinning reserve, thermal unit constraints, hydro constraints, fuel constraints and other constraints. Solution methods: priority list methods, forward dynamic programming approach. Numerical problems only in priority list method using full load average production cost. Statement of economic dispatch problem-cost of generation-incremental cost curve –co-ordination equations without loss and with loss- solution by direct method and lamda iteration method (No derivation of loss coefficients)
Python OpenCV Real Time projects
- 1. Python programming with Open CV Page 1 " PYTHON OPEnCV real time Projects " Prepared by: AMARjeetsingh thakur
- 2. Python programming with Open CV Page 2 1.The Face & Eyes Detection Step1: Open The Pycharm IDE
- 3. Python programming with Open CV Page 3 Step2: Source Code import numpy as np import cv2 #Import Open Source Computer Vision Package face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml') cap = cv2.VideoCapture(0) #Capture video from front camera of our laptop while 1: ret, img = cap.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #By default in CV the image is read in BGR format faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2) #Rectangular box size initialization roi_gray = gray[y:y + h, x:x + w] roi_color = img[y:y + h, x:x + w] eyes = eye_cascade.detectMultiScale(roi_gray) for (ex, ey, ew, eh) in eyes: cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2) cv2.imshow('img', img) k = cv2.waitKey(30) & 0xff # cv2.waitKey() will show the image for assigned time duration if k == 27: #If user presses Esc Key then video capturing will be terminated break cap.release() #Its mandatory to release video capture cv2.destroyAllWindows()#Once the task completes then we will close all the opened windows Step3: Run the code Result: 1. Showing my face and eyes detected and enclosed in a box
- 4. Python programming with Open CV Page 4 2. Showing face and eyes of mine and the person in my cell phone and also in photo detected and enclosed in a box
- 5. Python programming with Open CV Page 5 2. Nose Detection of Human Source Code: import cv2 import numpy as np nose_cascade = cv2.CascadeClassifier('haarcascade_mcs_nose.xml') cap = cv2.VideoCapture(0) ds_factor = 0.5 while True: ret, frame = cap.read() frame = cv2.resize(frame, None, fx=ds_factor, fy=ds_factor, interpolation=cv2.INTER_AREA) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) nose_rects = nose_cascade.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in nose_rects: cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3) break cv2.imshow('Nose Detected', frame) c = cv2.waitKey(1) if c == 27: break cap.release() cv2.destroyAllWindows() Result: Nose being detected
- 6. Python programming with Open CV Page 6 3. Mouth Detection of Human Source Code: import cv2 import numpy as np mouth_cascade = cv2.CascadeClassifier('haarcascade_mcs_mouth.xml') cap = cv2.VideoCapture(0) ds_factor = 0.5 while True: ret, frame = cap.read() frame = cv2.resize(frame, None, fx=ds_factor, fy=ds_factor, interpolation=cv2.INTER_AREA) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) mouth_rects = mouth_cascade.detectMultiScale(gray, 1.7, 11) for (x,y,w,h) in mouth_rects: y = int(y - 0.15*h) cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3) break cv2.imshow('Mouth Detected', frame) c = cv2.waitKey(1) if c == 27: break cap.release() cv2.destroyAllWindows() Result: Mouth being detected
- 7. Python programming with Open CV Page 7 For more information contact: [email protected] linkedin.com/in/amarjeetsingh-thakur-54915955 Mob: +91-9035508516