CATS VS DOGS (WEEK5)

!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/

!kaggle datasets download -d salader/dogs-vs-cats

import zipfile
zip_ref = zipfile.ZipFile('/content/dogs-vs-cats.zip', 'r')
zip_ref.extractall('/content')
zip_ref.close()

import tensorflow as tf
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,BatchNormalization,Dropout

# generators
train_ds = keras.utils.image_dataset_from_directory(
    directory = '/content/train',
    labels='inferred',
    label_mode = 'int',
    batch_size=32,
    image_size=(256,256)
)

validation_ds = keras.utils.image_dataset_from_directory(
    directory = '/content/test',
    labels='inferred',
    label_mode = 'int',
    batch_size=32,
    image_size=(256,256)
)


# Normalize
def process(image,label):
    image = tf.cast(image/255. ,tf.float32)
    return image,label

train_ds = train_ds.map(process)
validation_ds = validation_ds.map(process)


# create CNN model

model = Sequential()

model.add(Conv2D(32,kernel_size=(3,3),padding='valid',activation='relu',input_shape=(256,256,3)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding='valid'))

model.add(Conv2D(64,kernel_size=(3,3),padding='valid',activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding='valid'))

model.add(Conv2D(128,kernel_size=(3,3),padding='valid',activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding='valid'))

model.add(Flatten())

model.add(Dense(128,activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(1,activation='sigmoid'))


model.summary()


model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = model.fit(train_ds,epochs=10,validation_data=validation_ds)

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'],color='red',label='train')
plt.plot(history.history['val_accuracy'],color='blue',label='validation')
plt.legend()
plt.show()

plt.plot(history.history['accuracy'],color='red',label='train')
plt.plot(history.history['val_accuracy'],color='blue',label='validation')
plt.legend()
plt.show()

plt.plot(history.history['loss'],color='red',label='train')
plt.plot(history.history['val_loss'],color='blue',label='validation')
plt.legend()
plt.show()

plt.plot(history.history['loss'],color='red',label='train')
plt.plot(history.history['val_loss'],color='blue',label='validation')
plt.legend()
plt.show()

import cv2
test_img = cv2.imread('/content/cat.jpg')

plt.imshow(test_img)

test_img.shape

test_img = cv2.resize(test_img,(256,256))
test_input = test_img.reshape((1,256,256,3))
model.predict(test_input)





WEEK-4

ADDITION/SUBTRACTION

from tkinter import *
def Add():
 a=int(t1.get(1.0, "end-1c"))
 b=int(t2.get(1.0, "end-1c"))
 rea.config(text=str(a+b))
def Sub():
 a=int(t1.get(1.0, "end-1c"))
 b=int(t2.get(1.0, "end-1c"))
 res.config(text=str(abs(a-b)))
a = Tk()
a.geometry("500x500")
a.title("test")
l1 = Label(a, text = "enter first variable")
t1 = Text(a, height = 2, width = 10)
l1.pack()
t1.pack()
l2 = Label(a, text = "enter second variable")
t2 = Text(a, height = 2, width = 10)
l2.pack()
t2.pack()
Button(a,text='Addition',command=Add).pack()
l3 = Label(a, text = "Result")
l3.pack()
rea = Label(a, text = "")
rea.pack()
Button(a,text='subtraction',command=Sub).pack()
l4= Label(a, text = "Result")
l4.pack()
res = Label(a, text = "")
res.pack()
a.mainloop()


CONVERSION

rom tkinter import *
def convert():
 a=e1.get()
 euros = float(a) / 88.46
 l3.config(text=str(euros))
a = Tk()
a.geometry("300x200")
a.title("convert")
l1 = Label(a, text = "ENTER AMOUNT IN RUPEES")
l1.pack()
e1= Entry(a)
e1.pack()
l2=Label(a,text='AMOUNT IN EUROS')
l2.pack()
Button(a,text='convert',command=convert).pack()
l3=Label(a,text='')
l3.pack()
a.mainloop()

STUDENT DETAILS
import tkinter as tk
from tkinter import *
a = Tk()
a.geometry("600x600")
a.title("student details")
l1=Label(a,text='enter your name')
e1=Entry(a)
l1.pack()
e1.pack()
l2=Label(a,text='enter your roll number')
e2=Entry(a)
l2.pack()
e2.pack()
l3=Label(a,text='enter your emailID')
e3=Entry(a)
l3.pack()
e3.pack()
g=Label(a,text='GENDER')
g.pack()
ge=StringVar()
R1 = Radiobutton(a, text="Male", variable=ge, value='Male')
R1.pack()
R2 = Radiobutton(a, text="Female", variable=ge, value='Female')
R2.pack()
course_label = tk.Label(a, text="Course:")
course_label.pack()
course_listbox = tk.Listbox(a)
course_listbox.insert(1, "B.Tech")
course_listbox.insert(2, "M.Tech")
course_listbox.insert(3, "PhD")
course_listbox.pack()
br=Label(a,text='CHOOSE BRANCH')
br.pack()
b=Listbox(a)
b.insert(1,'CSE')
b.insert(2,'CIVIL')
b.insert(3,'IT')
b.insert(4,'ECE')
b.insert(5,'EEE')
b.pack()
Button(a,text='SUBMIT').pack()
a.mainloop()


WEEK-2

FROM IMAGE

import numpy as np
import cv2
csc= r"haarcascade_eye.xml"
csc2= r"haarcascade_frontalface_default.xml"
face_cascade=cv2.CascadeClassifier(csc2)
eye_cascade=cv2.CascadeClassifier(csc)
img=cv2.imread("C:\WEEK2DL\img_1.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 10)
for(x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    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)
    cv2.waitKey(0)
cv2.destroyAllWindows()

from cam

import cv2

cap = cv2.VideoCapture(0)  # 0 for default camera, change if necessary

while True:
    ret, frame = cap.read()  # read a frame from the camera

    if ret:  # if frame is successfully read
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # convert to grayscale

        # apply face detection
        face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
        eye_cascade=cv2.CascadeClassifier('haarcascade_eye.xml')
        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
        eyes = eye_cascade.detectMultiScale(gray, 1.3, 5)

        # draw rectangles around detected faces
        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        for (x, y, w, h) in eyes:
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # show the captured frame
        cv2.imshow('frame', frame)

    # exit the loop if 'q' key is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()  # release the camera
cv2.destroyAllWindows()  # close all windows