bilinear_interpolate.py

"""
linear_interpolate - function to bilinear interpolatation trasformation
Parameters:
	origi_img	I/P	image input
	scaled_img	I/P	scaled image input - 2d array of mapped pixels
	h_ratio		I/P	scaling ratio for height
	w_ratio		I/P	scaling ratio for width
			O/P	transformed image
"""
def linear_interpolate(origi_img, scaled_img, h_ratio, w_ratio):
	#get height and width of scaled_img and origi_img
	height = scaled_img.shape[0]
	width = scaled_img.shape[1]
	h = origi_img.shape[0]
	w = origi_img.shape[1]

	#visit each pixel and map back to original image
	#extra variables are pre-computed in order to prevent re-computing within for loops
	for row in range(height):
		#get x, floor and ceil of x, compute weight b
		x = row / h_ratio
		fx = np.int(np.floor(x))
		cx = np.int(np.ceil(x))

		#if out-of-bound, fill w/ zero
		if not fx in range(h) or not cx in range(h):
			continue
		#compute weight
		b = x - fx
		nb = 1 - b
			
		for col in range(width):
			#get y, floor and ceil of y, and compute weight a
			y = col / w_ratio
			fy = np.int(np.floor(y))
			cy = np.int(np.ceil(y))
			
			#compute transformed pixels
			if fy in range(w) and cy in range(w): #if out-of-bound, fill with zeros 
				#compute weight
				a = y - fy
				na = 1 - a
				scaled_img[row, col] = na * nb * origi_img[fx, fy] + na * b * origi_img[fx, cy] + a * nb * origi_img[cx, fy] + a * b * origi_img[cx, cy]
			else:
				pass
	return scaled_img
	"""		linear_interpolate		"""