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How to use d_x1 method in fMBT
Best Python code snippet using fMBT_python
fista_para.py
Source:fista_para.py 
1# -*- coding: utf-8 -*-2"""3Created on Tue Mar 12 13:59:42 201945@author: 619956"""7#import pandas8#import tensorflow as tf9import foa_image as foai10import foa_convolution as foac11import foa_saliency as foas12import numpy as np13import scipy.io as scio14import cv215#from data import *16import torch17import time18#import matplotlib.pyplot as plt19#from mpl_toolkits.mplot3d import Axes3D202122#def dpcn_detect(input_img,A,B,C,d_x1,d_u1,batch_size):23# img = np.zeros([batch_size,20,20,1])24# img[:,:,:,0] =input_img25# imgout = np.zeros([batch_size,2,2,144])26# for kkk in range(batch_size):27# for iii in range(6,20,8):28# for jjj in range(6,20,8):29# imgout[kkk,int((iii-6)/8),int((jjj-6)/8),:]=(img[kkk,iii-6:iii+6,jjj-6:jjj+6,0]).flatten()30# imgout = np.transpose(imgout,[3,1,2,0])31# 32# x_hat = np.zeros([d_x1,2,2,batch_size])33# cause1,state_cell1 = fista(x_hat,C,B,A,imgout,0,d_x1,d_u1,batch_size)34# return cause1 35def dpcn_detect(input_img,A,B,C,d_x1,d_u1,batch_size):3637 #img = torch.zeros([batch_size,20,20,1])#batch_size38 img = input_img.unsqueeze(-1)39 #imgout = torch.zeros([batch_size,2,2,144])40 41 imgout = img.unfold(1, 12, 8).unfold(2, 12, 8).squeeze()42 imgout = imgout.permute(3,4,1,2,0)43 44 imgout = imgout.reshape([144,-1])45 46 x_hat = torch.zeros([d_x1,2,2,batch_size])4748 49 cause1 = fista(x_hat,C,B,A,imgout,0,d_x1,d_u1,batch_size)5051 return cause1 52def pre_process(im):5354 mn = im.mean()5556 sd = im.std()5758 5960 k = np.ones([5,5])6162 k = k/k.sum()6364 6566 im = (im-mn)/sd6768 6970 lmn = cv2.filter2D(im,-1,k)7172 lmnsq = cv2.filter2D(im**2,-1,k)7374 lvar = lmnsq - lmn**27576 lvar[lvar<0] = 07778 lstd = np.sqrt(lvar)7980 lstd[lstd<1] = 18182 8384 im = im - lmn8586 im = im / lstd8788 return im 89 90 91 92 93 94 95 96 97 98 99 100#def fista(x_hat,C,B,A,imgout,lamda1,d_x1,d_u,batch_size):101# state_cell = []102# for rl in range(2):103# for cl in range(2):104# d= {'mu':0.01/d_x1,'L': np.ones([1,batch_size]),'eta':2 , 'tk' :1,'x_hat':x_hat[:,rl,cl,:],'xk':x_hat[:,rl,cl,:],'z_k':x_hat[:,rl,cl,:],'tk_1':1,'CTy':np.matmul(np.transpose(C),imgout[:,rl,cl,:]),'y':imgout[:,rl,cl,:],'xk_1':x_hat[:,rl,cl,:]}105# 106# state_cell.append(d)107# CTC = np.matmul(np.transpose(C),C)108# sumx1 = np.abs(state_cell[0]['xk'])+np.abs(state_cell[1]['xk'])+np.abs(state_cell[2]['xk'])+np.abs(state_cell[3]['xk'])109# #sumx1 = np.maximum(np.maximum(np.maximum(np.abs(state_cell[0]['xk']),np.abs(state_cell[1]['xk'])),np.abs(state_cell[2]['xk'])),np.abs(state_cell[3]['xk']))110# keep_going = 1;111# cause= {'L': np.ones([1,batch_size]),'eta':1.5 , 'tk' :1,'uk':np.zeros([d_u,batch_size]),'uk_1':np.zeros([d_u,batch_size]),'z_k':np.zeros([d_u,batch_size]),'tk_1':1,'x':sumx1,'gamma_bar':1e-3,'gamma':0.5}112# gama1output = (1+np.exp(-np.matmul(B,cause['uk'])))/2113# keep_going = 1;114# nz_x=np.zeros([4,d_x1,batch_size])115# for reg in range(4):116# nz_x[reg,:,:] = (np.abs(state_cell[reg]['xk']) >= 2.2204e-15)117# maxprotect = 0118# while keep_going:119# for reg in range(4):120## alpha = (state_cell[reg]['z_k']-state_cell[reg]['x_hat'])/state_cell[reg]['mu']#(d_x1,4)121## alpha[alpha > 1] =1;122## alpha[alpha < -1] = -1123# grad_zk =(np.matmul(CTC,state_cell[reg]['z_k']) - state_cell[reg]['CTy'])#+ lamda1*alpha;124# const = 0.5*np.sum((np.matmul(C,state_cell[reg]['z_k'])-state_cell[reg]['y'])**2,0)# + lamda1*np.sum(np.abs(state_cell[reg]['z_k'] - state_cell[reg]['x_hat']),0);125# stop_linesearch = np.zeros([batch_size,1])126# protect = 0127# 128# while np.sum(stop_linesearch) != batch_size:129# gk=state_cell[reg]['z_k'] - grad_zk/state_cell[reg]['L']130# state_cell[reg]['xk'] = np.sign(gk)*np.maximum((np.abs(gk)-gama1output/state_cell[reg]['L']),0)131# 132# #lossx.append(0.5*np.sum((np.matmul(C,state_cell[reg]['xk'])-state_cell[reg]['y'])**2) + lamda1*np.sum(np.abs(state_cell[reg]['xk'] - state_cell[reg]['x_hat']))+0*1/batch_size*np.sum(gama1output*cause['x']))133# #lossx.append(np.sum(const))134# temp1 = 0.5*np.sum((state_cell[reg]['y'] - np.matmul(C,state_cell[reg]['xk']))**2,0)# + lamda1*np.sum(abs(state_cell[reg]['xk'] - state_cell[reg]['x_hat']),0) #(y-x)**2 x(144,5)-->(1,5)135# temp2 = const + np.sum((state_cell[reg]['xk'] - state_cell[reg]['z_k'])*grad_zk,0) + np.sum((state_cell[reg]['L']/2),0)*np.sum(((state_cell[reg]['xk'] - state_cell[reg]['z_k']))**2,0)#(1,5)+(1,5).*sum((x-x')**2)136# indx = (temp1<= temp2) 137# stop_linesearch[indx] = True;138# decay = np.ones([batch_size,1])139# decay = (1-stop_linesearch)*state_cell[reg]['eta']140# decay[decay==0] = 1141# state_cell[reg]['L'] = np.transpose(decay)*state_cell[reg]['L']142# protect += 1 143# if protect>5:144# break145# #print(count)146#147# 148# 149# #state_cell[reg]['L'][indx] = self.eta*state_cell[reg]['L'](~indx);150# state_cell[reg]['tk_1'] = (1 + np.sqrt(4*state_cell[reg]['tk']**2 + 1))/2151# state_cell[reg]['z_k'] = state_cell[reg]['xk'] + (state_cell[reg]['tk'] - 1)/(state_cell[reg]['tk_1'])*(state_cell[reg]['xk'] - state_cell[reg]['xk_1']);152# state_cell[reg]['xk_1'] = state_cell[reg]['xk']153# state_cell[reg]['tk'] = state_cell[reg]['tk_1'];154# X = state_cell[reg]['xk']155# cause['x']=np.abs(state_cell[0]['xk'])+np.abs(state_cell[1]['xk'])+np.abs(state_cell[2]['xk'])+np.abs(state_cell[3]['xk'])156# #sess.run(u1_int, feed_dict={u1_p_r:cause['z_k'],})157# exp_func = np.exp(-np.matmul(B,cause['z_k']))/2 #np.array(sess.run(gama1t))[:,0,0,:]-0.5#exp(-Para.B*self.z_k)/2;158# 159# grad_zk = -np.matmul(np.transpose(B),(exp_func*cause['x']))#'*(exp_func.*self.x);160# const = np.sum(cause['x']*np.exp(-np.matmul(B,cause['z_k']))/2,0)#sum(alpha*(self.x.*exp(-Para.B*self.z_k)/2),1);161# stop_linesearch = np.zeros([batch_size,1]);162# protect = 0163# while np.sum(stop_linesearch) != batch_size:164# gk=cause['z_k'] - grad_zk/cause['L']165# cause['uk'] = np.sign(gk)*np.maximum((np.abs(gk)-cause['gamma']/cause['L']),0)166# 167# #lossu.append(0*0.5*np.sum((np.matmul(C,state_cell[reg]['xk'])-state_cell[reg]['y'])**2,0) + 0*lamda1*np.sum(np.abs(state_cell[reg]['xk'] - state_cell[reg]['x_hat']),0)+1/batch_size*np.sum(gama1output*cause['x']))168# #lossu.append(np.sum(const))169# #sess.run(u1_int, feed_dict={u1_p_r:cause['uk'],})170# temp1 = np.sum(cause['x']*np.exp(-np.matmul(B,cause['uk']))/2,0)171# temp2 = const+np.sum((cause['uk'] - cause['z_k'])*grad_zk,0)+ np.sum((cause['L']/2),0)*np.sum(((cause['uk'] - cause['z_k']))**2,0)172# indx = (temp1<= temp2) 173# stop_linesearch[indx] = True;174# decay = np.ones([batch_size,1])175# decay = (1-stop_linesearch)*cause['eta']176# decay[decay==0] = 1177# cause['L'] = np.transpose(decay)*cause['L']178# protect += 1179# if protect>5:180# break181#182# 183# cause['gamma'] = np.maximum(0.99*cause['gamma'],cause['gamma_bar']);184# cause['tk_1'] = (1 + np.sqrt(4*cause['tk']**2 + 1))/2185# cause['z_k'] = cause['uk'] + (cause['tk'] - 1)/(cause['tk_1'])*(cause['uk'] - cause['uk_1']);186# cause['uk_1'] = cause['uk']187# cause['tk'] = cause['tk_1'];188# 189# gama1output = (1+np.exp(-np.matmul(B,cause['uk'])))/2190# 191# 192# 193# 194# #keep_going -= 1195# #print(keep_going)196# #U = cause['uk']197# 198# nz_x_prev = nz_x199# nz_x=np.zeros([4,d_x1,batch_size])200# for reg in range(4):201# nz_x[reg,:,:] = (np.abs(state_cell[reg]['xk']) >= 2.2204e-15)202# 203# num_changes_active = 0204# for reg in range(4):205# num_changes_active = num_changes_active+np.sum(nz_x[reg,:,:] != nz_x_prev[reg,:,:])206# num_nz_x = np.sum(nz_x)207# if num_nz_x >= 1:208# criterionActiveSet = num_changes_active / num_nz_x209# keep_going = (criterionActiveSet > 0.01)210# maxprotect += 1211# if maxprotect >=20:212# keep_going=0213# 214# return cause,state_cell 215def fista(x_hat,C,B,A,imgout,lamda1,d_x1,d_u,batch_size):216 217 state_cell = []218 for rl in range(1):219 for cl in range(1):220 d= {'mu':0.01/d_x1,'L': torch.ones([1,int(batch_size*4)]),'eta':2 , 'tk' :1,'x_hat':torch.zeros([d_x1,int(batch_size*4)]),'xk':torch.zeros([d_x1,int(batch_size*4)]),'z_k':torch.zeros([d_x1,int(batch_size*4)]),'tk_1':1,'CTy':torch.mm(C.T,imgout),'y':imgout,'xk_1':torch.zeros([d_x1,int(batch_size*4)])}221 222 state_cell.append(d)223 224 225 CTC = torch.mm(C.T,C)226 sumx1 = torch.abs(state_cell[0]['xk'])+torch.abs(state_cell[0]['xk'])+torch.abs(state_cell[0]['xk'])+torch.abs(state_cell[0]['xk'])227 228 229 cause= {'L': torch.ones([1,batch_size]),'eta':1.5 , 'tk' :1,'uk':torch.zeros([d_u,batch_size]),'uk_1':torch.zeros([d_u,batch_size]),'z_k':torch.zeros([d_u,batch_size]),'tk_1':1,'x':sumx1,'gamma_bar':1e-3,'gamma':0.5}230 gama1output = torch.zeros([d_x1,2,2,batch_size])231 for rl in range(2):232 for cl in range(2):233 gama1output[:,rl,cl,:] = (1+torch.exp(-torch.mm(B,cause['uk'])))/2234 gama1output = gama1output.reshape([d_x1,-1])235 keep_going = 1236 nz_x=torch.zeros([4,d_x1,batch_size])237 for reg in range(4):238 nz_x[reg,:,:] = (state_cell[0]['xk'].reshape([d_x1,4,batch_size])[:,reg,:] >= 2.2204e-15)239 maxprotect = 0240241 while keep_going:242 243 for reg in range(1):#4244 245# alpha = (state_cell[reg]['z_k']-state_cell[reg]['x_hat'])/state_cell[reg]['mu']#(d_x1,4)246# alpha[alpha > 1] =1;247# alpha[alpha < -1] = -1248 grad_zk =(torch.mm(CTC,state_cell[reg]['z_k']) - state_cell[reg]['CTy'])#+ lamda1*alpha;249 const = 0.5*torch.sum((torch.mm(C,state_cell[reg]['z_k'])-state_cell[reg]['y'])**2,dim = 0)# + lamda1*torch.sum(torch.abs(state_cell[reg]['z_k'] - state_cell[reg]['x_hat']),dim = 0);250 stop_linesearch = torch.zeros([int(4*batch_size),1])251 protect = 0252 #print(reg)253 254 while torch.sum(stop_linesearch) != int(batch_size*4):255 gk=state_cell[reg]['z_k'] - grad_zk/state_cell[reg]['L']256 #mks, _ = torch.max((torch.abs(gk)-gama1output/state_cell[reg]['L']),dim = 0)257 mks = torch.abs(gk)-gama1output/state_cell[reg]['L']258 mks[mks<0] = 0259260 state_cell[reg]['xk'] = torch.sign(gk)*mks261 262# lsx = 0.5*torch.sum((torch.mm(C,state_cell[reg]['xk'])-state_cell[reg]['y'])**2)# + lamda1*torch.sum(torch.abs(state_cell[reg]['xk'] - state_cell[reg]['x_hat']))+0*1/batch_size*torch.sum(gama1output*cause['x'])263# lossx.append(np.asscalar(torch.sum(lsx).cpu().numpy()))264 temp1 = 0.5*torch.sum((state_cell[reg]['y'] - torch.mm(C,state_cell[reg]['xk']))**2,dim = 0)# + lamda1*torch.sum(abs(state_cell[reg]['xk'] - state_cell[reg]['x_hat']),dim = 0) #(y-x)**2 x(144,5)-->(1,5)265 temp2 = const + torch.sum((state_cell[reg]['xk'] - state_cell[reg]['z_k'])*grad_zk,0) + torch.sum((state_cell[reg]['L']/2),dim = 0)*torch.sum(((state_cell[reg]['xk'] - state_cell[reg]['z_k']))**2,dim = 0)#(1,5)+(1,5).*sum((x-x')**2)266 indx = (temp1<= temp2)267 stop_linesearch[indx] = True268 decay = torch.ones([int(batch_size*4),1])269 decay = (1-stop_linesearch)*state_cell[reg]['eta']270 decay[decay==0] = 1271 state_cell[reg]['L'] = (decay.T)*state_cell[reg]['L']272 protect += 1273 if protect>5:274 break 275 #print('xk')276 state_cell[reg]['tk_1'] = (1 + np.sqrt(4*state_cell[reg]['tk']**2 + 1))/2277 state_cell[reg]['z_k'] = state_cell[reg]['xk'] + (state_cell[reg]['tk'] - 1)/(state_cell[reg]['tk_1'])*(state_cell[reg]['xk'] - state_cell[reg]['xk_1']);278 state_cell[reg]['xk_1'] = state_cell[reg]['xk']279 state_cell[reg]['tk'] = state_cell[reg]['tk_1']280281 282 stc = state_cell[0]['xk'].reshape([d_x1,4,batch_size])283 cause['x']=torch.abs(stc[:,0,:])+torch.abs(stc[:,1,:])+torch.abs(stc[:,2,:])+torch.abs(stc[:,3,:]) 284 285 exp_func = torch.exp(-torch.mm(B,cause['z_k']))/2286 grad_zk = -torch.mm(B.T,(exp_func*cause['x']))287 const = torch.sum(cause['x']*torch.exp(-torch.mm(B,cause['z_k']))/2,dim = 0)288 stop_linesearch = torch.zeros([batch_size,1])289 protect = 0290291 292 while torch.sum(stop_linesearch) != batch_size:293 #print('uk')294 gk=cause['z_k'] - grad_zk/cause['L']295 #mks, _ = torch.max((torch.abs(gk)-cause['gamma']/cause['L']),dim = 0)296 297 mks = torch.abs(gk)-cause['gamma']/cause['L']298 mks[mks<0] = 0299 300 cause['uk'] = torch.sign(gk) * mks301 302 303# lsu = (1/batch_size*torch.sum(gama1output*cause['x']))304# lossu.append(np.asscalar(torch.sum(const).cpu().numpy()))305 306 temp1 = torch.sum(cause['x']*torch.exp(-torch.mm(B,cause['uk']))/2,dim = 0)307 temp2 = const+torch.sum((cause['uk'] - cause['z_k'])*grad_zk,dim = 0)+ torch.sum((cause['L']/2),dim = 0)*torch.sum(((cause['uk'] - cause['z_k']))**2,dim =0)308 indx = (temp1<= temp2) 309 stop_linesearch[indx] = True;310 decay = torch.ones([batch_size,1])311 decay = (1-stop_linesearch)*cause['eta']312 decay[decay==0] = 1313 cause['L'] = (decay.T)*cause['L']314 protect += 1315 if protect>5:316 break 317 318 cause['gamma'] = np.maximum(0.99*cause['gamma'],cause['gamma_bar']);319 cause['tk_1'] = (1 + np.sqrt(4*cause['tk']**2 + 1))/2320 cause['z_k'] = cause['uk'] + (cause['tk'] - 1)/(cause['tk_1'])*(cause['uk'] - cause['uk_1']);321 cause['uk_1'] = cause['uk']322 cause['tk'] = cause['tk_1'];323 324 gama1output = torch.zeros([d_x1,2,2,batch_size])325 for rl in range(2):326 for cl in range(2):327 gama1output[:,rl,cl,:] = (1+torch.exp(-torch.mm(B,cause['uk'])))/2328 gama1output = gama1output.reshape([d_x1,-1])329 nz_x_prev = nz_x330 nz_x=torch.zeros([4,d_x1,batch_size])331 for reg in range(4):332 nz_x[reg,:,:] = (torch.abs(state_cell[0]['xk'].reshape([d_x1,4,batch_size])[:,reg,:]) >= 2.2204e-15)333 num_changes_active = 0334 for reg in range(4):335 num_changes_active = num_changes_active+torch.sum(nz_x[reg,:,:] != nz_x_prev[reg,:,:])336 num_nz_x = torch.sum(nz_x)337 if num_nz_x >= 1:338 criterionActiveSet = num_changes_active / num_nz_x339 keep_going = (criterionActiveSet > 0.2)340 maxprotect += 1341 if maxprotect >=20:342 keep_going=0343 return cause344345346def Gamma(test_image,k=np.array([1, 20, 1, 30, 1, 35], dtype=float),mu = np.array([4, 4, 4, 4, 4, 4], dtype=float)):347 test_image = foai.ImageObject(test_image)348 foveation_prior = foac.matlab_style_gauss2D(test_image.modified.shape, 300)349 kernel = foac.gamma_kernel(test_image,k = k,mu = mu)350 image_height = 240351 image_width = 256352 rankCount = 8 # Number of maps scans to run353 img_sz = 32354 processed_patch = np.empty((1, rankCount, img_sz, img_sz, 3))355 processed_location = np.empty((1, rankCount, 2))356 357 foac.convolution(test_image, kernel, foveation_prior)358 map1 = foas.salience_scan(test_image, rankCount=rankCount)359 processed_patch = (test_image.patch).astype(np.uint8)360 processed_location = (test_image.location).astype(np.int)361 #map1 = (test_image.salience_map).astype(np.int)362 return processed_patch,processed_location, map1363364#scio.savemat('state21.mat',mdict={'state': processed_patch})365366def DPCN_score(processed_patch,d_x1,d_u1,d_input1,batch_size,A,B,C,ind_ac):367 length = len(processed_patch)368 state = processed_patch/255369 state = 0.2989 * state[:,:,:,0] + 0.5870 * state[:,:,:,1] + 0.1140 * state[:,:,:,2]370 for i in range(length):371 state[i,:,:] = pre_process(state[i,:,:])372 img_sz = 32373 AA = np.zeros([length,img_sz-20,20,20,img_sz-20])374 for ss in range(length):375 for i in range(img_sz-20):376 for j in range(img_sz-20):377 AA[ss,i,:,:,j] = state[ss,i:i+20,j:j+20]378 AA = np.transpose(AA,[0,2,3,1,4])379 AA = np.reshape(AA,[length,20,20,-1]) 380 AA = np.transpose(AA,[0,3,1,2])381 AA = np.reshape(AA,[-1,20,20])382# output = np.zeros([d_u1,1,batch_size])383 384 for i in range(1):385 #print(i)386 #input_img = AA[:,:,:,i]387 388 input_img = torch.Tensor(AA) 389 390 U = dpcn_detect(input_img,A,B,C,d_x1,d_u1,int(length*batch_size))391392# output[:,i,:] = U['uk']393 output = np.reshape(U['uk'].cpu().numpy(),[d_u1,length,batch_size])394395 #output = np.reshape(output,[60,12,12])396 output[ind_ac,:,:] = 0397 output[output<1.2] = 0398 a = np.sum(output,0)399 a = np.reshape(a,[length,batch_size])400401 return a402403404def thresholding(count_patch,processed_patch,mario):405 if mario and (np.where(count_patch!=0))[0]!=[]:406 407 pt = (np.where(count_patch==np.max(count_patch)))[0]408 else:409 pt = (np.where(count_patch!=0))[0]410 #411 ptt = np.zeros([5,])412 ptt[0:len(pt)] = pt[0:5]413 ind = True414 if len(pt) == 0:415 ind = False416 417 if len(pt) <5 and len(pt)>0:418 for i in range(5-len(pt)):419 ptt[-(i+1)] = pt[-1]420 421 422 ptt = ptt.astype(np.int64) 423 #ptt = np.concatenate([ptt]) 424 425 426# batch = (np.flip(processed_patch[ptt,:,:,:],-1)/255).astype(np.float32) #color channel for different api427# batch = batch[:,2:30,2:30,:] 428# batch_concat = np.concatenate([batch,batch_anchor],0)429 return ptt,ind,len(pt)430431432433def scores_locations(img,A,B,C,ind_ac,processed_patch,processed_location,mario = False):434# start = time.time()435 test_image = img436 if mario:437 ppp = []438 for i in range(len(processed_patch)):439 ppp.append(cv2.flip(processed_patch[i],1))440 ppp = np.array(ppp)441 #processed_patch,processed_location = Gamma(test_image)442# scores_patch = np.zeros([8,])#rank443 #count_patch = np.zeros([8,])#rank444 445 446 447 for i in range(1):#rank448 a = DPCN_score(processed_patch,150,60,144,int(12*12),A,B,C,ind_ac)449 scores_patch = np.sum(a,1)450 count_patch = (np.sum(a,1)>0).astype(np.int)451 452 if mario:453 for i in range(1):#rank454 a_f = DPCN_score(ppp,150,60,144,int(12*12),A,B,C,ind_ac)455 scores_patch_f = np.sum(a_f,1)456 count_patch_f = (np.sum(a,1)>0).astype(np.int)457 458 a = np.maximum(a,a_f)459 scores_patch = np.maximum(scores_patch,scores_patch_f)460 count_patch = np.maximum(count_patch,count_patch_f)461462 ptt,ind,num_object = thresholding(scores_patch,processed_patch,mario)463# stop = time.time()464# print("Salience Map Generation: ", stop - start, " seconds")465 return (processed_patch[ptt,:])[0:num_object,:],(processed_location[ptt,:])[0:num_object,:],num_object,ind,a466467468 469470471def labeling(batch_concat,batch_size_c,net,device,num_object):472 oringinal_data = IICDataset(ip = batch_concat,transform = 1)473 474 475 feed_x = torch.ones((batch_size_c,3,28,28), dtype=torch.float)476 for s in range(batch_size_c):477 sample = oringinal_data[s] 478 feed_x[s,:,:,:] = sample['image']479 feed_x.to(device)480 latent_x,_ =net(feed_x)481 lab_a = (latent_x.cpu().detach().numpy())482 483 return lab_a[0:num_object]484485486487def TK(location_p,location):488 #_,location,_ = Gamma(frame)489 490 location_r = np.zeros(location_p.shape)491 for i in range(np.size(location_p,0)):492 dis_list = []493 for j in range(len(location)):494 dis = (location[j][0]-location_p[i][0])**2+(location[j][1]-location_p[i][1])**2495 dis_list.append(dis)496 dis_list = np.array(dis_list)497 index = np.where(dis_list == np.min(dis_list))[0][0]498 location_r[i,:] = location[index]499 500 return location_r501502503def Merge(processed_location3):504 processed_location3_1 = np.zeros(processed_location3.shape)505 biaoji = np.arange(len(processed_location3))506 for i in range(len(processed_location3)):507 for j in range(len(processed_location3)-1-i):508 if np.abs(processed_location3[i,1] - processed_location3[j,1])<5:509 processed_location3[j,0] =min(processed_location3[j,0],processed_location3[i,0])510 biaoji[i] = 100511 512 return processed_location3[np.where(biaoji!=100)]513514515def truth_matching(img_gray,template):516517#template = img[177:189,114:143,:]518 # print("GT")519 w, h = template.shape[::-1]520 521 res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)522 523 524 threshold = 0.7525 return np.where( res >= threshold)526527# to accurately detect mario vs blocks528def truth_matching_mario(img_gray,template):529530#template = img[177:189,114:143,:]531 # print("GT")532 w, h = template.shape[::-1]533 534 res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)535 536 537 threshold = 0.75538 return np.where( res >= threshold)539540if __name__ == "__main__":
...
corrCUDA.py
Source:corrCUDA.py
1import numpy as np2import accelerate.cuda.blas as blas3import accelerate.cuda.fft as ft4from numba import cuda5def corr_td_single (x1,x2):6 c_12 = blas.dot(x1,x2)7 return c_128def best_grid_size(size, tpb):9 bpg = np.ceil(np.array(size, dtype=np.float) / tpb).astype(np.int).tolist()10 return tuple(bpg)11@cuda.jit('void(float32[:], float32[:])')12def mult_inplace(img, resp):13 i = cuda.grid(1)14 img[i] *= resp[i]15def corr_FD(x1,x2):16 threadperblock = 32, 817 blockpergrid = best_grid_size(tuple(reversed(x1.shape)), threadperblock)18 print('kernel config: %s x %s' % (blockpergrid, threadperblock))19 # Trigger initialization the cuFFT system.20 # This takes significant time for small dataset.21 # We should not be including the time wasted here22 #ft.FFTPlan(shape=x1.shape, itype=np.float32, otype=np.complex64)23 X1 = x1.astype(np.float32)24 X2 = x2.astype(np.float32)25 stream1 = cuda.stream()26 stream2 = cuda.stream()27 fftplan1 = ft.FFTPlan(shape=x1.shape, itype=np.float32,28 otype=np.complex64, stream=stream1)29 fftplan2 = ft.FFTPlan(shape=x2.shape, itype=np.float32,30 otype=np.complex64, stream=stream2)31 # pagelock memory32 with cuda.pinned(X1, X2):33 # We can overlap the transfer of response_complex with the forward FFT34 # on image_complex.35 d_X1 = cuda.to_device(X1, stream=stream1)36 d_X2 = cuda.to_device(X2, stream=stream2)37 fftplan1.forward(d_X1, out=d_X1)38 fftplan2.forward(d_X2, out=d_X2)39 print ('d_X1 is ',np.shape(d_X1),type(d_X1),np.max(d_X1))40 print ('d_X2 is ',np.shape(d_X2),type(d_X2),np.max(d_X2))41 stream2.synchronize()42 mult_inplace[blockpergrid, threadperblock, stream1](d_X1, d_X2)43 fftplan1.inverse(d_X1, out=d_X1)44 # implicitly synchronizes the streams45 c = d_X1.copy_to_host().real / np.prod(x1.shape)...
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