resize.py

"""Multiple image resizing techniques"""

importnumpyasnp
fromcv2importdestroyAllWindows, imread, imshow, waitKey


classNearestNeighbour:
"""
 Simplest and fastest version of image resizing.
 Source: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
 """

def__init__(self, img, dst_width: int, dst_height: int):
ifdst_width<0ordst_height<0:
raiseValueError("Destination width/height should be > 0")

self.img=img
self.src_w=img.shape[1]
self.src_h=img.shape[0]
self.dst_w=dst_width
self.dst_h=dst_height

self.ratio_x=self.src_w/self.dst_w
self.ratio_y=self.src_h/self.dst_h

self.output=self.output_img= (
np.ones((self.dst_h, self.dst_w, 3), np.uint8) *255
 )

defprocess(self):
foriinrange(self.dst_h):
forjinrange(self.dst_w):
self.output[i][j] =self.img[self.get_y(i)][self.get_x(j)]

defget_x(self, x: int) ->int:
"""
 Get parent X coordinate for destination X
 :param x: Destination X coordinate
 :return: Parent X coordinate based on `x ratio`
 >>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
 ... 1), 100, 100)
 >>> nn.ratio_x = 0.5
 >>> nn.get_x(4)
 2
 """
returnint(self.ratio_x*x)

defget_y(self, y: int) ->int:
"""
 Get parent Y coordinate for destination Y
 :param y: Destination X coordinate
 :return: Parent X coordinate based on `y ratio`
 >>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
 ... 1), 100, 100)
 >>> nn.ratio_y = 0.5
 >>> nn.get_y(4)
 2
 """
returnint(self.ratio_y*y)


if__name__=="__main__":
dst_w, dst_h=800, 600
im=imread("image_data/lena.jpg", 1)
n=NearestNeighbour(im, dst_w, dst_h)
n.process()

imshow(
f"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
 )
waitKey(0)
destroyAllWindows()