def main():
parser = argparse.ArgumentParser()
parser.add_argument('--out_path', type=str)
parser = DataModule.add_data_model_specific_args(parser)
args = parser.parse_args()
train(args)
def extract_match(
data_match, j, register_out,
id): #,match_feature): #,average_match,count_match,register_out):
tic = timeit.default_timer()
print("output card:", id)
#print("count----------", count)
#CUDA_VISIBLE_DEVICES = id
extractor = pfextractor('PED_EXT_001.pkl', id)
toc = timeit.default_timer()
print('all time: %.2f' % ((toc - tic) * 1000))
print("process are ", j)
match_feature = {}
for i in range(0, len(data_match)): #len(data_match)):
fea = extractor.extract(data_match[i])
one_feature = {}
one_feature['camera_id'] = data_match[i]
one_feature['time_stamp'] = datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S')
one_feature['feature'] = [fea]
match_feature[i] = one_feature
del one_feature
print("------------------", len(match_feature))
dm = DataModule(cache_dir)
mm = MatchingModule(MM_CONFIG, dm)
tic = timeit.default_timer()
matching_out = mm.match(match_feature, register=False, rank=5)
toc = timeit.default_timer()
print('match------- time: %.2f' % ((toc - tic) * 1000))
print("per----------time:%.2f" % ((toc - tic) * 1000 / len(matching_out)))
mm.free()
del match_feature
print('result is----------------', len(matching_out))
#calculate precisoin
candidate_path_ids = []
for i in range(len(cam1_image_list)): #len(cam1_image_list)):
# print(i)
id = cam1_image_list[i].split('/')[-2]
candidate_path_id = candidate_path + id
#print("mkdir is ",candidate_path_id)
if not os.path.exists(str(candidate_path_id)):
os.system('mkdir ' + candidate_path_id)
candidate_path_ids.append(candidate_path_id)
for mkey in matching_out:
mobject_id = matching_out[mkey]['object_id'][0]
mcamera_id = matching_out[mkey]['camera_id']
file_name = mcamera_id.split('/')[2]
allName = candidate_path + str(file_name) + "/"
#print('file location is:',allName)
for rkey in register_out.keys():
robject_id = register_out[rkey]['object_id'][0]
rcamera_id = register_out[rkey]['camera_id']
if robject_id == mobject_id:
print("----------------find--------------")
os.system('cp ' + rcamera_id + ' ' + allName)
def train(args):
model = RNN()
data_module = DataModule(args)
callbacks_list = None
if args.val_path:
callbacks_list = []
callbacks_list.append(callbacks.EarlyStopping(monitor='val_acc', patience=PATIENCE))
callbacks_list.append(callbacks.ModelCheckpoint(filepath=args.out_path, monitor='val_acc', prefix='rnn'))
gpus = N_GPU if torch.cuda.is_available() else None
trainer = pl.Trainer(gpus=gpus, max_epochs=MAX_EPOCHS, callbacks=callbacks_list)
trainer.fit(model, datamodule=data_module)
def main():
num = 0
for dirpath, dirnames, filenames in os.walk(cam2_path):
for f in filenames:
if num == 100:
break
print(f)
if '.jpg' in f:
cam2_image_list.append(join(dirpath, f))
num += 1
print("cam2 images num:")
print(len(cam2_image_list))
average_register = len(cam2_image_list) // 4
# calculate precisoin
for i in range(4):
if i == 3:
multiprocessing_register.append(cam2_image_list[i *
average_register:])
else:
multiprocessing_register.append(
cam2_image_list[i * average_register:(i + 1) *
average_register])
num = 0
for dirpath, dirnames, filenames in os.walk(cam1_path):
for f in filenames:
if num == 50:
break
print(f)
if '.jpg' in f:
cam1_image_list.append(join(dirpath, f))
num += 1
print("cam1 images num:")
print(len(cam1_image_list))
#num_match=len(cam1_image_list)//4
num_match = len(cam1_image_list) // 4
print(num_match)
for i in range(num_match):
if i == 3:
multiprocessing_match.append(cam1_image_list[i * num_match:])
else:
multiprocessing_match.append(
cam1_image_list[i * num_match:(i + 1) * num_match])
register_feature = multiprocessing.Manager().dict()
tic = timeit.default_timer()
plist_register = []
for count in range(4):
length = count * average_register
p = multiprocessing.Process(
target=extract_register,
args=(multiprocessing_register[count], count, length,
register_feature,
count)) #,average_register,register_feature,count_register))
p.start()
plist_register.append(p)
for p_register in plist_register:
p_register.join()
print("--------------------", len(register_feature))
dm = DataModule(cache_dir)
mm = MatchingModule(MM_CONFIG, dm)
register_out = mm.register(register_feature)
print(len(register_out))
#save_obj(register_out,"register_out1")
mm.free()
toc = timeit.default_timer()
print('register time: %.2f' % ((toc - tic) * 1000))
#register_out=load_obj("register_out2")
print("1111111111111111111111")
tic = timeit.default_timer()
print(num_match)
# for j in range(0,num_match,4):
# plist_match = []
# process=num_match-j if (j+3)>num_match else 4
# for count in range(process):
# print(j+count)
# p = multiprocessing.Process(target=extract_match,args=(multiprocessing_match[j+count],lock,count,register_out,count%3+1)) #,match_feature)) #,average_match,count_match,register_out))
# p.start()
# plist_match.append(p)
#
# for p_match in plist_match:
# p_match.join()
# del plist_match
plist_match = []
for j in range(4):
p = multiprocessing.Process(
target=extract_match,
args=(multiprocessing_match[j], j, register_out, j)
) #,match_feature)) #,average_match,count_match,register_out))
p.start()
plist_match.append(p)
for p_match in plist_match:
p_match.join()
del plist_match
toc = timeit.default_timer()
print('match time: %.2f' % ((toc - tic) * 1000))
def extract_match(
data_match, lock, j, length, register_out,
id): #,match_feature): #,average_match,count_match,register_out):
#lock.acquire()
#global count_match
#lock.acquire()
tic = timeit.default_timer()
print("output card:", id)
#print(torch.cuda.current_device())
CUDA_VISIBLE_DEVICES = id
#torch.cuda.set_device(id)
extractor = pfextractor('PED_EXT_001.pkl', id)
toc = timeit.default_timer()
print('all time: %.2f' % ((toc - tic) * 1000))
print("process are ", j)
#lock.release()
match_feature = {}
for i in range(0, len(data_match)): #len(data_match)):
# s = getsize(data_match[i])
#try:
fea = extractor.extract(data_match[i])
one_feature = {}
one_feature['camera_id'] = data_match[i]
one_feature['time_stamp'] = datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S')
one_feature['feature'] = [fea]
#match_feature[i+count_match.value] = one_feature
match_feature[i] = one_feature
del one_feature
#print("process is ",j)
# print("finished ext cam1 images features")
# print("finished ext cam1 images features")
# for ii in range(len(match_feature)):
# print(j,len(match_feature), match_feature[ii])
# matching
print("------------------", len(match_feature))
dm = DataModule(cache_dir)
mm = MatchingModule(MM_CONFIG, dm)
matching_out = mm.match(match_feature, register=False, rank=5)
mm.free()
del match_feature
print('result is----------------', len(matching_out))
#calculate precisoin
candidate_path_ids = []
for i in range(len(cam1_image_list)): #len(cam1_image_list)):
# print(i)
id = cam1_image_list[i].split('/')[-2]
candidate_path_id = candidate_path + id
print("mkdir is ", candidate_path_id)
if not os.path.exists(str(candidate_path_id)):
os.system('mkdir ' + candidate_path_id)
candidate_path_ids.append(candidate_path_id)
# print candidate_path_ids
# print("wenjian list is",len(candidate_path_ids))
# print( "------------------------------------------")
for mkey in matching_out:
# try:
mobject_id = matching_out[mkey]['object_id'][0]
mcamera_id = matching_out[mkey]['camera_id']
file_name = mcamera_id.split('/')[2]
allName = ''
allName = candidate_path + str(file_name) + "/"
print('file location is:', allName)
# print(len(register_out))
for rkey in register_out.keys():
robject_id = register_out[rkey]['object_id'][0]
rcamera_id = register_out[rkey]['camera_id']
print(robject_id)
print(rcamera_id)
if robject_id == mobject_id:
print("find-------------")
print mobject_id
print
#print('cp ' + rcamera_id + ' ' + allName)
os.system('cp ' + rcamera_id + ' ' + allName)
def main():
num = 0
for dirpath, dirnames, filenames in os.walk(cam2_path):
for f in filenames:
if num == 100:
break
print(f)
if '.jpg' in f:
cam2_image_list.append(join(dirpath, f))
num += 1
print("cam2 images num:")
print(len(cam2_image_list))
average_register = len(cam2_image_list) // 4
# calculate precisoin
for i in range(4):
if i == 3:
multiprocessing_register.append(cam2_image_list[i *
average_register:])
else:
multiprocessing_register.append(
cam2_image_list[i * average_register:(i + 1) *
average_register])
num = 0
for dirpath, dirnames, filenames in os.walk(cam1_path):
for f in filenames:
if num == 50:
break
print(f)
if '.jpg' in f:
cam1_image_list.append(join(dirpath, f))
num += 1
print("cam1 images num:")
print(len(cam1_image_list))
average_match = len(cam1_image_list) // 4
for i in range(4):
if i == 3:
multiprocessing_match.append(cam1_image_list[i * average_match:])
else:
multiprocessing_match.append(
cam1_image_list[i * average_match:(i + 1) * average_match])
plist_register = []
tic = timeit.default_timer()
register_feature = multiprocessing.Manager().dict()
# average_register = multiprocessing.Value("d", average_register)
# count_register=multiprocessing.Value("d", 0)
for count in range(4):
length = count * average_register
p = multiprocessing.Process(
target=extract_register,
args=(multiprocessing_register[count], lock, count, length,
register_feature,
count)) #,average_register,register_feature,count_register))
p.start()
plist_register.append(p)
for p_register in plist_register:
p_register.join()
print("--------------------", len(register_feature))
dm = DataModule(cache_dir)
mm = MatchingModule(MM_CONFIG, dm)
register_out = mm.register(register_feature)
mm.free()
toc = timeit.default_timer()
print('register time: %.2f' % ((toc - tic) * 1000))
tic = timeit.default_timer()
plist_match = []
for j in range(4):
length = j * average_match
p = multiprocessing.Process(
target=extract_match,
args=(multiprocessing_match[j], lock, j, length, register_out, j)
) #,match_feature)) #,average_match,count_match,register_out))
p.start()
plist_match.append(p)
for p_match in plist_match:
p_match.join()
toc = timeit.default_timer()
print('match time: %.2f' % ((toc - tic) * 1000))
def extract_match():
for i in range(0, len(cam1_image_list)):
s = getsize(cam1_image_list[i])
#try:
fea = extractor.extract(cam1_image_list[i])
one_feature = {}
one_feature['camera_id'] = cam1_image_list[i]
one_feature['time_stamp'] = datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S')
one_feature['feature'] = [fea]
match_feature[i] = one_feature
print i
#except:
#continue
print "finished ext cam1 images features"
# parse matching feature
# register
dm = DataModule(cache_dir)
mm = MatchingModule(MM_CONFIG, dm)
register_out = mm.register(register_feature)
mm.free()
# matching
dm = DataModule(cache_dir)
mm = MatchingModule(MM_CONFIG, dm)
tic = timeit.default_timer()
matching_out = mm.match(match_feature, register=False, rank=5)
toc = timeit.default_timer()
print('match time: %.2f' % ((toc - tic) * 1000))
print('match time: %.2f' % ((toc - tic) * 1000 / len(matching_out)))
# with open('register_out.json', 'w') as file:
# json.dump(register_out, file, indent=4)
# json.encoder.FLOAT_REPR = lambda x: format(x, '.2f')
# with open('matching_out.json', 'w') as file:
# json.dump(matching_out, file, indent=4)
# calculate precisoin
candidate_path_ids = []
for i in range(0, len(cam1_image_list)):
print i
id = cam1_image_list[i].split('/')[-2]
candidate_path_id = candidate_path + id
if not os.path.exists(str(candidate_path_id)):
os.system('mkdir ' + candidate_path_id)
candidate_path_ids.append(candidate_path_id)
# precision = 0
# feature_num = 0
print "------------------------------------------"
print candidate_path_ids
for mkey in matching_out.keys():
try:
mobject_id = matching_out[mkey]['object_id'][0]
mcamera_id = matching_out[mkey]['camera_id']
file_name = mcamera_id.split('/')[2]
allName = ''
allName = candidate_path + str(file_name) + "/"
print('file location is:', allName)
print mobject_id
print mcamera_id
for rkey in register_out.keys():
robject_id = register_out[rkey]['object_id'][0]
rcamera_id = register_out[rkey]['camera_id']
print robject_id
print rcamera_id
if robject_id == mobject_id:
print "find-------------"
os.system('cp ' + rcamera_id + ' ' + allName)
break
except:
continue
# print 'rank=', len(matching_out[mkey]['object_id']), len(matching_out[mkey]['score'])
#print 'matched_id=', mobject_id, ', register_id=', robject_id, ',score=', matching_out[mkey]['score'][0]
# precision += 1 if mobject_id == robject_id else 0
# feature_num += 1
#print 'precision =', precision * 100.0 / feature_num, '%'
mm.free()