delete module directory

module/coordsi.py

@@ -1,143 +0,0 @@
-import os, sys
-import pandas as pd
-import numpy as np
-import sklearn as sk
-from sklearn import preprocessing
-import math
-'''
-coordsi.py is a module for convert coordinates to SI unit.
-
-Warning: This module convert coordinates with heuristic method.
-This moudle will be deprecated
-'''
-
-def readCam (filepath):
-    '''
-    Create a dataframe from cam*.csv file.
-    You must specify camera id. default is 1.
-    '''
-    try:
-        df = pd.read_csv(filepath, header=0, dtype={'x': np.float64, 'y': np.float64, 'z': np.float64, 'frame': np.int64})
-        return df
-    except Exception as e:
-        print(e)
-
-def summaryOriginCoord (root, camid=0):
-    '''
-    Show brief information of listed surgeon coordinate.
-    '''
-    d = root
-    try:
-        if camid == 1 or camid == 2 :
-            df = pd.DataFrame(columns=['approx_total_frame', 'x_min', 'x_max', 'y_min', 'y_max', 'z_min', 'z_max'])
-            subdirs = [x[0] for x in os.walk(d)]
-            for d in subdirs[1:]:
-                row = []
-                #get a list of files in directory
-                files = os.listdir(d)
-                count_svo = len([s for s in files if "_1.csv" in s])
-                filpath = os.path.join(d,'cam'+str(camid)+'.csv')
-                if os.path.isfile(filpath):
-                    arr = readCam(os.path.join(d,'cam'+str(camid)+'.csv'))
-                    row = [3600 *count_svo, arr['x'].min(), arr['x'].max(), arr['y'].min(), arr['y'].max(), arr['z'].min(), arr['z'].max()]
-                    df.loc[d] = row
-                else:
-                    print('Warning: '+filpath+' is not exist.')
-                    continue
-            return df
-        else:
-            print("Error: Camera ID must be 1 or 2. type camid=1 or camid=2 after dirpath")
-    except Exception as e:
-        print(e)
-
-    
-
-
-def convertToSI (origin, w, l, h):
-    '''
-    Waring: This function will be deprecated!
-    Convert coordinates to SI unit with w, l, h.
-    w, l, h are heuristic value. It will be changed after calibration and each surgeon room will geat unique value.
-    '''
-    try:
-        df = origin.copy()
-        #normailze dataframe[0] from -1 to 1
-        df['x'] = preprocessing.minmax_scale(df['x'], feature_range=(-(w/2), (w/2)), axis=0, copy=True)
-        #normailze dataframe[1] from -0.75 to 0.75
-        df['y'] = preprocessing.minmax_scale(df['y'], feature_range=(-(l/2), (l/2)), axis=0, copy=True)
-        #subtract dataframe[z] from int(4) and normailze from 0.93m to 1.8m (0 as floor)
-        df['z'] = h - df['z']
-        df['z'] = preprocessing.minmax_scale(df['z'], feature_range=(0.93,1.8), axis=0, copy=True)
-        #change column order x, y to y, x
-        df = df[['y', 'x', 'z', 'frame']]
-        #change column name y to X, x to Y
-        df = df.rename(columns={'y': 'x', 'x': 'y'})
-        return df
-    except Exception as e:
-        print(e)
-
-def getVelocity (dataframe):
-    try:
-        df  = dataframe
-        df['positionvectorvalue'] = (df['x']**2 + df['y']**2 + df['z']**2)
-        buffer1, buffer2 = df['positionvectorvalue'].copy(), df['positionvectorvalue'].copy()
-        buffer1, buffer2 = buffer1.to_frame(), buffer2.to_frame()
-        buffer1, buffer2 = list(buffer1.loc[:, 'positionvectorvalue']), list(buffer2.loc[:, 'positionvectorvalue'])
-        buffer3 = [0.0]
-        for x in range(0, len(buffer2)-1):
-            buffer3.append(buffer2[x])
-        buffer3  = np.array(buffer1) - np.array(buffer3)
-        for x in range(0, len(buffer3)):
-            buffer3[x] = math.sqrt(abs(buffer3[x]))
-        buffer3[0] = 0.0
-        df['velocity'] = buffer3
-        return df
-    except Exception as e:
-        print(e)
-
-def getAcceleration (dataframe):
-    try:
-        df = dataframe
-        buffer1, buffer2 = df['velocity'].copy(), df['velocity'].copy()
-        buffer1, buffer2 = buffer1.to_frame(), buffer2.to_frame()
-        buffer1, buffer2 = list(buffer1.loc[:, 'velocity']), list(buffer2.loc[:, 'velocity'])
-        buffer3 = [0.0]
-        for x in range(0, len(buffer2)-1):
-            buffer3.append(buffer2[x])
-        buffer3  = np.array(buffer1) - np.array(buffer3)
-        buffer3[1] = 0.0
-        df['acceleration'] = buffer3
-        return df
-    except Exception as e:
-        print(e)
-
-def getFullTrace (dataframe, approx_total_frame):
-    try:
-        df = dataframe
-        total_frame = int(approx_total_frame)
-        handletrace = pd.DataFrame(columns=['x', 'y', 'z', 'frame', 'positionvectorvalue', 'velocity', 'acceleration', 'isValid'])
-        handletrace = pd.concat([handletrace, pd.DataFrame(np.zeros((total_frame,8)), columns=['x', 'y', 'z', 'frame', 'positionvectorvalue','velocity', 'acceleration', 'isValid'])], axis=0)
-        for x in range(0, len(df)):
-            handletrace.loc[df.loc[x, 'frame']] = df.loc[x]
-        handletrace['isValid'] = handletrace['isValid'].fillna(0)
-        return handletrace
-    except Exception as e:
-        print(e)
-
-def addValidDetection (dataframe, threshold = 25.0):
-    '''
-    threshold value is suitable for only 1st surgeon room(Seoul 801).
-    We can make threshold in every surgeon room by using epipolar geometry.(but needs more cameras with different positions)
-    '''
-    try:
-        df = dataframe
-        top = threshold
-        bottom = -threshold
-        for x in range(0, len(df)):
-            if bottom < df['acceleration'][x] < top:
-                df['isValid'][x] = 1
-            else:
-                df['isValid'][x] = 0
-        return df
-    except Exception as e:
-        print(e)        

module/wavacc.py

@@ -1,34 +0,0 @@
-import sys, os, math, time
-import numpy as np
-import pandas as pd
-'''
-wavacc.py for interpolate accelerometer data and extract feature as stroke.
-Denosing step is not implemented this module. See addValidDetection function in coordsi.py for denoising.
-'''
-def interpolation(df):
-    try:
-        #implement data here
-    except Exception as e:
-        print(e)
-        return df
-    
-def decomposition(df):
-    try:
-        #implemnet decompositino with wavelet transform here
-    except Exception as e:
-        print(e)
-        return df
-
-def getStroke(df):
-    try:
-        #implement get stroke here
-    except Exception as e:
-        print(e)
-        return df
-
-def mergeToDataFrame(df):
-    try:
-        #implement merge to dataframe here
-    except Exception as e:
-        print(e)
-        return df