def init_delete_imgfile(log_oper,path_1,path_2,path_3,path_4):
for root,dirs,files in os.walk(path_1["path_ref"]):
img_path_list=[]
for file in files:
if os.path.splitext(file)[1]=='.jpg':
img_path_list.append(os.path.join(path_1["path_ref"],file))
delete_file(log_oper,img_path_list)
#
for root,dirs,files in os.walk(path_2["path_ref"]):
img_path_list=[]
for file in files:
if os.path.splitext(file)[1]=='.jpg':
img_path_list.append(os.path.join(path_2["path_ref"],file))
delete_file(log_oper,img_path_list)
#
for root,dirs,files in os.walk(path_3["path_ref"]):
img_path_list=[]
for file in files:
if os.path.splitext(file)[1]=='.jpg':
img_path_list.append(os.path.join(path_3["path_ref"],file))
delete_file(log_oper,img_path_list)
#
for root,dirs,files in os.walk(path_4["path_ref"]):
img_path_list=[]
for file in files:
if os.path.splitext(file)[1]=='.jpg':
img_path_list.append(os.path.join(path_4["path_ref"],file))
delete_file(log_oper,img_path_list)
def thread_load_camimg3(log_oper,path):
while True:
try:
for root,dirs,files in os.walk(path["path_ref"]):
if len(files)>0:
img_path_list=[]
time.sleep(0.5)
for file in files:
if os.path.splitext(file)[1]=='.jpg':
if os.path.exists(os.path.join(path["path_ref"],file)):
img_info={}
img_info["img_path"]=os.path.join(path["path_ref"],file)
img_info["img_name"]=file
img_info['img_data']=cv2.imread(img_info["img_path"], 0)
if img_info['img_data'] is None:
continue
image_cache_cam3.put(img_info)
img_path_list.append(img_info["img_path"])
delete_file(log_oper,img_path_list)
else:
time.sleep(0.1)
except Exception as err:
log_oper.add_log("Error>>相机3图像载入线程中Bug->{}".format(err))
continue
def steel_char_detect(path_1, path_2, path_3, path_4):
try:
Log_oper = Log("Log_ASC")
Log_oper.add_log("Normal>>开始载入跟踪模型")
steel_model = YOLO()
Log_oper.add_log("Normal>>完成载入跟踪模型成功")
Log_oper.add_log("Normal>>开始载入识别模型")
phi = 2
weighted_bifpn = True
model_path = 'char_model/char_model.h5'
image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
image_size = image_sizes[phi]
classes = {
value['id']: value['name']
for value in json.load(open('char_model/char_class.json',
'r')).values()
}
num_classes = len(classes)
score_threshold = 0.5
colors = [
np.random.randint(0, 256, 3).tolist() for _ in range(num_classes)
]
_, char_model = efficientdet(phi=phi,
weighted_bifpn=weighted_bifpn,
num_classes=num_classes,
score_threshold=score_threshold)
char_model.load_weights(model_path, by_name=True)
char_model_param = {
'image_size': image_size,
'classes': classes,
'score_threshold': score_threshold,
'colors': colors
}
Log_oper.add_log("Normal>>完成载入识别模型成功")
Log_oper.add_log("Normal>>开始载入mini识别模型")
phi_mini = 0
weighted_bifpn_mini = True
model_path_mini = 'char_model_mini/char_model.h5'
image_sizes_mini = (512, 640, 768, 896, 1024, 1280, 1408)
image_size_mini = image_sizes_mini[phi_mini]
classes_mini = {
value['id']: value['name']
for value in json.load(open('char_model_mini/char_class.json',
'r')).values()
}
num_classes_mini = len(classes_mini)
score_threshold_mini = 0.5
colors_mini = [
np.random.randint(0, 256, 3).tolist()
for _ in range(num_classes_mini)
]
_, char_model_mini = efficientdet(phi=phi_mini,
weighted_bifpn=weighted_bifpn_mini,
num_classes=num_classes_mini,
score_threshold=score_threshold_mini)
char_model_mini.load_weights(model_path_mini, by_name=True)
char_model_param_mini = {
'image_size': image_size_mini,
'classes': classes_mini,
'score_threshold': score_threshold_mini,
'colors': colors_mini
}
Log_oper.add_log("Normal>>完成载入mini识别模型成功")
Log_oper.add_log("Normal>>开始载入字符分类模型")
table1 = get_convert_from_class_table(
'char_model_class/steel_class_table_char.xml', Log_oper)
table2, img_size, class_num, model_mark = get_convert_from_ini_config_file(
'char_model_class/steel_classifier_interaction.ini', Log_oper)
classifier = Classifier(20, 28,
'char_model_class/steel_model_classifier.h5')
classifier.load_model()
Log_oper.add_log("Normal>>完成载入字符分类模型成功")
Log_oper.add_log("Normal>>开始创建二级连接")
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(('191.168.162.192', 12001))
send_heartbeat = threading.Thread(target=thread_send_heartbeat_to_l2,
args=(
s,
Log_oper,
))
send_heartbeat.start()
#recv_info = threading.Thread(target=thread_recv_info_from_l2,args=(s,Log_oper,))
#recv_info.start()
t_recv_info = HThreadRecvInfo((
s,
Log_oper,
), "recv")
t_recv_info.start()
Log_oper.add_log("Normal>>完成成功连接到二级")
judge_num_sbm = 0
SCD_SBM_Oper = SteelCharDetect(1, Log_oper, steel_model, char_model,
char_model_param, char_model_mini,
char_model_param_mini, classifier, s,
10, path_1)
judge_num_htf1 = 0
SCD_HTF1_Oper = SteelCharDetect(2, Log_oper, steel_model, char_model,
char_model_param, char_model_mini,
char_model_param_mini, classifier, s,
10, path_2)
judge_num_htf2 = 0
SCD_HTF2_Oper = SteelCharDetect(3, Log_oper, steel_model, char_model,
char_model_param, char_model_mini,
char_model_param_mini, classifier, s,
10, path_3)
judge_num_print = 0
SCD_PRINT_Oper = SteelCharDetect(4, Log_oper, steel_model, char_model,
char_model_param, char_model_mini,
char_model_param_mini, classifier, s,
10, path_4)
steel_no_send = {
"SBM": False,
"HTF1": False,
"HTF2": False,
"PRINT": False
} #根据一级信号判断钢板是否经过
last_recv_res = {
"SBM": [1, 1, 1],
"HTF1": [1, 1, 1],
"HTF2": [1, 1, 1],
"PRINT": [1, 1, 1]
}
time.sleep(
5) #延时一会，让二级信息接收到后，再对变量进行更新，不让其使用默认值，另外感觉recv_res得到的是变量的地址，会跟着变
recv_res = t_recv_info.get_result()
last_recv_res["SBM"] = copy.deepcopy(recv_res["SBM"])
last_recv_res["HTF1"] = copy.deepcopy(recv_res["HTF1"])
last_recv_res["HTF2"] = copy.deepcopy(recv_res["HTF2"])
last_recv_res["PRINT"] = copy.deepcopy(recv_res["PRINT"])
char_roi = None
enable_l2_final_send = {
"SBM": False,
"HTF1": False,
"HTF2": False,
"PRINT": False
} #程序重启后钢板不在识别区时不发送数据
last_recv_res_plan_counter = {
"SBM": last_recv_res["SBM"][0],
"HTF1": last_recv_res["HTF1"][0],
"HTF2": last_recv_res["HTF2"][0],
"PRINT": last_recv_res["PRINT"][0]
} #通过2级发送的第一位计数判断是否还给二级发送，如果计数已变化则不再发送
#第一位：二级是否搜到计划 1为未搜到 0为搜到 第二位：钢板是否在识别区 1为在识别区 0为不在识别区 第三位：辊道转速情况 1为正转 0为停止 -1为倒转
#清空图像目录
for root, dirs, files in os.walk(path_1["path_ref"]):
img_path_list = []
for file in files:
if os.path.splitext(file)[1] == '.jpg':
img_path_list.append(os.path.join(path_1["path_ref"],
file))
delete_file(Log_oper, img_path_list)
#
for root, dirs, files in os.walk(path_2["path_ref"]):
img_path_list = []
for file in files:
if os.path.splitext(file)[1] == '.jpg':
img_path_list.append(os.path.join(path_2["path_ref"],
file))
delete_file(Log_oper, img_path_list)
#
for root, dirs, files in os.walk(path_3["path_ref"]):
img_path_list = []
for file in files:
if os.path.splitext(file)[1] == '.jpg':
img_path_list.append(os.path.join(path_3["path_ref"],
file))
delete_file(Log_oper, img_path_list)
#
for root, dirs, files in os.walk(path_4["path_ref"]):
img_path_list = []
for file in files:
if os.path.splitext(file)[1] == '.jpg':
img_path_list.append(os.path.join(path_4["path_ref"],
file))
delete_file(Log_oper, img_path_list)
#
except Exception as err:
Log_oper.add_log("Error>>执行中出现错误{},程序自动重启".format(err))
os.system('taskkill /IM ArNTSteelDefectsClassifier64.exe /F')
#钢板状态更新
def total_steel_state_update():
#抛丸机
if recv_res["SBM"][1] == 1:
enable_l2_final_send["SBM"] = True
if recv_res["SBM"][1] == 0 and last_recv_res["SBM"][1] == 1: #钢板退出时执行
if recv_res["SBM"][2] >= 0 and enable_l2_final_send["SBM"] is True:
if steel_no_send["SBM"] is not True:
steel_no, steel_type, steel_size, img_path = SCD_SBM_Oper.get_send_char(
char_roi, cache_limit=False)
if steel_no is not None:
SCD_SBM_Oper.send_char_to_l2(steel_no, steel_type,
steel_size, img_path)
else:
if SCD_SBM_Oper.curr_char_img_info[1] is not None:
SCD_SBM_Oper.send_char_to_l2_not_steel_no()
else:
SCD_SBM_Oper.send_char_to_l2_not_img()
elif recv_res["SBM"][1] == 1 and last_recv_res["SBM"][
1] == 0: #钢板重新进入后，上一次发送的板号置0
steel_no_send["SBM"] = False
last_recv_res_plan_counter["SBM"] = copy.deepcopy(
recv_res["SBM"][0])
SCD_SBM_Oper.last_steel_no = "00000000000000"
SCD_SBM_Oper.curr_char_img_info = [None, None]
last_recv_res["SBM"] = copy.deepcopy(recv_res["SBM"])
#1号炉
if recv_res["HTF1"][1] == 1:
enable_l2_final_send["HTF1"] = True
if recv_res["HTF1"][1] == 0 and last_recv_res["HTF1"][1] == 1: #钢板退出时执行
if recv_res["HTF1"][2] >= 0 and enable_l2_final_send[
"HTF1"] is True:
if steel_no_send["HTF1"] is not True:
steel_no, steel_type, steel_size, img_path = SCD_HTF1_Oper.get_send_char(
char_roi, cache_limit=False)
if steel_no is not None:
SCD_HTF1_Oper.send_char_to_l2(steel_no, steel_type,
steel_size, img_path)
else:
if SCD_HTF1_Oper.curr_char_img_info[1] is not None:
SCD_HTF1_Oper.send_char_to_l2_not_steel_no()
else:
SCD_HTF1_Oper.send_char_to_l2_not_img()
elif recv_res["HTF1"][1] == 1 and last_recv_res["HTF1"][
1] == 0: #钢板重新进入后，上一次发送的板号置0
steel_no_send["HTF1"] = False
last_recv_res_plan_counter["HTF1"] = copy.deepcopy(
recv_res["HTF1"][0])
#针对炉前来回倒板的情况，为避免重复识别发送
#SCD_HTF1_Oper.last_steel_no="00000000000000"
SCD_HTF1_Oper.curr_char_img_info = [None, None]
last_recv_res["HTF1"] = copy.deepcopy(recv_res["HTF1"])
#2号炉
if recv_res["HTF2"][1] == 1:
enable_l2_final_send["HTF2"] = True
if recv_res["HTF2"][1] == 0 and last_recv_res["HTF2"][1] == 1: #钢板退出时执行
if recv_res["HTF2"][2] >= 0 and enable_l2_final_send[
"HTF2"] is True:
if steel_no_send["HTF2"] is not True:
steel_no, steel_type, steel_size, img_path = SCD_HTF2_Oper.get_send_char(
char_roi, cache_limit=False)
if steel_no is not None:
SCD_HTF2_Oper.send_char_to_l2(steel_no, steel_type,
steel_size, img_path)
else:
if SCD_HTF2_Oper.curr_char_img_info[1] is not None:
SCD_HTF2_Oper.send_char_to_l2_not_steel_no()
else:
SCD_HTF2_Oper.send_char_to_l2_not_img()
elif recv_res["HTF2"][1] == 1 and last_recv_res["HTF2"][
1] == 0: #钢板重新进入后，上一次发送的板号置0
steel_no_send["HTF2"] = False
last_recv_res_plan_counter["HTF2"] = copy.deepcopy(
recv_res["HTF2"][0])
#针对炉前来回倒板的情况，为避免重复识别发送
#SCD_HTF2_Oper.last_steel_no="00000000000000"
SCD_HTF2_Oper.curr_char_img_info = [None, None]
last_recv_res["HTF2"] = copy.deepcopy(recv_res["HTF2"])
#标印
if recv_res["PRINT"][1] == 1:
enable_l2_final_send["PRINT"] = True
if recv_res["PRINT"][1] == 0 and last_recv_res["PRINT"][
1] == 1: #钢板退出时执行
if recv_res["PRINT"][2] >= 0 and enable_l2_final_send[
"PRINT"] is True:
if steel_no_send["PRINT"] is not True:
steel_no, steel_type, steel_size, img_path = SCD_PRINT_Oper.get_send_char(
char_roi, cache_limit=False)
if steel_no is not None:
SCD_PRINT_Oper.send_char_to_l2(steel_no, steel_type,
steel_size, img_path)
else:
if SCD_PRINT_Oper.curr_char_img_info[1] is not None:
SCD_PRINT_Oper.send_char_to_l2_not_steel_no()
else:
SCD_PRINT_Oper.send_char_to_l2_not_img()
elif recv_res["PRINT"][1] == 1 and last_recv_res["PRINT"][
1] == 0: #钢板重新进入后，上一次发送的板号置0
steel_no_send["PRINT"] = False
last_recv_res_plan_counter["PRINT"] = copy.deepcopy(
recv_res["PRINT"][0])
SCD_PRINT_Oper.last_steel_no = "00000000000000"
SCD_PRINT_Oper.curr_char_img_info = [None, None]
last_recv_res["PRINT"] = copy.deepcopy(recv_res["PRINT"])
while True:
try:
Log_oper.add_log("Normal>>获得文件夹图像路径列表")
time.sleep(1)
Log_oper.add_log(
"Normal>>-----------------抛丸机前字符识别-----------------")
for root, dirs, files in os.walk(path_1["path_ref"]):
img_path_list = []
if len(files) > 0: #确定是否钢板已走完
judge_num_sbm = 0
else:
judge_num_sbm = judge_num_sbm + 1
num = 0
for file in files:
if os.path.splitext(file)[1] == '.jpg':
num = num + 1
if num > 10:
break
img_path_list.append(
os.path.join(path_1["path_ref"], file))
char_label, char_roi, img_name, top_offset, bottom_offset = SCD_SBM_Oper.detect_steel(
file)
if char_roi is not None:
if char_label != 'tchar':
SCD_SBM_Oper.detect_char(
char_roi, img_name, top_offset,
bottom_offset)
else:
SCD_SBM_Oper.detect_char_mini(
char_roi, img_name, top_offset,
bottom_offset)
steel_no, steel_type, steel_size, img_path = SCD_SBM_Oper.get_send_char(
char_roi, cache_limit=True)
if steel_no is not None:
recv_res = t_recv_info.get_result()
if recv_res["SBM"][0] == last_recv_res_plan_counter[
"SBM"] and recv_res["SBM"][
2] >= 0: #还未依据钢板号查到计划且辊道速度不为负
SCD_SBM_Oper.send_char_to_l2(
steel_no, steel_type, steel_size, img_path)
steel_no_send["SBM"] = True
recv_res = t_recv_info.get_result() #一级状态
total_steel_state_update()
delete_file(Log_oper, img_path_list)
Log_oper.add_log(
"Normal>>-----------------一号加热炉前字符识别-----------------")
for root, dirs, files in os.walk(path_2["path_ref"]):
img_path_list = []
if len(files) > 0: #确定是否钢板已走完
judge_num_htf1 = 0
else:
judge_num_htf1 = judge_num_htf1 + 1
num = 0
for file in files:
if os.path.splitext(file)[1] == '.jpg':
num = num + 1
if num > 10:
break
img_path_list.append(
os.path.join(path_2["path_ref"], file))
char_label, char_roi, img_name, top_offset, bottom_offset = SCD_HTF1_Oper.detect_steel(
file)
if char_roi is not None:
if char_label != 'tchar':
SCD_HTF1_Oper.detect_char(
char_roi, img_name, top_offset,
bottom_offset)
else:
SCD_HTF1_Oper.detect_char_mini(
char_roi, img_name, top_offset,
bottom_offset)
steel_no, steel_type, steel_size, img_path = SCD_HTF1_Oper.get_send_char(
char_roi, cache_limit=True)
if steel_no is not None:
recv_res = t_recv_info.get_result()
if recv_res["HTF1"][
0] == last_recv_res_plan_counter[
"HTF1"] and recv_res["HTF1"][
2] >= 0: #还未依据钢板号查到计划且辊道速度不为负
SCD_HTF1_Oper.send_char_to_l2(
steel_no, steel_type, steel_size, img_path)
steel_no_send["HTF1"] = True
recv_res = t_recv_info.get_result() #一级状态
total_steel_state_update()
delete_file(Log_oper, img_path_list)
Log_oper.add_log(
"Normal>>-----------------二号加热炉前字符识别-----------------")
for root, dirs, files in os.walk(path_3["path_ref"]):
img_path_list = []
if len(files) > 0: #确定是否钢板已走完
judge_num_htf2 = 0
else:
judge_num_htf2 = judge_num_htf2 + 1
num = 0
for file in files:
if os.path.splitext(file)[1] == '.jpg':
num = num + 1
if num > 10:
break
img_path_list.append(
os.path.join(path_3["path_ref"], file))
char_label, char_roi, img_name, top_offset, bottom_offset = SCD_HTF2_Oper.detect_steel(
file)
if char_roi is not None:
if char_label != 'tchar':
SCD_HTF2_Oper.detect_char(
char_roi, img_name, top_offset,
bottom_offset)
else:
SCD_HTF2_Oper.detect_char_mini(
char_roi, img_name, top_offset,
bottom_offset)
steel_no, steel_type, steel_size, img_path = SCD_HTF2_Oper.get_send_char(
char_roi, cache_limit=True)
if steel_no is not None:
recv_res = t_recv_info.get_result()
if recv_res["HTF2"][
0] == last_recv_res_plan_counter[
"HTF2"] and recv_res["HTF2"][
2] >= 0: #还未依据钢板号查到计划且辊道速度不为负
SCD_HTF2_Oper.send_char_to_l2(
steel_no, steel_type, steel_size, img_path)
steel_no_send["HTF2"] = True
recv_res = t_recv_info.get_result() #一级状态
total_steel_state_update()
delete_file(Log_oper, img_path_list)
Log_oper.add_log(
"Normal>>-----------------喷印前字符识别-----------------")
for root, dirs, files in os.walk(path_4["path_ref"]):
img_path_list = []
if len(files) > 0: #确定是否钢板已走完
judge_num_print = 0
else:
judge_num_print = judge_num_print + 1
num = 0
for file in files:
if os.path.splitext(file)[1] == '.jpg':
num = num + 1
if num > 10:
break
img_path_list.append(
os.path.join(path_4["path_ref"], file))
char_label, char_roi, img_name, top_offset, bottom_offset = SCD_PRINT_Oper.detect_steel(
file)
if char_roi is not None:
if char_label != 'tchar':
SCD_PRINT_Oper.detect_char(
char_roi, img_name, top_offset,
bottom_offset)
else:
SCD_PRINT_Oper.detect_char_mini(
char_roi, img_name, top_offset,
bottom_offset)
steel_no, steel_type, steel_size, img_path = SCD_PRINT_Oper.get_send_char(
char_roi, cache_limit=True)
if steel_no is not None:
recv_res = t_recv_info.get_result()
if recv_res["PRINT"][
0] == last_recv_res_plan_counter[
"PRINT"] and recv_res["PRINT"][
2] >= 0: #还未依据钢板号查到计划且辊道速度不为负
SCD_PRINT_Oper.send_char_to_l2(
steel_no, steel_type, steel_size, img_path)
steel_no_send["PRINT"] = True
recv_res = t_recv_info.get_result() #一级状态
total_steel_state_update()
delete_file(Log_oper, img_path_list)
except Exception as err:
Log_oper.add_log("Error>>执行中出现错误{},程序自动重启".format(err))
os.system('taskkill /IM ArNTSteelDefectsClassifier64.exe /F')
steel_model.close_session()