def _first_level_segment_upload(segm_i):
l = ds_segments_bounds[segm_i][0, 0]
r = ds_segments_bounds[segm_i][-1, 1]
segm_start, segm_end = np.searchsorted(
sp_mz_int_buf[:, 1], (l, r)) # mz expected to be in column 1
segm = sp_mz_int_buf[segm_start:segm_end]
return storage.put_cobject(msgpack.dumps(segm))
def upload_chunk(ch_i, storage):
chunk_sp_inds = chunks[ch_i]
# Get imzml_reader from COS because it's too big to include via pywren captured vars
imzml_reader = pickle.loads(
read_cloud_object_with_retry(storage, imzml_cobject))
n_spectra = sum(imzml_reader.mzLengths[sp_i] for sp_i in chunk_sp_inds)
sp_mz_int_buf = np.zeros((n_spectra, 3),
dtype=imzml_reader.mzPrecision)
chunk_start = 0
for sp_i, mzs, ints in get_spectra(ibd_path, imzml_reader,
chunk_sp_inds):
chunk_end = chunk_start + len(mzs)
sp_mz_int_buf[chunk_start:chunk_end, 0] = sp_id_to_idx[sp_i]
sp_mz_int_buf[chunk_start:chunk_end, 1] = mzs
sp_mz_int_buf[chunk_start:chunk_end, 2] = ints
chunk_start = chunk_end
by_mz = np.argsort(sp_mz_int_buf[:, 1])
sp_mz_int_buf = sp_mz_int_buf[by_mz]
del by_mz
chunk = msgpack.dumps(sp_mz_int_buf)
size = sys.getsizeof(chunk) * (1 / 1024**2)
logger.info(f'Uploading spectra chunk {ch_i} - %.2f MB' % size)
chunk_cobject = storage.put_cobject(chunk)
logger.info(f'Spectra chunk {ch_i} finished')
return chunk_cobject
def merge_spectra_chunk_segments(segm_cobjects, id, storage):
print(f'Merging segment {id} spectra chunks')
def _merge(ch_i):
segm_spectra_chunk = read_cloud_object_with_retry(
storage, segm_cobjects[ch_i], msgpack.load)
return segm_spectra_chunk
with ThreadPoolExecutor(max_workers=128) as pool:
segm = list(pool.map(_merge, range(len(segm_cobjects))))
segm = np.concatenate(segm)
# Alternative in-place sorting (slower) :
# segm.view(f'{ds_segm_dtype},{ds_segm_dtype},{ds_segm_dtype}').sort(order=['f1'], axis=0)
segm = segm[segm[:, 1].argsort()]
bounds_list = ds_segments_bounds[id]
segms_len = []
segms_cobjects = []
for segm_j in range(len(bounds_list)):
l, r = bounds_list[segm_j]
segm_start, segm_end = np.searchsorted(
segm[:, 1], (l, r)) # mz expected to be in column 1
sub_segm = segm[segm_start:segm_end]
segms_len.append(len(sub_segm))
base_id = sum([len(bounds) for bounds in ds_segments_bounds[:id]])
segm_i = base_id + segm_j
print(f'Storing dataset segment {segm_i}')
segms_cobjects.append(storage.put_cobject(msgpack.dumps(sub_segm)))
return segms_len, segms_cobjects
def store_formula_to_id_chunk(ch_i, ibm_cos):
print(f'Storing formula_to_id dictionary chunk {ch_i}')
start_id = (N_FORMULAS_SEGMENTS // N_FORMULA_TO_ID) * ch_i
end_id = (N_FORMULAS_SEGMENTS // N_FORMULA_TO_ID) * (ch_i + 1)
keys = [
f'{formulas_chunks_prefix}/{formulas_chunk}.msgpack'
for formulas_chunk in range(start_id, end_id)
]
def _get(key):
formula_chunk = read_object_with_retry(ibm_cos, bucket, key,
pd.read_msgpack)
formula_to_id_chunk = dict(
zip(formula_chunk.formula, formula_chunk.index))
return formula_to_id_chunk
with ThreadPoolExecutor(max_workers=128) as pool:
results = list(pool.map(_get, keys))
formula_to_id = {}
for chunk_dict in results:
formula_to_id.update(chunk_dict)
ibm_cos.put_object(Bucket=bucket,
Key=f'{formula_to_id_chunks_prefix}/{ch_i}.msgpack',
Body=msgpack.dumps(formula_to_id))
def merge_spectra_chunk_segments(segm_i, ibm_cos):
print(f'Merging segment {segm_i} spectra chunks')
keys = list_keys(bucket, f'{ds_segments_prefix}/chunk/{segm_i}/', ibm_cos)
def _merge(key):
segm_spectra_chunk = read_object_with_retry(ibm_cos, bucket, key, msgpack.load)
return segm_spectra_chunk
with ThreadPoolExecutor(max_workers=128) as pool:
segm = list(pool.map(_merge, keys))
segm = np.concatenate(segm)
# Alternative in-place sorting (slower) :
# segm.view(f'{segm_dtype},{segm_dtype},{segm_dtype}').sort(order=['f1'], axis=0)
segm = segm[segm[:, 1].argsort()]
clean_from_cos(None, bucket, f'{ds_segments_prefix}/chunk/{segm_i}/', ibm_cos)
bounds_list = ds_segments_bounds[segm_i]
segms_len = []
for segm_j in range(len(bounds_list)):
l, r = bounds_list[segm_j]
segm_start, segm_end = np.searchsorted(segm[:, 1], (l, r)) # mz expected to be in column 1
sub_segm = segm[segm_start:segm_end]
segms_len.append(len(sub_segm))
base_id = sum([len(bounds) for bounds in ds_segments_bounds[:segm_i]])
id = base_id + segm_j
print(f'Storing dataset segment {id}')
ibm_cos.put_object(Bucket=bucket,
Key=f'{ds_segments_prefix}/{id}.msgpack',
Body=msgpack.dumps(sub_segm))
return segms_len
def convert_to_bytes(obj):
"""
Serialize an object.
Returns:
Implementation-dependent bytes-like object.
"""
return msgpack_numpy.dumps(obj, use_bin_type=True)
def _first_level_segment_upload(segm_i):
l = ds_segments_bounds[segm_i][0, 0]
r = ds_segments_bounds[segm_i][-1, 1]
segm_start, segm_end = np.searchsorted(sp_mz_int_buf[:, 1], (l, r)) # mz expected to be in column 1
segm = sp_mz_int_buf[segm_start:segm_end]
ibm_cos.put_object(Bucket=bucket,
Key=f'{ds_segments_prefix}/chunk/{segm_i}/{id}.msgpack',
Body=msgpack.dumps(segm))
def check_face_img(face_img):
# pose_predict(姿势): [[pitch, yaw, roll]](Pitch: 俯仰; Yaw: 摇摆; Roll: 倾斜)
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": str(time.time())
}
result = requests.post(angle_url, data=request)
try:
if result.status_code == 200:
pose_predict = json.loads(result.content)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write('\t'.join(map(str, ['pose filter request'])) +
'\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(
base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write(
'\t'.join(map(str, ['pose filter detect'])) + '\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < pitch_threshold and math.fabs(
yaw) < yaw_threshold and math.fabs(
roll) < roll_threshold:
log_file.write('\t'.join(
map(str, ['pose not filter',
str(pose_predict[0])])) + '\n')
log_file.close()
return True
else:
log_file.write('\t'.join(
map(str,
['pose filter threshold',
str(pose_predict[0])])) + '\n')
log_file.close()
return False
else:
return False
except:
traceback.print_exc()
log_file.close()
return False
def _upload_chunk(ch_i, sp_mz_int_buf):
chunk = msgpack.dumps(sp_mz_int_buf)
key = f'{input_data["ds_chunks"]}/{ch_i}.msgpack'
size = sys.getsizeof(chunk) * (1 / 1024 ** 2)
logger.info(f'Uploading spectra chunk {ch_i} - %.2f MB' % size)
cos_client.put_object(Bucket=config["storage"]["ds_bucket"],
Key=key,
Body=chunk)
logger.info(f'Spectra chunk {ch_i} finished')
return key
def check_face_img(self, face_img, image_id):
# 计算角度
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
# 姿势检测
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": image_id,
}
url = "http://%s:%d/" % (check_ip, check_port)
result = image_request(request, url)
try:
pose_predict = json.loads(result)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write(
'\t'.join(map(str, [image_id, 'pose filter request'])) +
'\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(
base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write(
'\t'.join(map(str, [image_id, 'pose filter detect'])) +
'\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < self.pitch_threshold and \
math.fabs(yaw) < self.yaw_threshold and \
math.fabs(roll) < self.roll_threshold:
log_file.close()
return True
else:
log_file.write('\t'.join(
map(str, [image_id, 'pose filter threshold'])) + '\n')
log_file.close()
return False
except:
traceback.print_exc()
log_file.close()
return False
def get_all_img_feature():
feature_result_file = '/tmp/annotate_light_cnn_feature.p'
f = open(feature_result_file, 'w')
folder = '/tmp/all_images'
person_list = os.listdir(folder)
for person in person_list:
person_path = os.path.join(folder, person)
pic_list = os.listdir(person_path)
for pic in pic_list:
pic_path = os.path.join(person_path, pic)
feature = np.asarray(extract_feature_from_file(pic_path)[0])
f.write(base64.b64encode(msgpack_numpy.dumps((feature, person_path)))+'\n')
f.close()
def emit(self, topic, data):
""" Emits data of some topic over TCP """
if isinstance(topic, str):
topic = topic.encode()
log.debug("Emitting message: %s %s" % (topic, data))
try:
self.publisher.send_multipart([topic, dumps(data)])
except (NameError, AttributeError):
pass # No dumps defined
if topic == b'results':
self.recorder_queue.put(data)
elif topic == b'status' or topic == b'progress':
self.monitor_queue.put((topic.decode(), data))
def emit(self, topic, data):
""" Emits data of some topic over TCP """
if isinstance(topic, str):
topic = topic.encode()
log.debug("Emitting message: %s %s" % (topic, data))
try:
self.publisher.send_multipart([topic, dumps(data)])
except (NameError, AttributeError):
pass # No dumps defined
if topic == b'results':
self.recorder_queue.put(data)
elif topic == b'status' or topic == b'progress':
self.monitor_queue.put((topic.decode(), data))
def get_pic_feature(pic_folder, result_file):
f_result = open(result_file, 'w')
pic_list = os.listdir(pic_folder)
for pic in pic_list:
try:
pic_path = os.path.join(pic_folder, pic)
feature = extract_feature_from_file(pic_path)
# print feature, feature.shape
f_result.write(pic_path.decode('gbk')+'\t'+base64.b64encode(msgpack_numpy.dumps(feature))+'\n')
print pic_path.decode('gbk')
except:
traceback.print_exc()
continue
f_result.close()
def extract_all_feature(folder, result_file, extract_func):
f = open(result_file, 'w')
f.write('pic_path'+'\t'+'feature'+'\n')
person_list = os.listdir(folder)
for person in person_list:
print person
person_path = os.path.join(folder, person)
pic_list = os.listdir(person_path)
for pic in pic_list:
pic_path = os.path.join(person_path, pic)
# feature = extract_feature_from_file(pic_path)
feature = extract_func(pic_path)
feature_str = base64.b64encode(msgpack_numpy.dumps(feature))
f.write(pic_path+'\t'+feature_str+'\n')
f.close()
def get_all_img_feature():
feature_result_file = '/tmp/annotate_light_cnn_feature.p'
f = open(feature_result_file, 'w')
folder = '/tmp/all_images'
person_list = os.listdir(folder)
for person in person_list:
person_path = os.path.join(folder, person)
pic_list = os.listdir(person_path)
for pic in pic_list:
pic_path = os.path.join(person_path, pic)
feature = np.asarray(extract_feature_from_file(pic_path)[0])
f.write(
base64.b64encode(msgpack_numpy.dumps((feature, person_path))) +
'\n')
f.close()
def get_pic_feature(pic_folder, result_file):
f_result = open(result_file, 'w')
pic_list = os.listdir(pic_folder)
for pic in pic_list:
try:
pic_path = os.path.join(pic_folder, pic)
feature = extract_feature_from_file(pic_path)
# print feature, feature.shape
f_result.write(
pic_path.decode('gbk') + '\t' +
base64.b64encode(msgpack_numpy.dumps(feature)) + '\n')
print pic_path.decode('gbk')
except:
traceback.print_exc()
continue
f_result.close()
def store_formula_to_id(storage, bucket, formula_to_id_chunks_prefix,
formulas_df):
num_formulas = len(formulas_df)
n_formula_to_id = int(
math.ceil(num_formulas * 200 / (FORMULA_TO_ID_CHUNK_MB * 1024**2)))
for ch_i in range(n_formula_to_id):
print(f'Storing formula_to_id dictionary chunk {ch_i}')
start_idx = num_formulas * ch_i // n_formula_to_id
end_idx = num_formulas * (ch_i + 1) // n_formula_to_id
formula_to_id = formulas_df.iloc[start_idx:end_idx].formula.to_dict()
storage.put_object(Bucket=bucket,
Key=f'{formula_to_id_chunks_prefix}/{ch_i}.msgpack',
Body=msgpack.dumps(formula_to_id))
return n_formula_to_id
def valid_one_pic_pose(face_img, image_id):
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": image_id,
}
requestPOST = urllib2.Request(data=urllib.urlencode(request),
url="http://10.160.164.26:%d/" % check_port)
requestPOST.get_method = lambda: "POST"
try:
s = urllib2.urlopen(requestPOST).read()
except urllib2.HTTPError, e:
print e.code
def post(self):
request_type = self.get_body_argument('request_type')
if request_type == 'check_pose':
try:
image_id = self.get_body_argument("image_id")
face_img_str = self.get_body_argument("face_img_str")
print "receive image", image_id, time.time()
face_img = msgpack_numpy.loads(base64.b64decode(face_img_str))
start = time.time()
pose_predict = angle_calculate_server.calculate_angle(face_img, image_id)
end = time.time()
pose_predict = base64.b64encode(msgpack_numpy.dumps(pose_predict))
print 'pose predict time :', (end - start)
self.write(json.dumps({"pose_predict": pose_predict}))
except:
traceback.print_exc()
return
def check_face_img(self, face_img, image_id):
# 计算角度
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
# 姿势检测
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": image_id,
}
url = "http://%s:%d/" % (check_ip, check_port)
result = image_request(request, url)
try:
pose_predict = json.loads(result)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write('\t'.join(map(str, [image_id, 'pose filter request'])) + '\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write('\t'.join(map(str, [image_id, 'pose filter detect'])) + '\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < self.pitch_threshold and \
math.fabs(yaw) < self.yaw_threshold and \
math.fabs(roll) < self.roll_threshold:
log_file.close()
return True
else:
log_file.write('\t'.join(map(str, [image_id, 'pose filter threshold'])) + '\n')
log_file.close()
return False
except:
traceback.print_exc()
log_file.close()
return False
def check_face_img(face_img):
# pose_predict(姿势): [[pitch, yaw, roll]](Pitch: 俯仰; Yaw: 摇摆; Roll: 倾斜)
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {"request_type": 'check_pose', "face_img_str": face_img_str, "image_id": str(time.time())}
result = requests.post(angle_url, data=request)
try:
if result.status_code == 200:
pose_predict = json.loads(result.content)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write('\t'.join(map(str, ['pose filter request'])) + '\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write('\t'.join(map(str, ['pose filter detect'])) + '\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < pitch_threshold and math.fabs(yaw) < yaw_threshold and math.fabs(roll) < roll_threshold:
log_file.write('\t'.join(map(str, ['pose not filter', str(pose_predict[0])])) + '\n')
log_file.close()
return True
else:
log_file.write('\t'.join(map(str, ['pose filter threshold', str(pose_predict[0])])) + '\n')
log_file.close()
return False
else:
return False
except:
traceback.print_exc()
log_file.close()
return False
def valid_one_pic_pose(face_img, image_id):
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": image_id,
}
requestPOST = urllib2.Request(
data=urllib.urlencode(request),
url="http://10.160.164.26:%d/" % check_port
)
requestPOST.get_method = lambda: "POST"
try:
s = urllib2.urlopen(requestPOST).read()
except urllib2.HTTPError, e:
print e.code
def post(self):
request_type = self.get_body_argument('request_type')
if request_type == 'check_pose':
try:
image_id = self.get_body_argument("image_id")
face_img_str = self.get_body_argument("face_img_str")
print "receive image", image_id, time.time()
face_img = msgpack_numpy.loads(base64.b64decode(face_img_str))
start = time.time()
pose_predict = angle_calculate_server.calculate_angle(
face_img, image_id)
end = time.time()
pose_predict = base64.b64encode(
msgpack_numpy.dumps(pose_predict))
print 'pose predict time :', (end - start)
self.write(json.dumps({"pose_predict": pose_predict}))
except:
traceback.print_exc()
return
def recognize_one_feature(self, im_feature, image_id):
'''
根据特征确定label
:param image_id : 大图的文件名+face_id(第几个人脸) --- 方便定位
'''
start = time.time()
feature_str = base64.b64encode(msgpack_numpy.dumps(im_feature))
# im_feature = msgpack_numpy.loads(base64.b64decode(feature_str))
current_day = get_current_day()
log_file = open(os.path.join(self.log_dir, current_day + '.txt'), 'a')
current_time = get_current_time()
log_file.write('\t'.join(map(str, [current_time, "receive image", image_id])) + '\n')
try:
# 流程 : 找距离最近的图片 ; 计算prob ; 在线聚类 ; 加入LSH Forest
try:
current_time = float(image_id)
nearest_sim_list = self.cal_nearest_sim(current_time=current_time, current_feature=im_feature)
# print 'current_time :', current_time, 'nearest_sim_list :', nearest_sim_list
except:
traceback.print_exc()
nearest_sim_list = []
# 找距离最近的图片 --- 用LSH Forest 找出最近的10张图片,然后分别计算距离
dist_label_list = self.find_k_neighbors_with_lsh(im_feature)
dist_label_list.extend(nearest_sim_list)
dist_label_list = self.filter_result(dist_label_list)
dist_label_list.sort(key=lambda x: x[0], reverse=True)
# 计算
if dist_label_list == None:
# 不考虑new_person的情况,小于阈值的都判断为new_person
this_id = self.must_be_not_same_id
this_label = 'new_person'
# this_id = self.must_be_not_same_id
# this_label = self.new_person_str + str(self.current_new_person_id)
else:
# 计算prob --- 根据距离计算prob
this_id, this_label = self.evaluate_result(dist_label_list)
# 在线聚类 --- 根据dist确定是重新增加一个人还是加入到已有的人中
if dist_label_list != None and len(dist_label_list) > 0:
current_time = get_current_time()
log_file.write('\t'.join(map(str, [current_time, 'dist_label_list :', map(str, dist_label_list)])) + '\n')
# need_add 决定是否加入LSHForest ; need_save决定是否存入数据库
if this_id == self.same_pic_id:
need_add = False
need_save = True
elif this_id == self.must_be_same_id:
need_add = False
need_save = True
elif this_id == self.must_be_not_same_id:
# 现在的版本不用加入新人, 不能识别的全部返回new_person
this_label = 'new_person'
need_save = True
need_add = False
# this_label = self.new_person_str + str(self.current_new_person_id)
# self.current_new_person_id += 1
# need_add = True
# need_save = True
elif this_id == self.maybe_same_id:
need_add = False
need_save = False
else:
current_time = get_current_time()
log_file.write('\t'.join(map(str, [current_time, 'error para :', this_id])) + '\n')
return self.unknown, str(self.max_dist_threshold), feature_str, str(False)
self.nearest.append((this_label, im_feature, image_id))
# 现在不在增加new_person
# # 加入LSH Forest --- partial_fit
# if need_add:
# # 只将新人的图片加入LSHForest并保存到文件
# self.add_one_pic(im_feature, this_label)
# write_start = time.time()
# tmp_file = open(self.all_feature_label_file, 'a')
# tmp_file.write(base64.b64encode(msgpack_numpy.dumps((im_feature, this_label)))+'\n')
# tmp_file.close()
# print 'write time :', (time.time() - write_start)
# # 根据label和image_id可以存生成文件名,确定是否要存储文件[可以选择在服务器和本地同时存储]
# 统计有多少图片在gray area
log_file.write('\t'.join(map(str, ['stat', 'recognize_id', self.trans_dic[this_id], 'recog time :', (time.time() - start)])) + '\n')
log_file.close()
if this_id == self.same_pic_id or this_id == self.must_be_not_same_id or this_id == self.must_be_same_id:
if this_label == None or dist_label_list == None:
# 数据库里可能一个人也没有, 这时this_label = None
return self.unknown, str(self.max_dist_threshold), feature_str, str(False)
else:
return this_label.replace(self.must_same_str, ''), str(dist_label_list[0][0]), feature_str, str(need_save)
else:
# 灰度区域,不显示人名
# return this_label.replace(self.maybe_same_str, ''), tr(dist_label_list[0][0]), str(has_save_num), str(need_add)
return self.unknown, str(dist_label_list[0][0]), feature_str, str(need_save)
except:
traceback.print_exc()
log_file.close()
return self.unknown, str(self.max_dist_threshold), feature_str, str(False)
def recognize_online_cluster(self, image, image_id):
'''
:param image: 将得到的图片进行识别,加入的LSH Forest,根据距离计算proba(不同的距离对应不同的准确率,根据已有的dist计算阈值);
和已经设定的阈值判断是不是一个新出现的人,确定是原来已有的人,还是不确定是原来已有的人
:return:
'''
start = time.time()
need_add = False
need_save = False
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
log_file.write('\t'.join(map(str, ["receive image", image_id, time.time()])) + '\n')
feature_str = ''
try:
image = base64.decodestring(image)
image = zlib.decompress(image)
im = cv2.imdecode(np.fromstring(image, dtype=np.uint8), 1)
log_file.write('\t'.join(map(str, ['shape :', im.shape[0], im.shape[1]])) + '\n')
# 图片尺寸过滤
if im.shape[0] < size_threshold or im.shape[1] < size_threshold:
log_file.write('\t'.join(map(str, ['stat recognize_time :', (time.time() - start), 'small_size'])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 清晰度过滤
blur_sign, blur_var = is_blur(cv2.resize(im, (96, 96)))
if blur_sign:
log_file.write('\t'.join(map(str, ['stat recognize_time :', (time.time() - start), 'blur_filter', blur_var])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 保存传过来的图片
# img_file = '/tmp/research_face/%s.jpg' %image_id
time_slot = get_time_slot(image_id)
if time_slot == None:
time_slot = 'error'
time_slot_dir = os.path.join(tmp_face_dir, time_slot)
if not os.path.exists(time_slot_dir):
os.makedirs(time_slot_dir)
img_file = os.path.join(time_slot_dir, image_id+'.jpg')
cv2.imwrite(img_file, im)
except:
traceback.print_exc()
log_file.close()
return self.unknown, 1.0, feature_str, need_save
try:
# 流程 : 找距离最近的图片 ; 计算prob ; 在线聚类 ; 加入LSH Forest
result = self.extract_pic_feature(img_file)
if result == None:
log_file.write('\t'.join(map(str, ['stat not_find_face', 'time :', (time.time() - start)]))+'\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
face_pic, im_feature = result
try:
# nearest_sim_list的格式和dist_label_list的格式一样,这样可以将两个list合并,一起计算(这样不用考虑时间的因素)
# 在识别出人名后将人名和feature放入到self.nearest
nearest_sim_list = self.cal_nearest_sim(current_feature=im_feature)
except:
traceback.print_exc()
nearest_sim_list = []
log_file.write('\t'.join(map(str, ['nearest_sim_list :', map(str, nearest_sim_list)])) + '\n')
feature_str = base64.b64encode(msgpack_numpy.dumps(im_feature))
log_file.write('\t'.join(map(str, ['extract_feature_time :', (time.time() - start)]))+'\n')
# 找距离最近的图片 --- 用LSH Forest 找出最近的10张图片,然后分别计算距离
tmp_list = self.find_k_neighbors_with_lsh(im_feature)
nearest_sim_list.sort(key=lambda x: x[0], reverse=True)
nearest_sim_list.extend(tmp_list)
dist_label_list = nearest_sim_list[:]
# 计算
log_file.write('\t'.join(map(str, ['dist_label_list :', map(str, dist_label_list)])) + '\n')
if dist_label_list == None:
this_id = self.must_be_not_same_id
this_label = self.new_person_str + str(self.current_new_person_id)
else:
# 计算prob --- 根据距离计算prob
this_id, this_label = self.evaluate_result(dist_label_list)
# 不管概率, 都要将最新的一张图片加入到self.nearest
self.nearest.append((this_label, im_feature))
log_file.write('\t'.join(map(str, ['self.nearest :', map(str, self.nearest)])) + '\n')
# 在线聚类 --- 根据dist确定是重新增加一个人还是加入到已有的人中
if this_id == self.same_pic_id:
need_add = False
elif this_id == self.must_be_same_id:
need_add = False
need_save = True
this_person_pic_folder = os.path.join(self.all_pic_data_folder, this_label+self.must_same_str)
this_person_feature_folder = os.path.join(self.all_pic_feature_data_folder, this_label+self.must_same_str)
elif this_id == self.must_be_not_same_id:
this_label = self.new_person_str + str(self.current_new_person_id)
self.current_new_person_id += 1
this_person_pic_folder = os.path.join(self.all_pic_data_folder, this_label)
this_person_feature_folder = os.path.join(self.all_pic_feature_data_folder, this_label)
need_add = True
need_save = True
elif this_id == self.maybe_same_id:
this_person_pic_folder = os.path.join(self.all_pic_data_folder, this_label + self.maybe_same_str)
this_person_feature_folder = os.path.join(self.all_pic_feature_data_folder, this_label + self.maybe_same_str)
need_add = False # prob在灰度区域的不如入,其余情况加入
need_save = True
else:
log_file.write('\t'.join(map(str, ['error para :', this_id]))+'\n')
if need_save:
try:
if not os.path.exists(this_person_pic_folder):
os.makedirs(this_person_pic_folder)
if not os.path.exists(this_person_feature_folder):
os.makedirs(this_person_feature_folder)
# 直接存储图片对应的特征, 同时保存图片文件
this_pic_feature_name = os.path.join(this_person_feature_folder, image_id+'.p')
msgpack_numpy.dump(im_feature, open(this_pic_feature_name, 'wb'))
this_pic_face_name = os.path.join(this_person_pic_folder, image_id+'.jpg')
cv2.imwrite(this_pic_face_name, face_pic)
except:
traceback.print_exc()
return self.unknown, 1.0, feature_str, False
# 加入LSH Forest --- partial_fit
if need_add:
self.add_one_pic(im_feature, this_label)
# 根据label和image_id可以存生成文件名,确定是否要存储文件[可以选择在服务器和本地同时存储]
if this_id == self.same_pic_id or this_id == self.must_be_not_same_id or this_id == self.must_be_same_id:
end = time.time()
log_file.write('\t'.join(map(str, ['stat recognize_time :',(end - start), 'this_id :', self.trans_dic.get(this_id)]))+'\n')
log_file.close()
need_save = True
return this_label.replace(self.must_same_str, ''), str(dist_label_list[0][0]), str(feature_str), str(need_save)
else:
# 灰度区域,不显示人名
end = time.time()
log_file.write('\t'.join(map(str, ['stat gray_area :',(end - start)]))+'\n')
log_file.close()
return self.unknown, str(dist_label_list[0][0]), str(feature_str), str(False)
except:
traceback.print_exc()
log_file.close()
return self.unknown, str(100.0), str(feature_str), str(False)
def recognize_online_cluster(self, image, image_id):
'''
:param image: 将得到的图片进行识别,加入的LSH Forest,根据距离计算proba(不同的距离对应不同的准确率,根据已有的dist计算阈值);
和已经设定的阈值判断是不是一个新出现的人,确定是原来已有的人,还是不确定是原来已有的人
:return:
'''
start = time.time()
need_add = False
need_save = False
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
log_file.write(
'\t'.join(map(str, ["receive image", image_id,
time.time()])) + '\n')
feature_str = ''
try:
image = base64.decodestring(image)
image = zlib.decompress(image)
im = cv2.imdecode(np.fromstring(image, dtype=np.uint8), 1)
log_file.write(
'\t'.join(map(str, ['shape :', im.shape[0], im.shape[1]])) +
'\n')
# 图片尺寸过滤
if im.shape[0] < size_threshold or im.shape[1] < size_threshold:
log_file.write('\t'.join(
map(str, [
'stat recognize_time :', (time.time() -
start), 'small_size'
])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 清晰度过滤
blur_sign, blur_var = is_blur(cv2.resize(im, (96, 96)))
if blur_sign:
log_file.write('\t'.join(
map(str, [
'stat recognize_time :',
(time.time() - start), 'blur_filter', blur_var
])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 保存传过来的图片
# img_file = '/tmp/research_face/%s.jpg' %image_id
time_slot = get_time_slot(image_id)
if time_slot == None:
time_slot = 'error'
time_slot_dir = os.path.join(tmp_face_dir, time_slot)
if not os.path.exists(time_slot_dir):
os.makedirs(time_slot_dir)
img_file = os.path.join(time_slot_dir, image_id + '.jpg')
cv2.imwrite(img_file, im)
except:
traceback.print_exc()
log_file.close()
return self.unknown, 1.0, feature_str, need_save
try:
# 流程 : 找距离最近的图片 ; 计算prob ; 在线聚类 ; 加入LSH Forest
result = self.extract_pic_feature(img_file)
if result == None:
log_file.write('\t'.join(
map(str, [
'stat not_find_face', 'time :', (time.time() - start)
])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
face_pic, im_feature = result
try:
# nearest_sim_list的格式和dist_label_list的格式一样,这样可以将两个list合并,一起计算(这样不用考虑时间的因素)
# 在识别出人名后将人名和feature放入到self.nearest
nearest_sim_list = self.cal_nearest_sim(
current_feature=im_feature)
except:
traceback.print_exc()
nearest_sim_list = []
log_file.write('\t'.join(
map(str, ['nearest_sim_list :',
map(str, nearest_sim_list)])) + '\n')
feature_str = base64.b64encode(msgpack_numpy.dumps(im_feature))
log_file.write('\t'.join(
map(str, ['extract_feature_time :', (time.time() - start)])) +
'\n')
# 找距离最近的图片 --- 用LSH Forest 找出最近的10张图片,然后分别计算距离
tmp_list = self.find_k_neighbors_with_lsh(im_feature)
nearest_sim_list.sort(key=lambda x: x[0], reverse=True)
nearest_sim_list.extend(tmp_list)
dist_label_list = nearest_sim_list[:]
# 计算
log_file.write('\t'.join(
map(str, ['dist_label_list :',
map(str, dist_label_list)])) + '\n')
if dist_label_list == None:
this_id = self.must_be_not_same_id
this_label = self.new_person_str + str(
self.current_new_person_id)
else:
# 计算prob --- 根据距离计算prob
this_id, this_label = self.evaluate_result(dist_label_list)
# 不管概率, 都要将最新的一张图片加入到self.nearest
self.nearest.append((this_label, im_feature))
log_file.write(
'\t'.join(map(str, ['self.nearest :',
map(str, self.nearest)])) + '\n')
# 在线聚类 --- 根据dist确定是重新增加一个人还是加入到已有的人中
if this_id == self.same_pic_id:
need_add = False
elif this_id == self.must_be_same_id:
need_add = False
need_save = True
this_person_pic_folder = os.path.join(
self.all_pic_data_folder, this_label + self.must_same_str)
this_person_feature_folder = os.path.join(
self.all_pic_feature_data_folder,
this_label + self.must_same_str)
elif this_id == self.must_be_not_same_id:
this_label = self.new_person_str + str(
self.current_new_person_id)
self.current_new_person_id += 1
this_person_pic_folder = os.path.join(self.all_pic_data_folder,
this_label)
this_person_feature_folder = os.path.join(
self.all_pic_feature_data_folder, this_label)
need_add = True
need_save = True
elif this_id == self.maybe_same_id:
this_person_pic_folder = os.path.join(
self.all_pic_data_folder, this_label + self.maybe_same_str)
this_person_feature_folder = os.path.join(
self.all_pic_feature_data_folder,
this_label + self.maybe_same_str)
need_add = False # prob在灰度区域的不如入,其余情况加入
need_save = True
else:
log_file.write('\t'.join(map(str, ['error para :', this_id])) +
'\n')
if need_save:
try:
if not os.path.exists(this_person_pic_folder):
os.makedirs(this_person_pic_folder)
if not os.path.exists(this_person_feature_folder):
os.makedirs(this_person_feature_folder)
# 直接存储图片对应的特征, 同时保存图片文件
this_pic_feature_name = os.path.join(
this_person_feature_folder, image_id + '.p')
msgpack_numpy.dump(im_feature,
open(this_pic_feature_name, 'wb'))
this_pic_face_name = os.path.join(this_person_pic_folder,
image_id + '.jpg')
cv2.imwrite(this_pic_face_name, face_pic)
except:
traceback.print_exc()
return self.unknown, 1.0, feature_str, False
# 加入LSH Forest --- partial_fit
if need_add:
self.add_one_pic(im_feature, this_label)
# 根据label和image_id可以存生成文件名,确定是否要存储文件[可以选择在服务器和本地同时存储]
if this_id == self.same_pic_id or this_id == self.must_be_not_same_id or this_id == self.must_be_same_id:
end = time.time()
log_file.write('\t'.join(
map(str, [
'stat recognize_time :', (end - start), 'this_id :',
self.trans_dic.get(this_id)
])) + '\n')
log_file.close()
need_save = True
return this_label.replace(self.must_same_str, ''), str(
dist_label_list[0][0]), str(feature_str), str(need_save)
else:
# 灰度区域,不显示人名
end = time.time()
log_file.write(
'\t'.join(map(str, ['stat gray_area :', (end - start)])) +
'\n')
log_file.close()
return self.unknown, str(
dist_label_list[0][0]), str(feature_str), str(False)
except:
traceback.print_exc()
log_file.close()
return self.unknown, str(100.0), str(feature_str), str(False)
