@@ -1,60 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# coding:utf-8 | |||||
import threading | |||||
import taskexecutor | |||||
import time | |||||
import common.RedisUtil as f | |||||
import common.config as config | |||||
import annotation as annotation | |||||
import luascript.starttaskscript as start_script | |||||
import logging | |||||
import common.select_gpu as gpu | |||||
logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', | |||||
level=logging.DEBUG) | |||||
if __name__ == '__main__': | |||||
"""Automatic annotation algorithm entry.""" | |||||
gpu.select_gpu() | |||||
jsonData = config.loadJsonData(config.configPath) | |||||
redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) | |||||
logging.info('init redis client %s', redisClient) | |||||
t = threading.Thread(target=taskexecutor.delayKeyThread, args=(redisClient,)) | |||||
t.setDaemon(True) | |||||
t.start() | |||||
annotation._init() | |||||
while 1: | |||||
try: | |||||
if config.loadJsonData(config.sign) == 0: | |||||
logging.info('not to execute new task') | |||||
time.sleep(1) | |||||
else: | |||||
logging.info('get one task') | |||||
element = redisClient.eval(start_script.startTaskLua, 1, config.queue, | |||||
config.annotationStartQueue, int(time.time())) | |||||
if len(element) > 0: | |||||
taskexecutor.annotationExecutor(redisClient, element[0]); | |||||
else: | |||||
logging.info('task queue is empty.') | |||||
time.sleep(1) | |||||
except Exception as e: | |||||
logging.error('except:', e) | |||||
time.sleep(1) |
@@ -1,67 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# coding:utf-8 | |||||
import json | |||||
import threading | |||||
import time | |||||
import imagenet as imagenet | |||||
import common.RedisUtil as f | |||||
import common.config as config | |||||
import luascript.starttaskscript as start_script | |||||
import logging | |||||
import common.select_gpu as gpu | |||||
logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', | |||||
level=logging.DEBUG) | |||||
if __name__ == '__main__': | |||||
"""Imagenet algorithm entry.""" | |||||
gpu.select_gpu() | |||||
jsonData = config.loadJsonData(config.configPath) | |||||
redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) | |||||
logging.info('init redis client %s', redisClient) | |||||
t = threading.Thread(target=imagenet.delayKeyThread, args=(redisClient,)) | |||||
t.setDaemon(True) | |||||
t.start() | |||||
imagenet._init() | |||||
while 1: | |||||
try: | |||||
if config.loadJsonData(config.sign) == 0: | |||||
logging.info('not to execute new task') | |||||
time.sleep(1) | |||||
else: | |||||
logging.info('get one task') | |||||
element = redisClient.eval(start_script.startTaskLua, 1, config.imagenetTaskQueue, | |||||
config.imagenetStartQueue, int(time.time())) | |||||
if len(element) > 0: | |||||
key = element[0].decode() | |||||
jsonStr = f.getByKey(redisClient, key.replace('"', '')); | |||||
result = imagenet.process(jsonStr, element[0]) | |||||
logging.info("result:", json.dumps(result)) | |||||
logging.info('save result to redis') | |||||
f.pushToQueue(redisClient, config.imagenetFinishQueue, json.dumps(result)) | |||||
redisClient.zrem(config.imagenetStartQueue, element[0]) | |||||
else: | |||||
logging.info('task queue is empty.') | |||||
time.sleep(2) | |||||
except Exception as e: | |||||
logging.error('except:', e) | |||||
time.sleep(1) |
@@ -1,54 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
import threading | |||||
import time | |||||
import common.RedisUtil as f | |||||
import luascript.starttaskscript as start_script | |||||
import common.config as config | |||||
import logging | |||||
import imgprocess | |||||
logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', | |||||
level=logging.DEBUG) | |||||
if __name__ == '__main__': | |||||
"""Enhancement algorithm entry.""" | |||||
jsonData = config.loadJsonData(config.configPath) | |||||
redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) | |||||
logging.info('init redis client %s', redisClient) | |||||
t = threading.Thread(target=imgprocess.delayKeyThread, args=(redisClient,)) | |||||
t.setDaemon(True) | |||||
t.start() | |||||
while 1: | |||||
try: | |||||
if config.loadJsonData(config.sign) == 0: | |||||
logging.info('not to execute new task') | |||||
time.sleep(5) | |||||
else: | |||||
enhanceTaskId = redisClient.eval(start_script.startTaskLua, 1, config.imgProcessTaskQueue, | |||||
config.imgProcessStartQueue, int(time.time())) | |||||
if len(enhanceTaskId) > 0: | |||||
imgprocess.start_enhance_task(enhanceTaskId, redisClient) | |||||
else: | |||||
logging.info('task queue is empty.') | |||||
time.sleep(5) | |||||
except Exception as e: | |||||
logging.error('except:', e) | |||||
time.sleep(1) |
@@ -1,77 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# coding:utf-8 | |||||
import os | |||||
import json | |||||
import threading | |||||
import time | |||||
import common.RedisUtil as f | |||||
import common.config as config | |||||
import luascript.starttaskscript as start_script | |||||
import logging | |||||
import traceback | |||||
import ofrecord | |||||
logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s',level=logging.DEBUG) | |||||
basePath = '/nfs/' | |||||
descPath = 'ofrecord/train' | |||||
if __name__ == '__main__': | |||||
"""Ofrecord algorithm entry.""" | |||||
jsonData = config.loadJsonData(config.configPath) | |||||
redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) | |||||
logging.info('init redis client %s', redisClient) | |||||
t = threading.Thread(target=ofrecord.delayKeyThread, args=(redisClient,)) | |||||
t.setDaemon(True) | |||||
t.start() | |||||
while 1: | |||||
try: | |||||
if config.loadJsonData(config.sign) == 0: | |||||
logging.info('not to execute new task') | |||||
time.sleep(1) | |||||
else: | |||||
element = redisClient.eval(start_script.startTaskLua, 1, config.ofrecordTaskQueue, | |||||
config.ofrecordStartQueue, int(time.time())) | |||||
if len(element) > 0: | |||||
key = element[0].decode() | |||||
detail = f.getByKey(redisClient, key.replace('"', '')) | |||||
jsonStr = json.loads(detail.decode()) | |||||
label_map = {} | |||||
index = 0 | |||||
for item in jsonStr["datasetLabels"].keys(): | |||||
if index >= 0 and item != '@type': | |||||
label_map[item] = jsonStr["datasetLabels"][item] | |||||
index += 1 | |||||
ofrecord.execute(os.path.join(basePath, jsonStr["datasetPath"]), | |||||
os.path.join(basePath, jsonStr["datasetPath"], descPath), | |||||
label_map, | |||||
jsonStr["files"], | |||||
jsonStr["partNum"], | |||||
element[0]) | |||||
logging.info('save result to redis') | |||||
f.pushToQueue(redisClient, config.ofrecordFinishQueue, key) | |||||
redisClient.zrem(config.ofrecordStartQueue, element[0]) | |||||
else: | |||||
logging.info('task queue is empty.') | |||||
time.sleep(2) | |||||
except Exception as e: | |||||
logging.error('except:', e) | |||||
redisClient.zrem(config.ofrecordStartQueue, element[0]) | |||||
time.sleep(1) |
@@ -1,60 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# coding:utf-8 | |||||
import threading | |||||
import time | |||||
import common.RedisUtil as f | |||||
import common.config as config | |||||
import luascript.starttaskscript as start_script | |||||
import logging | |||||
import track | |||||
logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', | |||||
level=logging.DEBUG) | |||||
if __name__ == '__main__': | |||||
"""Track algorithm entry.""" | |||||
jsonData = config.loadJsonData(config.configPath) | |||||
redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) | |||||
logging.info('init redis client %s', redisClient) | |||||
t = threading.Thread(target=track.delayKeyThread, args=(redisClient,)) | |||||
t.setDaemon(True) | |||||
t.start() | |||||
while 1: | |||||
try: | |||||
if config.loadJsonData(config.sign) == 0: | |||||
logging.info('not to execute new task') | |||||
time.sleep(1) | |||||
else: | |||||
logging.info('get one task') | |||||
element = redisClient.eval(start_script.startTaskLua, 1, config.trackTaskQueue, | |||||
config.trackStartQueue, int(time.time())) | |||||
if len(element) > 0: | |||||
key = element[0].decode() | |||||
jsonStr = f.getByKey(redisClient, key.replace('"', '')); | |||||
if track.trackProcess(jsonStr, element[0]): | |||||
f.pushToQueue(redisClient, config.trackFinishQueue, key) | |||||
redisClient.zrem(config.trackStartQueue, element[0]) | |||||
logging.info('success') | |||||
else: | |||||
logging.info('task queue is empty.') | |||||
time.sleep(1) | |||||
except Exception as e: | |||||
logging.error('except:', e) | |||||
time.sleep(1) |
@@ -1,62 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
import json | |||||
import threading | |||||
from datetime import datetime | |||||
import time | |||||
import common.RedisUtil as f | |||||
import luascript.starttaskscript as start_script | |||||
import common.config as config | |||||
import logging | |||||
import videosample | |||||
logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', | |||||
level=logging.DEBUG) | |||||
if __name__ == '__main__': | |||||
"""VideoSample algorithm entry.""" | |||||
jsonData = config.loadJsonData(config.configPath) | |||||
redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) | |||||
logging.info('init redis client %s', redisClient) | |||||
t = threading.Thread(target=videosample.delayKeyThread, args=(redisClient,)) | |||||
t.setDaemon(True) | |||||
t.start() | |||||
while 1: | |||||
try: | |||||
if config.loadJsonData(config.sign) == 0: | |||||
logging.info('not to execute new task') | |||||
time.sleep(5) | |||||
else: | |||||
logging.info("read redis:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) | |||||
sampleTask = redisClient.eval(start_script.startTaskLua, 1, config.videoPendingQueue, | |||||
config.videoStartQueue, int(time.time())) | |||||
logging.info(int(time.time())) | |||||
if len(sampleTask) > 0: | |||||
datasetId = json.loads(sampleTask[0])['datasetIdKey'] | |||||
taskParameters = json.loads(redisClient.get("videoSample:" + str(datasetId))) | |||||
path = taskParameters['path'] | |||||
frameList = taskParameters['frames'] | |||||
videosample.sampleProcess(datasetId, path, frameList, redisClient) | |||||
else: | |||||
logging.info('task queue is empty.') | |||||
time.sleep(5) | |||||
except Exception as e: | |||||
logging.error('except:', e) | |||||
time.sleep(1) |
@@ -1,45 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# coding:utf-8 | |||||
import time | |||||
import sys | |||||
sys.path.append(r"./common") | |||||
import predict_with_print_box as yolo_demo | |||||
from common.log_config import setup_log | |||||
label_log = setup_log('dev', 'label.log') | |||||
def _init(): | |||||
print('init yolo_obj') | |||||
global yolo_obj | |||||
yolo_obj = yolo_demo.YoloInference(label_log) | |||||
def _annotation(type_, image_path_list, id_list, label_list, coco_flag=0): | |||||
"""Perform automatic annotation task.""" | |||||
image_num = len(image_path_list) | |||||
if image_num < 16: | |||||
for i in range(16 - image_num): | |||||
image_path_list.append(image_path_list[0]) | |||||
id_list.append(id_list[0]) | |||||
image_num = len(image_path_list) | |||||
annotations = yolo_obj.yolo_inference(type_, id_list, image_path_list, label_list, coco_flag) | |||||
return annotations[0:image_num] |
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -25,6 +25,11 @@ port = 6379 | |||||
db = 0 | db = 0 | ||||
password = '' | password = '' | ||||
# text_classification | |||||
textClassificationQueue = 'text_classification_task_queue' | |||||
textClassificationStartQueue = 'text_classification_processing_queue' | |||||
textClassificationFinishQueue = 'text_classification_finished_queue' | |||||
# annotation | # annotation | ||||
queue = 'annotation_task_queue' | queue = 'annotation_task_queue' | ||||
annotationStartQueue = 'annotation_processing_queue' | annotationStartQueue = 'annotation_processing_queue' | ||||
@@ -44,6 +49,7 @@ ofrecordFinishQueue = 'ofrecord_finished_queue' | |||||
trackTaskQueue = 'track_task_queue' | trackTaskQueue = 'track_task_queue' | ||||
trackStartQueue = 'track_processing_queue' | trackStartQueue = 'track_processing_queue' | ||||
trackFinishQueue = 'track_finished_queue' | trackFinishQueue = 'track_finished_queue' | ||||
trackFailedQueue = 'track_failed_queue' | |||||
# videosample | # videosample | ||||
videoPendingQueue = "videoSample_unprocessed" | videoPendingQueue = "videoSample_unprocessed" | ||||
@@ -51,6 +57,11 @@ videoStartQueue = "videoSample_processing" | |||||
videoFinishQueue = "videoSample_finished" | videoFinishQueue = "videoSample_finished" | ||||
videoFailedQueue = "videoSample_failed" | videoFailedQueue = "videoSample_failed" | ||||
# lungsegmentation | |||||
dcmTaskQueue = "dcm_task_queue" | |||||
dcmStartQueue = "dcm_processing_queue" | |||||
dcmFinishQueue = "dcm_finished_queue" | |||||
# imgprocess | # imgprocess | ||||
imgProcessTaskQueue = 'imgProcess_unprocessed' | imgProcessTaskQueue = 'imgProcess_unprocessed' | ||||
imgProcessFinishQueue = 'imgProcess_finished' | imgProcessFinishQueue = 'imgProcess_finished' | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -34,7 +34,7 @@ sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) | |||||
def init_resnet(): | def init_resnet(): | ||||
"""Initialize ResNet with pretrained weights""" | """Initialize ResNet with pretrained weights""" | ||||
model_load_dir = 'of_model/resnet_v15_of_best_model_val_top1_773/' | |||||
model_load_dir = '../of_model/resnet_v15_of_best_model_val_top1_773/' | |||||
assert os.path.isdir(model_load_dir) | assert os.path.isdir(model_load_dir) | ||||
check_point = flow.train.CheckPoint() | check_point = flow.train.CheckPoint() | ||||
check_point.load(model_load_dir) | check_point.load(model_load_dir) | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -17,6 +17,7 @@ | |||||
*/ | */ | ||||
""" | """ | ||||
import os | import os | ||||
import random | |||||
import sys | import sys | ||||
import pynvml | import pynvml | ||||
import logging | import logging | ||||
@@ -27,6 +28,7 @@ pynvml.nvmlInit() | |||||
def select_gpu(): | def select_gpu(): | ||||
deviceCount = pynvml.nvmlDeviceGetCount() | deviceCount = pynvml.nvmlDeviceGetCount() | ||||
gpu_usable = [] | |||||
for i in range(deviceCount): | for i in range(deviceCount): | ||||
logging.info('-------------get GPU information--------------') | logging.info('-------------get GPU information--------------') | ||||
handle = pynvml.nvmlDeviceGetHandleByIndex(i) | handle = pynvml.nvmlDeviceGetHandleByIndex(i) | ||||
@@ -34,8 +36,12 @@ def select_gpu(): | |||||
gpu_info = pynvml.nvmlDeviceGetMemoryInfo(handle) | gpu_info = pynvml.nvmlDeviceGetMemoryInfo(handle) | ||||
logging.info('free:%s MB', gpu_info.free / (1000 * 1000)) | logging.info('free:%s MB', gpu_info.free / (1000 * 1000)) | ||||
if gpu_info.free / (1000 * 1000) > 3072: | if gpu_info.free / (1000 * 1000) > 3072: | ||||
os.environ["CUDA_VISIBLE_DEVICES"] = str(i) | |||||
logging.info('use GPU:%s %s', i, pynvml.nvmlDeviceGetName(handle)) | |||||
return | |||||
logging.info('No GPU is currently available') | |||||
sys.exit() | |||||
gpu_usable.append(i) | |||||
gpu_usable_num = len(gpu_usable) | |||||
if gpu_usable_num == 0: | |||||
logging.info('No GPU is currently available') | |||||
sys.exit() | |||||
random_gpu = random.randint(0, gpu_usable_num - 1) | |||||
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_usable[random_gpu]) | |||||
logging.info('use GPU:%s %s', gpu_usable[random_gpu], pynvml.nvmlDeviceGetName(handle)) | |||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,80 +0,0 @@ | |||||
person | |||||
bicycle | |||||
car | |||||
motorbike | |||||
aeroplane | |||||
bus | |||||
train | |||||
truck | |||||
boat | |||||
traffic light | |||||
fire hydrant | |||||
stop sign | |||||
parking meter | |||||
bench | |||||
bird | |||||
cat | |||||
dog | |||||
horse | |||||
sheep | |||||
cow | |||||
elephant | |||||
bear | |||||
zebra | |||||
giraffe | |||||
backpack | |||||
umbrella | |||||
handbag | |||||
tie | |||||
suitcase | |||||
frisbee | |||||
skis | |||||
snowboard | |||||
sports ball | |||||
kite | |||||
baseball bat | |||||
baseball glove | |||||
skateboard | |||||
surfboard | |||||
tennis racket | |||||
bottle | |||||
wine glass | |||||
cup | |||||
fork | |||||
knife | |||||
spoon | |||||
bowl | |||||
banana | |||||
apple | |||||
sandwich | |||||
orange | |||||
broccoli | |||||
carrot | |||||
hot dog | |||||
pizza | |||||
donut | |||||
cake | |||||
chair | |||||
sofa | |||||
pottedplant | |||||
bed | |||||
diningtable | |||||
toilet | |||||
tvmonitor | |||||
laptop | |||||
mouse | |||||
remote | |||||
keyboard | |||||
cell phone | |||||
microwave | |||||
oven | |||||
toaster | |||||
sink | |||||
refrigerator | |||||
book | |||||
clock | |||||
vase | |||||
scissors | |||||
teddy bear | |||||
hair drier | |||||
toothbrush |
@@ -1,93 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
#!/usr/bin/env python3 | |||||
# -*- coding: utf-8 -*- | |||||
import sched | |||||
import sys | |||||
sys.path.append(r"./common") | |||||
import logging | |||||
import time | |||||
import json | |||||
import common.of_cnn_resnet as of_cnn_resnet | |||||
import numpy as np | |||||
import luascript.delaytaskscript as delay_script | |||||
import common.config as config | |||||
from datetime import datetime | |||||
schedule = sched.scheduler(time.time, time.sleep) | |||||
base_path = "/nfs/" | |||||
delayId = "" | |||||
def _init(): | |||||
of_cnn_resnet.init_resnet() | |||||
logging.info('env init finished') | |||||
def process(task_dict, key): | |||||
"""Imagenet task method. | |||||
Args: | |||||
task_dict: imagenet task details. | |||||
key: imagenet task key. | |||||
""" | |||||
global delayId | |||||
delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" | |||||
task_dict = json.loads(task_dict) | |||||
id_list = [] | |||||
image_path_list = [] | |||||
for file in task_dict["files"]: | |||||
id_list.append(file["id"]) | |||||
image_path_list.append(base_path + file["url"]) | |||||
label_list = task_dict["labels"] | |||||
image_num = len(image_path_list) | |||||
annotations = [] | |||||
for inds in range(len(image_path_list)): | |||||
temp = {} | |||||
temp['id'] = id_list[inds] | |||||
score, ca_id = of_cnn_resnet.resnet_inf(image_path_list[inds]) | |||||
temp['annotation'] = [{'category_id': int(ca_id), 'score': np.float(score)}] | |||||
temp['annotation'] = json.dumps(temp['annotation']) | |||||
annotations.append(temp) | |||||
result = {"annotations": annotations, "task": key.decode()} | |||||
return result | |||||
def delaySchduled(inc, redisClient): | |||||
"""Delay task method. | |||||
Args: | |||||
inc: scheduled task time. | |||||
redisClient: redis client. | |||||
""" | |||||
try: | |||||
print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) | |||||
redisClient.eval(delay_script.delayTaskLua, 1, config.imagenetStartQueue, delayId, int(time.time())) | |||||
schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) | |||||
except Exception as e: | |||||
print("delay error" + e) | |||||
def delayKeyThread(redisClient): | |||||
"""Delay task thread. | |||||
Args: | |||||
redisClient: redis client. | |||||
""" | |||||
schedule.enter(0, 0, delaySchduled, (5, redisClient)) | |||||
schedule.run() |
@@ -1,189 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# !/usr/bin/env python3 | |||||
# -*- coding: utf-8 -*- | |||||
from datetime import datetime | |||||
import sched | |||||
import os | |||||
import cv2 | |||||
import numpy as np | |||||
import logging | |||||
import time | |||||
import json | |||||
import argparse | |||||
import sys | |||||
import codecs | |||||
import shutil | |||||
import luascript.delaytaskscript as delay_script | |||||
import common.config as config | |||||
from common.augment_utils.ACE import ACE_color | |||||
from common.augment_utils.dehaze import deHaze, addHaze | |||||
from common.augment_utils.hist_equalize import adaptive_hist_equalize | |||||
from common.log_config import setup_log | |||||
schedule = sched.scheduler(time.time, time.sleep) | |||||
delayId = "" | |||||
finish_key = {} | |||||
re_task_id = {} | |||||
sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) | |||||
# task url suffix | |||||
img_pro_url = 'api/data/datasets/' | |||||
# arguments | |||||
parser = argparse.ArgumentParser(description="config for image augmentation server") | |||||
parser.add_argument("-m", "--mode", type=str, default="test", required=False) | |||||
args = parser.parse_args() | |||||
# url concat(ip + port + suffix) | |||||
url_json = './common/config/url.json' | |||||
with open(url_json) as f: | |||||
url_dict = json.loads(f.read()) | |||||
img_pro_url = url_dict[args.mode] + img_pro_url | |||||
# creat task quene | |||||
base_path = "/nfs/" | |||||
# create log path and file | |||||
des_folder = os.path.join('./log', args.mode) | |||||
if not os.path.exists(des_folder): | |||||
os.makedirs(des_folder) | |||||
logging = setup_log(args.mode, 'enhance-' + args.mode + '.log') | |||||
enhanceTaskId = "" | |||||
def start_enhance_task(enhanceTaskId, redisClient): | |||||
"""Enhance task method. | |||||
Args: | |||||
enhanceTaskId: enhance task id. | |||||
redisClient: redis client. | |||||
""" | |||||
global delayId | |||||
detailKey = 'imgProcess:' + eval(str(enhanceTaskId[0], encoding="utf-8")) | |||||
delayId = "\"" + eval(str(enhanceTaskId[0], encoding="utf-8")) + "\"" | |||||
print(detailKey) | |||||
taskParameters = json.loads(redisClient.get(detailKey).decode()) | |||||
dataset_id = taskParameters['id'] | |||||
img_save_path = taskParameters['enhanceFilePath'] | |||||
ann_save_path = taskParameters["enhanceAnnotationPath"] | |||||
file_list = taskParameters['fileDtos'] | |||||
nums_, img_path_list, ann_path_list = img_ann_list_gen(file_list) | |||||
process_type = taskParameters['type'] | |||||
re_task_id = eval(str(enhanceTaskId[0], encoding="utf-8")) | |||||
img_process_config = [dataset_id, img_save_path, | |||||
ann_save_path, img_path_list, | |||||
ann_path_list, process_type, re_task_id] | |||||
image_enhance_process(img_process_config, redisClient) | |||||
logging.info(str(nums_) + ' images for augment') | |||||
def img_ann_list_gen(file_list): | |||||
"""Analyze the json request and convert to list""" | |||||
nums_ = len(file_list) | |||||
img_list = [] | |||||
ann_list = [] | |||||
for i in range(nums_): | |||||
img_list.append(file_list[i]['filePath']) | |||||
ann_list.append(file_list[i]['annotationPath']) | |||||
return nums_, img_list, ann_list | |||||
def image_enhance_process(img_task, redisClient): | |||||
"""The implementation of image augmentation thread""" | |||||
global img_pro_url | |||||
global finish_key | |||||
global re_task_id | |||||
logging.info('img_process server start'.center(66, '-')) | |||||
logging.info(img_pro_url) | |||||
try: | |||||
dataset_id = img_task[0] | |||||
img_save_path = img_task[1] | |||||
ann_save_path = img_task[2] | |||||
img_list = img_task[3] | |||||
ann_list = img_task[4] | |||||
method = img_task[5] | |||||
re_task_id = img_task[6] | |||||
suffix = '_enchanced_' + re_task_id | |||||
logging.info("dataset_id " + str(dataset_id)) | |||||
finish_key = {"processKey": re_task_id} | |||||
finish_data = {"id": re_task_id, | |||||
"suffix": suffix} | |||||
for j in range(len(ann_list)): | |||||
img_path = img_list[j] | |||||
ann_path = ann_list[j] | |||||
img_process(suffix, img_path, ann_path, | |||||
img_save_path, ann_save_path, method) | |||||
redisClient.lpush(config.imgProcessFinishQueue, json.dumps(finish_key, separators=(',', ':'))) | |||||
redisClient.set("imgProcess:finished:" + re_task_id, json.dumps(finish_data)) | |||||
redisClient.zrem(config.imgProcessStartQueue, "\"" + re_task_id + "\"") | |||||
logging.info('suffix:' + suffix) | |||||
logging.info("End img_process of dataset:" + str(dataset_id)) | |||||
except Exception as e: | |||||
redisClient.lpush(config.imgProcessFailedQueue, json.dumps(finish_key, separators=(',', ':'))) | |||||
redisClient.zrem(config.imgProcessStartQueue, "\"" + re_task_id + "\"") | |||||
logging.info(img_pro_url) | |||||
logging.error("Error imgProcess") | |||||
logging.error(e) | |||||
time.sleep(0.01) | |||||
def img_process(suffix, img_path, ann_path, img_save_path, ann_save_path, method_ind): | |||||
"""Process images and save in specified path""" | |||||
inds2method = {1: deHaze, 2: addHaze, 3: ACE_color, 4: adaptive_hist_equalize} | |||||
method = inds2method[method_ind] | |||||
img_raw = cv2.imdecode(np.fromfile(img_path.encode('utf-8'), dtype=np.uint8), 1) | |||||
img_suffix = os.path.splitext(img_path)[-1] | |||||
ann_name = ann_path.replace(ann_save_path, '') | |||||
if method_ind <= 3: | |||||
processed_img = method(img_raw / 255.0) * 255 | |||||
else: | |||||
processed_img = method(img_raw) | |||||
cv2.imwrite(img_save_path + ann_name + suffix + img_suffix, | |||||
processed_img.astype(np.uint8)) | |||||
shutil.copyfile(ann_path.encode('utf-8'), (ann_path + suffix).encode('utf-8')) | |||||
def delaySchduled(inc, redisClient): | |||||
"""Delay task method. | |||||
Args: | |||||
inc: scheduled task time. | |||||
redisClient: redis client. | |||||
""" | |||||
try: | |||||
logging.info("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S") + ":" + delayId) | |||||
redisClient.eval(delay_script.delayTaskLua, 1, config.imgProcessStartQueue, delayId, int(time.time())) | |||||
schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) | |||||
except Exception as e: | |||||
print("delay error" + e) | |||||
def delayKeyThread(redisClient): | |||||
"""Delay task thread. | |||||
Args: | |||||
redisClient: redis client. | |||||
""" | |||||
schedule.enter(0, 0, delaySchduled, (5, redisClient)) | |||||
schedule.run() |
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,181 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# -*- coding: utf-8 -*- | |||||
import logging | |||||
import json | |||||
import os | |||||
import struct | |||||
import cv2 | |||||
import sched | |||||
import numpy as np | |||||
import oneflow.core.record.record_pb2 as of_record | |||||
import luascript.delaytaskscript as delay_script | |||||
import time | |||||
import common.config as config | |||||
from datetime import datetime | |||||
schedule = sched.scheduler(time.time, time.sleep) | |||||
delayId = "" | |||||
class ImageCoder(object): | |||||
"""Helper class that provides image coding utilities.""" | |||||
def __init__(self, size=None): | |||||
self.size = size | |||||
def _resize(self, image_data): | |||||
if self.size is not None and image_data.shape[:2] != self.size: | |||||
return cv2.resize(image_data, self.size) | |||||
return image_data | |||||
def image_to_jpeg(self, image_data): | |||||
image_data = cv2.imdecode(np.fromstring(image_data, np.uint8), 1) | |||||
image_data = self._resize(image_data) | |||||
return cv2.imencode(".jpg", image_data)[1].tostring( | |||||
), image_data.shape[0], image_data.shape[1] | |||||
def _process_image(filename, coder): | |||||
"""Process a single image file. | |||||
Args: | |||||
filename: string, path to an image file e.g., '/path/to/example.JPG'. | |||||
coder: instance of ImageCoder to provide image coding utils. | |||||
Returns: | |||||
image_buffer: string, JPEG encoding of RGB image. | |||||
height: integer, image height in pixels. | |||||
width: integer, image width in pixels. | |||||
""" | |||||
# Read the image file. | |||||
with open(filename, 'rb') as f: | |||||
image_data = f.read() | |||||
image_data, height, width = coder.image_to_jpeg(image_data) | |||||
return image_data, height, width | |||||
def _bytes_feature(value): | |||||
"""Wrapper for inserting bytes features into Example proto.""" | |||||
return of_record.Feature(bytes_list=of_record.BytesList(value=[value])) | |||||
def dense_to_one_hot(labels_dense, num_classes): | |||||
"""Convert class labels from scalars to one-hot vectors.""" | |||||
num_labels = labels_dense.shape[0] | |||||
index_offset = np.arange(num_labels) * num_classes | |||||
labels_one_hot = np.zeros((num_labels, num_classes)) | |||||
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 | |||||
return labels_one_hot | |||||
def extract_img_label(names, path): | |||||
"""Extract the images and labels into np array [index]. | |||||
Args: | |||||
f: A file object that contain images and annotations. | |||||
Returns: | |||||
data: A 4D uint8 np array [index, h, w, depth]. | |||||
labels: a 1D uint8 np array. | |||||
num_img: the number of images. | |||||
""" | |||||
train_img = os.path.join(path, 'origin/') | |||||
train_label = os.path.join(path, 'annotation/') | |||||
num_imgs = len(names) | |||||
data = [] | |||||
labels = [] | |||||
print('^^^^^^^^^^ start img_set for sycle') | |||||
for i in names: | |||||
name = os.path.splitext(i)[0] | |||||
print(name) | |||||
coder = ImageCoder((224, 224)) | |||||
image_buffer, height, width = _process_image( | |||||
os.path.join(train_img, i), coder) | |||||
data += [image_buffer] | |||||
if os.path.exists(os.path.join(train_label, name)): | |||||
with open(os.path.join(train_label, name), "r", encoding='utf-8') as jsonFile: | |||||
la = json.load(jsonFile) | |||||
if la: | |||||
labels += [la[0]['category_id']] | |||||
else: | |||||
data.pop() | |||||
num_imgs -= 1 | |||||
else: | |||||
print('File is not found') | |||||
print('^^^^^^^^^ img_set for end') | |||||
data = np.array(data) | |||||
labels = np.array(labels) | |||||
print(data.shape, labels.shape) | |||||
return num_imgs, data, labels | |||||
def execute(src_path, desc, label_map, files, part_id, key): | |||||
"""Execute ofrecord task method.""" | |||||
global delayId | |||||
delayId = delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" | |||||
logging.info(part_id) | |||||
num_imgs, images, labels = extract_img_label(files, src_path) | |||||
keys = sorted(list(map(int, label_map.keys()))) | |||||
for i in range(len(keys)): | |||||
label_map[str(keys[i])] = i | |||||
if not num_imgs: | |||||
return False, 0, 0 | |||||
try: | |||||
os.makedirs(desc) | |||||
except Exception as e: | |||||
print('{} exists.'.format(desc)) | |||||
for i in range(num_imgs): | |||||
filename = 'part-{}'.format(part_id) | |||||
filename = os.path.join(desc, filename) | |||||
f = open(filename, 'wb') | |||||
print(filename) | |||||
img = images[i] | |||||
label = label_map[str(labels[i])] | |||||
sample = of_record.OFRecord(feature={ | |||||
'class/label': of_record.Feature(int32_list=of_record.Int32List(value=[label])), | |||||
'encoded': _bytes_feature(img) | |||||
}) | |||||
size = sample.ByteSize() | |||||
f.write(struct.pack("q", size)) | |||||
f.write(sample.SerializeToString()) | |||||
if f: | |||||
f.close() | |||||
def delaySchduled(inc, redisClient): | |||||
"""Delay task method. | |||||
Args: | |||||
inc: scheduled task time. | |||||
redisClient: redis client. | |||||
""" | |||||
try: | |||||
print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) | |||||
redisClient.eval(delay_script.delayTaskLua, 1, config.ofrecordStartQueue, delayId, int(time.time())) | |||||
schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) | |||||
except Exception as e: | |||||
print("delay error" + e) | |||||
def delayKeyThread(redisClient): | |||||
"""Delay task thread. | |||||
Args: | |||||
redisClient: redis client. | |||||
""" | |||||
schedule.enter(0, 0, delaySchduled, (5, redisClient)) | |||||
schedule.run() |
@@ -1,2 +0,0 @@ | |||||
# oneflow | |||||
application by oneflow |
@@ -1,167 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# -*- coding: utf-8 -*- | |||||
import json | |||||
import math | |||||
import os | |||||
import struct | |||||
import cv2 | |||||
import numpy as np | |||||
import oneflow.core.record.record_pb2 as of_record | |||||
class ImageCoder(object): | |||||
"""Helper class that provides image coding utilities.""" | |||||
def __init__(self, size=None): | |||||
self.size = size | |||||
def _resize(self, image_data): | |||||
if self.size is not None and image_data.shape[:2] != self.size: | |||||
return cv2.resize(image_data, self.size) | |||||
return image_data | |||||
def image_to_jpeg(self, image_data): | |||||
image_data = cv2.imdecode(np.fromstring(image_data, np.uint8), 1) | |||||
image_data = self._resize(image_data) | |||||
return cv2.imencode(".jpg", image_data)[1].tostring( | |||||
), image_data.shape[0], image_data.shape[1] | |||||
def _process_image(filename, coder): | |||||
"""Process a single image file. | |||||
Args: | |||||
filename: string, path to an image file e.g., '/path/to/example.JPG'. | |||||
coder: instance of ImageCoder to provide image coding utils. | |||||
Returns: | |||||
image_buffer: string, JPEG encoding of RGB image. | |||||
height: integer, image height in pixels. | |||||
width: integer, image width in pixels. | |||||
""" | |||||
# Read the image file. | |||||
with open(filename, 'rb') as f: | |||||
image_data = f.read() | |||||
image_data, height, width = coder.image_to_jpeg(image_data) | |||||
return image_data, height, width | |||||
def _bytes_feature(value): | |||||
"""Wrapper for inserting bytes features into Example proto.""" | |||||
return of_record.Feature(bytes_list=of_record.BytesList(value=[value])) | |||||
def dense_to_one_hot(labels_dense, num_classes): | |||||
"""Convert class labels from scalars to one-hot vectors.""" | |||||
num_labels = labels_dense.shape[0] | |||||
index_offset = np.arange(num_labels) * num_classes | |||||
labels_one_hot = np.zeros((num_labels, num_classes)) | |||||
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 | |||||
return labels_one_hot | |||||
def extract_img_label(f): | |||||
"""Extract the images and labels into np array [index]. | |||||
Args: | |||||
f: A file object that contain images and annotations. | |||||
Returns: | |||||
data: A 4D uint8 np array [index, h, w, depth]. | |||||
labels: a 1D uint8 np array. | |||||
num_img: the number of images. | |||||
""" | |||||
train_img = os.path.join(f, 'origin/') | |||||
train_label = os.path.join(f, 'annotation/') | |||||
img_set = os.listdir(train_img) | |||||
num_imgs = len(img_set) | |||||
data = [] | |||||
labels = [] | |||||
print('^^^^^^^^^^ start img_set for sycle') | |||||
for i in img_set: | |||||
name = os.path.splitext(i)[0] | |||||
coder = ImageCoder((224, 224)) | |||||
image_buffer, height, width = _process_image( | |||||
os.path.join(train_img, i), coder) | |||||
data += [image_buffer] | |||||
if os.path.exists(os.path.join(train_label, name)): | |||||
with open(os.path.join(train_label, name), "r", encoding='utf-8') as jsonFile: | |||||
la = json.load(jsonFile) | |||||
if la: | |||||
labels += [la[0]['category_id']] | |||||
else: | |||||
data.pop() | |||||
num_imgs -= 1 | |||||
else: | |||||
print('File is not found') | |||||
print('^^^^^^^^^ img_set for end') | |||||
data = np.array(data) | |||||
labels = np.array(labels) | |||||
print(data.shape, labels.shape) | |||||
return num_imgs, data, labels | |||||
def read_data_sets(src, desc, label_map, | |||||
part_num=8): | |||||
""" | |||||
Args: | |||||
src: The path where image and annotations saved. | |||||
desc: The path where OfRecord will be writen in. | |||||
part_num: The OfRecord will be writen in part_num parts. | |||||
label_map: id and its corresponding label | |||||
Returns: | |||||
Whether there is image for converting to ofRecord | |||||
num_images: The number of images. | |||||
part_num: The OfRecord will be writen in part_num parts. | |||||
""" | |||||
print('************** start read_data_sets func **********************') | |||||
num_images, images, labels = extract_img_label(src) | |||||
print('************** read_data_sets end **********************') | |||||
keys = sorted(list(map(int, label_map.keys()))) | |||||
for i in range(len(keys)): | |||||
label_map[str(keys[i])] = i | |||||
if not num_images: | |||||
return False, 0, 0 | |||||
os.makedirs(desc) | |||||
part_size = num_images / int(part_num) | |||||
part_id = -1 | |||||
print('************** start for range num_images') | |||||
for i in range(num_images): | |||||
p = math.floor(i / part_size) | |||||
if p != part_id and p < part_num: | |||||
part_id = p | |||||
filename = 'part-{}'.format(part_id) | |||||
filename = os.path.join(desc, filename) | |||||
f = open(filename, 'wb') | |||||
print(filename) | |||||
img = images[i] | |||||
label = label_map[str(labels[i])] | |||||
sample = of_record.OFRecord(feature={ | |||||
'class/label': of_record.Feature(int32_list=of_record.Int32List(value=[label])), | |||||
'encoded': _bytes_feature(img) | |||||
}) | |||||
size = sample.ByteSize() | |||||
f.write(struct.pack("q", size)) | |||||
f.write(sample.SerializeToString()) | |||||
print('********************* end for range') | |||||
if f: | |||||
f.close() | |||||
return True, num_images, part_num |
@@ -1,161 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
#!/usr/bin/env python3 | |||||
# -*- coding: utf-8 -*- | |||||
import web | |||||
import os | |||||
import string | |||||
import _thread | |||||
import logging | |||||
import urllib | |||||
from queue import Queue | |||||
import time | |||||
import random | |||||
import json | |||||
import argparse | |||||
import sys | |||||
import codecs | |||||
import of_cnn_resnet | |||||
import numpy as np | |||||
from log_config import setup_log | |||||
from upload_config import Upload_cfg, MyApplication | |||||
urls = ('/auto_annotate', 'Upload') | |||||
sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) | |||||
label_url = "api/data/datasets/files/annotations/auto/" | |||||
parser = argparse.ArgumentParser(description="config for imagenet label server") | |||||
parser.add_argument("-p", "--port", type=int, required=True) | |||||
parser.add_argument("-m", "--mode", type=str, default="test", required=False) | |||||
args = parser.parse_args() | |||||
url_json = './config/url.json' | |||||
with open(url_json) as f: | |||||
url_dict = json.loads(f.read()) | |||||
label_url = url_dict[args.mode] + label_url | |||||
port = args.port | |||||
taskQueue = Queue() | |||||
taskInImages = {} | |||||
base_path = "/nfs/" | |||||
des_folder = os.path.join('./log', args.mode) | |||||
if not os.path.exists(des_folder): | |||||
os.makedirs(des_folder) | |||||
logging = setup_log(args.mode, 'imagenet-' + args.mode + '.log') | |||||
#############################label_server##################################### | |||||
def get_code(): | |||||
return ''.join(random.sample(string.ascii_letters + string.digits, 8)) | |||||
def get_32code(): | |||||
return ''.join(random.sample(string.ascii_letters + string.digits, 32)) | |||||
class Upload(Upload_cfg): | |||||
"""Recieve and analyze the post request""" | |||||
def POST(self): | |||||
try: | |||||
super().POST() | |||||
x = web.data() | |||||
x = json.loads(x.decode()) | |||||
type_ = x['annotateType'] | |||||
if_imagenet = x['labelType'] | |||||
task_id = get_code() | |||||
task_images = {} | |||||
task_images[task_id] = { | |||||
"input": { | |||||
'type': type_, 'data': x}, "output": { | |||||
"annotations": []}, 'if_imagenet': if_imagenet} | |||||
logging.info(task_id) | |||||
web.t_queue.put(task_images) | |||||
return {"code": 200, "msg": "", "data": task_id} | |||||
except Exception as e: | |||||
logging.error("Error post") | |||||
logging.error(e) | |||||
return 'post error' | |||||
def imagenetProcess(): | |||||
"""The implementation of imageNet auto labeling thread""" | |||||
global taskQueue | |||||
global label_url | |||||
logging.info('ImageNet auto labeling server start'.center(66,'-')) | |||||
logging.info(label_url) | |||||
while True: | |||||
try: | |||||
task_dict = taskQueue.get() | |||||
for task_id in task_dict: | |||||
id_list = [] | |||||
image_path_list = [] | |||||
type_ = task_dict[task_id]["input"]['type'] | |||||
if_imagenet = task_dict[task_id]['if_imagenet'] | |||||
for file in task_dict[task_id]["input"]['data']["files"]: | |||||
id_list.append(file["id"]) | |||||
image_path_list.append(base_path + file["url"]) | |||||
label_list = task_dict[task_id]["input"]['data']["labels"] | |||||
image_num = len(image_path_list) | |||||
logging.info(image_num) | |||||
logging.info(image_path_list) | |||||
annotations = [] | |||||
if if_imagenet == 2: | |||||
for inds in range(len(image_path_list)): | |||||
temp = {} | |||||
temp['id'] = id_list[inds] | |||||
score, ca_id = of_cnn_resnet.resnet_inf( | |||||
image_path_list[inds]) | |||||
temp['annotation'] = [ | |||||
{'category_id': int(ca_id), 'score': np.float(score)}] | |||||
temp['annotation'] = json.dumps(temp['annotation']) | |||||
annotations.append(temp) | |||||
result = {"annotations": annotations} | |||||
logging.info(result) | |||||
send_data = json.dumps(result).encode() | |||||
task_url = label_url + task_id | |||||
headers = {'Content-Type': 'application/json'} | |||||
req = urllib.request.Request(task_url, headers=headers) | |||||
response = urllib.request.urlopen( | |||||
req, data=send_data, timeout=5) | |||||
logging.info(task_url) | |||||
logging.info(response.read()) | |||||
logging.info("End imagenet") | |||||
except Exception as e: | |||||
logging.error("Error imagenet_Process") | |||||
logging.error(e) | |||||
logging.info(label_url) | |||||
time.sleep(0.01) | |||||
def imagenet_thread(no, interval): | |||||
"""Running the imageNet auto labeling thread""" | |||||
imagenetProcess() | |||||
if __name__ == "__main__": | |||||
of_cnn_resnet.init_resnet() | |||||
_thread.start_new_thread(imagenet_thread, (5, 5)) | |||||
app = MyApplication(urls, globals()) | |||||
web.t_queue = taskQueue | |||||
web.taskInImages = taskInImages | |||||
app.run(port=port) |
@@ -1,191 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# !/usr/bin/env python3 | |||||
# -*- coding: utf-8 -*- | |||||
import web | |||||
import os | |||||
import string | |||||
import cv2 | |||||
import numpy as np | |||||
import _thread | |||||
import logging | |||||
import urllib | |||||
from queue import Queue | |||||
import time | |||||
import random | |||||
import json | |||||
import argparse | |||||
import sys | |||||
import codecs | |||||
import shutil | |||||
from augment_utils.ACE import ACE_color | |||||
from augment_utils.dehaze import deHaze, addHaze | |||||
from augment_utils.hist_equalize import adaptive_hist_equalize | |||||
from log_config import setup_log | |||||
from upload_config import Upload_cfg, MyApplication | |||||
urls = ('/img_process', 'Image_augmentation') | |||||
sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) | |||||
# task url suffix | |||||
img_pro_url = 'api/data/datasets/' | |||||
# arguments | |||||
parser = argparse.ArgumentParser(description="config for image augmentation server") | |||||
parser.add_argument("-p", "--port", type=int, required=True) | |||||
parser.add_argument("-m", "--mode", type=str, default="test", required=False) | |||||
args = parser.parse_args() | |||||
# url concat(ip + port + suffix) | |||||
url_json = './config/url.json' | |||||
with open(url_json) as f: | |||||
url_dict = json.loads(f.read()) | |||||
img_pro_url = url_dict[args.mode] + img_pro_url | |||||
port = args.port | |||||
# creat task quene | |||||
imageProcessQuene = Queue() | |||||
base_path = "/nfs/" | |||||
# create log path and file | |||||
des_folder = os.path.join('./log', args.mode) | |||||
if not os.path.exists(des_folder): | |||||
os.makedirs(des_folder) | |||||
logging = setup_log(args.mode, 'enhance-' + args.mode + '.log') | |||||
class Image_augmentation(Upload_cfg): | |||||
"""Recieve and analyze the post request""" | |||||
def POST(self): | |||||
try: | |||||
super().POST() | |||||
x = web.data() | |||||
x = json.loads(x.decode()) | |||||
dataset_id = x['id'] | |||||
img_save_path = x['enhanceFilePath'] | |||||
ann_save_path = x["enhanceAnnotationPath"] | |||||
file_list = x['fileDtos'] | |||||
nums_, img_path_list, ann_path_list = img_ann_list_gen(file_list) | |||||
process_type = x['type'] | |||||
re_task_id = ''.join(random.sample(string.ascii_letters + string.digits, 8)) | |||||
img_process_config = [dataset_id, img_save_path, | |||||
ann_save_path, img_path_list, | |||||
ann_path_list, process_type, re_task_id] | |||||
web.t_queue2.put(img_process_config) | |||||
logging.info(str(nums_) + ' images for augment') | |||||
return {"code": 200, "msg": "", "data": re_task_id} | |||||
except Exception as e: | |||||
print(e) | |||||
print("Error Post") | |||||
logging.error("Error post") | |||||
logging.error(e) | |||||
return 'post error' | |||||
def image_process_thread(): | |||||
"""The implementation of image augmentation thread""" | |||||
global img_pro_url | |||||
global imageProcessQuene | |||||
logging.info('img_process server start'.center(66, '-')) | |||||
logging.info(img_pro_url) | |||||
task_cond = [] | |||||
while True: | |||||
try: | |||||
img_task = imageProcessQuene.get() | |||||
if img_task and img_task[0] not in task_cond: | |||||
index = len(task_cond) | |||||
task_cond.append(img_task[0]) | |||||
dataset_id = img_task[0] | |||||
img_save_path = img_task[1] | |||||
ann_save_path = img_task[2] | |||||
img_list = img_task[3] | |||||
ann_list = img_task[4] | |||||
method = img_task[5] | |||||
re_task_id = img_task[6] | |||||
suffix = '_enchanced_' + re_task_id | |||||
logging.info("dataset_id " + str(dataset_id)) | |||||
for j in range(len(ann_list)): | |||||
img_path = img_list[j] | |||||
ann_path = ann_list[j] | |||||
img_process(suffix, img_path, ann_path, | |||||
img_save_path, ann_save_path, method) | |||||
task_url = img_pro_url + 'enhance/finish' | |||||
send_data = {"id": re_task_id, | |||||
"suffix": suffix} | |||||
headers = {'Content-Type': 'application/json'} | |||||
req = urllib.request.Request(task_url, | |||||
data=json.dumps(send_data).encode(), | |||||
headers=headers) | |||||
response = urllib.request.urlopen(req, timeout=5) | |||||
logging.info('suffix:' + suffix) | |||||
logging.info(task_url) | |||||
logging.info(response.read()) | |||||
logging.info("End img_process of dataset:" + str(dataset_id)) | |||||
task_cond.pop(index) | |||||
else: | |||||
continue | |||||
except Exception as e: | |||||
logging.info(img_pro_url) | |||||
logging.error("Error imgProcess") | |||||
logging.error(e) | |||||
time.sleep(0.01) | |||||
def img_ann_list_gen(file_list): | |||||
"""Analyze the json request and convert to list""" | |||||
nums_ = len(file_list) | |||||
img_list = [] | |||||
ann_list = [] | |||||
for i in range(nums_): | |||||
img_list.append(file_list[i]['filePath']) | |||||
ann_list.append(file_list[i]['annotationPath']) | |||||
return nums_, img_list, ann_list | |||||
def img_process(suffix, img_path, ann_path, img_save_path, ann_save_path, method_ind): | |||||
"""Process images and save in specified path""" | |||||
inds2method = {1: deHaze, 2: addHaze, 3: ACE_color, 4: adaptive_hist_equalize} | |||||
method = inds2method[method_ind] | |||||
img_raw = cv2.imdecode(np.fromfile(img_path.encode('utf-8'), dtype=np.uint8), 1) | |||||
img_suffix = os.path.splitext(img_path)[-1] | |||||
ann_name = ann_path.replace(ann_save_path, '') | |||||
if method_ind <= 3: | |||||
processed_img = method(img_raw / 255.0) * 255 | |||||
else: | |||||
processed_img = method(img_raw) | |||||
cv2.imwrite(img_save_path + ann_name + suffix + img_suffix, | |||||
processed_img.astype(np.uint8)) | |||||
shutil.copyfile(ann_path.encode('utf-8'), (ann_path + suffix).encode('utf-8')) | |||||
def img_process_thread(no, interval): | |||||
"""Running the image augmentation thread""" | |||||
image_process_thread() | |||||
if __name__ == "__main__": | |||||
_thread.start_new_thread(img_process_thread, (5, 5)) | |||||
app = MyApplication(urls, globals()) | |||||
web.t_queue2 = imageProcessQuene | |||||
app.run(port=port) |
@@ -1,180 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# !/usr/bin/env python3 | |||||
# -*- coding: utf-8 -*- | |||||
import _thread | |||||
import argparse | |||||
import codecs | |||||
import json | |||||
import os | |||||
import shutil | |||||
import sys | |||||
import time | |||||
import urllib | |||||
from queue import Queue | |||||
import web | |||||
from upload_config import Upload_cfg, MyApplication | |||||
import gen_ofrecord as ofrecord | |||||
from log_config import setup_log | |||||
urls = ('/gen_ofrecord', 'Ofrecord') | |||||
sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) | |||||
parser = argparse.ArgumentParser(description="config for label server") | |||||
parser.add_argument("-p", "--port", type=int, required=True) | |||||
parser.add_argument("-m", "--mode", type=str, default="test", required=False) | |||||
args = parser.parse_args() | |||||
base_path = "/nfs/" | |||||
record_url = 'api/data/datasets/versions/' | |||||
url_json = './config/url.json' | |||||
with open(url_json) as f: | |||||
url_dict = json.loads(f.read()) | |||||
record_url = url_dict[args.mode] + record_url | |||||
port = args.port | |||||
of_que = Queue() | |||||
of_cond = [] | |||||
des_folder = os.path.join('./log', args.mode) | |||||
if not os.path.exists(des_folder): | |||||
os.makedirs(des_folder) | |||||
of_log = setup_log(args.mode, 'ofrecord-' + args.mode + '.log') | |||||
class Ofrecord(Upload_cfg): | |||||
"""Recieve and analyze the post request""" | |||||
def POST(self): | |||||
try: | |||||
super().POST() | |||||
x = web.data() | |||||
x = json.loads(x.decode()) | |||||
print(x) | |||||
dataset_version_id = x['id'] | |||||
label_map = x['datasetLabels'] | |||||
if dataset_version_id not in web.of_cond: | |||||
web.of_cond.append(dataset_version_id) | |||||
src_path = base_path + x['datasetPath'] | |||||
save_path = base_path + x['datasetPath'] + '/ofrecord' | |||||
# transform the windows path to linux path | |||||
src_path = '/'.join(src_path.split('\\')) | |||||
save_path = '/'.join(save_path.split('\\')) | |||||
of_config = [dataset_version_id, src_path, save_path,label_map] | |||||
of_log.info('Recv of_config:%s' % of_config) | |||||
web.t_queue1.put(of_config) | |||||
else: | |||||
pass | |||||
return {"code": 200, "msg": "", "data": dataset_version_id} | |||||
except Exception as e: | |||||
of_log.error("Error post") | |||||
of_log.error(e) | |||||
return 'post error' | |||||
def gen_ofrecord_thread(): | |||||
"""The implementation of ofRecord generating thread""" | |||||
global record_url | |||||
global of_que | |||||
of_log.info('ofrecord server start'.center(66, '-')) | |||||
of_log.info(record_url) | |||||
while True: | |||||
try: | |||||
of_task = of_que.get() | |||||
debug_msg = '-------- OfRecord gen start: %s --------' % of_task[0] if of_task else '' | |||||
of_log.info(debug_msg) | |||||
if not of_task: | |||||
continue | |||||
dataset_version_id = of_task[0] | |||||
src_path = of_task[1] | |||||
save_path = of_task[2] | |||||
label_map = of_task[3] | |||||
of_log.info('[%s] not in of_cond' % dataset_version_id) | |||||
if os.path.exists(save_path): | |||||
shutil.rmtree(save_path) | |||||
os.makedirs(save_path) | |||||
task_url = record_url + str(dataset_version_id) + '/convert/finish' | |||||
of_log.info('key: label, type: int32') | |||||
of_log.info('key: img_raw, type: bytes') | |||||
desc = os.path.join(save_path, 'train') | |||||
of_log.info('desc: %s' % desc) | |||||
try: | |||||
con, num_images, num_part = ofrecord.read_data_sets( | |||||
src_path, desc,label_map) | |||||
except Exception as e: | |||||
error_msg = 'Error happened in ofrecord.read_data_sets' | |||||
of_log.error(error_msg) | |||||
if of_task[0] in web.of_cond: | |||||
web.of_cond.remove(of_task[0]) | |||||
# send messages to DataManage | |||||
url_dbg = 'Request to [%s]' % task_url | |||||
of_log.info(url_dbg) | |||||
headers = {'Content-Type': 'application/json'} | |||||
req_body = bytes(json.dumps({'msg': str(e)}), 'utf8') | |||||
req = urllib.request.Request( | |||||
task_url, data=req_body, headers=headers) | |||||
response = urllib.request.urlopen(req, timeout=5) | |||||
debug_msg = "response.read(): %s; ret_code: %s" % ( | |||||
response.read(), response.getcode()) | |||||
of_log.info(debug_msg) | |||||
raise e | |||||
if not con: | |||||
error_msg = 'No annotated images, No ofrecord will be created' | |||||
of_log.warning(error_msg) | |||||
of_log.info( | |||||
'train: {} images in {} part files.\n'.format( | |||||
num_images, num_part)) | |||||
of_log.info('generate ofrecord file done') | |||||
url_dbg = 'Request to [%s]' % task_url | |||||
of_log.info(url_dbg) | |||||
headers = {'Content-Type': 'application/json'} | |||||
req_body = {'msg': 'ok'} | |||||
req = urllib.request.Request( | |||||
task_url, data=json.dumps(req_body).encode(), headers=headers) | |||||
response = urllib.request.urlopen(req, timeout=5) | |||||
debug_msg = "response.read(): %s; ret_code: %s" % ( | |||||
response.read(), response.getcode()) | |||||
of_log.info(debug_msg) | |||||
web.of_cond.remove(of_task[0]) | |||||
except Exception as e: | |||||
of_log.error("Error ofProcess") | |||||
of_log.error(e) | |||||
of_log.info(record_url) | |||||
debug_msg = '-------- OfRecord gen end --------' | |||||
of_log.info(debug_msg) | |||||
time.sleep(0.01) | |||||
def of_thread(no, interval): | |||||
"""Running the ofRecord generating thread""" | |||||
gen_ofrecord_thread() | |||||
if __name__ == "__main__": | |||||
_thread.start_new_thread(of_thread, (5, 5)) | |||||
app = MyApplication(urls, globals()) | |||||
web.of_cond = of_cond | |||||
web.t_queue1 = of_que | |||||
app.run(port=port) |
@@ -1,156 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# !/usr/bin/env python3 | |||||
# -*- coding: utf-8 -*- | |||||
import _thread | |||||
import argparse | |||||
import codecs | |||||
import json | |||||
import os | |||||
import random | |||||
import string | |||||
import sys | |||||
import time | |||||
import urllib | |||||
from queue import Queue | |||||
import predict_with_print_box as yolo_demo | |||||
import web | |||||
from upload_config import Upload_cfg, MyApplication | |||||
from log_config import setup_log | |||||
'''Config urls and chinese coding''' | |||||
urls = ('/auto_annotate', 'Upload') | |||||
sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) | |||||
'''Set port and mode''' | |||||
parser = argparse.ArgumentParser(description="config for label server") | |||||
parser.add_argument("-p", "--port", type=int, required=True) | |||||
parser.add_argument("-m", "--mode", type=str, default="test", required=False) | |||||
args = parser.parse_args() | |||||
'''Set path''' | |||||
base_path = "/nfs/" | |||||
label_url = "api/data/datasets/files/annotations/auto/" | |||||
url_json = './config/url.json' | |||||
'''Init task queue and log''' | |||||
with open(url_json) as f: | |||||
url_dict = json.loads(f.read()) | |||||
label_url = url_dict[args.mode] + label_url | |||||
port = args.port | |||||
taskQueue = Queue() | |||||
taskInImages = {} | |||||
des_folder = os.path.join('./log', args.mode) | |||||
if not os.path.exists(des_folder): | |||||
os.makedirs(des_folder) | |||||
label_log = setup_log(args.mode, 'label-' + args.mode + '.log') | |||||
def get_code(): | |||||
"""Generate task_id""" | |||||
return ''.join(random.sample(string.ascii_letters + string.digits, 8)) | |||||
class Upload(Upload_cfg): | |||||
"""Recieve and analyze the post request""" | |||||
def POST(self): | |||||
try: | |||||
super().POST() | |||||
x = web.data() | |||||
x = json.loads(x.decode()) | |||||
type_ = x['annotateType'] | |||||
task_id = get_code() | |||||
task_images = {} | |||||
task_images[task_id] = {"input": {'type': type_, 'data': x}, "output": {"annotations": []}} | |||||
print("Random_code:", task_id) | |||||
label_log.info(task_id) | |||||
label_log.info('web.t_queue length:%s' % web.t_queue.qsize()) | |||||
label_log.info('Recv task_images:%s' % task_images) | |||||
web.t_queue.put(task_images) | |||||
return {"code": 200, "msg": "", "data": task_id} | |||||
except Exception as e: | |||||
label_log.error("Error post") | |||||
label_log.error(e) | |||||
return 'post error' | |||||
def bgProcess(): | |||||
"""The implementation of automatic_label generating thread""" | |||||
global taskQueue | |||||
global label_url | |||||
label_log.info('auto label server start'.center(66, '-')) | |||||
label_log.info(label_url) | |||||
while True: | |||||
try: | |||||
task_dict = taskQueue.get() | |||||
for task_id in task_dict: | |||||
id_list = [] | |||||
image_path_list = [] | |||||
type_ = task_dict[task_id]["input"]['type'] | |||||
for file in task_dict[task_id]["input"]['data']["files"]: | |||||
id_list.append(file["id"]) | |||||
image_path_list.append(base_path + file["url"]) | |||||
label_list = task_dict[task_id]["input"]['data']["labels"] | |||||
coco_flag = 0 | |||||
if "labelType" in task_dict[task_id]["input"]['data']: | |||||
label_type = task_dict[task_id]["input"]['data']["labelType"] | |||||
if label_type == 3: | |||||
coco_flag = 80 | |||||
label_log.info(coco_flag) | |||||
image_num = len(image_path_list) | |||||
if image_num < 16: | |||||
for i in range(16 - image_num): | |||||
image_path_list.append(image_path_list[0]) | |||||
id_list.append(id_list[0]) | |||||
label_log.info(image_num) | |||||
label_log.info(image_path_list) | |||||
annotations = yolo_obj.yolo_inference(type_, id_list, image_path_list, label_list, coco_flag) | |||||
annotations = annotations[0:image_num] | |||||
result = {"annotations": annotations} | |||||
label_log.info('Inference complete %s' % task_id) | |||||
send_data = json.dumps(result).encode() | |||||
task_url = label_url + task_id | |||||
headers = {'Content-Type': 'application/json'} | |||||
req = urllib.request.Request(task_url, headers=headers) | |||||
response = urllib.request.urlopen(req, data=send_data, timeout=2) | |||||
label_log.info(task_url) | |||||
label_log.info(response.read()) | |||||
label_log.info("End automatic label") | |||||
except Exception as e: | |||||
label_log.error("Error bgProcess") | |||||
label_log.error(e) | |||||
label_log.info(label_url) | |||||
time.sleep(0.01) | |||||
def bg_thread(no, interval): | |||||
"""Running the automatic_label generating thread""" | |||||
bgProcess() | |||||
if __name__ == "__main__": | |||||
yolo_obj = yolo_demo.YoloInference(label_log) | |||||
_thread.start_new_thread(bg_thread, (5, 5)) | |||||
app = MyApplication(urls, globals()) | |||||
web.t_queue = taskQueue | |||||
web.taskInImages = taskInImages | |||||
app.run(port=port) |
@@ -1,46 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
import web | |||||
class Upload_cfg: | |||||
"""Recieve and analyze the post request""" | |||||
def GET(self): | |||||
web.header("Access-Control-Allow-Origin", "*") | |||||
web.header("Access-Control-Allow-Credentials", "true") | |||||
web.header('Access-Control-Allow-Headers', | |||||
'Content-Type, Access-Control-Allow-Origin, Access-Control-Allow-Headers, X-Requested-By, Access-Control-Allow-Methods') | |||||
web.header('Access-Control-Allow-Methods', 'POST, GET, PUT, DELETE') | |||||
return """<html><head></head><body>please send data in post | |||||
</body></html>""" | |||||
def POST(self): | |||||
web.header("Access-Control-Allow-Origin", "*") | |||||
web.header("Access-Control-Allow-Credentials", "true") | |||||
web.header('Access-Control-Allow-Headers', | |||||
'Content-Type, Access-Control-Allow-Origin, Access-Control-Allow-Headers, X-Requested-By, Access-Control-Allow-Methods') | |||||
web.header('Access-Control-Allow-Methods', 'POST, GET, PUT, DELETE') | |||||
class MyApplication(web.application): | |||||
def run(self, port, *middleware): | |||||
func = self.wsgifunc(*middleware) | |||||
return web.httpserver.runsimple(func, ('0.0.0.0', port)) |
@@ -1,277 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
import json | |||||
import time | |||||
import cv2 | |||||
import numpy as np | |||||
import oneflow_yolov3 | |||||
from yolo_net import YoloPredictNet | |||||
import oneflow as flow | |||||
'''Init oneflow config''' | |||||
model_load_dir = "of_model/yolov3_model_python/" | |||||
label_to_name_file = "data/coco.names" | |||||
use_tensorrt = 0 | |||||
gpu_num_per_node = 1 | |||||
batch_size = 16 | |||||
image_height = 608 | |||||
image_width = 608 | |||||
flow.config.load_library(oneflow_yolov3.lib_path()) | |||||
func_config = flow.FunctionConfig() | |||||
func_config.default_distribute_strategy(flow.distribute.consistent_strategy()) | |||||
func_config.default_data_type(flow.float) | |||||
if use_tensorrt != 0: | |||||
func_config.use_tensorrt(True) | |||||
label_2_name = [] | |||||
with open(label_to_name_file, 'r') as f: | |||||
label_2_name = f.readlines() | |||||
nms = True | |||||
print("nms:", nms) | |||||
input_blob_def_dict = { | |||||
"images": flow.FixedTensorDef((batch_size, 3, image_height, image_width), dtype=flow.float), | |||||
"origin_image_info": flow.FixedTensorDef((batch_size, 2), dtype=flow.int32), | |||||
} | |||||
def xywh_2_x1y1x2y2(x, y, w, h, origin_image): | |||||
"""The format of box transform""" | |||||
x1 = (x - w / 2.) * origin_image[1] | |||||
x2 = (x + w / 2.) * origin_image[1] | |||||
y1 = (y - h / 2.) * origin_image[0] | |||||
y2 = (y + h / 2.) * origin_image[0] | |||||
return x1, y1, x2, y2 | |||||
def batch_boxes(positions, probs, origin_image_info): | |||||
"""The images postprocessing""" | |||||
batch_size = positions.shape[0] | |||||
batch_list = [] | |||||
if nms == True: | |||||
for k in range(batch_size): | |||||
box_list = [] | |||||
for i in range(1, 81): | |||||
for j in range(positions.shape[2]): | |||||
if positions[k][i][j][2] != 0 and positions[k][i][j][3] != 0 and probs[k][i][j] != 0: | |||||
x1, y1, x2, y2 = xywh_2_x1y1x2y2(positions[k][i][j][0], positions[k][i][j][1], | |||||
positions[k][i][j][2], positions[k][i][j][3], | |||||
origin_image_info[k]) | |||||
bbox = [i - 1, x1, y1, x2, y2, probs[k][i][j]] | |||||
box_list.append(bbox) | |||||
batch_list.append(np.asarray(box_list)) | |||||
else: | |||||
for k in range(batch_size): | |||||
box_list = [] | |||||
for j in range(positions.shape[1]): | |||||
for i in range(1, 81): | |||||
if positions[k][j][2] != 0 and positions[k][j][3] != 0 and probs[k][j][i] != 0: | |||||
x1, y1, x2, y2 = xywh_2_x1y1x2y2(positions[k][j][0], positions[k][j][1], positions[k][j][2], | |||||
positions[k][j][3], origin_image_info[k]) | |||||
bbox = [i - 1, x1, y1, x2, y2, probs[k][j][i]] | |||||
box_list.append(bbox) | |||||
batch_list.append(np.asarray(box_list)) | |||||
return batch_list | |||||
@flow.function(func_config) | |||||
def yolo_user_op_eval_job(images=input_blob_def_dict["images"], | |||||
origin_image_info=input_blob_def_dict["origin_image_info"]): | |||||
"""The model inference""" | |||||
yolo_pos_result, yolo_prob_result = YoloPredictNet(images, origin_image_info, trainable=False) | |||||
yolo_pos_result = flow.identity(yolo_pos_result, name="yolo_pos_result_end") | |||||
yolo_prob_result = flow.identity(yolo_prob_result, name="yolo_prob_result_end") | |||||
return yolo_pos_result, yolo_prob_result, origin_image_info | |||||
def yolo_show(image_path_list, batch_list): | |||||
"""Debug the result of Yolov3""" | |||||
font = cv2.FONT_HERSHEY_SIMPLEX | |||||
for img_path, batch in zip(image_path_list, batch_list): | |||||
result_list = batch.tolist() | |||||
img = cv2.imread(img_path) | |||||
for result in result_list: | |||||
cls = int(result[0]) | |||||
bbox = result[1:-1] | |||||
score = result[-1] | |||||
print('img_file:', img_path) | |||||
print('cls:', cls) | |||||
print('bbox:', bbox) | |||||
c = ((int(bbox[0]) + int(bbox[2])) / 2, (int(bbox[1] + int(bbox[3])) / 2)) | |||||
cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0, 255, 255), 1) | |||||
cv2.putText(img, str(cls), (int(c[0]), int(c[1])), font, 1, (0, 0, 255), 1) | |||||
result_name = img_path.split('/')[-1] | |||||
cv2.imwrite("data/results/" + result_name, img) | |||||
def resize_image(img, origin_h, origin_w, image_height, image_width): | |||||
"""The resize of image preprocessing""" | |||||
w = image_width | |||||
h = image_height | |||||
resized = np.zeros((3, image_height, image_width), dtype=np.float32) | |||||
part = np.zeros((3, origin_h, image_width), dtype=np.float32) | |||||
w_scale = (float)(origin_w - 1) / (w - 1) | |||||
h_scale = (float)(origin_h - 1) / (h - 1) | |||||
for c in range(w): | |||||
if c == w - 1 or origin_w == 1: | |||||
val = img[:, :, origin_w - 1] | |||||
else: | |||||
sx = c * w_scale | |||||
ix = int(sx) | |||||
dx = sx - ix | |||||
val = (1 - dx) * img[:, :, ix] + dx * img[:, :, ix + 1] | |||||
part[:, :, c] = val | |||||
for r in range(h): | |||||
sy = r * h_scale | |||||
iy = int(sy) | |||||
dy = sy - iy | |||||
val = (1 - dy) * part[:, iy, :] | |||||
resized[:, r, :] = val | |||||
if r == h - 1 or origin_h == 1: | |||||
continue | |||||
resized[:, r, :] = resized[:, r, :] + dy * part[:, iy + 1, :] | |||||
return resized | |||||
def batch_image_preprocess_v2(img_path_list, image_height, image_width): | |||||
"""The images preprocessing""" | |||||
result_list = [] | |||||
origin_info_list = [] | |||||
for img_path in img_path_list: | |||||
img = cv2.imread(img_path, cv2.IMREAD_COLOR) | |||||
img = img.transpose(2, 0, 1).astype(np.float32) # hwc->chw | |||||
img = img / 255 # /255 | |||||
img[[0, 1, 2], :, :] = img[[2, 1, 0], :, :] # bgr2rgb | |||||
w = image_width | |||||
h = image_height | |||||
origin_h = img.shape[1] | |||||
origin_w = img.shape[2] | |||||
new_w = origin_w | |||||
new_h = origin_h | |||||
if w / origin_w < h / origin_h: | |||||
new_w = w | |||||
new_h = origin_h * w // origin_w | |||||
else: | |||||
new_h = h | |||||
new_w = origin_w * h // origin_h | |||||
resize_img = resize_image(img, origin_h, origin_w, new_h, new_w) | |||||
dw = (w - new_w) // 2 | |||||
dh = (h - new_h) // 2 | |||||
padh_before = int(dh) | |||||
padh_after = int(h - new_h - padh_before) | |||||
padw_before = int(dw) | |||||
padw_after = int(w - new_w - padw_before) | |||||
result = np.pad(resize_img, pad_width=((0, 0), (padh_before, padh_after), (padw_before, padw_after)), | |||||
mode='constant', constant_values=0.5) | |||||
origin_image_info = [origin_h, origin_w] | |||||
result_list.append(result) | |||||
origin_info_list.append(origin_image_info) | |||||
results = np.asarray(result_list).astype(np.float32) | |||||
origin_image_infos = np.asarray(origin_info_list).astype(np.int32) | |||||
return results, origin_image_infos | |||||
def coco_format(type_, id_list, file_list, result_list, label_list, coco_flag=0): | |||||
"""Transform the annotations to coco format""" | |||||
annotations = [] | |||||
for i, result in enumerate(result_list): | |||||
temp = {} | |||||
id_name = id_list[i] | |||||
file_path = file_list[i] | |||||
temp['id'] = id_name | |||||
temp['annotation'] = [] | |||||
im = cv2.imread(file_path) | |||||
height, width, _ = im.shape | |||||
if result.shape[0] == 0: | |||||
temp['annotation'] = json.dumps(temp['annotation']) | |||||
annotations.append(temp) | |||||
continue | |||||
else: | |||||
for j in range(result.shape[0]): | |||||
cls_id = int(result[j][0]) + 1 + coco_flag | |||||
x1 = result[j][1] | |||||
x2 = result[j][3] | |||||
y1 = result[j][2] | |||||
y2 = result[j][4] | |||||
score = result[j][5] | |||||
width = max(0, x2 - x1) | |||||
height = max(0, y2 - y1) | |||||
if cls_id in label_list: | |||||
temp['annotation'].append({ | |||||
'area': width * height, | |||||
'bbox': [x1, y1, width, height], | |||||
'category_id': cls_id, | |||||
'iscrowd': 0, | |||||
'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]], | |||||
'score': score | |||||
}) | |||||
if type_ == 2 and len(temp['annotation']) > 0: | |||||
temp['annotation'] = [temp['annotation'][0]] | |||||
temp['annotation'][0].pop('area') | |||||
temp['annotation'][0].pop('bbox') | |||||
temp['annotation'][0].pop('iscrowd') | |||||
temp['annotation'][0].pop('segmentation') | |||||
temp['annotation'] = json.dumps(temp['annotation']) | |||||
annotations.append(temp) | |||||
return annotations | |||||
class YoloInference(object): | |||||
"""Yolov3 detection inference""" | |||||
def __init__(self, label_log): | |||||
self.label_log = label_log | |||||
flow.config.gpu_device_num(gpu_num_per_node) | |||||
flow.env.ctrl_port(9789) | |||||
check_point = flow.train.CheckPoint() | |||||
if not model_load_dir: | |||||
check_point.init() | |||||
else: | |||||
check_point.load(model_load_dir) | |||||
print("Load check_point success") | |||||
self.label_log.info("Load check_point success") | |||||
def yolo_inference(self, type_, id_list, image_path_list, label_list, coco_flag=0): | |||||
annotations = [] | |||||
try: | |||||
if len(image_path_list) == 16: | |||||
t0 = time.time() | |||||
images, origin_image_info = batch_image_preprocess_v2(image_path_list, image_height, image_width) | |||||
yolo_pos, yolo_prob, origin_image_info = yolo_user_op_eval_job(images, origin_image_info).get() | |||||
batch_list = batch_boxes(yolo_pos, yolo_prob, origin_image_info) | |||||
annotations = coco_format(type_, id_list, image_path_list, batch_list, label_list, coco_flag) | |||||
t1 = time.time() | |||||
print('t1-t0:', t1 - t0) | |||||
except: | |||||
print("Forward Error") | |||||
self.label_log.error("Forward Error") | |||||
for i, image_path in enumerate(image_path_list): | |||||
temp = {} | |||||
id_name = id_list[i] | |||||
temp['id'] = id_name | |||||
temp['annotation'] = [] | |||||
temp['annotation'] = json.dumps(temp['annotation']) | |||||
annotations.append(temp) | |||||
return annotations |
@@ -1,100 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# coding:utf-8 | |||||
import codecs | |||||
import sched | |||||
import sys | |||||
import json | |||||
import logging | |||||
import time | |||||
import common.RedisUtil as f | |||||
import common.config as config | |||||
import annotation as annotation | |||||
from datetime import datetime | |||||
import luascript.delaytaskscript as delay_script | |||||
logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', | |||||
level=logging.DEBUG) | |||||
schedule = sched.scheduler(time.time, time.sleep) | |||||
sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) | |||||
delayId = "" | |||||
def annotationExecutor(redisClient, key): | |||||
"""Annotation task method. | |||||
Args: | |||||
redisClient: redis client. | |||||
key: annotation task key. | |||||
""" | |||||
global delayId | |||||
print('-------------process one-----------------') | |||||
try: | |||||
delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" | |||||
logging.info('get element is {0}'.format(key)) | |||||
key = key.decode() | |||||
jsonStr = f.getByKey(redisClient, key.replace('"', '')); | |||||
print(jsonStr) | |||||
jsonObject = json.loads(jsonStr.decode('utf-8')); | |||||
image_path_list = [] | |||||
id_list = [] | |||||
label_list = [] | |||||
label_list = jsonObject['labels'] | |||||
for fileObject in jsonObject['files']: | |||||
image_path_list.append('/nfs/' + fileObject['url']) | |||||
id_list.append(fileObject['id']) | |||||
print(image_path_list) | |||||
print(id_list) | |||||
print(label_list) | |||||
coco_flag = 0 | |||||
if "labelType" in jsonObject: | |||||
label_type = jsonObject['labelType'] | |||||
if label_type == 3: | |||||
coco_flag = 80 | |||||
annotations = annotation._annotation(0, image_path_list, id_list, label_list, coco_flag); | |||||
result = {"task": key, "annotations": annotations} | |||||
f.pushToQueue(redisClient, config.annotationFinishQueue, json.dumps(result)) | |||||
redisClient.zrem(config.annotationStartQueue, key) | |||||
except Exception as e: | |||||
print(e) | |||||
def delaySchduled(inc, redisClient): | |||||
"""Delay task method. | |||||
Args: | |||||
inc: scheduled task time. | |||||
redisClient: redis client. | |||||
""" | |||||
try: | |||||
print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) | |||||
redisClient.eval(delay_script.delayTaskLua, 1, config.annotationStartQueue, delayId, int(time.time())) | |||||
schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) | |||||
except Exception as e: | |||||
print("delay error" + e) | |||||
def delayKeyThread(redisClient): | |||||
"""Delay task thread. | |||||
Args: | |||||
redisClient: redis client. | |||||
""" | |||||
schedule.enter(0, 0, delaySchduled, (5, redisClient)) | |||||
schedule.run() |
@@ -1,93 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
# -*- coding: utf-8 -*- | |||||
import sched | |||||
import common.config as config | |||||
import luascript.delaytaskscript as delay_script | |||||
from track_only.hog_track import * | |||||
schedule = sched.scheduler(time.time, time.sleep) | |||||
delayId = "" | |||||
def trackProcess(task, key): | |||||
"""Track task method. | |||||
Args: | |||||
task: dataset id. | |||||
key: video file path. | |||||
Returns: | |||||
True: track success | |||||
False: track failed | |||||
""" | |||||
global delayId | |||||
delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" | |||||
task = json.loads(task.decode('utf-8')) | |||||
image_list = [] | |||||
label_list = [] | |||||
images_data = task['images'] | |||||
path = task['path'] | |||||
for file in images_data: | |||||
filePath = path + "/origin/" + file | |||||
annotationPath = path + "/annotation/" + file.split('.')[0] | |||||
if not os.path.exists(filePath): | |||||
continue | |||||
if not os.path.exists(annotationPath): | |||||
continue | |||||
image_list.append(filePath) | |||||
label_list.append(annotationPath) | |||||
image_num = len(label_list) | |||||
track_det = Detector( | |||||
'xxx.avi', | |||||
min_confidence=0.35, | |||||
max_cosine_distance=0.2, | |||||
max_iou_distance=0.7, | |||||
max_age=30, | |||||
out_dir='results/') | |||||
track_det.write_img = False | |||||
RET = track_det.run_track(image_list, label_list) | |||||
if RET == 'OK': | |||||
return True | |||||
else: | |||||
return False | |||||
def delaySchduled(inc, redisClient): | |||||
"""Delay task method. | |||||
Args: | |||||
inc: scheduled task time. | |||||
redisClient: redis client. | |||||
""" | |||||
try: | |||||
print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) | |||||
redisClient.eval(delay_script.delayTaskLua, 1, config.trackStartQueue, delayId, int(time.time())) | |||||
schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) | |||||
except Exception as e: | |||||
print("delay error" + e) | |||||
def delayKeyThread(redisClient): | |||||
"""Delay task thread. | |||||
Args: | |||||
redisClient: redis client. | |||||
""" | |||||
schedule.enter(0, 0, delaySchduled, (5, redisClient)) | |||||
schedule.run() |
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -2,7 +2,7 @@ | |||||
# -*- coding: utf-8 -*- | # -*- coding: utf-8 -*- | ||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,18 +1,14 @@ | |||||
""" | """ | ||||
MIT License | MIT License | ||||
Copyright (c) 2020 Ziqiang | Copyright (c) 2020 Ziqiang | ||||
Permission is hereby granted, free of charge, to any person obtaining a copy | Permission is hereby granted, free of charge, to any person obtaining a copy | ||||
of this software and associated documentation files (the "Software"), to deal | of this software and associated documentation files (the "Software"), to deal | ||||
in the Software without restriction, including without limitation the rights | in the Software without restriction, including without limitation the rights | ||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||||
copies of the Software, and to permit persons to whom the Software is | copies of the Software, and to permit persons to whom the Software is | ||||
furnished to do so, subject to the following conditions: | furnished to do so, subject to the following conditions: | ||||
The above copyright notice and this permission notice shall be included in all | The above copyright notice and this permission notice shall be included in all | ||||
copies or substantial portions of the Software. | copies or substantial portions of the Software. | ||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||||
@@ -1,6 +1,6 @@ | |||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,18 +1,14 @@ | |||||
""" | """ | ||||
MIT License | MIT License | ||||
Copyright (c) 2020 Ziqiang | Copyright (c) 2020 Ziqiang | ||||
Permission is hereby granted, free of charge, to any person obtaining a copy | Permission is hereby granted, free of charge, to any person obtaining a copy | ||||
of this software and associated documentation files (the "Software"), to deal | of this software and associated documentation files (the "Software"), to deal | ||||
in the Software without restriction, including without limitation the rights | in the Software without restriction, including without limitation the rights | ||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||||
copies of the Software, and to permit persons to whom the Software is | copies of the Software, and to permit persons to whom the Software is | ||||
furnished to do so, subject to the following conditions: | furnished to do so, subject to the following conditions: | ||||
The above copyright notice and this permission notice shall be included in all | The above copyright notice and this permission notice shall be included in all | ||||
copies or substantial portions of the Software. | copies or substantial portions of the Software. | ||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||||
@@ -20,6 +16,7 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||||
SOFTWARE. | SOFTWARE. | ||||
*/ | |||||
""" | """ | ||||
import numpy as np | import numpy as np | ||||
@@ -1,18 +1,14 @@ | |||||
""" | """ | ||||
MIT License | MIT License | ||||
Copyright (c) 2020 Ziqiang | Copyright (c) 2020 Ziqiang | ||||
Permission is hereby granted, free of charge, to any person obtaining a copy | Permission is hereby granted, free of charge, to any person obtaining a copy | ||||
of this software and associated documentation files (the "Software"), to deal | of this software and associated documentation files (the "Software"), to deal | ||||
in the Software without restriction, including without limitation the rights | in the Software without restriction, including without limitation the rights | ||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||||
copies of the Software, and to permit persons to whom the Software is | copies of the Software, and to permit persons to whom the Software is | ||||
furnished to do so, subject to the following conditions: | furnished to do so, subject to the following conditions: | ||||
The above copyright notice and this permission notice shall be included in all | The above copyright notice and this permission notice shall be included in all | ||||
copies or substantial portions of the Software. | copies or substantial portions of the Software. | ||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||||
@@ -1,18 +1,14 @@ | |||||
""" | """ | ||||
MIT License | MIT License | ||||
Copyright (c) 2020 Ziqiang | Copyright (c) 2020 Ziqiang | ||||
Permission is hereby granted, free of charge, to any person obtaining a copy | Permission is hereby granted, free of charge, to any person obtaining a copy | ||||
of this software and associated documentation files (the "Software"), to deal | of this software and associated documentation files (the "Software"), to deal | ||||
in the Software without restriction, including without limitation the rights | in the Software without restriction, including without limitation the rights | ||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||||
copies of the Software, and to permit persons to whom the Software is | copies of the Software, and to permit persons to whom the Software is | ||||
furnished to do so, subject to the following conditions: | furnished to do so, subject to the following conditions: | ||||
The above copyright notice and this permission notice shall be included in all | The above copyright notice and this permission notice shall be included in all | ||||
copies or substantial portions of the Software. | copies or substantial portions of the Software. | ||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||||
@@ -1,18 +1,14 @@ | |||||
""" | """ | ||||
MIT License | MIT License | ||||
Copyright (c) 2020 Ziqiang | Copyright (c) 2020 Ziqiang | ||||
Permission is hereby granted, free of charge, to any person obtaining a copy | Permission is hereby granted, free of charge, to any person obtaining a copy | ||||
of this software and associated documentation files (the "Software"), to deal | of this software and associated documentation files (the "Software"), to deal | ||||
in the Software without restriction, including without limitation the rights | in the Software without restriction, including without limitation the rights | ||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||||
copies of the Software, and to permit persons to whom the Software is | copies of the Software, and to permit persons to whom the Software is | ||||
furnished to do so, subject to the following conditions: | furnished to do so, subject to the following conditions: | ||||
The above copyright notice and this permission notice shall be included in all | The above copyright notice and this permission notice shall be included in all | ||||
copies or substantial portions of the Software. | copies or substantial portions of the Software. | ||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||||
@@ -2,7 +2,7 @@ | |||||
# -*- coding: utf-8 -*- | # -*- coding: utf-8 -*- | ||||
""" | """ | ||||
/** | /** | ||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* Copyright 2020 Tianshu AI Platform. All Rights Reserved. | |||||
* | * | ||||
* Licensed under the Apache License, Version 2.0 (the "License"); | * Licensed under the Apache License, Version 2.0 (the "License"); | ||||
* you may not use this file except in compliance with the License. | * you may not use this file except in compliance with the License. | ||||
@@ -1,18 +1,14 @@ | |||||
""" | """ | ||||
MIT License | MIT License | ||||
Copyright (c) 2020 Ziqiang | Copyright (c) 2020 Ziqiang | ||||
Permission is hereby granted, free of charge, to any person obtaining a copy | Permission is hereby granted, free of charge, to any person obtaining a copy | ||||
of this software and associated documentation files (the "Software"), to deal | of this software and associated documentation files (the "Software"), to deal | ||||
in the Software without restriction, including without limitation the rights | in the Software without restriction, including without limitation the rights | ||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||||
copies of the Software, and to permit persons to whom the Software is | copies of the Software, and to permit persons to whom the Software is | ||||
furnished to do so, subject to the following conditions: | furnished to do so, subject to the following conditions: | ||||
The above copyright notice and this permission notice shall be included in all | The above copyright notice and this permission notice shall be included in all | ||||
copies or substantial portions of the Software. | copies or substantial portions of the Software. | ||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||||
@@ -1,100 +0,0 @@ | |||||
""" | |||||
/** | |||||
* Copyright 2020 Zhejiang Lab. All Rights Reserved. | |||||
* | |||||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||||
* you may not use this file except in compliance with the License. | |||||
* You may obtain a copy of the License at | |||||
* | |||||
* http://www.apache.org/licenses/LICENSE-2.0 | |||||
* | |||||
* Unless required by applicable law or agreed to in writing, software | |||||
* distributed under the License is distributed on an "AS IS" BASIS, | |||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||||
* See the License for the specific language governing permissions and | |||||
* limitations under the License. | |||||
* ============================================================= | |||||
*/ | |||||
""" | |||||
import json | |||||
import os | |||||
import sched | |||||
import time | |||||
from datetime import datetime | |||||
import luascript.finishtaskscript as finish_script | |||||
import luascript.failedtaskscript as failed_script | |||||
import luascript.delaytaskscript as delay_script | |||||
import common.config as config | |||||
import cv2 | |||||
schedule = sched.scheduler(time.time, time.sleep) | |||||
datasetIdKey = "" | |||||
def sampleProcess(datasetId, path, frameList, redisClient): | |||||
"""Video sampling method. | |||||
Args: | |||||
datasetId: dataset id. | |||||
path: video file path. | |||||
frameList: picture frame number list. | |||||
redisClient: redis client. | |||||
""" | |||||
global datasetIdKey | |||||
datasetIdJson = {'datasetIdKey': datasetId} | |||||
datasetIdKey = json.dumps(datasetIdJson, separators=(',', ':')) | |||||
try: | |||||
videoName = path.split('/')[-1] | |||||
save_path = path.split(videoName)[0].replace("video", "origin") | |||||
is_exists = os.path.exists(save_path) | |||||
if not is_exists: | |||||
os.makedirs(save_path) | |||||
print('path of %s is build' % save_path) | |||||
else: | |||||
print('path of %s already exist and start' % save_path) | |||||
cap = cv2.VideoCapture(path) | |||||
for i in frameList: | |||||
cap.set(cv2.CAP_PROP_POS_FRAMES, i) | |||||
success, video_capture = cap.read() | |||||
# 保存图片 | |||||
if success is True and video_capture is not None: | |||||
save_name = save_path + videoName.split('.')[0] + '_' + str(i) + '.jpg' | |||||
cv2.imwrite(save_name, video_capture) | |||||
redisClient.lpush("videoSample_pictures:" + datasetId, | |||||
'{' + '\"pictureName\":' + "\"" + save_name + "\"" + '}') | |||||
print('image of %s is saved' % save_name) | |||||
print('video is all read') | |||||
redisClient.eval(finish_script.finishTaskLua, 3, config.videoStartQueue, config.videoFinishQueue, | |||||
"videoSample:" + str(datasetId), | |||||
datasetIdKey, str(datasetIdKey)) | |||||
except Exception as e: | |||||
print(e) | |||||
redisClient.eval(failed_script.failedTaskLua, 4, config.videoStartQueue, config.videoFailedQueue, | |||||
"videoSample_pictures:" + datasetId, | |||||
"videoSample:" + str(datasetId), | |||||
datasetIdKey, str(datasetIdKey)) | |||||
def delaySchduled(inc, redisClient): | |||||
"""Delay task method. | |||||
Args: | |||||
inc: scheduled task time. | |||||
redisClient: redis client. | |||||
""" | |||||
try: | |||||
print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) | |||||
redisClient.eval(delay_script.delayTaskLua, 1, config.videoStartQueue, datasetIdKey, int(time.time())) | |||||
schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) | |||||
except Exception as e: | |||||
print("delay error" + e) | |||||
def delayKeyThread(redisClient): | |||||
"""Delay task thread. | |||||
Args: | |||||
redisClient: redis client. | |||||
""" | |||||
schedule.enter(0, 0, delaySchduled, (5, redisClient)) | |||||
schedule.run() |