GANs consist of three terms Generative Adversarial Network. Let's understand these three terms first. Generative : A Generative Model takes input training sample from some distribution and learns to represents that distribution. Adversarial : It basically means Conflicting or Opposing. Networks : These are basically neural networks. So,Generative Adversarial Networks are deep neural network architecture comprising of two neural networks compete with each other to make a generative model. A GAN consist of two class models : Discriminative Model :- It is the one that discriminate between two different classes of data.It tries to identify real data from fakes created by the generator Generative Model :- The Generator turns noise into an imitation of the data to try to trick the discriminator Mathematically, A Generative Model 'G' to be trained on training data 'X' sampled from some true distribution 'D' is the one which, given some standard random distrib...
Media files tends to have a lot of information and processing it need a lot of time and computation. Resizing of image and videos is done to prevent computational strain Resizing is basically modifying the width and height. Many image recognition and machine learning applications benefit from scaling. By Resizing, the training time of a neural network can be significantly reduced.
We'll use CV2.resize() method :-
cv2.resize(src, dsize, interpolation)
src - takes the input image
dsize - take the output dimension as input in Tuple
Interpolation take three method as input
cv2.INTER_AREA : This is used when we need to shrink an image.It is the preferred methodcv2.INTER_CUBIC : A Bicubic method, is slow but more efficient.cv2.INTER_LINEAR : This is primarily used when zooming is required. This is the default interpolation technique in OpenCV
The Function defined below will always work for Images, Video and Live Camera Feed.
CODE FOR RESIZING.
import cv2
frame= cv2.imread("cam.jpg") #read the input image
def rescaleFrame(frame,scale_value=0.50):
width = int(frame.shape[1] * scale_value)
height = int(frame.shape[0] * scale_value)
dim = (width,height)
return cv2.resize(frame,dim)
img = rescaleFrame(frame)
cv2.imshow('image',img)
cv2.waitKey(0)
NOTE :
Change the value of 'scale_value' according to your preference it is basically the percent by which you want to change the size of image.
'frame.shape[1] ' - is the width of the input image
'frame.shape[0] ' - is the height of the input image
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