answer_76.py

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

# Grayscale
def BGR2GRAY(img):
	# Grayscale
	gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]
	return gray

# Bi-Linear interpolation
def bl_interpolate(img, ax=1., ay=1.):
	if len(img.shape) > 2:
		H, W, C = img.shape
	else:
		H, W = img.shape
		C = 1

	aH = int(ay * H)
	aW = int(ax * W)

	# get position of resized image
	y = np.arange(aH).repeat(aW).reshape(aW, -1)
	x = np.tile(np.arange(aW), (aH, 1))

	# get position of original position
	y = (y / ay)
	x = (x / ax)

	ix = np.floor(x).astype(np.int)
	iy = np.floor(y).astype(np.int)

	ix = np.minimum(ix, W-2)
	iy = np.minimum(iy, H-2)

	# get distance 
	dx = x - ix
	dy = y - iy

	if C > 1:
		dx = np.repeat(np.expand_dims(dx, axis=-1), C, axis=-1)
		dy = np.repeat(np.expand_dims(dy, axis=-1), C, axis=-1)

	# interpolation
	out = (1-dx) * (1-dy) * img[iy, ix] + dx * (1 - dy) * img[iy, ix+1] + (1 - dx) * dy * img[iy+1, ix] + dx * dy * img[iy+1, ix+1]

	out = np.clip(out, 0, 255)
	out = out.astype(np.uint8)

	return out

# make image pyramid
def make_pyramid(gray):
	# first element
	pyramid = [gray]
	# each scale
	for i in range(1, 6):
		# define scale
		a = 2. ** i

		# down scale
		p = bl_interpolate(gray, ax=1./a, ay=1. / a)

		# up scale
		p = bl_interpolate(p, ax=a, ay=a)

		# add pyramid list
		pyramid.append(p.astype(np.float32))

	return pyramid

# make saliency map
def saliency_map(pyramid):
	# get shape
	H, W = pyramid[0].shape

	# prepare out image
	out = np.zeros((H, W), dtype=np.float32)

	# add each difference
	out += np.abs(pyramid[0] - pyramid[1])
	out += np.abs(pyramid[0] - pyramid[3])
	out += np.abs(pyramid[0] - pyramid[5])
	out += np.abs(pyramid[1] - pyramid[4])
	out += np.abs(pyramid[2] - pyramid[3])
	out += np.abs(pyramid[3] - pyramid[5])

	# normalization
	out = out / out.max() * 255

	return out


# Read image
img = cv2.imread("imori.jpg").astype(np.float)

# grayscale
gray = BGR2GRAY(img)

# pyramid
pyramid = make_pyramid(gray)
    
# pyramid -> saliency
out = saliency_map(pyramid)

out = out.astype(np.uint8)

# Save result
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.imwrite("out.jpg", out)