def __get_costs(self, iml, imr): costcensus = mtc.census(iml, imr, self.d, self.__censw).astype(np.float64) costncc = mtc.nccNister(iml, imr, self.d, self.__nccw) costncc = fte.swap_axes(costncc) sobl = mtc.sobel(iml) sobr = mtc.sobel(imr) costsob = mtc.sadsob(sobl, sobr, self.d, 5).astype(np.float64) costsob = fte.swap_axes(costsob) costsad = mtc.zsad(iml, imr, self.d, self.__sadw).astype(np.float64) costsad = fte.swap_axes(costsad) return costcensus, costncc, costsob, costsad
def __create_samples_mem(self, iml, imr, index): w, h, ndisp = self.__read_calib(index) gt = pfm.load(self.__data_path + self.__trainset[index] + "/disp0GT.pfm")[0] gt = np.reshape(gt, [gt.shape[0] * gt.shape[1], 1]) infs = np.concatenate((np.argwhere(gt == np.inf), np.argwhere(gt < 0)), axis=0) # infs = np.empty((0,2)) gt = np.delete(gt, infs[:, 0], axis=0) gt = np.round(gt) gt = gt.astype(np.int32) #print ("loading gt ... gt shape = {}".format(gt.shape)) random_samples = fte.generate_d_indices(gt, ndisp, 1) assert random_samples.shape[ 1] == 3 # here : 3 means 1 positive sample + 2 negative ones; samples = np.empty( (random_samples.shape[0] * random_samples.shape[1], 21)) #print ("samples shape = {}".format(samples.shape)) #print ("staring census ...") ################## Census compute ########################################################## #print ('w = {}, h = {}, ndisp = {}, censW = {}'.format(w, h, ndisp, self.__censw)) #print ('last iml = {}, last imr = {}'.format(iml[h-1,w-1], imr[h-1,w-1])) costcensus = mtc.census(iml, imr, ndisp, self.__censw).astype(np.float64) #print ('costcensus shape = {}'.format(costcensus.shape)) costcensusR = fte.get_right_cost(costcensus) costcensus = np.reshape( costcensus, [costcensus.shape[0] * costcensus.shape[1], costcensus.shape[2]]) costcensusR = np.reshape(costcensusR, [ costcensusR.shape[0] * costcensusR.shape[1], costcensusR.shape[2] ]) costcensus = np.delete(costcensus, infs[:, 0], axis=0) samples[:, 0] = fte.get_samples(costcensus, random_samples) samples[:, 4] = fte.extract_ratio(costcensus, random_samples, .01) samples[:, 8] = fte.extract_likelihood(costcensus, random_samples, self.__cens_sigma) del costcensus #print ("census done!") r_pkrn = fte.extract_ratio(costcensusR, .01) r_pkrn = np.reshape(r_pkrn, [h, w, ndisp]) r_pkrn = fte.get_left_cost(r_pkrn) r_pkrn = np.reshape( r_pkrn, [r_pkrn.shape[0] * r_pkrn.shape[1], r_pkrn.shape[2]]) r_pkrn = np.delete(r_pkrn, infs[:, 0], axis=0) samples[:, 12] = fte.get_samples(r_pkrn, random_samples) del r_pkrn r_aml = fte.extract_likelihood(costcensusR, self.__cens_sigma) r_aml = np.reshape(r_aml, [h, w, ndisp]) r_aml = fte.get_left_cost(r_aml) r_aml = np.reshape(r_aml, [r_aml.shape[0] * r_aml.shape[1], r_aml.shape[2]]) r_aml = np.delete(r_aml, infs[:, 0], axis=0) samples[:, 16] = fte.get_samples(r_aml, random_samples) del r_aml del costcensusR ###################################################################################### ############################### NCC compute ########################################## costncc = mtc.nccNister(iml, imr, ndisp, self.__nccw) costncc = fte.swap_axes(costncc) costnccR = fte.get_right_cost(costncc) costncc = np.reshape( costncc, [costncc.shape[0] * costncc.shape[1], costncc.shape[2]]) costnccR = np.reshape( costnccR, [costnccR.shape[0] * costnccR.shape[1], costnccR.shape[2]]) costncc = np.delete(costncc, infs[:, 0], axis=0) samples[:, 1] = fte.get_samples(costncc, random_samples) samples[:, 5] = fte.extract_ratio(costncc, random_samples, 1.01) samples[:, 9] = fte.extract_likelihood(costncc, random_samples, self.__ncc_sigma) del costncc r_pkrn = fte.extract_ratio(costnccR, 1.01) r_pkrn = np.reshape(r_pkrn, [h, w, ndisp]) r_pkrn = fte.get_left_cost(r_pkrn) r_pkrn = np.reshape( r_pkrn, [r_pkrn.shape[0] * r_pkrn.shape[1], r_pkrn.shape[2]]) r_pkrn = np.delete(r_pkrn, infs[:, 0], axis=0) samples[:, 13] = fte.get_samples(r_pkrn, random_samples) del r_pkrn r_aml = fte.extract_likelihood(costnccR, self.__ncc_sigma) r_aml = np.reshape(r_aml, [h, w, ndisp]) r_aml = fte.get_left_cost(r_aml) r_aml = np.reshape(r_aml, [r_aml.shape[0] * r_aml.shape[1], r_aml.shape[2]]) r_aml = np.delete(r_aml, infs[:, 0], axis=0) samples[:, 17] = fte.get_samples(r_aml, random_samples) del r_aml del costnccR ###################################################################################### ############################### Sob compute ########################################## sobl = mtc.sobel(iml) sobr = mtc.sobel(imr) costsob = mtc.sadsob(sobl, sobr, ndisp, 5).astype(np.float64) costsob = fte.swap_axes(costsob) costsobR = fte.get_right_cost(costsob) costsob = np.reshape( costsob, [costsob.shape[0] * costsob.shape[1], costsob.shape[2]]) costsobR = np.reshape( costsobR, [costsobR.shape[0] * costsobR.shape[1], costsobR.shape[2]]) costsob = np.delete(costsob, infs[:, 0], axis=0) samples[:, 2] = fte.get_samples(costsob, random_samples) samples[:, 6] = fte.extract_ratio(costsob, random_samples, .01) samples[:, 10] = fte.extract_likelihood(costsob, random_samples, self.__sad_sigma) del costsob r_pkrn = fte.extract_ratio(costsobR, .01) r_pkrn = np.reshape(r_pkrn, [h, w, ndisp]) r_pkrn = fte.get_left_cost(r_pkrn) r_pkrn = np.reshape( r_pkrn, [r_pkrn.shape[0] * r_pkrn.shape[1], r_pkrn.shape[2]]) r_pkrn = np.delete(r_pkrn, infs[:, 0], axis=0) samples[:, 14] = fte.get_samples(r_pkrn, random_samples) del r_pkrn r_aml = fte.extract_likelihood(costsobR, self.__sad_sigma) r_aml = np.reshape(r_aml, [h, w, ndisp]) r_aml = fte.get_left_cost(r_aml) r_aml = np.reshape(r_aml, [r_aml.shape[0] * r_aml.shape[1], r_aml.shape[2]]) r_aml = np.delete(r_aml, infs[:, 0], axis=0) samples[:, 18] = fte.get_samples(r_aml, random_samples) del r_aml del costsobR ###################################################################################### ############################### Sad compute ########################################## costsad = mtc.zsad(iml, imr, ndisp, self.__sadw).astype(np.float64) costsad = fte.swap_axes(costsad) costsadR = fte.get_right_cost(costsad) costsad = np.reshape( costsad, [costsad.shape[0] * costsad.shape[1], costsad.shape[2]]) costsadR = np.reshape( costsadR, [costsadR.shape[0] * costsadR.shape[1], costsadR.shape[2]]) costsad = np.delete(costsad, infs[:, 0], axis=0) samples[:, 3] = fte.get_samples(costsad, random_samples) samples[:, 7] = fte.extract_ratio(costsad, random_samples, .01) samples[:, 11] = fte.extract_likelihood(costsad, random_samples, self.__sad_sigma) del costsad r_pkrn = fte.extract_ratio(costsadR, .01) r_pkrn = np.reshape(r_pkrn, [h, w, ndisp]) r_pkrn = fte.get_left_cost(r_pkrn) r_pkrn = np.reshape( r_pkrn, [r_pkrn.shape[0] * r_pkrn.shape[1], r_pkrn.shape[2]]) r_pkrn = np.delete(r_pkrn, infs[:, 0], axis=0) samples[:, 15] = fte.get_samples(r_pkrn, random_samples) del r_pkrn r_aml = fte.extract_likelihood(costsadR, self.__sad_sigma) r_aml = np.reshape(r_aml, [h, w, ndisp]) r_aml = fte.get_left_cost(r_aml) r_aml = np.reshape(r_aml, [r_aml.shape[0] * r_aml.shape[1], r_aml.shape[2]]) r_aml = np.delete(r_aml, infs[:, 0], axis=0) samples[:, 19] = fte.get_samples(r_aml, random_samples) del r_aml del costsadR samples[:, 20] = fte.generate_labels(random_samples) return samples