saurabhpal97

#import the required libraries
import numpy as np 
import cv2 
import matplotlib.pyplot as plt 
%matplotlib inline
#read the image
image = cv2.imread('coins.jpg') 
#calculate the edges using Canny edge algorithm
edges = cv2.Canny(image,100,200) 
#plot the edges

saurabhpal97

#importing the required libraries 
import numpy as np 
import cv2 
import matplotlib.pyplot as plt 
%matplotlib inline 
image = cv2.imread('index.png') 
#using the averaging kernel for image smoothening 
averaging_kernel = np.ones((3,3),np.float32)/9 
filtered_image = cv2.filter2D(image,-1,kernel) 
plt.imshow(dst) 

saurabhpal97

#import required libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#show OpenCV version
print(cv2.__version__)
#read the iamge and convert to grayscale
image = cv2.imread('index.png')
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

saurabhpal97

#import required libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#show OpenCV version
print(cv2.__version__)
#read image and convert to grayscale
image = cv2.imread('index.png')
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

saurabhpal97

import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline

#reading images in grayscale format
image1 = cv2.imread('messi.jpg',0)
image2 = cv2.imread('team.jpg',0)

#finding out the keypoints and their descriptors

saurabhpal97

#import required libraries
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
%matplotlib inline

#load the classifiers downloaded 
face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv.CascadeClassifier('haarcascade_eye.xml')
#read the image and convert to grayscale format

saurabhpal97

#importing required modules
from keras.applications import VGG16
#loading the saved model
#we are using the complete architecture thus include_top=True
model = VGG16(weights='imagenet',include_top=True)
#show the summary of model
model.summary()

saurabhpal97

#creating a mapping of layer name ot layer details 
#we will create a dictionary layers_info which maps a layer name to its charcteristics
layers_info = {}
for i in model.layers:
    layers_info[i.name] = i.get_config()

#here the layer_weights dictionary will map every layer_name to its corresponding weights
layer_weights = {}
for i in model.layers:
    layer_weights[i.name] = i.get_weights()

saurabhpal97

#importing the required modules
from vis.visualization import visualize_activation
from vis.utils import utils
from keras import activations
from keras import applications
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (18,6)
#creating a VGG16 model using fully connected layers also because then we can 
#visualize the patterns for individual category

saurabhpal97

#importing required libraries and functions
from keras.models import Model
#defining names of layers from which we will take the output
layer_names = ['block1_conv1','block2_conv1','block3_conv1','block4_conv2']
outputs = []
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
#extracting the output and appending to outputs
for layer_name in layer_names:
    intermediate_layer_model = Model(inputs=model.input,outputs=model.get_layer(layer_name).output)
    intermediate_output = intermediate_layer_model.predict(image)