commit of early 22 implements

README.md

@@ -1,3 +0,0 @@
-# mailsys_server
-
-Repository for M.A.I.L system's analysis server.

auto_coordinate.py

@@ -1,41 +0,0 @@
-import os
-import sys
-import glob
-import time
-
-
-hdd_root = '/hdd/*'
-
-def main():
-    surgery_list = glob.glob(hdd_root)
-
-    for id in surgery_list:
-        if 'lost+found' in id:
-            surgery_list.remove(id)
-        else:
-            pass
-
-    for id in surgery_list:
-        file_list=glob.glob(id + '/*')
-        for file in file_list:
-            if 'log.txt' in file:
-                f = open(file, 'rt')
-                logs = f.readlines()
-                f.close()
-                if len(logs) == 4:
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('global coordinate successfully starts at ' + str(r_time) + '\n')
-                    f.close()
-                    #FIXME: change coordinate_g.py when IMU sensor works
-                    os.system('docker run --rm --name coordinate --gpus '"device=1"' -v /dev:/dev -v /home/mc365/sources:/sources -v /hdd:/hdd ellishuntingmoon/mailsys:0.4 python3 /sources/coordinate_g.py ' + id + '/ >> /dev/null')
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('global coordinate successfully ends at ' + str(r_time) + '\n')
-                    f.close()
-                else:
-                    pass
-
-if __name__ == '__main__':
-    while True:
-        main()

auto_extract.py

@@ -1,91 +0,0 @@
-import os
-import sys
-import glob
-import time
-import threading
-import paramiko
-
-
-hdd_root = '/hdd/*'
-
-def cmd_run(index, shell_cmd):
-    sh = shell_cmd[index]
-    os.system(sh)
-
-def main():
-    surgery_list = glob.glob(hdd_root)
-
-    for id in surgery_list:
-        if 'lost+found' in id:
-            surgery_list.remove(id)
-        else:
-            pass
-
-    for id in surgery_list:
-        
-        file_list=glob.glob(id + '/*')
-        for file in file_list:
-            if 'log.txt' in file:
-                f = open(file, 'rt')
-                logs = f.readlines()
-                f.close()                
-                if len(logs) == 2:
-                    thread_list = []
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('exectue is successfully starts at ' + str(r_time) + '\n')
-                    f.close()
-                    cmd_list = ['docker run --rm --name extract_2 --gpus '"device=1"' -v /dev:/dev -v /home/mc365/sources:/sources -v /hdd:/hdd ellishuntingmoon/mailsys:0.5 python3 /sources/extract_02.py ' + id + '/', 'echo ext_1 start']
-                    for index in range(0, len(cmd_list)):
-                        thread_list.append(threading.Thread(target=cmd_run,args=(index,cmd_list)))
-                        thread_list[index].start()         
-                    os.system('docker run --rm --name extract_1 --gpus '"device=0"' -v /dev:/dev -v /home/mc365/sources:/sources -v /hdd:/hdd ellishuntingmoon/mailsys:0.5 python3 /sources/extract_01.py ' + id + '/')       
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('exectue is successfully ends at ' + str(r_time) + '\n')
-                    f.close()
-
-                    #NOTE: Starting global coordinate
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('global coordinate successfully starts at ' + str(r_time) + '\n')
-                    f.close()
-                    #FIXME: change coordinate_g.py when IMU sensor works
-                    os.system('docker run --rm --name coordinate --gpus '"device=1"' -v /dev:/dev -v /home/mc365/sources:/sources -v /hdd:/hdd ellishuntingmoon/mailsys:0.5 python3 /sources/coordinate_g.py ' + id + '/ >> /dev/null')
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('global coordinate successfully ends at ' + str(r_time) + '\n')
-                    f.close()
-
-
-                    #NOTE:
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('kpi successfully starts at ' + str(r_time) + '\n')
-                    f.close()
-                    #FIXME: change coordinate_g.py when IMU sensor works and get generic optimizer for DBSCAN
-                    os.system('docker run --rm --name kpi --gpus '"device=1"' -v /dev:/dev -v /home/mc365/sources:/sources -v /hdd:/hdd ellishuntingmoon/mailsys:0.5 python3 /sources/kpi.py ' + id + '/ >> /dev/null')
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('kpi successfully ends at ' + str(r_time) + '\n')
-                    f.close()
-
-                    transport = paramiko.Transport(('365mc.iptime.org', 63122))
-                    transport.connect(username='mc365', password='tkadbrdhMC1!')
-                    sftp = paramiko.SFTPClient.from_transport(transport)
-                    #FIXME: send file recursively
-                    full_list = glob.glob(id + "/*")
-                    sftp.mkdir('/data/'+full_list[0][5:22])
-                    for f in full_list:
-                        sftp.put(f,'/data/'+str(f[5:]))
-                    sftp.close()
-                    transport.close()      
-                    os.system('rm -rf '+id)     
-
-                else:
-                    pass
-
-if __name__ == '__main__':
-    while True:
-        main()
-        time.sleep(900)

auto_kpi.py

@@ -1,60 +0,0 @@
-import os
-import sys
-import glob
-import time
-import paramiko
-
-
-
-hdd_root = '/hdd/*'
-
-
-hdd_root = '/hdd/*'
-
-def main():
-    surgery_list = glob.glob(hdd_root)
-
-    for id in surgery_list:
-        #id = /hdd/********
-        if 'lost+found' in id:
-            surgery_list.remove(id)
-        else:
-            pass
-
-    for id in surgery_list:
-        file_list=glob.glob(id + '/*')
-
-        for file in file_list:
-            #file = /hdd/**********/*.csv, svo, txt
-            if 'log.txt' in file:
-                f = open(file, 'rt')
-                logs = f.readlines()
-                f.close()
-                if len(logs) == 6:
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('kpi successfully starts at ' + str(r_time) + '\n')
-                    f.close()
-                    #FIXME: change coordinate_g.py when IMU sensor works and get generic optimizer for DBSCAN
-                    os.system('docker run --rm --name kpi --gpus '"device=1"' -v /dev:/dev -v /home/mc365/sources:/sources -v /hdd:/hdd ellishuntingmoon/mailsys:0.4 python3 /sources/kpi.py ' + id + '/ >> /dev/null')
-                    f = open(file, 'at')
-                    r_time = time.ctime(time.time())
-                    f.write('kpi successfully ends at ' + str(r_time) + '\n')
-                    f.close()
-
-                    transport = paramiko.Transport(('365mc.iptime.org', 63122))
-                    transport.connect(username='mc365', password='tkadbrdhMC1!')
-                    sftp = paramiko.SFTPClient.from_transport(transport)
-                    #FIXME: send file recursively
-                    for file in file_list:
-                        sftp.put(file,'/data/'+str(file[5:]))
-                    sftp.close()
-                    transport.close()      
-                    os.system('rm -rf '+id)          
-                else:
-                    pass
-        
-
-if __name__ == '__main__':
-    while True:
-        main()

cfg/log_reboot.txt

@@ -0,0 +1,2 @@
+This file automatically created at 2022/02/04 19:02:51
+[REBOOT] Device reboot executed at 2022/02/05 19:23:39

configuration.json

@@ -0,0 +1,39 @@
+{
+    "confDate": "2022/02/09 19:18:45",
+    "branch": "",
+    "room": "",
+    "workingDir": "/home/mc365/data",
+    "storageDir": "/hdd",
+    "mainCam": "ZED 29498854",
+    "recFrame": 1800,
+    "cam1": {
+        "SN": "ZED 29498854",
+        "angle": 10.848755325589863,
+        "d_center": 1.77,
+        "headCoord": [
+            1149,
+            540
+        ],
+        "d_headCoord": 1.78,
+        "tailCoord": [
+            700,
+            540
+        ],
+        "d_tailCoord": 1.8
+    },
+    "cam2": {
+        "SN": "ZED 27972057",
+        "angle": 10.848755325589863,
+        "d_center": 1.77,
+        "headCoord": [
+            700,
+            540
+        ],
+        "d_headCoord": 1.78,
+        "tailCoord": [
+            1149,
+            540
+        ],
+        "d_tailCoord": 1.8
+    }
+}

configuration.json.bak

@@ -0,0 +1,25 @@
+{
+    "confDate": "2022/02/09 03:57:27",
+    "workingDir": "/home/mc365/data",
+    "storageDir": "/hdd",
+    "mainCam": "ZED 22842807",
+    "recFrame": 1800,
+    "cam1": {
+        "SN": "ZED 22842807",
+        "angle": 8.925006049019956,
+        "d_center": 0,
+        "headCoord": "",
+        "d_headCoord": 0,
+        "tailCoord": "",
+        "d_tailCoord": 0
+    },
+    "cam2": {
+        "SN": "",
+        "angle": "",
+        "d_center": "",
+        "headCoord": "",
+        "d_headCoord": "",
+        "tailCoord": "",
+        "d_tailCoord": ""
+    }
+}

coordinate_g.py

@@ -1,54 +0,0 @@
-import pandas as pd
-import os
-import sys
-
-target_dir = str(sys.argv[1])
-
-full_list = os.listdir(target_dir)
-csv_list = [file for file in full_list if file.startswith('cam')]
-csv_list = sorted(csv_list)
-#csv_list = [cam1.csv,cam2.csv]
-#print(csv_list)
-
-d1 = pd.read_csv(os.path.join(target_dir,csv_list[0]))
-
-if len(csv_list) != 1 :
-    d2 = pd.read_csv(os.path.join(target_dir,csv_list[1]))
-
-    d2['x'] = 1920 - d2['x']
-
-    df_conc = pd.concat([d1,d2])
-else:
-    df_conc = d1
-if len(d1) == 0 and len(d2) == 0:
-    dummy_dic = {'x':[0,1920], 'y':[1080,0], 'z':[1.0,5.0], 'frame':[1,1900]}
-    df_conc = pd.DataFrame(dummy_dic)
-df_conc.sort_values(by='frame')
-df_conc.drop_duplicates(['frame'])
-#print(df_conc)
-
-df_global = pd.DataFrame(index=range(0,int(df_conc['frame'].iloc[-1])+1), columns=['x','y','z','frame'])
-df_global['frame'] = list(range(1, int(df_conc['frame'].iloc[-1]+2)))
-df_global = df_global.astype({'frame':'int'})
-#print(df_global)
-
-df_global = pd.concat([df_global,df_conc])
-
-df_global=df_global.sort_values(by = ['frame'])
-df_global = df_global.drop_duplicates(['frame'], keep='last')
-df_global = df_global.reset_index(drop=True)
-df_global = df_global.interpolate()
-df_global = df_global.interpolate(method='values')
-#print(df_global)
-
-
-df_global['x'] = 10 * ((df_global['x'] - df_global['x'].min()) / (df_global['x'].max() - df_global['x'].min())) - 5
-df_global['y'] = 10 * ((df_global['y'] - df_global['y'].min()) / (df_global['y'].max() - df_global['y'].min())) - 5
-df_global['z'] = 2.7 * ((df_global['z'] - df_global['z'].min()) / (df_global['z'].max() - df_global['z'].min())) + 0.3
-#print(df_global)
-
-df_global = df_global.fillna(0)
-df_summary = df_global[df_global['frame']% 30 == 1]
-#print(df_summary)
-df_global.to_csv(os.path.join(target_dir,'coordinate.csv'))
-df_summary.to_csv(os.path.join(target_dir,'coordinate_summary.csv'))

data.data

@@ -1,5 +0,0 @@
-classes=2
-train=/tmp/darknet/365mc/dataset_v4/train.txt
-valid=/tmp/darknet/365mc/dataset_v4/valid.txt
-names=/sources/names.names
-backup=/tmp/darknet/365mc/dataset_v4/backup

depthsensing.py

@@ -1,37 +1,33 @@
 #!python3
 """
 Python 3 wrapper for identifying objects in images
-
 Requires DLL compilation
-
 Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py
 Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py
-
 @author: Philip Kahn, Aymeric Dujardin
 @date: 20180911
 """
 # pylint: disable=R, W0401, W0614, W0703
-import cv2
-import pyzed.sl as sl
-from ctypes import *
-import math
-import random
 import os
-import numpy as np
-import statistics
 import sys
-import getopt
-from random import randint
 import time
+import logging
+import random
+from random import randint
+import math
+import statistics
+import getopt
+from ctypes import *
+import numpy as np
+import cv2
+import pyzed.sl as sl
 import pandas as pd
-from pandas import  DataFrame as df
-import  subprocess
+from pandas import DataFrame as df
 
 
-#NOTE at 2020-12-22: if svo's resulotion changed, then you have to edit WIDTH, HEIGHT and DEPTH_MAX
-WIDTH = 1920
-HEIGHT = 1080
-DEPTH_MAX = 8
+# Get the top-level logger object
+log = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
 
 
 def sample(probs):
@@ -64,7 +60,13 @@ class DETECTION(Structure):
                 ("prob", POINTER(c_float)),
                 ("mask", POINTER(c_float)),
                 ("objectness", c_float),
-                ("sort_class", c_int)]
+                ("sort_class", c_int),
+                ("uc", POINTER(c_float)),
+                ("points", c_int),
+                ("embeddings", POINTER(c_float)),
+                ("embedding_size", c_int),
+                ("sim", c_float),
+                ("track_id", c_int)]
 
 
 class IMAGE(Structure):
@@ -86,7 +88,6 @@ if os.name == "nt":
     cwd = os.path.dirname(__file__)
     os.environ['PATH'] = cwd + ';' + os.environ['PATH']
     winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll")
-    #print(winGPUdll)
     winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll")
     envKeys = list()
     for k, v in os.environ.items():
@@ -97,7 +98,7 @@ if os.name == "nt":
             if tmp in ["1", "true", "yes", "on"]:
                 raise ValueError("ForceCPU")
             else:
-                pass#print("Flag value '"+tmp+"' not forcing CPU mode")
+                log.info("Flag value '"+tmp+"' not forcing CPU mode")
         except KeyError:
             # We never set the flag
             if 'CUDA_VISIBLE_DEVICES' in envKeys:
@@ -109,8 +110,8 @@ if os.name == "nt":
                     raise ValueError("ForceCPU")
             except NameError:
                 pass
-            # #print(os.environ.keys())
-            # #print("FORCE_CPU flag undefined, proceeding with GPU")
+            # log.info(os.environ.keys())
+            # log.warning("FORCE_CPU flag undefined, proceeding with GPU")
         if not os.path.exists(winGPUdll):
             raise ValueError("NoDLL")
         lib = CDLL(winGPUdll, RTLD_GLOBAL)
@@ -118,13 +119,13 @@ if os.name == "nt":
         hasGPU = False
         if os.path.exists(winNoGPUdll):
             lib = CDLL(winNoGPUdll, RTLD_GLOBAL)
-            #print("Notice: CPU-only mode")
+            log.warning("Notice: CPU-only mode")
         else:
             # Try the other way, in case no_gpu was
             # compile but not renamed
             lib = CDLL(winGPUdll, RTLD_GLOBAL)
-            #print("Environment variables indicated a CPU run, but we didn't find `" +
-            #      winNoGPUdll+"`. Trying a GPU run anyway.")
+            log.warning("Environment variables indicated a CPU run, but we didn't find `" +
+                        winNoGPUdll+"`. Trying a GPU run anyway.")
 else:
     lib = CDLL("/root/darknet/libdarknet.so", RTLD_GLOBAL)
 lib.network_width.argtypes = [c_void_p]
@@ -220,10 +221,10 @@ def classify(net, meta, im):
     res = []
     for i in range(meta.classes):
         if altNames is None:
-            nameTag = meta.names[i]
+            name_tag = meta.names[i]
         else:
-            nameTag = altNames[i]
-        res.append((nameTag, out[i]))
+            name_tag = altNames[i]
+        res.append((name_tag, out[i]))
     res = sorted(res, key=lambda x: -x[1])
     return res
 
@@ -246,21 +247,16 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False):
         do_nms_sort(dets, num, meta.classes, nms)
     res = []
     if debug:
-        pass
-        #print("about to range")
+        log.debug("about to range")
     for j in range(num):
         for i in range(meta.classes):
-            try:
-                if dets[j].prob[i] > 0:
-                    b = dets[j].bbox
-                    if altNames is None:
-                        nameTag = meta.names[i]
-                    else:
-                        nameTag = altNames[i]
-                    res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h), i))
-            except ValueError as e:
-                #print(e)
-                pass
+            if dets[j].prob[i] > 0:
+                b = dets[j].bbox
+                if altNames is None:
+                    name_tag = meta.names[i]
+                else:
+                    name_tag = altNames[i]
+                res.append((name_tag, dets[j].prob[i], (b.x, b.y, b.w, b.h), i))
     res = sorted(res, key=lambda x: -x[1])
     free_detections(dets, num)
     return res
@@ -271,7 +267,20 @@ metaMain = None
 altNames = None
 
 
-def getObjectDepth(depth, bounds):
+def get_object_depth(depth, bounds):
+    '''
+    Calculates the median x, y, z position of top slice(area_div) of point cloud
+    in camera frame.
+    Arguments:
+        depth: Point cloud data of whole frame.
+        bounds: Bounding box for object in pixels.
+            bounds[0]: x-center
+            bounds[1]: y-center
+            bounds[2]: width of bounding box.
+            bounds[3]: height of bounding box.
+    Return:
+        x, y, z: Location of object in meters.
+    '''
     area_div = 2
 
     x_vect = []
@@ -286,22 +295,29 @@ def getObjectDepth(depth, bounds):
                 y_vect.append(depth[i, j, 1])
                 z_vect.append(z)
     try:
-        x = statistics.median(x_vect)
-        y = statistics.median(y_vect)
-        z = statistics.median(z_vect)
+        x_median = statistics.median(x_vect)
+        y_median = statistics.median(y_vect)
+        z_median = statistics.median(z_vect)
     except Exception:
-        x = -1
-        y = -1
-        z = -1
+        x_median = -1
+        y_median = -1
+        z_median = -1
         pass
 
-    return x, y, z
+    return x_median, y_median, z_median
 
 
-def generateColor(metaPath):
+def generate_color(meta_path):
+    '''
+    Generate random colors for the number of classes mentioned in data file.
+    Arguments:
+    meta_path: Path to .data file.
+    Return:
+    color_array: RGB color codes for each class.
+    '''
     random.seed(42)
-    f = open(metaPath, 'r')
-    content = f.readlines()
+    with open(meta_path, 'r') as f:
+        content = f.readlines()
     class_num = int(content[0].split("=")[1])
     color_array = []
     for x in range(0, class_num):
@@ -312,62 +328,58 @@ def generateColor(metaPath):
 def main(argv):
 
     thresh = 0.25
-    configPath = "/sources/cfg.cfg"
-    weightPath = "/sources/mailsys.weight"
-    metaPath = "/sources/data.data"
-    svoPath = None
-
-    help_str = 'darknet_zed.py -c <config> -w <weight> -m <meta> -t <threshold> -s <svo_file> -o <output_path>'
+    darknet_path="../libdarknet/"
+    config_path = darknet_path + "cfg/yolov3-tiny.cfg"
+    weight_path = "yolov3-tiny.weights"
+    meta_path = "coco.data"
+    svo_path = None
+    zed_id = 0
+
+    help_str = 'darknet_zed.py -c <config> -w <weight> -m <meta> -t <threshold> -s <svo_file> -z <zed_id>'
     try:
         opts, args = getopt.getopt(
-            argv, "hc:w:m:t:s:o:", ["config=", "weight=", "meta=", "threshold=", "svo_file=", "output_path="])
+            argv, "hc:w:m:t:s:z:o:", ["config=", "weight=", "meta=", "threshold=", "svo_file=", "zed_id=", "output_path="])
     except getopt.GetoptError:
-        print (help_str)
+        log.exception(help_str)
         sys.exit(2)
     for opt, arg in opts:
         if opt == '-h':
-            print (help_str)
+            log.info(help_str)
             sys.exit()
         elif opt in ("-c", "--config"):
-            configPath = arg
+            config_path = arg
         elif opt in ("-w", "--weight"):
-            weightPath = arg
+            weight_path = arg
         elif opt in ("-m", "--meta"):
-            metaPath = arg
+            meta_path = arg
         elif opt in ("-t", "--threshold"):
             thresh = float(arg)
         elif opt in ("-s", "--svo_file"):
-            svoPath = arg
+            svo_path = arg
+        elif opt in ("-z", "--zed_id"):
+            zed_id = int(arg)
         elif opt in ("-o", "--output_path"):
             output_path = arg
 
-    init = sl.InitParameters()
-    init.coordinate_units = sl.UNIT.METER
-
-
-
-    if svoPath is not None:
-        init.set_from_svo_file(svoPath)
-
-    dirname = os.path.splitext(svoPath)[0]
-    dir, filename = os.path.split(dirname)
-    #print(output_path)
-    #print(filename)
+            output_path_nms = arg[:-4] + '_nms' + arg[-4:]
 
-    #try:
-    #    if not (os.path.isdir(output_path)):
-    #        os.makedirs(os.path.join(output_path))
-    #except OSError as e:
-    #    #print(e)
+    input_type = sl.InputType()
+    if svo_path is not None:
+        log.info("SVO file : " + svo_path)
+        input_type.set_from_svo_file(svo_path)
+    else:
+        # Launch camera by id
+        input_type.set_from_camera_id(zed_id)
 
+    init = sl.InitParameters(input_t=input_type)
+    init.coordinate_units = sl.UNIT.METER
 
     cam = sl.Camera()
     if not cam.is_opened():
-        #print("Opening ZED Camera...")
-        pass
+        log.info("Opening ZED Camera...")
     status = cam.open(init)
     if status != sl.ERROR_CODE.SUCCESS:
-        #print(repr(status))
+        log.error(repr(status))
         exit()
 
     runtime = sl.RuntimeParameters()
@@ -376,31 +388,32 @@ def main(argv):
     mat = sl.Mat()
     point_cloud_mat = sl.Mat()
 
-    # Import the global variables. This lets us instance Darknet once, then just call performDetect() again without instancing again
+    # Import the global variables. This lets us instance Darknet once,
+    # then just call performDetect() again without instancing again
     global metaMain, netMain, altNames  # pylint: disable=W0603
     assert 0 < thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
-    if not os.path.exists(configPath):
+    if not os.path.exists(config_path):
         raise ValueError("Invalid config path `" +
-                         os.path.abspath(configPath)+"`")
-    if not os.path.exists(weightPath):
+                         os.path.abspath(config_path)+"`")
+    if not os.path.exists(weight_path):
         raise ValueError("Invalid weight path `" +
-                         os.path.abspath(weightPath)+"`")
-    if not os.path.exists(metaPath):
+                         os.path.abspath(weight_path)+"`")
+    if not os.path.exists(meta_path):
         raise ValueError("Invalid data file path `" +
-                         os.path.abspath(metaPath)+"`")
+                         os.path.abspath(meta_path)+"`")
     if netMain is None:
-        netMain = load_net_custom(configPath.encode(
-            "ascii"), weightPath.encode("ascii"), 0, 1)  # batch size = 1
+        netMain = load_net_custom(config_path.encode(
+            "ascii"), weight_path.encode("ascii"), 0, 1)  # batch size = 1
     if metaMain is None:
-        metaMain = load_meta(metaPath.encode("ascii"))
+        metaMain = load_meta(meta_path.encode("ascii"))
     if altNames is None:
         # In thon 3, the metafile default access craps out on Windows (but not Linux)
         # Read the names file and create a list to feed to detect
         try:
-            with open(metaPath) as metaFH:
-                metaContents = metaFH.read()
+            with open(meta_path) as meta_fh:
+                meta_contents = meta_fh.read()
                 import re
-                match = re.search("names *= *(.*)$", metaContents,
+                match = re.search("names *= *(.*)$", meta_contents,
                                   re.IGNORECASE | re.MULTILINE)
                 if match:
                     result = match.group(1)
@@ -408,41 +421,27 @@ def main(argv):
                     result = None
                 try:
                     if os.path.exists(result):
-                        with open(result) as namesFH:
-                            namesList = namesFH.read().strip().split("\n")
-                            altNames = [x.strip() for x in namesList]
+                        with open(result) as names_fh:
+                            names_list = names_fh.read().strip().split("\n")
+                            altNames = [x.strip() for x in names_list]
                 except TypeError:
                     pass
         except Exception:
             pass
 
-    color_array = generateColor(metaPath)
+    color_array = generate_color(meta_path)
 
-    #print("Running...")
-    #df1 = df(data={'frame': [], 'label': [], 'x': [], 'y': [], 'depth': []})
+    #log.info("Running...")
     df1 = df(data={'x': [], 'y': [], 'z': [], 'frame': []})
-    #print(df1)
-    start = time.time()
-    key = ''
+    df_nms = df(data={'x': [], 'y': [], 'z': [],'nms':[] ,'frame': []})
     count = 0
     frame = 1
-
-    #image_size = cam.get_resolution()
-    #width = image_size.width
-    #height = image_size.height
-    #width_sbs = width * 2
-
-    # Prepare side by side image container equivalent to CV_8UC4
-    #svo_image_sbs_rgba = np.zeros((height, width_sbs, 4), dtype=np.uint8)
-    fourcc = cv2.VideoWriter_fourcc('M', '4', 'S', '2')
-
-    # 컬러 영상 저장시
-    ##print(cam.get_camera_fps(), "!!!!!!!!!!!")
-    #video_path = os.path.join(output_path, filename)
-    #writer = cv2.VideoWriter(str(video_path)+"_output.avi", fourcc, cam.get_camera_fps(), (width, height))
-    ##print(video_path)
-    ##print(writer)
-    while key != 113:  # for 'q' key
+    svo_frame = cam.get_svo_number_of_frames()
+    
+    quit_timer = time.time()
+    key = ''
+    while frame != svo_frame:  # for 'q' key        
+        start_time = time.time() # start time of the loop
         err = cam.grab(runtime)
         if err == sl.ERROR_CODE.SUCCESS:
             cam.retrieve_image(mat, sl.VIEW.LEFT)
@@ -455,75 +454,56 @@ def main(argv):
             # Do the detection
             detections = detect(netMain, metaMain, image, thresh)
 
-            #print(chr(27) + "[2J"+"**** " +
-                #  str(len(detections)) + " Results ****")
-            frame += 1
+            log.info(chr(27) + "[2J"+"**** " + str(len(detections)) + " Results ****")
+            
             for detection in detections:
+                log.info(detection)
                 label = detection[0]
                 confidence = detection[1]
                 pstring = label+": "+str(np.rint(100 * confidence))+"%"
-                #print(pstring)
+                log.info(pstring)
                 bounds = detection[2]
-                yExtent = int(bounds[3])
-                xEntent = int(bounds[2])
+                y_extent = int(bounds[3])
+                x_extent = int(bounds[2])
                 # Coordinates are around the center
-                xCoord = int(bounds[0] - bounds[2]/2)
-                yCoord = int(bounds[1] - bounds[3]/2)
-                boundingBox = [[xCoord, yCoord], [xCoord, yCoord + yExtent], [xCoord + xEntent, yCoord + yExtent], [xCoord + xEntent, yCoord] ]
+                x_coord = int(bounds[0] - bounds[2]/2)
+                y_coord = int(bounds[1] - bounds[3]/2)
+                #boundingBox = [[x_coord, y_coord], [x_coord, y_coord + y_extent], [x_coord + x_extent, y_coord + y_extent], [x_coord + x_extent, y_coord]]
                 thickness = 1
-                x, y, z = getObjectDepth(depth, bounds)
-                distance_xyz = math.sqrt(x * x + y * y + z * z)
-
-                distance = "{:.4f}".format(distance_xyz)
-
-                #print(label, distance, xCoord, yCoord)
-                cv2.rectangle(image, (xCoord-thickness, yCoord-thickness), (xCoord + xEntent+thickness, yCoord+(18 +thickness*4)), color_array[detection[3]], -1)
-                cv2.putText(image, label + " " +  (str(distance) + " m"), (xCoord+(thickness*4), yCoord+(10 +thickness*4)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 2)
-                cv2.rectangle(image, (xCoord-thickness, yCoord-thickness), (xCoord + xEntent+thickness, yCoord + yExtent+thickness), color_array[detection[3]], int(thickness*2))
-                x = xCoord-thickness
-                x1 = xCoord + xEntent+thickness
-                y = yCoord-thickness
-                y1 = yCoord + yExtent+thickness
-                
-
-                #If need normalize, erase # and change df1.loc[count]
-                #norm_x = (x + x1) / 2 / WIDTH
-                #norm_y = (y + y1) / 2 / HEIGHT
-                #norm_z = distance_xyz / DEPTH_MAX
-
-
-                
-                df1.loc[count] = [int(xCoord), int(yCoord), distance_xyz, int(frame)]
-                #df1.columns = ['idx','frame','label','x','y','depth']
-                cv2.line(image, (int((x+x1)/2), int((y+y1)/2)), (int((x+x1)/2), int((y+y1)/2)), (0, 0, 255), 10)
-
+                x, y, z = get_object_depth(depth, bounds)
+                distance = math.sqrt(x * x + y * y + z * z)
+                distance = "{:.8f}".format(distance)
+                cv2.rectangle(image, (x_coord - thickness, y_coord - thickness),
+                              (x_coord + x_extent + thickness, y_coord + (18 + thickness*4)),
+                              color_array[detection[3]], -1)
+                cv2.putText(image, label + " " +  (str(distance) + " m"),
+                            (x_coord + (thickness * 4), y_coord + (10 + thickness * 4)),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+                cv2.rectangle(image, (x_coord - thickness, y_coord - thickness),
+                              (x_coord + x_extent + thickness, y_coord + y_extent + thickness),
+                              color_array[detection[3]], int(thickness*2))
+                x = x_coord-thickness
+                x1 = x_coord + x_extent+thickness
+                y = y_coord-thickness
+                y1 = y_coord + y_extent+thickness
+                df1.loc[count] = [int(x_coord), int(y_coord), distance, int(frame)]
+                df_nms.loc[count] = [int(x_coord), int(y_coord), distance, np.rint(100*confidence), int(frame)]
                 count += 1
-                image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
-            #print(frame)
-            image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
-            #writer.write(image)
-            # cv2.imshow("ZED", image)
-           # key = cv2.waitKey(5)
-            # #print(cam.get_svo_number_of_frames())
-            if frame == cam.get_svo_number_of_frames():
-                break
-        else:
-            #print('something happend')
+            #cv2.imshow("ZED", image)
             #key = cv2.waitKey(5)
+            log.info("FPS: {}".format(1.0 / (time.time() - start_time)))
+            log.info("\n"+str(frame))
+            frame += 1        
+        else:        
             pass
-    #print("time :", time.time() - start)
+            #key = cv2.waitKey(5)
+    #cv2.destroyAllWindows()
 
     cam.close()
-    #writer.release()
-    #cv2.destroyAllWindows()
-    #print("\nFINISH")
-    ##print(df1)
-    
     df1.to_csv(output_path, index=False)
-    #print("csv save")
+    df_nms.to_csv(output_path_nms, index=False)
+    log.info("\nFINISH")
 
 
 if __name__ == "__main__":
-
-    #print(sys.argv)
-    main(sys.argv[1:])
+    main(sys.argv[1:])

detect_new_folder.py

@@ -1,23 +0,0 @@
-import os
-import time
-
-name_time_list = []
-target_dir = '/home/mc365/data/dummy'
-while True:
-    time.sleep(60)
-    root_path = "/home/mc365/data/"
-    filenames = os.listdir(root_path)
-    for f in filenames:
-        w_time = os.path.getctime(root_path + f)
-        name_time_list.append((f, w_time))
-    sorted_list = sorted(name_time_list, key=lambda x: x[1], reverse=True)
-    
-    
-    recent_file = sorted_list[0]
-    tmp_recent = str(root_path) + str(recent_file[0])
-    if target_dir != tmp_recent:
-        time.sleep(5)
-        command = 'docker run --rm --name mail_recorder --gpus '"device=1"' --privileged -v /dev:/dev -v /home/mc365/sources:/sources -v /home/mc365/data/'+str(recent_file[0])+':/data/'+str(recent_file[0])+' stereolabs/zed:3.5-gl-devel-cuda11.1-ubuntu18.04 python3 /sources/recorder.py.bak'
-        os.system(str(command))        
-    target_dir = str(root_path) + str(recent_file[0])    
-    

extract_01.py

@@ -1,35 +0,0 @@
-import sys
-import os
-import glob
-import pandas as pd
-
-#ssd_dir = /data/36536536520201231/
-target_dir = str(sys.argv[1])
-
-
-svo_list = os.listdir(target_dir)
-svo_list = [file for file in svo_list if file.endswith('1.svo')]
-svo_list = sorted(svo_list)
-del svo_list[-1]
-
-
-
-for svo in svo_list:
-  if svo[-5:] == '1.svo':   
-    os.system('python3 /sources/depthsensing.py -c /sources/cfg.cfg -w /sources/mailsys.weights -m /sources/data.data -s ' + target_dir + svo + ' -o ' + target_dir + svo[:5] + '.csv')
-  else:
-    pass
-
-full_list = os.listdir(target_dir)
-csv_1_list = [file for file in full_list if file.endswith("1.csv")]
-csv_1_list = sorted(csv_1_list)
-
-conc_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'frame': []})
-FRAME_COUNTER = 0
-N_OF_FRAME = 1800
-for x in csv_1_list:
-  df = pd.read_csv(os.path.join(target_dir,x), header=0)
-  df['frame'] = df['frame'] + FRAME_COUNTER
-  conc_df = pd.concat([conc_df,df])
-  FRAME_COUNTER += N_OF_FRAME
-conc_df.to_csv(os.path.join(target_dir,'cam1.csv'), index=False)

extract_02.py

@@ -1,33 +0,0 @@
-import sys
-import os
-import glob
-import pandas as pd
-
-#target_dir = /hdd/36536536520201231/
-target_dir = sys.argv[1]
-
-
-svo_list = os.listdir(target_dir)
-svo_list = [file for file in svo_list if file.endswith('2.svo')]
-svo_list = sorted(svo_list)
-del svo_list[-1]
-
-for svo in svo_list:
-  if svo[-5:] == '2.svo':
-    os.system('python3 /sources/depthsensing.py -c /sources/cfg.cfg -w /sources/mailsys.weights -m /sources/data.data -s ' + target_dir + svo + ' -o ' + target_dir + svo[:5] + '.csv')
-  else:
-    pass
-
-full_list = os.listdir(target_dir)
-csv_2_list = [file for file in full_list if file.endswith("2.csv")]
-csv_2_list = sorted(csv_2_list)
-
-conc_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'frame': []})
-FRAME_COUNTER = 0
-N_OF_FRAME = 1800
-for x in csv_2_list:
-  df = pd.read_csv(os.path.join(target_dir,x), header=0)
-  df['frame'] = df['frame'] + FRAME_COUNTER
-  conc_df = pd.concat([conc_df,df])
-  FRAME_COUNTER += N_OF_FRAME
-conc_df.to_csv(os.path.join(target_dir,'cam2.csv'), index=False)

kpi.py

@@ -1,233 +0,0 @@
-import os
-import time
-import math
-import sys
-import numpy as np
-from numpy.lib.function_base import append
-import pandas as pd
-from pandas import DataFrame as df
-from sklearn.cluster import DBSCAN
-import pymssql
-#import matplotlib.pyplot as plt
-
-
-ssd_dir = str(sys.argv[1])
-file_path = '/hdd/' + ssd_dir[5:]
-count = 1
-
-def put_kpi_to_db(kpi):
-    conn = pymssql.connect(server='52.231.39.219',port=1433, user='mc365', password='tkadbrdhmc1!', database='master')
-    #print("INSERT INTO tsfmc_mailsystem.dbo.KPI(surgeryID, totalStrokeCount, totalStrokeVelocity, totalStrokeDepth, upDownTotalStrokeCount, upDownTotalStrokeVelocity, upDownTotalStrokeDepth, leftRightTotalStrokeCount, leftRightTotalStrokeVelocity, leftRightTotalStrokeDepth, quadrant1TotalStrokeCount, quadrant1TotalStrokeVelocity, quadrant1TotalStrokeDepth, quadrant2TotalStrokeCount, quadrant2TotalStrokeVelocity, quadrant2TotalStrokeDepth, quadrant3TotalStrokeCount, quadrant3TotalStrokeVelocity, quadrant3TotalStrokeDepth, quadrant4TotalStrokeCount, quadrant4TotalStrokeVelocity, quadrant4TotalStrokeDepth) VALUES (" + str(kpi[0]) + ", "+str(kpi[1])+", "+str(kpi[2])+", "+str(kpi[3])+", "+str(kpi[4])+", "+str(kpi[5])+", "+str(kpi[6])+", "+str(kpi[7])+", "+str(kpi[8])+", "+str(kpi[9])+", "+str(kpi[10])+", "+str(kpi[11])+", "+str(kpi[12])+", "+str(kpi[13])+", "+str(kpi[14])+", "+str(kpi[15])+", "+str(kpi[16])+", "+str(kpi[17])+", "+str(kpi[18])+", "+str(kpi[19])+", "+str(kpi[20])+", "+str(kpi[21])+")")
-    cursor = conn.cursor()
-    cursor.execute("INSERT INTO tsfmc_mailsystem.dbo.KPI(surgeryID, totalStrokeCount, totalStrokeVelocity, totalStrokeDepth, upDownTotalStrokeCount, upDownTotalStrokeVelocity, upDownTotalStrokeDepth, leftRightTotalStrokeCount, leftRightTotalStrokeVelocity, leftRightTotalStrokeDepth, quadrant1TotalStrokeCount, quadrant1TotalStrokeVelocity, quadrant1TotalStrokeDepth, quadrant2TotalStrokeCount, quadrant2TotalStrokeVelocity, quadrant2TotalStrokeDepth, quadrant3TotalStrokeCount, quadrant3TotalStrokeVelocity, quadrant3TotalStrokeDepth, quadrant4TotalStrokeCount, quadrant4TotalStrokeVelocity, quadrant4TotalStrokeDepth) VALUES ( '" + str(kpi[0]) + "', '"+str(kpi[1])+"', '"+str(kpi[2])+"', '"+str(kpi[3])+"', '"+str(kpi[4])+"', '"+str(kpi[5])+"', '"+str(kpi[6])+"', '"+str(kpi[7])+"', '"+str(kpi[8])+"', '"+str(kpi[9])+"', '"+str(kpi[10])+"', '"+str(kpi[11])+"', '"+str(kpi[12])+"', '"+str(kpi[13])+"', '"+str(kpi[14])+"', '"+str(kpi[15])+"', '"+str(kpi[16])+"', '"+str(kpi[17])+"', '"+str(kpi[18])+"', '"+str(kpi[19])+"', '"+str(kpi[20])+"', '"+str(kpi[21])+"')")
-    conn.commit()
-    conn.close()
-
-
-def get_quad(df):
-    if df['x'] >= 0 and df['y'] >= 0:
-        return 1
-    elif df['x'] >= 0 and df['y'] < 0:
-        return 2
-    elif df['x'] < 0 and df['y'] < 0:
-        return 3
-    elif df['x'] < 0 and df['y'] >= 0:
-        return 4
-def get_inc_num(df):
-    global count
-    prev_count = 0
-    if abs(df['diff_diff']) > 0.0008:                
-        prev_count = df['inc_num']
-        df['inc_num'] = count
-        #print(prev_count)
-        return count        
-    else:
-        if prev_count != 0:            
-            count += 1
-            df['inc_num'] = 0
-            prev_count = df['inc_num']
-            return 0
-        else:
-            df['inc_num'] = 0
-            prev_count = df['inc_num']
-            return 0
-
-
-def count_inc(df):
-    e= 0
-    count  = 1
-    prev_count = 0
-    for x in df['diff_diff']:
-        if abs(x) > 8:
-            df['inc_num'][e] = count
-            prev_count = count
-            e +=1
-        else:
-            if prev_count != 0:
-                count += 1
-                df['inc_num'][e] = 0
-                prev_count = 0
-                e +=1
-            else:
-                df['inc_num'][e] = 0
-                prev_count = 0
-                e +=1
-
-def stroke_count(df):
-    e = 0
-    count = 1    
-    for x in df['diff_diff']:
-        if x > 1.2:
-            df['stroke_count'][e] = count
-            e +=1
-        else:
-            df['stroke_count'][e] = 0
-            e+=1
-            
-def get_sqrt(df):
-    e =0
-    count = 1
-    prev_x = 0.0
-    prev_y = 0.0
-    prev_z = 0.0
-    for x in range(len(df)):
-        df['sqrt'][e] = np.sqrt((df['x'][e] - prev_x) **2 + (df['y'][e] - prev_y) ** 2 +(df['z'][e] - prev_z) ** 2)
-        prev_x = df['x'][e]
-        prev_y = df['y'][e]
-        prev_z = df['z'][e]
-        e += 1
-
-        
-
-
-def dist():
-    file = os.path.join(file_path, 'coordinate.csv')
-    norm = pd.read_csv(file)
-    norm = norm.drop(norm.columns[0], axis=1)
-    norm = norm.fillna(0)
-    data = norm[['x', 'y', 'z']]
-    norm['sqrt'] = 0.0
-
-    get_sqrt(norm)
-
-    #norm = norm.apply(lambda x:np.sqrt(x) if x.name in ['sqrt'] else x, axis =1)
-    norm['diff'] = norm.diff()['sqrt']
-    norm['diff_diff'] = norm.diff()['diff']
-
-
-    norm['quad'] = norm.apply(get_quad, axis =1)
-    
-    
-    norm = norm.fillna(0)
-
-    norm['inc_num'] = 0
-    #norm['inc_num'] = norm.apply(get_inc_num, axis = 1)
-    count_inc(norm)
-    norm['stroke_count'] = 0
-    stroke_count(norm)
-    
-    #print('num of inc')
-    #print(norm['inc_num'].unique())
-
-    #print('total stroke')
-    #print(norm['stroke_count'].unique())
-    #print(norm)
-    diff_csv = norm[['frame','diff_diff']]
-    diff_csv.to_csv(os.path.join(file_path, 'diff.csv'))
-    #plt.plot(norm['frame'],norm['diff_diff'])
-    #plt.savefig('./diff_diff.png')    
-
-
-
-    kpi = pd.DataFrame(index=range(0,1))
-
-    quad_count = norm[norm['stroke_count'] != 0]
-    
-    quad1_c = quad_count[quad_count['quad'] == 1]
-    quad2_c = quad_count[quad_count['quad'] == 2]
-    quad3_c = quad_count[quad_count['quad'] == 3]
-    quad4_c = quad_count[quad_count['quad'] == 4]
-    #print(len(quad1_c))
-    #print(len(quad2_c))
-    #print(len(quad3_c))
-    #print(len(quad4_c))
-    #print(quad1_c)
-    kpi['PK'] = ssd_dir[5:] 
-    kpi['qaud1_c'] = len(quad1_c)
-    kpi['qaud2_c'] = len(quad2_c)
-    kpi['qaud3_c'] = len(quad3_c)
-    kpi['qaud4_c'] = len(quad4_c)
-    #FIXME: this values are huristic value. please change this values after research
-    kpi['qaud1_s'] = quad1_c['sqrt'].mean() / 30
-    kpi['qaud2_s'] = quad2_c['sqrt'].mean() / 30
-    kpi['qaud3_s'] = quad3_c['sqrt'].mean() / 30
-    kpi['qaud4_s'] = quad4_c['sqrt'].mean() / 30
-    kpi['qaud1_d'] = quad1_c['sqrt'].mean() / 45
-    kpi['qaud2_d'] = quad2_c['sqrt'].mean() / 45
-    kpi['qaud3_d'] = quad3_c['sqrt'].mean() / 45
-    kpi['qaud4_d'] = quad4_c['sqrt'].mean() / 45
-    kpi = kpi.fillna(0)
-
-
-    kpi_1 = {
-        "stroke": [len(quad1_c),len(quad2_c),len(quad3_c),len(quad4_c)],
-        "velocity": [quad1_c['sqrt'].mean() / 30, quad2_c['sqrt'].mean() / 30, quad3_c['sqrt'].mean() / 30, quad4_c['sqrt'].mean() / 30],
-        "depth": [quad1_c['sqrt'].mean() / 45, quad2_c['sqrt'].mean() / 45, quad3_c['sqrt'].mean() / 45, quad4_c['sqrt'].mean() / 45]
-    }
-
-
-    #print(kpi)
-    val_list = []
-
-    #PK =
-    val_list.append(str(ssd_dir[5:-1]))
-
-    #t_stroke = 
-    val_list.append(sum(kpi_1['stroke']))
-    #t_vel
-    val_list.append("{:.4f}".format(sum(kpi_1['velocity'])/4)) 
-    #t_dep
-    val_list.append("{:.4f}".format(sum(kpi_1['depth'])/4)) 
-    #ud_s_rate =
-    val_list.append("{:.4f}".format((kpi_1['stroke'][0] + kpi_1['stroke'][3] - kpi_1['stroke'][1] - kpi_1['stroke'][2]) / sum(kpi_1['stroke'])*100))
-    #ud_v_rate =
-    val_list.append("{:.4f}".format((kpi_1['velocity'][0] + kpi_1['velocity'][3] - kpi_1['velocity'][1] - kpi_1['velocity'][2])/2))
-    #ud_d_rate =
-    val_list.append("{:.4f}".format((kpi_1['depth'][0] + kpi_1['depth'][3] - kpi_1['depth'][1] - kpi_1['depth'][2])/2))
-
-    #lr_s_rate =
-    val_list.append("{:.4f}".format((kpi_1['stroke'][2] + kpi_1['stroke'][3] - kpi_1['stroke'][0] - kpi_1['stroke'][1]) / sum(kpi_1['stroke'])*100))
-    #lr_v_rate =
-    val_list.append("{:.4f}".format((kpi_1['velocity'][2] + kpi_1['velocity'][3] - kpi_1['velocity'][0] - kpi_1['velocity'][1])/2))
-    #lr_d_rate =
-    val_list.append("{:.4f}".format((kpi_1['depth'][2] + kpi_1['depth'][3] - kpi_1['depth'][0] - kpi_1['depth'][1])/2))
-
-    #q1 s, v, d
-    val_list.append(len(quad1_c))
-    val_list.append(quad1_c['sqrt'].mean() / 30)
-    val_list.append(quad1_c['sqrt'].mean() / 45)
-    val_list.append(len(quad2_c))
-    val_list.append(quad2_c['sqrt'].mean() / 30)
-    val_list.append(quad2_c['sqrt'].mean() / 45)
-    val_list.append(len(quad3_c))
-    val_list.append(quad3_c['sqrt'].mean() / 30)
-    val_list.append(quad3_c['sqrt'].mean() / 45)
-    val_list.append(len(quad4_c))
-    val_list.append(quad4_c['sqrt'].mean() / 30)
-    val_list.append(quad4_c['sqrt'].mean() / 45)
-
-
-
-
-    return val_list
-
-    #print(val_list)
-
-
-
-if __name__ == "__main__":
-    kpi = dist()
-    kpi_csv = pd.DataFrame([kpi])
-    kpi_csv = kpi_csv.fillna(0)
-    kpi_csv.to_csv(os.path.join(file_path,'kpi.csv'))
-    #put_kpi_to_db(kpi_csv)

mailanalysis.py

@@ -0,0 +1,20 @@
+import os
+import sys
+import pandas as pd
+import numpy as np
+import json
+import time
+
+def 
+
+
+
+
+
+
+
+def main():
+
+
+if __name__ == "__main":
+    main()

mailcalibrate.py

@@ -0,0 +1,171 @@
+import pyzed.sl as sl
+import numpy as np
+import sys
+import os
+import time
+import json
+import math
+import json
+
+JSON_PATH = '~/sources/configuration.json'
+
+
+def conf_depth(camobject=sl.Camera(), headCoord=(480,540), tailCoord=(1440,540)):
+    # Create a Camera object
+    hc = headCoord
+    tc = tailCoord
+
+
+    zed = sl.Camera()
+
+    # Create a InitParameters object and set configuration parameters
+    init = sl.InitParameters()
+    init.set_from_serial_number
+    init.depth_mode = sl.DEPTH_MODE.PERFORMANCE  # Use PERFORMANCE depth mode
+    init.coordinate_units = sl.UNIT.METER  # Use meter units (for depth measurements)
+    init.camera_resolution = sl.RESOLUTION.HD1080
+
+    # Open the camera
+    err = zed.open(init)
+    if err != sl.ERROR_CODE.SUCCESS:
+        exit(1)
+
+    # Create and set RuntimeParameters after opening the camera
+    runtime_parameters = sl.RuntimeParameters()
+    runtime_parameters.sensing_mode = sl.SENSING_MODE.STANDARD  # Use STANDARD sensing mode
+    # Setting the depth confidence parameters
+    runtime_parameters.confidence_threshold = 100
+    runtime_parameters.textureness_confidence_threshold = 100
+
+    # Capture 150 images and depth, then stop
+    i = 0
+    image = sl.Mat()
+    depth = sl.Mat()
+    point_cloud = sl.Mat()
+
+    mirror_ref = sl.Transform()
+    mirror_ref.set_translation(sl.Translation(2.75,4.0,0))
+    tr_np = mirror_ref.m
+    hx = 0
+    tx = 0    
+    distance_head_cliff = 0
+    distance_tail_cliff = 0
+    while i < 900:
+        # A new image is available if grab() returns SUCCESS
+        if zed.grab(runtime_parameters) == sl.ERROR_CODE.SUCCESS:
+            # Retrieve left image
+            zed.retrieve_image(image, sl.VIEW.LEFT)
+            # Retrieve depth map. Depth is aligned on the left image
+            zed.retrieve_measure(depth, sl.MEASURE.DEPTH)
+            # Retrieve colored point cloud. Point cloud is aligned on the left image.
+            zed.retrieve_measure(point_cloud, sl.MEASURE.XYZRGBA)
+
+            # Get and print distance value in mm at the center of the image
+            # We measure the distance camera - object using Euclidean distance
+            x = round(image.get_width() / 2)
+            y = round(image.get_height() / 2)
+            if hx == 0 :
+                hx = x
+                tx = x
+            err, point_cloud_value_center = point_cloud.get_value(x, y)
+
+            distance_center = math.sqrt(point_cloud_value_center[0] * point_cloud_value_center[0] +
+                                 point_cloud_value_center[1] * point_cloud_value_center[1] +
+                                 point_cloud_value_center[2] * point_cloud_value_center[2])
+            
+
+            err, point_cloud_value_head = point_cloud.get_value(hx,y)
+            distance_head = math.sqrt(point_cloud_value_head[0] * point_cloud_value_head[0] +
+                                 point_cloud_value_head[1] * point_cloud_value_head[1] +
+                                 point_cloud_value_head[2] * point_cloud_value_head[2])
+            if distance_head_cliff != 0:
+                pass
+            elif distance_head > (0.04 + distance_center):
+                if np.isnan(prev_distance_head) == True:
+                    pass
+                else:
+                    distance_head_cliff = prev_distance_head
+                    hx_cliff = hx
+            
+            if np.isnan(distance_head) == False:
+                prev_distance_head = distance_head
+
+            err, point_cloud_value_tail = point_cloud.get_value(tx,y)
+            distance_tail =  math.sqrt(point_cloud_value_tail[0] * point_cloud_value_tail[0] +
+                                 point_cloud_value_tail[1] * point_cloud_value_tail[1] +
+                                 point_cloud_value_tail[2] * point_cloud_value_tail[2])
+            
+            if distance_tail_cliff != 0:
+                pass
+            elif distance_tail > (0.04 + distance_center):
+                if np.isnan(prev_distance_tail) == True:
+                    pass
+                else:
+                    distance_tail_cliff = prev_distance_tail
+                    tx_cliff = tx - 1
+            if np.isnan(distance_tail) == False:
+                prev_distance_tail = distance_tail
+            
+
+            point_cloud_np = point_cloud.get_data()
+            point_cloud_np.dot(tr_np)
+            hx -=1
+            tx +=1
+
+            if not np.isnan(distance_center) and not np.isinf(distance_center):
+                print("Distance to Camera at ({}, {}) (image center): {:1.3} m".format(hx, y, distance_head))
+                print("Distance to Camera at ({}, {}) (image center): {:1.3} m".format(x, y, distance_center))                
+                print("Distance to Camera at ({}, {}) (image center): {:1.3} m".format(tx, y, distance_tail))
+                # Increment the loop
+                i = i + 1
+            else:
+                print("Can't estimate distance at this position.")
+                print("Your camera is probably too close to the scene, please move it backwards.\n")
+            sys.stdout.flush()
+
+    # Close the camera
+    zed.close()
+    return [distance_center,hx_cliff,distance_head_cliff,tx_cliff,distance_tail_cliff]
+
+
+
+def main():
+    cameras = sl.Camera.get_device_list()
+    if len(cameras) == 1:
+        with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "r") as json_file:
+            conf = json.load(json_file)
+        d_list = conf_depth(cameras[0],conf["cam1"]["headCoord"], conf["cam1"]["tailCoord"])
+
+        conf["cam1"]["d_center"] = d_list[0]             
+        conf["cam1"]["d_headCoord"] = d_list[2]        
+        conf["cam1"]["d_tailCoord"] = d_list[4]
+
+        #as maincam, head is left side of image
+        if conf["mainCam"] == cameras[0][0]:
+            conf["cam1"]["headCoord"] = [d_list[1], 540]
+            conf["cam1"]["tailCoord"] = [d_list[3], 540]
+        else:
+            conf["cam1"]["headCoord"] = [d_list[3], 540]
+            conf["cam1"]["tailCoord"] = [d_list[1], 540]
+        
+        with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "w") as json_file:
+            json.dump(conf,json_file, indent=4)
+    elif len(cameras) == 2:
+        with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "r") as json_file:
+            conf = json.load(json_file)
+        d_list = conf_depth(cameras[1],conf["cam2"]["headCoord"], conf["cam2"]["tailCoord"])
+
+        conf["cam2"]["d_center"] = d_list[0]             
+        conf["cam2"]["d_headCoord"] = d_list[2]        
+        conf["cam2"]["d_tailCoord"] = d_list[4]
+        if conf["mainCam"] == cameras[1][0]:            
+            conf["cam2"]["headCoord"] = [d_list[1], 540]
+            conf["cam2"]["tailCoord"] = [d_list[3], 540]
+        else:
+            conf["cam2"]["headCoord"] = [d_list[3], 540]
+            conf["cam2"]["tailCoord"] = [d_list[1], 540]
+        with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "w") as json_file:
+            json.dump(conf,json_file, indent=4)    
+
+if __name__ == "__main__":
+    main()

mailextract_01.py

@@ -0,0 +1,70 @@
+import os
+import sys
+import numpy
+import subprocess
+import pandas as pd
+import time
+
+SOURCE_DIR = '/home/mc365/sources/'
+STROAGE_DIR = '/hdd/'
+LOG_EXTRACT = '/log_extract.txt'
+#NOTE: CHANGE CFG, WEIGHT, .data
+CFG_FILE = 'yolov4_7gpu.cfg'
+WEIGHT_FILE = 'yolov4_7gpu_last.weights'
+DATA_FILE = 'obj.data'
+FRAME_COUNTER = 0
+N_OF_FRAME = 1800
+
+SURGEON_DIR = os.path.abspath(STROAGE_DIR+os.listdir('/hdd/')[0])
+CALL_DEPTH = ['python3',SOURCE_DIR+'depthsensing.py', '-c', SOURCE_DIR+CFG_FILE, '-w', SOURCE_DIR+WEIGHT_FILE, '-m', SOURCE_DIR+DATA_FILE]
+#print(SURGEON_DIR)
+def extract_surgeon ():    
+    svo1list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('1.svo')]
+    svo1list= sorted(svo1list)
+    del svo1list[-1]
+    for svo in svo1list:
+        REST_OF_CALL_DEPTH = ['-s',SURGEON_DIR+'/'+svo, '-o', SURGEON_DIR+'/'+svo[:5]+'.csv']
+        subprocess.call(CALL_DEPTH+REST_OF_CALL_DEPTH)
+
+def concat_csv():
+    csv1list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('1.csv')]
+    csv1list = sorted(csv1list)    
+    fc = FRAME_COUNTER
+    nf = N_OF_FRAME
+
+    conc_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'frame': []})
+    for csv in csv1list:
+        df = pd.read_csv(os.path.join(SURGEON_DIR,csv), header=0)
+        df['frame'] = df['frame'] + fc
+        conc_df = pd.concat([conc_df,df])
+        fc += nf
+    conc_df.to_csv(os.path.join(SURGEON_DIR,'cam1.csv'), index=False)
+
+def concat_csv_with_nms():
+    csv1list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('1_nms.csv')]
+    csv1list = sorted(csv1list)    
+    fc = FRAME_COUNTER
+    nf = N_OF_FRAME
+
+    conc_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'nms':[],'frame': []})
+    for csv in csv1list:
+        df = pd.read_csv(os.path.join(SURGEON_DIR,csv), header=0)
+        df['frame'] = df['frame'] + fc
+        conc_df = pd.concat([conc_df,df])
+        fc += nf
+    conc_df.to_csv(os.path.join(SURGEON_DIR,'cam1_nms.csv'), index=False)
+
+def log_write():
+    exttime = time.localtime()
+    csv1list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('1.csv')]
+    with open (os.path.abspath(SURGEON_DIR+LOG_EXTRACT), "a") as log_extract:
+        log_extract.write("\n[EXTRACT] EXTRACT %d svo files finishied at %04d/%02d/%02d %02d:%02d:%02d" % (len(csv1list)-1,exttime.tm_year, exttime.tm_mon, exttime.tm_mday, exttime.tm_hour, exttime.tm_min, exttime.tm_sec))
+
+def main():        
+    extract_surgeon()
+    concat_csv()
+    concat_csv_with_nms()    
+    log_write()
+
+if __name__ == '__main__':
+    main()

mailextract_02.py

@@ -0,0 +1,70 @@
+import os
+import sys
+import numpy
+import subprocess
+import pandas as pd
+import time
+
+SOURCE_DIR = '/home/mc365/sources/'
+STROAGE_DIR = '/hdd/'
+LOG_EXTRACT = '/log_extract.txt'
+#NOTE: CHANGE CFG, WEIGHT, .data
+CFG_FILE = 'yolov4_7gpu.cfg'
+WEIGHT_FILE = 'yolov4_7gpu_last.weights'
+DATA_FILE = 'obj.data'
+FRAME_COUNTER = 0
+N_OF_FRAME = 1800
+
+SURGEON_DIR = os.path.abspath(STROAGE_DIR+os.listdir('/hdd/')[0])
+CALL_DEPTH = ['python3',SOURCE_DIR+'depthsensing.py', '-c', SOURCE_DIR+CFG_FILE, '-w', SOURCE_DIR+WEIGHT_FILE, '-m', SOURCE_DIR+DATA_FILE]
+#print(SURGEON_DIR)
+def extract_surgeon ():    
+    svo2list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('2.svo')]
+    svo2list= sorted(svo2list)
+    del svo2list[-1]    
+    for svo in svo2list:
+        REST_OF_CALL_DEPTH = ['-s',SURGEON_DIR+'/'+svo, '-o', SURGEON_DIR+'/'+svo[:5]+'.csv']
+        subprocess.call(CALL_DEPTH+REST_OF_CALL_DEPTH)
+    
+def concat_csv():
+    csv2list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('2.csv')]
+    csv2list = sorted(csv2list)    
+    fc = FRAME_COUNTER
+    nf = N_OF_FRAME
+
+    conc_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'frame': []})
+    for csv in csv2list:
+        df = pd.read_csv(os.path.join(SURGEON_DIR,csv), header=0)
+        df['frame'] = df['frame'] + fc
+        conc_df = pd.concat([conc_df,df])
+        fc += nf
+    conc_df.to_csv(os.path.join(SURGEON_DIR,'cam2.csv'), index=False)
+
+def concat_csv_with_nms():
+    csv2list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('2_nms.csv')]
+    csv2list = sorted(csv2list)    
+    fc = FRAME_COUNTER
+    nf = N_OF_FRAME
+
+    conc_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'nms':[],'frame': []})
+    for csv in csv2list:
+        df = pd.read_csv(os.path.join(SURGEON_DIR,csv), header=0)
+        df['frame'] = df['frame'] + fc
+        conc_df = pd.concat([conc_df,df])
+        fc += nf
+    conc_df.to_csv(os.path.join(SURGEON_DIR,'cam2_nms.csv'), index=False)
+
+def log_write():
+    exttime = time.localtime()
+    csv2list = [file for file in os.listdir(os.path.abspath(SURGEON_DIR)) if file.endswith('2.csv')]
+    with open (os.path.abspath(SURGEON_DIR+LOG_EXTRACT), "a") as log_extract:
+        log_extract.write("\n[EXTRACT] EXTRACT %d svo files finishied at %04d/%02d/%02d %02d:%02d:%02d" % (len(csv2list)-1,exttime.tm_year, exttime.tm_mon, exttime.tm_mday, exttime.tm_hour, exttime.tm_min, exttime.tm_sec))
+
+def main():        
+    extract_surgeon()
+    concat_csv()
+    concat_csv_with_nms()    
+    log_write()
+
+if __name__ == '__main__':
+    main()

mailimage.py

@@ -0,0 +1,115 @@
+import os
+import sys
+import time
+import pandas as pd
+import json
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+#import sklearn.datasets as data
+#import hdbscan ##NOTE: hdbscan needs Cython. please install Cython before use.
+import seaborn as sns
+import pandas as pd
+
+
+
+
+def draw_plot_cam_1(dataframe, path):
+    df = dataframe
+    imgpath = path
+    sns.set_context('poster')
+    sns.set_style('white')
+    sns.set_color_codes()
+    plot_kwds = {'alpha': 0.5, 'linewidths':0 }
+    plt.rcParams['figure.figsize'] = 9, 7
+
+    fig = plt.figure()
+    ax = fig.add_subplot(projection = '3d')
+    ax.scatter(df['x'],df['y'],df['z'], color='g', s=0.3, **plot_kwds)
+    plt.savefig(path, dpi=660)
+    plt.clf()
+
+def draw_plot_cam_2(dataframe, path):
+    df = dataframe
+    sns.set_context('poster')
+    sns.set_style('white')
+    sns.set_color_codes()
+    plot_kwds = {'alpha': 0.5, 'linewidths':0 }
+    plt.rcParams['figure.figsize'] = 9, 7
+
+    fig = plt.figure()
+    ax = fig.add_subplot(projection = '3d')
+    ax.scatter(df['x'],df['y'],df['z'], color='r', s=0.3, **plot_kwds)
+    plt.savefig(path, dpi=660)
+    plt.clf()
+
+def draw_xy(dataframe,path):
+    df = dataframe
+    path_list=path.split('/')    
+    plt.rcParams['figure.figsize'] = 9, 7
+    fig=plt.figure()
+    plt.scatter(df['x'], df['y'], s=0.2)
+    plt.title(path_list[-1])
+    plt.savefig(path, dpi=600)
+    plt.clf()
+    
+
+def draw_yz(dataframe, path):
+    df = dataframe
+    path_list=path.split('/')    
+    plt.rcParams['figure.figsize'] = 9, 7
+    fig=plt.figure()
+    plt.scatter(df['y'], df['z'], s=0.2)
+    plt.title(path_list[-1])
+    plt.savefig(path, dpi=600)
+    plt.clf()
+
+def draw_xz(dataframe, path):
+    df = dataframe
+    path_list=path.split('/')    
+    plt.rcParams['figure.figsize'] = 9, 7
+    fig=plt.figure()
+    plt.scatter(df['z'], df['x'], s=0.2)
+    plt.title(path_list[-1])
+    plt.savefig(path, dpi=600)
+    plt.clf()
+
+def draw_plot_total(dataframe, path):
+    df = dataframe
+    sns.set_context('poster')
+    sns.set_style('white')
+    sns.set_color_codes()
+    plot_kwds = {'alpha': 0.5, 'linewidths':0 }
+    plt.rcParams['figure.figsize'] = 9, 7
+
+    fig = plt.figure()
+    ax = fig.add_subplot(projection = '3d')
+    ax.axes.set_xlim3d(left=-1.8, right=1.8)
+    ax.axes.set_ylim3d(bottom=-1.8, top=1.8)
+    ax.axes.set_zlim3d(bottom=0, top=1.8)
+    ax.set_xlabel('x (left_right)')
+    ax.set_ylabel('y (head_tail)')
+    ax.set_zlabel('z (floor_ceiling)')
+
+    cam_1 = df[df['cam'] == 1]
+    cam_2 = df[df['cam'] == 2]
+    ax.scatter(cam_1['x'],cam_1['y'],cam_1['z'], color='g', s=0.3, **plot_kwds)
+    ax.scatter(cam_2['x'],cam_2['y'],cam_2['z'], color='r', s=0.3, **plot_kwds)
+    
+    plt.savefig(path, dpi=660)
+    plt.clf()
+
+def draw_diff_diff(dataframe, path):
+    data = dataframe
+    plt.rcParams['figure.figsize'] =30, 7
+    #plt.axes().set_aspect('equal')
+    plt.xlim(data['frame'].min()-10, data['frame'].max()+10)
+    #l = range(0, len(data['diff_diff']))
+    #plt.xticks(l*10, data['diff_diff'])
+    plt.ylim(data['diff_diff'].min(), data['diff_diff'].max())
+    fig = plt.figure()
+    plt.plot(data['frame'], data['diff_diff'], color='g')
+    #plt.gca().set_aspect('auto')
+    plt.savefig('./diff.png', dpi=600)
+    plt.clf()
+

mailkpi.py

@@ -0,0 +1,250 @@
+import os
+import time
+import math
+import sys
+import numpy as np
+from numpy.lib.function_base import append
+import pandas as pd
+from pandas import DataFrame as df
+from sklearn.cluster import DBSCAN
+import pymssql
+#import matplotlib.pyplot as plt
+
+
+#ssd_dir = str(sys.argv[1])
+#file_path = '/hdd/' + ssd_dir[5:]
+
+
+def put_kpi_to_db(kpi):
+    try:
+        conn = pymssql.connect(server='52.231.39.219',port=1433, user='mc365', password='tkadbrdhmc1!', database='master')
+        #print("INSERT INTO tsfmc_mailsystem.dbo.KPI(surgeryID, totalStrokeCount, totalStrokeVelocity, totalStrokeDepth, upDownTotalStrokeCount, upDownTotalStrokeVelocity, upDownTotalStrokeDepth, leftRightTotalStrokeCount, leftRightTotalStrokeVelocity, leftRightTotalStrokeDepth, quadrant1TotalStrokeCount, quadrant1TotalStrokeVelocity, quadrant1TotalStrokeDepth, quadrant2TotalStrokeCount, quadrant2TotalStrokeVelocity, quadrant2TotalStrokeDepth, quadrant3TotalStrokeCount, quadrant3TotalStrokeVelocity, quadrant3TotalStrokeDepth, quadrant4TotalStrokeCount, quadrant4TotalStrokeVelocity, quadrant4TotalStrokeDepth) VALUES (" + str(kpi[0]) + ", "+str(kpi[1])+", "+str(kpi[2])+", "+str(kpi[3])+", "+str(kpi[4])+", "+str(kpi[5])+", "+str(kpi[6])+", "+str(kpi[7])+", "+str(kpi[8])+", "+str(kpi[9])+", "+str(kpi[10])+", "+str(kpi[11])+", "+str(kpi[12])+", "+str(kpi[13])+", "+str(kpi[14])+", "+str(kpi[15])+", "+str(kpi[16])+", "+str(kpi[17])+", "+str(kpi[18])+", "+str(kpi[19])+", "+str(kpi[20])+", "+str(kpi[21])+")")
+        #print("INSERT INTO tsfmc_mailsystem.dbo.KPI(surgeryID, totalStrokeCount, totalStrokeVelocity, totalStrokeDepth, upDownTotalStrokeCount, upDownTotalStrokeVelocity, upDownTotalStrokeDepth, leftRightTotalStrokeCount, leftRightTotalStrokeVelocity, leftRightTotalStrokeDepth, quadrant1TotalStrokeCount, quadrant1TotalStrokeVelocity, quadrant1TotalStrokeDepth, quadrant2TotalStrokeCount, quadrant2TotalStrokeVelocity, quadrant2TotalStrokeDepth, quadrant3TotalStrokeCount, quadrant3TotalStrokeVelocity, quadrant3TotalStrokeDepth, quadrant4TotalStrokeCount, quadrant4TotalStrokeVelocity, quadrant4TotalStrokeDepth) VALUES ( '" + str(kpi[0]) + "', '"+str(kpi[1])+"', '"+str(kpi[2])+"', '"+str(kpi[3])+"', '"+str(kpi[4])+"', '"+str(kpi[5])+"', '"+str(kpi[6])+"', '"+str(kpi[7])+"', '"+str(kpi[8])+"', '"+str(kpi[9])+"', '"+str(kpi[10])+"', '"+str(kpi[11])+"', '"+str(kpi[12])+"', '"+str(kpi[13])+"', '"+str(kpi[14])+"', '"+str(kpi[15])+"', '"+str(kpi[16])+"', '"+str(kpi[17])+"', '"+str(kpi[18])+"', '"+str(kpi[19])+"', '"+str(kpi[20])+"', '"+str(kpi[21])+"')")
+        cursor = conn.cursor()
+        cursor.execute("INSERT INTO tsfmc_mailsystem.dbo.KPI(surgeryID, totalStrokeCount, totalStrokeVelocity, totalStrokeDepth, upDownTotalStrokeCount, upDownTotalStrokeVelocity, upDownTotalStrokeDepth, leftRightTotalStrokeCount, leftRightTotalStrokeVelocity, leftRightTotalStrokeDepth, quadrant1TotalStrokeCount, quadrant1TotalStrokeVelocity, quadrant1TotalStrokeDepth, quadrant2TotalStrokeCount, quadrant2TotalStrokeVelocity, quadrant2TotalStrokeDepth, quadrant3TotalStrokeCount, quadrant3TotalStrokeVelocity, quadrant3TotalStrokeDepth, quadrant4TotalStrokeCount, quadrant4TotalStrokeVelocity, quadrant4TotalStrokeDepth) VALUES ( '" + str(kpi[0]) + "', '"+str(kpi[1])+"', '"+str(kpi[2])+"', '"+str(kpi[3])+"', '"+str(kpi[4])+"', '"+str(kpi[5])+"', '"+str(kpi[6])+"', '"+str(kpi[7])+"', '"+str(kpi[8])+"', '"+str(kpi[9])+"', '"+str(kpi[10])+"', '"+str(kpi[11])+"', '"+str(kpi[12])+"', '"+str(kpi[13])+"', '"+str(kpi[14])+"', '"+str(kpi[15])+"', '"+str(kpi[16])+"', '"+str(kpi[17])+"', '"+str(kpi[18])+"', '"+str(kpi[19])+"', '"+str(kpi[20])+"', '"+str(kpi[21])+"')")
+    
+        conn.commit()
+        conn.close()
+    except:
+        pass
+
+
+def get_quad(df):
+    if df['x'] >= 0 and df['y'] >= 0:
+        return 1
+    elif df['x'] >= 0 and df['y'] < 0:
+        return 2
+    elif df['x'] < 0 and df['y'] < 0:
+        return 3
+    elif df['x'] < 0 and df['y'] >= 0:
+        return 4
+def get_inc_num(df):
+    count = 1
+    prev_count = 0
+    if abs(df['diff_diff']) > 0.0008:                
+        prev_count = df['inc_num']
+        df['inc_num'] = count
+        #print(prev_count)
+        return count        
+    else:
+        if prev_count != 0:            
+            count += 1
+            df['inc_num'] = 0
+            prev_count = df['inc_num']
+            return 0
+        else:
+            df['inc_num'] = 0
+            prev_count = df['inc_num']
+            return 0
+
+
+def count_inc(df):
+    e= 0
+    count  = 1
+    prev_count = 0
+    for x in df['diff_diff']:
+        if abs(x) > 0.05:
+            df['inc_num'][e] = count
+            prev_count = count
+            e +=1
+        else:
+            if prev_count != 0:
+                count += 1
+                df['inc_num'][e] = 0
+                prev_count = 0
+                e +=1
+            else:
+                df['inc_num'][e] = 0
+                prev_count = 0
+                e +=1
+
+def stroke_count(df):
+    e = 0
+    count = 1    
+    for x in df['diff_diff']:
+        if x > 0.03:
+            df['stroke_count'][e] = count
+            e +=1
+        else:
+            df['stroke_count'][e] = 0
+            e+=1
+            
+def get_sqrt(df):
+    prev_x = 0.0
+    prev_y = 0.0
+    prev_z = 0.0
+
+    
+    for index, row in df.iterrows():        
+            df['sqrt'][index] = np.sqrt((df['x'][index] - prev_x) **2 + (df['y'][index] - prev_y) ** 2 +(df['z'][index] - prev_z) ** 2)
+            prev_x = df['x'][index]
+            prev_y = df['y'][index]
+            prev_z = df['z'][index]
+    df['sqrt'][0] = 0.0
+            
+
+        
+
+
+def dist(filepath):
+    file = filepath
+    surgeon_dir = file.split('/')
+    #print(surgeon_dir)
+
+    #filepath_slice = file.slice('/')
+    norm = pd.read_csv(file)
+    #print(norm)
+    #norm = norm.drop(norm.columns[0], axis=1)
+    #norm = norm.fillna(0)
+    data = norm[['x', 'y', 'z']]
+    norm['sqrt'] = 0.0
+
+    get_sqrt(norm)
+
+    #norm = norm.apply(lambda x:np.sqrt(x) if x.name in ['sqrt'] else x, axis =1)
+    norm['diff'] = norm.diff()['sqrt']
+    norm['diff_diff'] = norm.diff()['diff']
+
+
+    norm['quad'] = norm.apply(get_quad, axis =1)
+    
+    
+    norm = norm.fillna(0)
+
+    norm['inc_num'] = 0
+    #norm['inc_num'] = norm.apply(get_inc_num, axis = 1)
+    count_inc(norm)
+    norm['stroke_count'] = 0
+    stroke_count(norm)
+    
+    #print('num of inc')
+    #print(norm['inc_num'].unique())
+
+    #print('total stroke')
+    #print(norm['stroke_count'].unique())
+    #print(norm)
+    diff_csv = norm[['frame','diff_diff']]    
+    norm.to_csv(os.path.join(file[:-14]+ 'norm.csv'), index=False)
+    diff_csv.to_csv(os.path.join(file[:-14]+ 'diff.csv'))
+    #plt.plot(norm['frame'],norm['diff_diff'])
+    #plt.savefig('./diff_diff.png')    
+
+
+
+    kpi = pd.DataFrame(index=range(0,1))
+
+    quad_count = norm[norm['stroke_count'] != 0]
+    
+    quad1_c = quad_count[quad_count['quad'] == 1]
+    quad2_c = quad_count[quad_count['quad'] == 2]
+    quad3_c = quad_count[quad_count['quad'] == 3]
+    quad4_c = quad_count[quad_count['quad'] == 4]
+    #print(len(quad1_c))
+    #print(len(quad2_c))
+    #print(len(quad3_c))
+    #print(len(quad4_c))
+    #print(quad1_c)
+    kpi['PK'] = surgeon_dir[2]
+    kpi['qaud1_c'] = len(quad1_c)
+    kpi['qaud2_c'] = len(quad2_c)
+    kpi['qaud3_c'] = len(quad3_c)
+    kpi['qaud4_c'] = len(quad4_c)
+    #FIXME: this values are huristic value. please change this values after research
+    kpi['qaud1_s'] = quad1_c['sqrt'].mean() / 30
+    kpi['qaud2_s'] = quad2_c['sqrt'].mean() / 30
+    kpi['qaud3_s'] = quad3_c['sqrt'].mean() / 30
+    kpi['qaud4_s'] = quad4_c['sqrt'].mean() / 30
+    kpi['qaud1_d'] = quad1_c['sqrt'].mean() / 45
+    kpi['qaud2_d'] = quad2_c['sqrt'].mean() / 45
+    kpi['qaud3_d'] = quad3_c['sqrt'].mean() / 45
+    kpi['qaud4_d'] = quad4_c['sqrt'].mean() / 45
+    kpi = kpi.fillna(0)
+
+
+    kpi_1 = {
+        "stroke": [len(quad1_c),len(quad2_c),len(quad3_c),len(quad4_c)],
+        "velocity": [quad1_c['sqrt'].mean() / 30, quad2_c['sqrt'].mean() / 30, quad3_c['sqrt'].mean() / 30, quad4_c['sqrt'].mean() / 30],
+        "depth": [quad1_c['sqrt'].mean() / 45, quad2_c['sqrt'].mean() / 45, quad3_c['sqrt'].mean() / 45, quad4_c['sqrt'].mean() / 45]
+    }
+
+
+    #print(kpi)
+    val_list = []
+
+    #PK =
+    val_list.append(str(surgeon_dir[2]))
+
+    #t_stroke = 
+    val_list.append(sum(kpi_1['stroke']))
+    #t_vel
+    val_list.append("{:.4f}".format(sum(kpi_1['velocity'])/4)) 
+    #t_dep
+    val_list.append("{:.4f}".format(sum(kpi_1['depth'])/4)) 
+    #ud_s_rate =
+    val_list.append("{:.4f}".format((kpi_1['stroke'][0] + kpi_1['stroke'][3] - kpi_1['stroke'][1] - kpi_1['stroke'][2]) / sum(kpi_1['stroke'])*100))
+    #ud_v_rate =
+    val_list.append("{:.4f}".format((kpi_1['velocity'][0] + kpi_1['velocity'][3] - kpi_1['velocity'][1] - kpi_1['velocity'][2])/2))
+    #ud_d_rate =
+    val_list.append("{:.4f}".format((kpi_1['depth'][0] + kpi_1['depth'][3] - kpi_1['depth'][1] - kpi_1['depth'][2])/2))
+
+    #lr_s_rate =
+    val_list.append("{:.4f}".format((kpi_1['stroke'][2] + kpi_1['stroke'][3] - kpi_1['stroke'][0] - kpi_1['stroke'][1]) / sum(kpi_1['stroke'])*100))
+    #lr_v_rate =
+    val_list.append("{:.4f}".format((kpi_1['velocity'][2] + kpi_1['velocity'][3] - kpi_1['velocity'][0] - kpi_1['velocity'][1])/2))
+    #lr_d_rate =
+    val_list.append("{:.4f}".format((kpi_1['depth'][2] + kpi_1['depth'][3] - kpi_1['depth'][0] - kpi_1['depth'][1])/2))
+
+    #q1 s, v, d
+    val_list.append(str(len(quad1_c)))
+    val_list.append(str(quad1_c['sqrt'].mean() / 30))
+    val_list.append(str(quad1_c['sqrt'].mean() / 45))
+    val_list.append(str(len(quad2_c)))
+    val_list.append(str(quad2_c['sqrt'].mean() / 30))
+    val_list.append(str(quad2_c['sqrt'].mean() / 45))
+    val_list.append(str(len(quad3_c)))
+    val_list.append(str(quad3_c['sqrt'].mean() / 30))
+    val_list.append(str(quad3_c['sqrt'].mean() / 45))
+    val_list.append(str(len(quad4_c)))
+    val_list.append(str(quad4_c['sqrt'].mean() / 30))
+    val_list.append(str(quad4_c['sqrt'].mean() / 45))
+    
+    #print(val_list)
+    for x in range(0,len(val_list)):        
+        if val_list[x] == 'nan':
+            val_list[x] = 0        
+    #print(val_list)
+    return val_list    
+
+    #print(val_list)
+
+def main():
+    kpi = dist()
+    #kpi_csv = pd.DataFrame([kpi])
+    #kpi_csv = kpi_csv.fillna(0)
+    #kpi_csv.to_csv(os.path.join(file[:-14],'kpi.csv'))
+    #put_kpi_to_db(kpi_csv)
+
+
+
+if __name__ == "__main__":
+    main()

mailrequest.py

@@ -0,0 +1,104 @@
+from re import sub
+import sys
+import os
+import subprocess
+import time
+
+DEVICE_PASSWORD = b'tkadbrdhmc1!\n'
+SOURCE_DIR = '~/sources/'
+STORAGE_DIR = '/hdd/'
+LOG_REBOOT = '~/sources/cfg/log_reboot.txt'
+REC_COUNT = '/rec_count.txt'
+LOG_EXTRACT = '/log_extract.txt'
+CONTAINER_EXT_1 = ['docker', 'run', '--name', 'ext_01', '--gpus', ''"device=0"'', '--rm']
+CONTAINER_EXT_2 = ['docker', 'run', '--name', 'ext_02', '--gpus', ''"device=1"'', '--rm']
+CONTAINER_IMAGE_1 = ['ellishuntingmoon/mailsys:0.5','python3',os.path.join(os.path.abspath(os.path.expanduser(SOURCE_DIR)),'mailextract_01.py')]
+CONTAINER_IMAGE_2 = ['ellishuntingmoon/mailsys:0.5','python3',os.path.join(os.path.abspath(os.path.expanduser(SOURCE_DIR)),'mailextract_02.py')]
+
+def container_status():
+    clist = []    
+    docker_process = subprocess.Popen(('docker','ps','-a'), stdout=subprocess.PIPE)
+    try:
+        clist = subprocess.check_output(('grep','ext_'), stdin=docker_process.stdout).decode()
+        clist = clist.split()
+        clist= [i for i in clist if 'ext_' in i]
+        return clist
+    except subprocess.CalledProcessError as e:
+        return clist
+
+
+
+def request_extract():        
+    nowtime = time.localtime()    
+    nowdate = "%04d/%02d/%02d" % (nowtime.tm_year, nowtime.tm_mon, nowtime.tm_mday)
+    hdd_list = os.listdir(os.path.abspath(STORAGE_DIR))
+    reboot_list = []
+    container_list = container_status()
+    #print(container_list)
+
+
+    with open (os.path.abspath(os.path.expanduser(LOG_REBOOT)), "r") as log_reboot:
+            reboot_list = log_reboot.readlines()            
+    #NOTE: nowtime.tm_wday == 6 is sunday
+    if (nowtime.tm_wday == 6) and (nowdate not in reboot_list[-1]) and (len(hdd_list) == 1):
+        with open (os.path.abspath(os.path.expanduser(LOG_REBOOT)), "a+") as log_reboot:
+            log_reboot.write("\n[REBOOT] Device reboot executed at %04d/%02d/%02d %02d:%02d:%02d" % (nowtime.tm_year, nowtime.tm_mon, nowtime.tm_mday, nowtime.tm_hour, nowtime.tm_min, nowtime.tm_sec))
+        
+        #FIXME: MUST ERASE COMMENT WHEN YOU COMPLETE
+        #subprocess.call(('echo','rebooting...'))
+        #subprocess.Popen(('sudo','-S','shutdown','-r','now'), stdin=subprocess.PIPE, stderr=subprocess.PIPE).communicate(input=PASSWORD)
+    elif len(container_list) == 0 :
+        hdd_list = []
+        hdd_list = os.listdir(os.path.abspath(STORAGE_DIR))
+        hdd_list.remove('lost+found')
+        #roundrobin surgeons
+        for surgeon in hdd_list:
+            if len(container_list) != 0:
+                break
+            svocount = 0
+            surgeondatalist = os.listdir(os.path.abspath(STORAGE_DIR+surgeon))
+            #rec_count 내용 확인
+            with open (os.path.abspath(STORAGE_DIR+surgeon+REC_COUNT), "r") as log_reboot:
+                svocount = int(log_reboot.readline())
+
+            
+            svofulllist = [file for file in surgeondatalist if file.endswith('.svo')]
+            #svo 개수가 rec_count -2일때 and 파일 개수가 rec_count와 동일 하면 분석 시작    
+            if (len(svofulllist) == (svocount-2)) and len(surgeondatalist) == svocount:
+                exttime = time.localtime()                
+                with open (os.path.abspath(STORAGE_DIR+surgeon+LOG_EXTRACT), "w+") as log_extract:
+                    log_extract.write("\n[EXTRACT] EXTRACT %d svo files executed at %04d/%02d/%02d %02d:%02d:%02d" % (len(svofulllist),exttime.tm_year, exttime.tm_mon, exttime.tm_mday, exttime.tm_hour, exttime.tm_min, exttime.tm_sec))
+                
+                containermiddle = [\
+                    '-v', os.path.abspath(STORAGE_DIR+surgeon)+':'+ os.path.abspath(STORAGE_DIR+surgeon), \
+                    '-v', os.path.abspath(os.path.expanduser(SOURCE_DIR))+':'+os.path.abspath(os.path.expanduser(SOURCE_DIR)), \
+                    '-w', os.path.abspath(STORAGE_DIR+surgeon)                    
+                ]
+                #docker run --name ext_01 --gpus '"device=0"' --rm -v /hdd/{surgeon}:/hdd/{surgeon} -v /home/mc365/sources/:/home/mc365/sources/ -w /hdd/{surgeon} ellishuntingmoon/mailsys:0.5 python3 /home/mc365/sources/mailextract_01.py
+                #print(CONTAINER_EXT_1 + containermiddle + CONTAINER_IMAGE_1)                
+                subprocess.Popen(CONTAINER_EXT_1 + containermiddle + CONTAINER_IMAGE_1)
+                subprocess.Popen(CONTAINER_EXT_2 + containermiddle + CONTAINER_IMAGE_2)
+                
+                #svo1list = [file for file in svofulllist if file.endswith('1.svo')]
+                #svo2list = [file for file in svofulllist if file.endswith('2.svo')]
+                #print('ext start')
+                time.sleep(15)
+                container_list = container_status()
+            else:
+                #print('already finished')
+                pass
+                #print(svo1list, svo2list)
+                
+    else:
+        #print('extract is in progress... wait until it finish...')
+        pass
+    
+        
+
+def main():
+    while True:
+        request_extract()
+        time.sleep(15) 
+
+if __name__== '__main__':
+    main()

mailsetup.py

@@ -0,0 +1,137 @@
+import os
+import sys
+import pyzed.sl as sl
+import json
+import numpy as np 
+import math 
+import time
+import subprocess
+import mailcalibrate
+
+WDIR_DEFAULT = os.path.expanduser('~/data/')
+SDIR_DEFAULT = os.path.abspath('/hdd/')
+MAINCAM_DEFAULT = 'cam1'
+RECFRAME_DEFAULT = 1800
+JSON_PATH = '~/sources/configuration.json'
+LOG_REBOOT = '~/sources/cfg/log_reboot.txt'
+
+cameras = sl.Camera.get_device_list()
+
+def get_cam_info():
+    init = sl.InitParameters()
+    init.depth_mode = sl.DEPTH_MODE.NONE  # Use PERFORMANCE depth mode
+    init.coordinate_units = sl.UNIT.METER  # Use meter units (for depth measurements)
+    init.camera_resolution = sl.RESOLUTION.HD720
+    name_list = []
+    
+    zed_list = []
+    angle_list = []    
+
+    for cam in cameras:        
+        init.set_from_serial_number(cam.serial_number)
+        name_list.append("ZED {}".format(cam.serial_number))
+        zed = sl.Camera()
+        err = zed.open(init)
+        if err != sl.ERROR_CODE.SUCCESS:
+            print(repr(err))
+            zed.close()
+        info = zed.get_camera_information()
+        sensors_data = sl.SensorsData()
+        if zed.get_sensors_data(sensors_data, sl.TIME_REFERENCE.CURRENT) == sl.ERROR_CODE.SUCCESS :
+            #print(" - IMU:")
+            # Filtered orientation quaternion
+            quaternion = sensors_data.get_imu_data().get_pose().get_orientation().get()            
+            #NOTE: IS IT REALLY CORRECT ANGLE???
+            angle = (quaternion[0] * 900)/7+ 90
+            angle_list.append(angle)
+        print(angle)
+        
+    zed.close()
+    return_list = []
+    for x in range(0,len(name_list)):
+        return_list.append(name_list[x])
+        return_list.append(angle_list[x])
+
+    return return_list
+    
+
+def main():
+    #Initialize JSON
+    conf = {
+    "confDate" : "",
+    "branch" : "",
+    "room" : "",
+    "workingDir" : "",
+    "storageDir" : "",
+    "mainCam" : "cam1",
+    "recFrame" : 1800,
+    "cam1" : {
+        "SN" : "",
+        "angle" : "",
+        "d_center" : "",
+        "headCoord" : "",
+        "d_headCoord" : "",
+        "tailCoord" : "",
+        "d_tailCoord" : ""
+        },
+    "cam2" : {
+        "SN" : "",
+        "angle" : "",
+        "d_center" : "",
+        "headCoord" : "",
+        "d_headCoord" : "",
+        "tailCoord" : "",
+        "d_tailCoord" : ""
+        }
+    }
+
+    wdirpath = os.path.abspath(os.path.expanduser('~/data'))
+    sdirpath = os.path.abspath('/hdd')
+        
+    nowtime = time.localtime()
+    #NOTE: createdDate = "2022/02/04 21:07:54"
+    createdDate = "%04d/%02d/%02d %02d:%02d:%02d" % (nowtime.tm_year, nowtime.tm_mon, nowtime.tm_mday, nowtime.tm_hour, nowtime.tm_min, nowtime.tm_sec)
+
+    caminfo_list = get_cam_info()
+    
+    conf["confDate"] = createdDate
+    conf["workingDir"] = wdirpath
+    conf["storageDir"] = sdirpath
+    conf["mainCam"] = caminfo_list[0]
+    conf["recFrame"] = RECFRAME_DEFAULT
+    conf["cam1"]["SN"] = caminfo_list[0]
+    conf["cam1"]["angle"] = caminfo_list[1]
+    conf["cam1"]["d_center"] = 0
+    conf["cam1"]["headCoord"] = ''
+    conf["cam1"]["d_headCoord"] = 0
+    conf["cam1"]["tailCoord"] = ''
+    conf["cam1"]["d_tailCoord"] = 0
+    if len(caminfo_list) == 4:
+        conf["mainCam"] = "SN 00000000"
+        conf["cam2"]["SN"] = caminfo_list[2]
+        conf["cam2"]["angle"] = caminfo_list[3]
+        conf["cam2"]["d_center"] = 0
+        conf["cam2"]["headCoord"] = ''
+        conf["cam2"]["d_headCoord"] = 0
+        conf["cam2"]["tailCoord"] = ''
+        conf["cam2"]["d_tailCoord"] = 0
+    else:
+        pass
+
+    if os.path.isfile(os.path.expanduser(JSON_PATH)):
+        subprocess.call(('mv', os.path.expanduser(JSON_PATH), os.path.expanduser('~/sources/configuration.json.bak')))
+        print('Already configuration file exists.')
+        with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "w") as json_file:
+            json.dump(conf,json_file, indent=4)
+        print('configuration.json was created')
+    else:
+        with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "w") as json_file:
+            json.dump(conf,json_file, indent=4)
+
+    if os.path.isfile(os.path.expanduser(LOG_REBOOT)) == False:        
+        with open (os.path.abspath(os.path.expanduser(LOG_REBOOT)), "w+") as log_reboot:
+            log_reboot.write('This file automatically created at '+ createdDate)
+            print('log_reboot.txt was created')
+
+if __name__ == "__main__":
+    main()

mailsync.py

@@ -0,0 +1,230 @@
+import os
+import sys
+import time
+import pandas as pd
+import json
+import numpy as np
+import math
+import subprocess
+import paramiko
+sys.path.append(os.path.expanduser('~/sources/'))
+import mailimage
+import mailkpi
+
+
+
+WDIR_DEFAULT = os.path.expanduser('~/data/')
+SDIR_DEFAULT = os.path.abspath('/hdd/')
+RECFRAME_DEFAULT = 1800
+JSON_PATH = '~/sources/configuration.json'
+SOURCE_DIR = '~/sources/'
+STORAGE_DIR = '/hdd/'
+WIDTH_NORMAL = float(4) / 1920
+HEIGHT_NORMAL = float(2.25) / 1080
+
+
+#dataFrame = cam1.csv, conf_cam = conf['cam1'] or conf['cam2']
+def sync_coord_main(dataFrame, conf_cam):
+    df = dataFrame
+    cam = conf_cam
+    df.drop_duplicates(['frame'])
+    radian = np.pi/180 * cam['angle']
+    sin_a = np.sin(radian)
+    cos_a = np.cos(radian)
+    
+    df['x'] = df['x'] * WIDTH_NORMAL
+    df['y'] = df['y'] * HEIGHT_NORMAL
+
+    g_cam_x = math.sqrt((cam['d_headCoord']**2) - (cam['d_center']**2))
+    g_cam_y = cam['d_center'] * cos_a
+    g_cam_z = cam['d_center'] * sin_a
+
+    g_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'frame': []})
+    g_df['x'] = df['x'] + g_cam_x
+    g_df['frame'] = df['frame']
+
+    for index, row in df.iterrows():        
+        A = [[sin_a,-(cos_a)],[cos_a, sin_a]]
+        B = [row['y']-g_cam_y*sin_a-g_cam_z*cos_a, row['z']-g_cam_y*cos_a+g_cam_z*sin_a]
+        C = np.linalg.solve(A,B)
+        g_df['y'][index] = C[0]
+        g_df['z'][index] = C[1]
+    
+    g_result = pd.DataFrame(data={'x': g_df['y'], 'y': g_df['x'], 'z': g_df['z'], 'frame': g_df['frame']})
+    
+    return(g_result)
+    
+
+
+
+
+
+
+    
+
+
+
+def sync_coord_sub(dataFrame, conf_cam):
+    df = dataFrame
+    cam = conf_cam
+
+    df.drop_duplicates(['frame'])
+    radian = np.pi/180 * cam['angle']
+    sin_a = np.sin(radian)
+    cos_a = np.cos(radian)
+    
+    df['x'] = df['x'] * WIDTH_NORMAL
+    df['y'] = df['y'] * HEIGHT_NORMAL
+
+    g_cam_x = math.sqrt((cam['d_headCoord']**2) - (cam['d_center']**2))
+    g_cam_y = cam['d_center'] * cos_a
+    g_cam_z = cam['d_center'] * sin_a
+
+    g_df = pd.DataFrame(data={'x': [], 'y': [], 'z': [], 'frame': []})
+    g_df['x'] = df['x'] + g_cam_x
+
+    for index, row in df.iterrows():        
+        A = [[sin_a,cos_a],[cos_a,-(sin_a)]]
+        B = [g_cam_y*sin_a + g_cam_z*cos_a - row['y'],-(g_cam_y*cos_a)-g_cam_z*sin_a-row['z']]
+        C = np.linalg.solve(A,B)
+        g_df['y'][index] = C[0]
+        g_df['z'][index] = C[1]
+    
+    g_result = pd.DataFrame(data={'x': g_df['y'], 'y': g_df['x'], 'z': g_df['z'], 'frame': g_df['frame']})
+    
+    
+    return(g_result)
+
+
+
+
+def loop_sync():
+    with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "r") as json_file:
+        conf = json.load(json_file)
+    hdd_list = []
+    hdd_list = os.listdir(os.path.abspath(STORAGE_DIR))
+    hdd_list.remove('lost+found')
+    tmp_e = hdd_list[0]
+    hdd_list[0] = hdd_list[1]
+    hdd_list[1] = tmp_e
+
+
+    for surgeon in hdd_list:
+        surgeondatalist = os.listdir(os.path.abspath(STORAGE_DIR+surgeon))
+        surgeondatalist = sorted(surgeondatalist)
+        if 'kpi.csv' in surgeondatalist:
+            transport = paramiko.Transport(('mc365.asuscomm.com', 63122))
+            transport.connect(username='mc365', password='tkadbrdhMC1!')
+            sftp = paramiko.SFTPClient.from_transport(transport)
+            for surgeondata in surgeondatalist:
+                try:                
+                    sftp.put(os.path.abspath(STORAGE_DIR+surgeon+'/'+surgeondata), '/data/'+surgeon + '/' + surgeondata)
+                except:
+                    pass
+            sftp.close()
+            transport.close()
+            subprocess.Popen(['rm', '-rf', os.path.abspath(STORAGE_DIR+surgeon+'/')])            
+            
+        else:
+            if 'cam1.csv' in surgeondatalist:
+                df_1 = pd.read_csv(os.path.abspath(STORAGE_DIR+surgeon+'/cam1.csv'))                
+                if conf['mainCam'] == conf['cam1']['SN']:                
+                    g_cam1=sync_coord_main(df_1, conf['cam1'])
+                    g_cam1['cam'] = 1                    
+                    g_cam1.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord.csv'),index=False)
+                    mailimage.draw_plot_cam_1(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord.png'))
+                    mailimage.draw_xy(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord_xy.png'))
+                    mailimage.draw_yz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord_yz.png'))
+                    mailimage.draw_xz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord_xz.png'))
+                    if 'cam2.csv' not in surgeondatalist:                        
+                        g_cam1 = g_cam1.drop_duplicates(['frame'], keep='first')
+                        g_cam1.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.csv'),index=False)
+                        g_cam1.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_summary.csv'),index=False)
+                        mailimage.draw_plot_total(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.png'))
+                        mailimage.draw_xy(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xy.png'))
+                        mailimage.draw_yz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_yz.png'))
+                        mailimage.draw_xz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xz.png'))                        
+                else:
+                    g_cam1=sync_coord_sub(df_1, conf['cam1'])
+                    g_cam1['cam'] = 1                    
+                    g_cam1.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord.csv'),index=False)
+                    mailimage.draw_plot_cam_1(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord.png'))
+                    mailimage.draw_xy(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord_xy.png'))
+                    mailimage.draw_yz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord_yz.png'))
+                    mailimage.draw_xz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/cam1_coord_xz.png'))
+                    if 'cam2.csv' not in surgeondatalist:
+                        g_cam1 = g_cam1.drop_duplicates(['frame'], keep='first')
+                        g_cam1.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.csv'),index=False)
+                        g_cam1.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_summary.csv'),index=False)
+                        mailimage.draw_plot_total(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.png'))
+                        mailimage.draw_xy(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xy.png'))
+                        mailimage.draw_yz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_yz.png'))
+                        mailimage.draw_xz(g_cam1, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xz.png'))
+                        
+            if 'cam2.csv' in surgeondatalist:
+                df_2 = pd.read_csv(os.path.abspath(STORAGE_DIR+surgeon+'/cam2.csv'))
+
+                if conf['mainCam'] == conf['cam2']['SN']:                
+                    g_cam2 = sync_coord_main(df_2, conf['cam2'])
+                    g_cam2['cam'] = 2
+
+
+                    g_cam2.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord.csv'),index=False)
+                    mailimage.draw_plot_cam_2(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord.png'))
+                    mailimage.draw_xy(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord_xy.png'))
+                    mailimage.draw_yz(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord_yz.png'))
+                    mailimage.draw_xz(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord_xz.png'))
+                    g_coor = pd.concat([g_cam1,g_cam2])
+                    g_coor = g_coor.drop_duplicates(['frame'], keep='first')
+                    g_coor = g_coor.sort_values('frame')
+                    g_coor.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.csv'),index=False)
+                    g_coor.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_summary.csv'),index=False)
+                    mailimage.draw_plot_total(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.png'))
+                    mailimage.draw_xy(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xy.png'))
+                    mailimage.draw_yz(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_yz.png'))
+                    mailimage.draw_xz(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xz.png'))
+                else:
+                    g_cam2 = sync_coord_sub(df_2, conf['cam2'])
+                    g_cam2['cam'] = 2
+
+                    g_cam2.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord.csv'),index=False)
+                    mailimage.draw_plot_cam_2(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord.png'))
+                    mailimage.draw_xy(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord_xy.png'))
+                    mailimage.draw_yz(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord_yz.png'))
+                    mailimage.draw_xz(g_cam2, os.path.abspath(STORAGE_DIR+surgeon+'/cam2_coord_xz.png'))
+                    g_coor = pd.concat([g_cam1,g_cam2])
+                    g_coor = g_coor.drop_duplicates(['frame'], keep='first')
+                    g_coor = g_coor.sort_values('frame')
+                    g_coor.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.csv'),index=False)
+                    g_coor.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_summary.csv'),index=False)
+                    mailimage.draw_plot_total(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.png'))
+                    mailimage.draw_xy(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xy.png'))
+                    mailimage.draw_yz(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_yz.png'))
+                    mailimage.draw_xz(g_coor, os.path.abspath(STORAGE_DIR+surgeon+'/coordinate_xz.png'))
+            #difference of difference        
+            df_kpi = mailkpi.dist(os.path.abspath(STORAGE_DIR+surgeon+'/coordinate.csv'))
+            mailkpi.put_kpi_to_db(df_kpi)            
+            df_kpi = pd.DataFrame([df_kpi])
+            diff = pd.read_csv(os.path.abspath(STORAGE_DIR+surgeon+'/diff.csv'))
+            mailimage.draw_diff_diff(diff,os.path.abspath(STORAGE_DIR+surgeon+'/diff.png'))
+            df_kpi.to_csv(os.path.abspath(STORAGE_DIR+surgeon+'/kpi.csv'), index=False)
+            
+            
+            #subprocess ftp
+            
+            
+
+def main():    
+    while True:
+        loop_sync()
+        time.sleep(15)
+
+
+                
+        
+
+
+if __name__ == "__main__":
+    main()
+
+

names.names

@@ -1,2 +0,0 @@
-bind	
-point

obj.data

@@ -0,0 +1,5 @@
+classes = 1
+train = /workspace/configurations/v4_220204/train.txt
+valid = /workspace/configurations/v4_220204/valid.txt
+names = /home/mc365/sources/obj.names
+backup = /workspace/configurations/v4_220204/weights_backups/

obj.names

@@ -0,0 +1 @@
+handle

recorder.py → rec_ubuntu.py

@@ -1,3 +1,4 @@
+from asyncio import subprocess
 import sys
 import os
 import pyzed.sl as sl
@@ -5,6 +6,10 @@ import threading
 import time
 from signal import signal, SIGINT
 
+#FIXME: 2022/02/04 completed
+#FIXME: NOTE: rec_count.txt contains number of flies in target_path include itself.
+
+
 zed_list = []
 timestamp_list = []
 thread_list = []
@@ -16,15 +21,18 @@ record_time_per_svo = 1800
 name_time_list = []
 
 
-root_path = "/data/"
+root_path = os.path.abspath(os.path.expanduser('~/data/'))
+print(root_path)
 filenames = os.listdir(root_path)
 for f in filenames:
-    w_time = os.path.getctime(root_path + f)
+    w_time = os.path.getctime(root_path + '/'+f)
     name_time_list.append((f, w_time))
+
 sorted_list = sorted(name_time_list, key=lambda x: x[1], reverse=True)
+print(sorted_list)
 
 recent_file = sorted_list[0]
-target_dir = str(root_path) + str(recent_file[0])
+target_dir = str(root_path) + '/' + str(recent_file[0])
 
 
 def handler(signal, frame):
@@ -32,14 +40,23 @@ def handler(signal, frame):
     global zed_list
     global target_dir        
     stop_signal = True
+    #os.call(('wall', 'catch SIGINT'))
+    
+
+    #STOP RECORDING CAMERA
+    for cam in zed_list:
+        cam.disable_recording()
+        cam.close()
+
+    nowtime = time.localtime()
+    rec_done = "%04d/%02d/%02d %02d:%02d:%02d" % (nowtime.tm_year, nowtime.tm_mon, nowtime.tm_mday, nowtime.tm_hour, nowtime.tm_min, nowtime.tm_sec)
 
     filepath = '%s/log.txt'%(target_dir)
-    f = open(filepath, 'w')
-    f.write('record succesfully stopped')
+    f = open(filepath, 'a')
+    f.write('\n[RECORD] record succesfully stopped at ' + rec_done + '\n')
     f.close()
-    
-    time.sleep(2)
-    exit()
+    with open ('%s/rec_count.txt'%(target_dir), "w") as rec_count:
+        rec_count.write(str(len(os.listdir(target_dir))))
 
 def grab_run(index, cameras):
     global stop_signal
@@ -55,7 +72,7 @@ def grab_run(index, cameras):
     while not stop_signal:
         #I need loop and 1800ms
         if frame_list[index] % record_time_per_svo == 0:
-            filepath = '%s/%s_%s.svo'%(target_dir,str(shard_count).zfill(3),cameras[index].id+1)
+            filepath = '%s/%s_%s.svo'%(target_dir,str(shard_count).zfill(3),cameras[index].id+1)            
             record_params = sl.RecordingParameters(filepath, sl.SVO_COMPRESSION_MODE.H264)
             err = zed_list[index].enable_recording(record_params)
             #print('new shard created')
@@ -74,9 +91,11 @@ def grab_run(index, cameras):
     #print('saved')    
 
 def main():
-
+    global target_dir
+    #os.call(('wall', target_dir))
     signal(SIGINT, handler)
 
+
     #print('Running...')
     init = sl.InitParameters()
     init.camera_resolution = sl.RESOLUTION.HD1080
@@ -85,13 +104,15 @@ def main():
     name_list = []
 
     cameras = sl.Camera.get_device_list()
-    #print(cameras)
+    print(cameras)
     index = 0
     for cam in cameras:
         init.set_from_serial_number(cam.serial_number)
         name_list.append("ZED {}".format(cam.serial_number))
         print("Opening {}".format(name_list[index]))
+        
         zed_list.append(sl.Camera())
+        
         timestamp_list.append(0)
         frame_list.append(0)
         status = zed_list[index].open(init)
@@ -105,6 +126,7 @@ def main():
         if zed_list[index].is_opened():
             thread_list.append(threading.Thread(target=grab_run, args=(index,cameras)))
             thread_list[index].start()
+            
 
 
 

recorder.py.bak

@@ -1,107 +0,0 @@
-import sys
-import os
-import pyzed.sl as sl
-import threading
-import time
-from signal import signal, SIGINT
-
-zed_list = []
-timestamp_list = []
-thread_list = []
-stop_signal = False
-frame_list = []
-
-record_time_per_svo = 1800
-
-name_time_list = []
-
-
-root_path = "/data/"
-filenames = os.listdir(root_path)
-for f in filenames:
-    w_time = os.path.getctime(root_path + f)
-    name_time_list.append((f, w_time))
-sorted_list = sorted(name_time_list, key=lambda x: x[1], reverse=True)
-
-recent_file = sorted_list[0]
-target_dir = str(root_path) + str(recent_file[0])
-
-
-def handler(signal, frame):
-    global stop_signal
-    global zed_list
-    stop_signal = True    
-    time.sleep(2)
-    exit()
-
-def grab_run(index, cameras):
-    global stop_signal
-    global zed_list
-    global timestamp_list
-    global left_list
-    global depth_list
-    global frame_list
-    global taget_dir
-
-    runtime = sl.RuntimeParameters()
-    shard_count = 0
-    while not stop_signal:
-        #I need loop and 1800ms
-        if frame_list[index] % record_time_per_svo == 0:
-            filepath = '%s/%s_%s.svo'%(target_dir,str(shard_count).zfill(3),cameras[index].id+1)
-            record_params = sl.RecordingParameters(filepath, sl.SVO_COMPRESSION_MODE.H264)
-            err = zed_list[index].enable_recording(record_params)
-            #print('new shard created')
-            shard_count += 1
-            if zed_list[index].grab(runtime) == sl.ERROR_CODE.SUCCESS :
-                frame_list[index] += 1
-        #if zed_list[index].grab() == "SUCCESS":
-        #    zed_list[index].record()
-        #print("Frame count: " + str(frame_list[index]), end='\r')
-        if zed_list[index].grab(runtime) == sl.ERROR_CODE.SUCCESS :
-            frame_list[index] += 1
-
-    zed_list[index].disable_recording()
-    filepath = '%s/log.txt'%(target_dir)
-    f = open(filepath, 'w')
-    f.write('record succesfully stopped')
-    f.close()
-    #print('saved')    
-
-def main():
-
-    signal(SIGINT, handler)
-
-    #print('Running...')
-    init = sl.InitParameters()
-    init.camera_resolution = sl.RESOLUTION.HD1080
-    init.camera_fps = 30
-
-    name_list = []
-
-    cameras = sl.Camera.get_device_list()
-    #print(cameras)
-    index = 0
-    for cam in cameras:
-        init.set_from_serial_number(cam.serial_number)
-        name_list.append("ZED {}".format(cam.serial_number))
-        print("Opening {}".format(name_list[index]))
-        zed_list.append(sl.Camera())
-        timestamp_list.append(0)
-        frame_list.append(0)
-        status = zed_list[index].open(init)
-        if status != sl.ERROR_CODE.SUCCESS:
-            print(repr(status))
-            zed_list[index].close()
-        index = index +1
-
-    #Start camera threads
-    for index in range(0, len(zed_list)):
-        if zed_list[index].is_opened():
-            thread_list.append(threading.Thread(target=grab_run, args=(index,cameras)))
-            thread_list[index].start()
-
-
-
-if __name__ == "__main__":
-    main()

stop_ubuntu.py

@@ -0,0 +1,24 @@
+import subprocess
+import os
+import signal
+import json
+
+
+PROCESS_NAME = 'rec_ubuntu.py'
+
+
+devnull = open('/dev/null', 'w')
+
+def kill_recorder():
+    ps = subprocess.Popen(('ps', '-ef'), stdout=subprocess.PIPE)
+    output = subprocess.check_output(('grep', PROCESS_NAME), stdin=ps.stdout).decode()    
+    ps_number = output.split()    
+    os.kill(int(ps_number[1]), signal.SIGINT)
+    #with open (os.path.abspath(os.path.expanduser(JSON_PATH)), "w") as json_file:
+    #        json.dump(conf,json_file, indent=4)
+
+def main():
+    kill_recorder()
+
+if __name__ == "__main__":
+    main()

tiny-yolov4.cfg

@@ -0,0 +1,294 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=16
+width=1920
+height=1056
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.00261
+burn_in=1000
+
+max_batches = 100000
+policy=steps
+steps=1600,1800
+scales=.1,.1
+
+
+#weights_reject_freq=1001
+#ema_alpha=0.9998
+#equidistant_point=1000
+#num_sigmas_reject_badlabels=3
+#badlabels_rejection_percentage=0.2
+
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+##################################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=1
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+#new_coords=1
+#scale_x_y = 2.0
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 23
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=1
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+#new_coords=1
+#scale_x_y = 2.0

tools/checkstatus.py

@@ -0,0 +1,20 @@
+from asyncio import subprocess
+import sys
+import os
+import pyzed.sl as sl
+import threading
+import time
+from signal import signal, SIGINT
+
+
+def check_status():
+    cameras = sl.Camera.get_device_list()
+    print(cameras)    
+
+
+
+def main():
+    check_status()
+
+if __name__ == '__main__':
+    main()

tools/zedclose.py

@@ -0,0 +1,29 @@
+import os
+import pyzed.sl as sl
+import sys
+
+
+def close_zed():
+    zed_list = []
+
+    init = sl.InitParameters()
+    init.camera_resolution = sl.RESOLUTION.HD1080
+    init.camera_fps = 30
+
+
+    cameras = sl.Camera.get_device_list()
+    print(cameras)
+    for cam in cameras:
+        init.set_from_serial_number(cam.serial_number)        
+        zed_list.append(sl.Camera())
+    for index in range(0, len(zed_list)):
+        zed_list[index].close()
+
+    
+
+
+def main():
+    close_zed()
+
+if __name__ == '__main__':
+    main()

yolo_cfg/tiny_v4/obj.data

@@ -0,0 +1,5 @@
+classes = 1
+train = /workspace/configurations/tiny_v4_220204/train.txt
+valid = /workspace/configurations/tiny_v4_220204/valid.txt
+names = /home/mc365/sources/obj.names
+backup = /workspace/configurations/tiny_v4_220204/weights_backups/

yolo_cfg/tiny_v4/obj.names

@@ -0,0 +1 @@
+handle