anilsathyan7

from PIL import Image
import os, sys
import cv2
import numpy as np

'''
Converts all images in a directory to '.npy' format.
Use np.save and np.load to save and load the images.
Use it for training your neural networks in ML/DL projects. 
'''

anilsathyan7

,
[
[<]<+
>>[>]
,

]
<
[.<]

anilsathyan7

+++++ +++
[
> +++++ +
< -
]
>>>
,.
[
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anilsathyan7

+++++ +++++ initialize counter (cell #0) to 10
[ set the next four cells to 70 100 30 and 10 respectively
> +++++ ++ add 7 to cell #1
> +++++ +++++ add 10 to cell #2
> +++ add 3 to cell #3
> + add 1 to cell #4
<<<< - decrement counter (cell #0)
]

> ++ . print 'H' (H = ASC (72))

anilsathyan7

import time
import numba
from numba import jit
import numpy as np

#input matrices
matrix1 = np.random.rand(30,30)
matrix2 = np.random.rand(30,30)
rmatrix = np.zeros(shape=(30,30))

anilsathyan7

Sample Output

Similar shapes in the input image(left) are grouped into same clusters, using hu moments. Members of the same cluster are shown with same coloured bounding boxes in the output image(right).

anilsathyan7

Sample Output

Similar patches or objects in the input image(left) are grouped into same clusters, using Structural Similarity Index(SSIM). Members of the same cluster are shown with same coloured bounding boxes in the output image(right).

anilsathyan7

import numpy as np
from PIL import Image
from imageio import imsave
from skimage.util.shape import view_as_blocks

# Global shuffle key
map={}

'''
Args:-

anilsathyan7

from PIL import Image
import os, sys
from scipy.misc import imsave
import cv2
import numpy as np
import random
import argparse

'''
Creates synthetic images with custom backgrounds, using the default images and masks in the dataset.

anilsathyan7

import numpy as np
import cv2

cap = cv2.VideoCapture(0)
i=1
while(True):
	# Capture frame-by-frame
	ret, frame = cap.read()

	# Operations on the frame