# --*-- coding:utf-8 --*--
import math
import cv2
import os
import math
import numpy as np
from utils.rgbd_util import *
from utils.getCameraParam import *
'''
must use 'COLOR_BGR2GRAY' here, or you will get a different gray-value with what MATLAB gets.
'''
'''
C: Camera matrix
D: Depth image, the unit of each element in it is "meter"
RD: Raw depth image, the unit of each element in it is "meter"
'''
def getHHA(C, D, RD):
missingMask = (RD == 0);
pc, N, yDir, h, pcRot, NRot = processDepthImage(D * 100, missingMask, C);
tmp = np.multiply(N, yDir)
acosValue = np.minimum(1, np.maximum(-1, np.sum(tmp, axis=2)))
angle = np.array([math.degrees(math.acos(x)) for x in acosValue.flatten()])
angle = np.reshape(angle, h.shape)
'''
Must convert nan to 180 as the MATLAB program actually does.
Or we will get a HHA image whose border region is different
with that of MATLAB program's output.
'''
angle[np.isnan(angle)] = 180
pc[:, :, 2] = np.maximum(pc[:, :, 2], 100)
I = np.zeros(pc.shape)
# opencv-python save the picture in BGR order.
I[:, :, 2] = 20000 / pc[:, :, 2]*6
I[:, :, 1] = h/20
I[:, :, 0] = (angle + 128 - 90) + 10
# print(np.isnan(angle))
'''
np.uint8 seems to use 'floor', but in matlab, it seems to use 'round'.
So I convert it to integer myself.
'''
I = np.rint(I)
# np.uint8: 256->1, but in MATLAB, uint8: 256->255
I[I > 255] = 255
HHA = I.astype(np.uint8)
return HHA
if __name__ == "__main__":
for i in range(1448):
D = getImageD(i)
RD = getImageRD(i)
camera_matrix = getCameraParam('color')
print('max gray value: ', np.max(D)) # make sure that the image is in 'meter'
hha = getHHA(camera_matrix, D, RD)
# hha_complete = getHHA(camera_matrix, D, D)
cv2.imwrite('HHA/%d.png' % (i), hha)
# cv2.imwrite('hha_complete/%d.png' % (i), hha_complete)
''' multi-peocessing example '''
'''
from multiprocessing import Pool
def generate_hha(i):
# generate hha for the i-th image
return
processNum = 16
pool = Pool(processNum)
for i in range(img_num):
print(i)
pool.apply_async(generate_hha, args=(i,))
pool.close()
pool.join()
'''
