# A reimplemented version in public environments by Xiao Fu and Mu Hu

import numpy as np
import cv2

def kitti_colormap(disparity, maxval=-1):
	"""
	A utility function to reproduce KITTI fake colormap
	Arguments:
	  - disparity: numpy float32 array of dimension HxW
	  - maxval: maximum disparity value for normalization (if equal to -1, the maximum value in disparity will be used)
	
	Returns a numpy uint8 array of shape HxWx3.
	"""
	if maxval < 0:
		maxval = np.max(disparity)

	colormap = np.asarray([[0,0,0,114],[0,0,1,185],[1,0,0,114],[1,0,1,174],[0,1,0,114],[0,1,1,185],[1,1,0,114],[1,1,1,0]])
	weights = np.asarray([8.771929824561404,5.405405405405405,8.771929824561404,5.747126436781609,8.771929824561404,5.405405405405405,8.771929824561404,0])
	cumsum = np.asarray([0,0.114,0.299,0.413,0.587,0.701,0.8859999999999999,0.9999999999999999])

	colored_disp = np.zeros([disparity.shape[0], disparity.shape[1], 3])
	values = np.expand_dims(np.minimum(np.maximum(disparity/maxval, 0.), 1.), -1)
	bins = np.repeat(np.repeat(np.expand_dims(np.expand_dims(cumsum,axis=0),axis=0), disparity.shape[1], axis=1), disparity.shape[0], axis=0)
	diffs = np.where((np.repeat(values, 8, axis=-1) - bins) > 0, -1000, (np.repeat(values, 8, axis=-1) - bins))
	index = np.argmax(diffs, axis=-1)-1

	w = 1-(values[:,:,0]-cumsum[index])*np.asarray(weights)[index]


	colored_disp[:,:,2] = (w*colormap[index][:,:,0] + (1.-w)*colormap[index+1][:,:,0])
	colored_disp[:,:,1] = (w*colormap[index][:,:,1] + (1.-w)*colormap[index+1][:,:,1])
	colored_disp[:,:,0] = (w*colormap[index][:,:,2] + (1.-w)*colormap[index+1][:,:,2])

	return (colored_disp*np.expand_dims((disparity>0),-1)*255).astype(np.uint8)


def read_16bit_gt(path):
	"""
	A utility function to read KITTI 16bit gt
	Arguments:
	  - path: filepath 	
	Returns a numpy float32 array of shape HxW.
	"""
	gt = cv2.imread(path,-1).astype(np.float32)/256.
	return gt