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@@ -33,3 +33,43 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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Dockerfile

@@ -0,0 +1,25 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN apt-get update
+
+RUN apt install -y libgl1-mesa-glx
+
+RUN apt-get install python3-pygame -y
+
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+
+RUN apt-get update && apt-get install libgl1
+
+RUN apt-get update && apt-get install -y python3-opencv
+
+RUN pip install opencv-python
+
+COPY . .
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "main:app"]

USL/000000000000_keypoints.json

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+{"version":1.2,"people":[{"pose_keypoints_2d":[751.476,285.063,0.912371,711.287,518.561,0.85813,457.564,518.576,0.795016,0,0,0,0,0,0,975.083,518.618,0.799197,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,690.309,233.835,0.897561,782.309,234.172,0.8556,609.34,223.713,0.86976,853.427,244.176,0.813811,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"face_keypoints_2d":[],"hand_left_keypoints_2d":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"hand_right_keypoints_2d":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"pose_keypoints_3d":[],"face_keypoints_3d":[],"hand_left_keypoints_3d":[],"hand_right_keypoints_3d":[]}]}

USL/alphabet_recognition/alphabet_recognition.py

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+
+
+import USL.helper.move as move
+import USL.helper.helperFunc as helper
+import USL.helper.scale as scale
+
+import json
+import sqlite3
+import math
+import numpy as np
+
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+from keras.utils import to_categorical
+from keras.models import load_model
+
+
+model = load_model("data\\models\\alphabet_model.h5")
+print("alphabet model loaded")
+
+
+def match_ann(fileName):
+    js = json.loads(open(fileName ).read())
+    for items in js['people']:
+        handRight = items["hand_right_keypoints_2d"]
+
+    confPoints = helper.confidencePoints(handRight)
+    confidence = helper.confidence(confPoints)
+    if confidence > 10.2:
+        handPoints = helper.removePoints(handRight)
+
+        """
+        experimenting with scaling 
+        """
+        p1 = [handPoints[0], handPoints[1]]
+        p2 = [handPoints[18], handPoints[19]]
+        distance = math.sqrt( ((p1[0]-p2[0])**2)+((p1[1]-p2[1])**2) )
+
+        Result,Points = scale.scalePoints(handPoints,distance)
+        handRightResults,handRightPoints = move.centerPoints(handPoints)
+        
+        """
+        extracting data from db
+        """
+        connection = sqlite3.connect("data\\db\\main_dataset.db") 
+        crsr = connection.cursor()
+
+        # extracting x and y points
+        sql = 'SELECT x1,y1'
+        for x in range(2,22):
+            sql = sql + ',x'+str(x)+',y'+str(x)
+        sql = sql + ' FROM rightHandDataset WHERE 1'
+        crsr.execute(sql)
+        feature_res = crsr.fetchall()
+        feature_res = np.asarray(feature_res)
+        features=[]
+        for x in feature_res:
+            features.append(x)
+
+        # extracting labels
+        crsr.execute('SELECT label FROM rightHandDataset WHERE 1')
+        label_res = crsr.fetchall()
+        labels=[]
+        for x in label_res:
+            labels.append(x)
+    
+        #creating labelEncoder
+        le = preprocessing.LabelEncoder()
+        # Converting string labels into numbers.
+        label_encoded=le.fit_transform(labels)
+
+        label_encoded = to_categorical(label_encoded)
+
+        X_train, X_test, y_train, y_test = train_test_split(features, label_encoded, test_size=0.2)
+        
+        scaler = StandardScaler().fit(X_train)
+        X_train = scaler.transform(X_train)
+        X_test = scaler.transform(X_test)
+
+        y_pred = model.predict(scaler.transform(np.array([handRightResults])))
+
+        C = np.argmax(y_pred)
+
+        result = le.inverse_transform([C])
+
+        return result[0]
+    else:
+        return "no confidence"

USL/alphabet_recognition/train_alphabet_model.py

@@ -0,0 +1,176 @@
+
+
+import sqlite3
+import numpy as np
+
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils import to_categorical
+
+
+from sklearn.metrics import confusion_matrix, classification_report
+from matplotlib import pyplot as plt
+
+def train_alphabets():    
+    """
+    extracting data from db
+    """
+    connection = sqlite3.connect("data\\db\\main_dataset.db") 
+    crsr = connection.cursor()
+    
+    # extracting x and y points
+    sql = 'SELECT x1,y1'
+    for x in range(2,22):
+        sql = sql + ',x'+str(x)+',y'+str(x)
+    sql = sql + ' FROM rightHandDataset WHERE 1'
+    crsr.execute(sql)
+    feature_res = crsr.fetchall()
+    feature_res = np.asarray(feature_res)
+    features=[]
+    for x in feature_res:
+        features.append(x)
+    
+    # extracting labels
+    crsr.execute('SELECT label FROM rightHandDataset WHERE 1')
+    label_res = crsr.fetchall()
+    labels=[]
+    for x in label_res:
+        labels.append(x)
+        
+    #creating labelEncoder
+    le = preprocessing.LabelEncoder()
+    # Converting string labels into numbers.
+    label_encoded=le.fit_transform(labels)
+    
+    label_encoded = to_categorical(label_encoded)
+    
+    X_train, X_test, y_train, y_test = train_test_split(features, label_encoded, test_size=0.2)
+    
+    scaler = StandardScaler().fit(X_train)
+    X_train = scaler.transform(X_train)
+    X_test = scaler.transform(X_test)
+    
+    # Initialize the constructor
+    model = Sequential()
+    
+    # Add an input layer 
+    model.add(Dense(120, activation='relu', input_shape=(42,)))
+    
+    # Add one hidden layer 
+    model.add(Dense(64, activation='relu'))
+    
+    
+    # Add an output layer 
+    model.add(Dense(37, activation='sigmoid'))
+    
+    model.compile(loss='binary_crossentropy',
+                  optimizer='adam',
+                  metrics=['accuracy'])
+                       
+#    model.fit(X_train, y_train,epochs=20, batch_size=1, verbose=1)
+    
+#    model.save("..\\data\\models\\alphabet_model.h5")
+    
+    
+    
+    score = model.evaluate(X_test, y_test,verbose=1)
+    #
+    print("\n%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
+
+    history = model.fit(X_train, y_train,validation_split=0.20,epochs=25, batch_size=1, verbose=1)
+
+#    model.save("data\\models\\alphabet_model.h5")
+    
+    y_pred = model.predict(X_test)
+    score = model.evaluate(X_test, y_test,verbose=1)
+    #
+    print("\n%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
+    
+    # list all data in history
+    print(history.history.keys())
+    # summarize history for accuracy
+    plt.plot(history.history['acc'])
+    plt.plot(history.history['val_acc'])
+    plt.title('Accuracy vs Epoch')
+    plt.ylabel('accuracy')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'test'], loc='upper left')
+    plt.show()
+    # summarize history for loss
+    plt.plot(history.history['loss'])
+    plt.plot(history.history['val_loss'])
+    plt.title('model loss')
+    plt.ylabel('loss')
+    plt.xlabel('epoch')
+    plt.legend(['train', 'test'], loc='upper left')
+    #plt.figure(figsize = (30,30))
+    plt.show()
+    
+    
+    print(model.summary())
+    cm = confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))
+    
+    import itertools
+    plt.rcParams.update({'font.size': 16})
+    def plot_confusion_matrix(cm, classes,
+                              normalize=False,
+                              title='Confusion matrix',
+                              cmap=plt.cm.Blues):
+        """
+        This function prints and plots the confusion matrix.
+        Normalization can be applied by setting `normalize=True`.
+        """
+        if normalize:
+            cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+            print("Normalized confusion matrix")
+        else:
+            print('Confusion matrix, without normalization')
+    
+        print(cm)
+    
+        plt.imshow(cm, interpolation='nearest', cmap=cmap)
+        plt.title(title)
+        plt.colorbar()
+        tick_marks = np.arange(len(classes))
+        plt.xticks(tick_marks, classes, rotation=0)
+        plt.yticks(tick_marks, classes)
+    
+        fmt = '.2f' if normalize else 'd'
+        thresh = cm.max() / 2.
+        for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+            plt.text(j, i, format(cm[i, j], fmt),
+                     horizontalalignment="center",
+                     color="white" if cm[i, j] > thresh else "black")
+    
+        plt.tight_layout()
+        plt.ylabel('True label')
+        plt.xlabel('Predicted label')
+    
+    l = np.array(labels) 
+    l = np.unique(l)
+    
+    class_names = l
+    
+    plt.figure(figsize = (25,25))
+    plot_confusion_matrix(cm, classes=class_names, normalize=True,
+                          title='Normalized confusion matrix')
+    
+    plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+

USL/capture_alphabets.py

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+
+import USL.helper.db_helper as dbh
+import USL.helper.helperFunc as helper
+import USL.helper.move as move
+import USL.helper.scale as scale
+import USL.helper.plot as plot
+import USL.retrain as retrain
+
+# system imports
+import cv2
+import math
+import os
+import subprocess
+import json
+from matplotlib import pyplot as plt
+import time
+from datetime import datetime
+
+# for error handling
+import errno, stat, shutil
+from distutils.dir_util import copy_tree
+import sys, signal
+
+import eel
+
+
+"""
+Handling Errors While removing temp folders
+"""     
+def signal_handler(signal, frame):
+    # Removing Keypoints Folder
+    shutil.rmtree("Keypoints", ignore_errors=True, onerror=handleRemoveReadonly)
+    shutil.rmtree("gui\\captured_images", ignore_errors=True, onerror=handleRemoveReadonly)
+    shutil.rmtree("gui\\temp_images", ignore_errors=True, onerror=handleRemoveReadonly)
+    
+    print( 'All done')
+    sys.exit(0)
+
+signal.signal(signal.SIGINT, signal_handler)
+
+# if folder is read only raise exception
+def handleRemoveReadonly(func, path, exc):
+  excvalue = exc[1]
+  if func in (os.rmdir, os.remove) and excvalue.errno == errno.EACCES:
+      os.chmod(path, stat.S_IRWXU| stat.S_IRWXG| stat.S_IRWXO) # 0777
+      func(path)
+  else:
+      raise Exception
+
+
+
+def plotPose(posePoints, handRightPoints, handLeftPoints):
+    POSE_PAIRS = [[1, 0], [1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7],
+                  [1, 8], [0, 15], [15, 17], [0, 16], [16, 18]]
+
+    HAND_PAIRS = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10],
+                  [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18],
+                  [18, 19], [19, 20]]
+
+    colors = [[0, 0, 130], [0, 0, 175], [0, 0, 210], [0, 0, 250],
+              [0, 200, 160], [0, 180, 150], [0, 230, 186], [0, 255, 255],
+              [82, 201, 8], [82, 204, 0], [92, 230, 0], [102, 252, 6],
+              [197, 88, 17], [204, 82, 0], [179, 71, 0], [227, 94, 5],
+              [204, 0, 163], [200, 0, 163], [196, 0, 163], [230, 0, 184]]
+
+    background = "URDU\\BLACK_background.jpg"
+
+    frame = cv2.imread(background)
+
+    count = 0
+    # Draw Skeleton
+    for pair in POSE_PAIRS:
+        partA = pair[0]
+        partB = pair[1]
+
+        if posePoints[partA] and posePoints[partB] and posePoints[partA][0] != 0 and posePoints[partA][1] != 0 and \
+                posePoints[partB][0] != 0 and posePoints[partB][1] != 0:
+            cv2.line(frame, posePoints[partA], posePoints[partB], colors[count], 10)
+            cv2.circle(frame, posePoints[partA], 5, (0, 0, 255), thickness=10, lineType=cv2.FILLED)
+            cv2.circle(frame, posePoints[partB], 5, (255, 255, 255), thickness=15, lineType=cv2.FILLED)
+        count += 1
+
+    count = 0
+    for pair in HAND_PAIRS:
+        partA = pair[0]
+        partB = pair[1]
+
+        if handRightPoints[partA] and handRightPoints[partB]:
+            cv2.line(frame, handRightPoints[partA], handRightPoints[partB], colors[count], 10)
+            cv2.circle(frame, handRightPoints[partA], 5, (0, 0, 255), thickness=3, lineType=cv2.FILLED)
+            cv2.circle(frame, handRightPoints[partB], 5, (255, 255, 255), thickness=4, lineType=cv2.FILLED)
+        count += 1
+
+    count = 0
+    for pair in HAND_PAIRS:
+        partA = pair[0]
+        partB = pair[1]
+
+        if handLeftPoints[partA] and handLeftPoints[partB]:
+            cv2.line(frame, handLeftPoints[partA], handLeftPoints[partB], colors[count], 10)
+            cv2.circle(frame, handLeftPoints[partA], 5, (0, 0, 255), thickness=3, lineType=cv2.FILLED)
+            cv2.circle(frame, handLeftPoints[partB], 5, (255, 255, 255), thickness=4, lineType=cv2.FILLED)
+        count += 1
+
+    #    frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
+    return frame
+
+
+remfileNames = []
+
+@eel.expose
+def capture_alphabet_dataset(sec):
+    
+    global remfileNames
+    
+    """
+    ----------------------Start OpenPoseDemo.exe----------------------
+    --render_pose 0  --display 0
+    """
+    os.chdir('bin\\openpose')
+    print('Starting OpenPose')
+    subprocess.Popen('bin\\OpenPoseDemo.exe --hand  --write_json ..\\..\\Keypoints   --number_people_max 1', shell=True)
+    os.chdir('..\\..')
+    
+    
+    
+    """
+    ----------------------Creating temp folder----------------------
+    """
+    dirName = 'Keypoints'
+    init_file = 'URDU\\000000000000_keypoints.json'
+    
+    try:
+        # Create target Directory
+        os.mkdir(dirName)
+        os.mkdir("gui\\captured_images")
+        os.mkdir("gui\\temp_images")
+        
+        # copy initializing file in directory
+        shutil.copy(init_file, dirName)
+        print("Directory " , dirName ,  " Created ") 
+    except FileExistsError:
+        print("Directory " , dirName ,  " already exists")
+
+    
+    
+    """
+    ----------------------Live View----------------------
+    """
+    
+#    shutil.copy('URDU\\000000000000_keypoints.json', 'Keypoints')
+#    filePlotName = '000000000000_keypoints.json'
+    t = time.time() + sec
+    while time.time() <= t:
+        eel.sleep(0.05)
+#        for entry in os.scandir('Keypoints'):
+#            if entry.is_file():
+#                if os.path.splitext(entry)[1] == ".json":
+#                    filePlotName = entry.name
+#    
+#        try:
+#            js = json.loads(open('Keypoints\\' + filePlotName).read())
+#        except ValueError:
+#            print('Decoding JSON has failed')
+#            pass
+#    
+#        # extract 'hand_right_keypoints_2d' from json file
+#        for items in js['people']:
+#            pose = items["pose_keypoints_2d"]
+#            handRight = items["hand_right_keypoints_2d"]
+#            handLeft = items["hand_left_keypoints_2d"]
+#    
+#        pose_points = helper.removePoints(pose)
+#        posePoints = helper.join_points(pose_points)
+#    
+#        #  function to remove confidence points
+#        hand_right_Points = helper.removePoints(handRight)
+#        handRightPoints = helper.join_points(hand_right_Points)
+#    
+#        hand_left_points = helper.removePoints(handLeft)
+#        handLeftPoints = helper.join_points(hand_left_points)
+#    
+#        frame = plotPose(posePoints, handRightPoints, handLeftPoints)
+#    
+#        cv2.imwrite('gui\\temp_images\\' + filePlotName + '.jpg', frame)
+#        # reset image
+#        frame = cv2.imread("URDU\\BLACK_background.jpg")
+#        print("saad")
+#        eel.get_fileName(filePlotName)
+#        filePlotName = ''
+
+
+#    eel.sleep(sec)
+    #  Kill openpose
+    os.system("taskkill /f /im  OpenPoseDemo.exe")
+    
+    
+    """
+    ---------------------- Auto Remove files----------------------
+    """
+    conf_thershold = 10
+    fileNames = []
+    #scan temporary folder
+    for entry in os.scandir('Keypoints'):
+        # store the name if entry is file and file is of ext .json
+        if entry.is_file():
+            if os.path.splitext(entry)[1] == ".json":
+                fileNames.append(entry.name)
+                
+    # traverse fileNames[]
+    for x in range(len(fileNames)):
+        # load each file from fileName[]
+        js = json.loads(open('Keypoints\\' + fileNames[x]).read())
+        # extract 'hand_right_keypoints_2d' from json file
+        for items in js['people']:
+            handRight = items["hand_right_keypoints_2d"]
+        
+        # extract confidence points
+        confPoints = helper.confidencePoints(handRight)
+        # add all confidence points
+        confidence = helper.confidence(confPoints)
+        print(confidence)
+        # remove file if confidence is less than threshold
+        if confidence < conf_thershold:
+            os.remove('Keypoints\\' + fileNames[x])
+
+
+    """
+    ----------------------plot and save----------------------
+    """   
+    background = 'big_background.png'
+    fileNames = []
+    #scan temporary folder
+    for entry in os.scandir('Keypoints'):
+        # store the name if entry is file and file is of ext .json
+        if entry.is_file():
+            if os.path.splitext(entry)[1] == ".json":
+                fileNames.append(entry.name)
+                
+    
+    # read background image
+    frame = cv2.imread(background)
+    
+    i=1;
+    
+    for x in range(len(fileNames)):
+        # load each file from fileName[]
+        js = json.loads(open('Keypoints\\' + fileNames[x]).read())
+        # extract 'hand_right_keypoints_2d' from json file
+        for items in js['people']:
+            handRight = items["hand_right_keypoints_2d"]
+        
+        #  function to remove confidence points
+        handPoints = helper.removePoints(handRight)
+         
+        p1 = [handPoints[0], handPoints[1]]
+        p2 = [handPoints[18], handPoints[19]]
+        distance = math.sqrt( ((p1[0]-p2[0])**2)+((p1[1]-p2[1])**2) )
+        
+        Result,Points = scale.dummy_scalePoints(handPoints,distance)
+       
+        handRightResults,handRightPoints = move.dummy_centerPoints(Result)  
+    
+        
+        
+        frame = plot.plot_dataset(handRightPoints,'black') 
+        
+        cv2.imwrite('gui\\captured_images\\' + str(i) + '.jpg', frame)
+        i+=1
+        
+        
+        """
+        ----------------------get ref to delete files----------------------
+        """ 
+        
+        for entry in os.scandir('Keypoints'):
+            if entry.is_file():
+                if os.path.splitext(entry)[1] == ".json":
+                    remfileNames.append(entry.name)
+        
+        
+        """
+        ----------------------end capture_alphabet_dataset(sec)----------------------
+        """  
+
+@eel.expose
+def getFileCount():
+    Names = []
+    for entry in os.scandir('gui\\captured_images'):
+        Names.append(entry.name)
+    return str(len(Names))
+
+
+@eel.expose
+def delete_Image(i):
+    global remfileNames
+    
+    print(remfileNames)
+    
+    try:
+        os.remove('Keypoints\\' + remfileNames[i-1])            
+        os.remove('gui\\captured_images\\'+ str(i) + '.jpg')
+    except:
+        print("file not found")
+        pass
+
+
+
+
+@eel.expose
+def getlabel(a):
+
+    # remove whitespaces
+    label = a.strip()
+    print(label)
+
+    """
+    traverse 'dataset' folder ,
+    find subfolder matching 'label' ,
+    create folder with timestamp in  matched folder , 
+    and copy everything from 'Keypoints_temp' to created folder
+    """
+    for entry in os.scandir('data\\datasets\\alphabets_dataset'):
+        if entry.name == label:
+            # current date and time
+            now = datetime.now()
+            # create folder with timestamp
+            timestamp =  str(datetime.timestamp(now))
+            dir_name = "data\\datasets\\alphabets_dataset\\" + entry.name +"\\"+ timestamp
+            try:
+                # Create target Directory
+                os.mkdir(dir_name)
+                print("Directory " , dir_name ,  " Created ")
+            except FileExistsError:
+                print("Directory " , dir_name ,  " already exists")
+
+               # copy everything from 'Keypoints_temp' to created folder
+            copy_tree("Keypoints", "data\\datasets\\alphabets_dataset\\" + entry.name + "\\" + timestamp )
+
+
+    """ 
+    Remove temp folders 
+    """
+    try:
+        shutil.rmtree("Keypoints", ignore_errors=True, onerror=handleRemoveReadonly)
+        shutil.rmtree("gui\\captured_images", ignore_errors=True, onerror=handleRemoveReadonly)
+        shutil.rmtree("gui\\temp_images", ignore_errors=True, onerror=handleRemoveReadonly)
+        
+        print( 'Keypoints_temp folder removed')
+    except:
+        print("not removed")
+        pass
+    
+@eel.expose
+def db_train():
+    retrain.re_train(1)
+
+
+
+
+
+
+
+
+
+
+
+

USL/capture_words.py

@@ -0,0 +1,260 @@
+
+
+# local imports
+import helper.helperFunc as helper
+import helper.scale as scale
+import helper.db_helper as dbh
+import helper.normalize as norm
+import helper.plot_body as plot_body
+
+# system imports
+import cv2
+import math
+import os
+import subprocess
+import json
+from matplotlib import pyplot as plt
+import time
+from datetime import datetime
+
+# for error handling
+import errno, stat, shutil
+from distutils.dir_util import copy_tree
+import sys, signal
+
+
+"""
+Handling Errors While removing temp folders
+"""     
+def signal_handler(signal, frame):
+    # Removing Keypoints Folder
+    shutil.rmtree("Keypoints", ignore_errors=True, onerror=handleRemoveReadonly)
+    print( 'All done')
+    sys.exit(0)
+
+signal.signal(signal.SIGINT, signal_handler)
+
+# if folder is read only raise exception
+def handleRemoveReadonly(func, path, exc):
+  excvalue = exc[1]
+  if func in (os.rmdir, os.remove) and excvalue.errno == errno.EACCES:
+      os.chmod(path, stat.S_IRWXU| stat.S_IRWXG| stat.S_IRWXO) # 0777
+      func(path)
+  else:
+      raise Exception
+
+
+""" 
+Remove 'Keypoints_temp' folder if exists
+"""
+
+shutil.rmtree("Keypoints_temp", ignore_errors=True, onerror=handleRemoveReadonly)
+
+      
+"""
+Starting OpenPoseDemo.exe
+and storing json files to temporary folder [Keypoints_temp] 
+"""
+sec = int(input('Enter Seconds OpenPose should run for : '))
+os.chdir('..\\bin\\openpose')
+print('Starting OpenPose')
+output = subprocess.Popen('bin\\OpenPoseDemo.exe --hand  --write_json ..\\..\\URDU\\Keypoints_temp  --net_resolution 128x128  --number_people_max 1', shell=True)
+os.chdir('..\\..\\URDU')
+
+
+"""
+Creating temp folder and initializing with zero padded json file
+"""
+dirName = 'Keypoints_temp'
+init_file = '000000000000_keypoints.json'
+
+try:
+    # Create target Directory
+    os.mkdir(dirName)
+    # copy initializing file in directory
+    shutil.copy(init_file, dirName)
+    print("Directory " , dirName ,  " Created ") 
+except FileExistsError:
+    print("Directory " , dirName ,  " already exists")
+    
+#  wait for openpose to start     
+time.sleep(5) 
+#  wait for user  
+time.sleep(sec) 
+#  Kill openpose
+os.system("taskkill /f /im  OpenPoseDemo.exe")
+
+
+"""
+Remove files with confidence less then threshold
+"""
+conf_thershold = 10
+fileNames = []
+#scan temporary folder
+for entry in os.scandir('Keypoints_temp'):
+    # store the name if entry is file and file is of ext .json
+    if entry.is_file():
+        if os.path.splitext(entry)[1] == ".json":
+            fileNames.append(entry.name)
+            
+# traverse fileNames[]
+for x in range(len(fileNames)):
+    # load each file from fileName[]
+    js = json.loads(open('Keypoints_temp\\' + fileNames[x]).read())
+    # extract 'hand_right_keypoints_2d' from json file
+    for items in js['people']:
+        handRight = items["hand_right_keypoints_2d"]
+        handLeft = items["hand_left_keypoints_2d"]
+    
+    # extract confidence points
+    RightConfPoints = helper.confidencePoints(handRight)
+    LeftConfPoints = helper.confidencePoints(handLeft)
+    # add all confidence points
+    RightConfidence = helper.confidence(RightConfPoints)
+    LeftConfidence = helper.confidence(LeftConfPoints)
+    # remove file if confidence is less than threshold
+    if RightConfidence < 10:
+        os.remove('Keypoints_temp\\' + fileNames[x])
+        
+    elif LeftConfidence < conf_thershold and LeftConfidence > 2 :
+        os.remove('Keypoints_temp\\' + fileNames[x])   
+
+    
+
+"""
+plot remaining files
+and delete if user inputs "N"  
+"""   
+# points to draw lines between
+POSE_PAIRS = [ [0,1],[1,2],[2,3],[3,4],[0,5],[5,6],[6,7],[7,8],[0,9],[9,10],[10,11],[11,12],[0,13],[13,14],[14,15],[15,16],[0,17],[17,18],[18,19],[19,20] ]
+background = 'big_background.png'
+fileNames = []
+#scan temporary folder
+for entry in os.scandir('Keypoints_temp'):
+    # store the name if entry is file and file is of ext .json
+    if entry.is_file():
+        if os.path.splitext(entry)[1] == ".json":
+            fileNames.append(entry.name)
+            
+
+# read background image
+frame = cv2.imread(background)
+for x in range(len(fileNames)):
+    # load each file from fileName[]
+    js = json.loads(open('Keypoints_temp\\' + fileNames[x]).read())
+    # extract 'hand_right_keypoints_2d' from json file
+    for items in js['people']:
+        pose = items["pose_keypoints_2d"]
+        handRight = items["hand_right_keypoints_2d"]
+        handLeft = items["hand_left_keypoints_2d"]
+        
+    pose_points = helper.removePoints(pose)
+    p1 = [pose_points[0], pose_points[1]]
+    p2 = [pose_points[2], pose_points[3]]
+    distance = math.sqrt( ((p1[0]-p2[0])**2)+((p1[1]-p2[1])**2) )
+    scaled_results,scaled_points = norm.scaleBody(pose_points,distance)
+    poseResults,posePoints = norm.moveBody(scaled_results)
+    
+    
+    hand_right_points = helper.removePoints(handRight)
+    p1 = [hand_right_points[0], hand_right_points[1]]
+    p2 = [hand_right_points[18], hand_right_points[19]]
+    distance = math.sqrt( ((p1[0]-p2[0])**2)+((p1[1]-p2[1])**2) )
+    RightResult,Points = scale.scalePoints(hand_right_points,distance)
+    handRightResults,handRightPoints = norm.move_to_wrist(RightResult,poseResults[8],poseResults[9])
+    
+    
+    if LeftConfidence > 3:
+        hand_left_points = helper.removePoints(handLeft)
+        p1 = [hand_left_points[0], hand_left_points[1]]
+        p2 = [hand_left_points[18], hand_left_points[19]]
+        distance = math.sqrt( ((p1[0]-p2[0])**2)+((p1[1]-p2[1])**2) )
+        if distance != 0: 
+            LeftResult,Points = scale.scalePoints(hand_left_points,distance)
+            handLeftResults,handLeftPoints = norm.move_to_wrist(LeftResult,poseResults[14],poseResults[15])
+        else: 
+            handLeftResults,handLeftPoints = norm.move_to_wrist(hand_left_points,poseResults[14],poseResults[15])
+    
+    else:
+        handLeftPoints = [(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0),(0, 0)]
+        handLeftResults = [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,]
+    
+
+    
+    
+    frame = plot_body.plotPose(posePoints,handRightPoints,handLeftPoints) 
+    
+    
+    fig2 = plt.figure(figsize = (15,15)) # create a 20 x 20 figure 
+    ax3 = fig2.add_subplot(111)
+    ax3.imshow(frame, interpolation='none')
+    
+    plt.imshow(frame)
+    plt.show()
+    
+    
+    
+    # delete file if user rejects it
+    choice = input('do you want to keep it? Y/N: ')
+    if choice == 'N' or choice == 'n':
+        os.remove('Keypoints_temp\\' + fileNames[x])
+        print("File Removed")
+
+# store label
+label = input('Enter label for these files: ')
+# remove whitespaces
+label = label.strip()
+
+choice = input('do you want to put label = '+label+' ? Y/N: ')
+if choice == 'N' or choice == 'n':
+    # store label
+    label = input('Enter label for these files: ')
+    # remove whitespaces
+    label = label.strip()
+elif choice == 'Y' or choice == 'y':
+    """
+    traverse 'dataset' folder ,
+    find subfolder matching 'label' ,
+    create folder with timestamp in  matched folder , 
+    and copy everything from 'Keypoints_temp' to created folder
+    """
+    for entry in os.scandir('..\\data\\datasets\\words_dataset'):
+        if entry.name == label:
+            # current date and time
+            now = datetime.now()
+            
+            # create folder with timestamp
+            timestamp =  str(datetime.timestamp(now))
+            dir_name = "..\\data\\datasets\\words_dataset\\" + entry.name +"\\"+ timestamp
+            try:
+                # Create target Directory
+                os.mkdir(dir_name)
+                print("Directory " , dir_name ,  " Created ") 
+            except FileExistsError:
+                print("Directory " , dir_name ,  " already exists")
+                
+            # copy everything from 'Keypoints_temp' to created folder    
+            copy_tree("Keypoints_temp", "..\\data\\datasets\\words_dataset\\" + entry.name + "\\" + timestamp )
+    
+    
+    # create table if not exists
+    dbh.create_pose_table()
+    # add all records from 'dataset' to 'db\main_databse.db'
+    dbh.populate_words()
+    
+    
+    
+""" 
+Remove 'Keypoints_temp' folder 
+"""
+
+shutil.rmtree("Keypoints_temp", ignore_errors=True, onerror=handleRemoveReadonly)
+print( 'Keypoints_temp folder removed')
+
+        
+
+
+
+
+
+

USL/data/datasets/words_dataset/good/1564405243.754477/000000000001_keypoints.json

@@ -0,0 +1 @@
+{"version":1.3,"people":[{"person_id":[-1],"pose_keypoints_2d":[711.072,172.976,0.865881,751.659,335.277,0.872606,609.359,335.311,0.883848,507.695,518.571,0.906813,660.126,487.748,0.815168,863.364,315.359,0.843981,974.807,497.894,0.68327,985.26,599.552,0.29148,751.27,690.945,0.792042,640.163,691.107,0.747708,619.876,934.405,0.171245,0,0,0,842.592,690.728,0.790617,924.207,924.207,0.157552,0,0,0,690.96,132.408,0.927396,761.405,132.569,0.917004,680.373,162.957,0.437018,792.153,182.893,0.856458,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"face_keypoints_2d":[],"hand_left_keypoints_2d":[1009.89,575.042,0.0302895,1016.17,597.794,0.0273345,973.802,598.579,0.0129452,972.233,597.794,0.0129548,973.018,654.283,0.00576005,962.818,607.209,0.0157419,943.204,613.486,0.0164846,988.709,629.177,0.0118225,997.339,629.961,0.00905518,986.355,615.055,0.0119401,998.908,629.961,0.0188129,993.416,628.392,0.0165809,999.693,629.177,0.0107825,998.124,613.486,0.0116951,1005.18,634.669,0.0206705,1001.26,633.884,0.0222454,997.339,629.961,0.0130985,929.082,617.408,0.0188582,940.851,617.408,0.0191024,1004.4,644.868,0.0199488,1001.26,639.376,0.013104],"hand_right_keypoints_2d":[645.08,489.878,0.56507,667.375,456.05,0.617107,683.52,425.298,0.796485,701.971,400.696,0.76958,705.815,379.17,0.872175,717.348,449.131,0.830243,747.331,442.212,0.744034,736.568,437.599,0.538112,723.498,438.368,0.426773,720.423,473.733,0.772022,749.637,462.201,0.830327,737.336,458.357,0.616486,721.96,462.201,0.445763,721.96,496.028,0.649564,744.256,482.189,0.790552,737.336,479.883,0.644268,724.267,480.652,0.459893,719.654,516.017,0.640393,737.336,505.254,0.590242,736.568,499.872,0.683161,726.573,499.103,0.508596],"pose_keypoints_3d":[],"face_keypoints_3d":[],"hand_left_keypoints_3d":[],"hand_right_keypoints_3d":[]}]}

USL/data/datasets/words_dataset/good/1564405243.754477/000000000002_keypoints.json

@@ -0,0 +1 @@
+{"version":1.3,"people":[{"person_id":[-1],"pose_keypoints_2d":[701.269,173.056,0.876828,751.424,335.277,0.859636,609.231,345.258,0.889252,497.953,518.638,0.894063,640.014,497.828,0.816606,853.672,305.258,0.856551,964.671,497.908,0.668743,1005.23,609.63,0.29021,751.362,691.035,0.774036,649.967,691.221,0.734425,609.463,924.255,0.134358,0,0,0,842.686,690.779,0.768943,924.224,924.193,0.102074,0,0,0,690.76,132.45,0.923417,761.264,132.534,0.919074,680.059,163.025,0.360531,792.099,182.94,0.857891,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"face_keypoints_2d":[],"hand_left_keypoints_2d":[1047.16,609.795,0.031987,1030.04,612.242,0.0309442,1018.62,607.348,0.02016,1026.77,605.717,0.0115887,1079.79,613.057,0.00502428,1033.3,653.024,0.0215367,1034.11,683.203,0.0183799,1038.19,676.678,0.00949141,1052.87,669.337,0.00575537,1033.3,645.683,0.0243699,1033.3,689.728,0.0251009,994.963,679.94,0.0104869,1061.03,665.259,0.00527603,1042.27,647.314,0.0250417,1029.22,700.331,0.0216332,1029.22,705.225,0.0153,1016.98,700.331,0.00882543,1068.37,654.655,0.0303428,981.097,617.136,0.0116016,1012.09,705.225,0.019704,1014.54,704.409,0.0116831],"hand_right_keypoints_2d":[650.617,488.693,0.586119,671.022,456.951,0.667331,684.626,426.72,0.797986,704.276,402.536,0.757695,707.299,379.862,0.890551,717.88,450.905,0.797714,748.111,444.859,0.749466,736.019,440.324,0.467101,720.903,441.08,0.422225,720.903,476.601,0.723817,749.623,465.265,0.80112,736.774,460.73,0.599919,721.659,464.509,0.461643,722.415,499.274,0.682511,745.088,486.426,0.769263,736.774,482.647,0.652596,724.682,484.159,0.464571,721.659,518.169,0.64753,739.798,506.076,0.64177,735.263,502.297,0.634378,724.682,504.565,0.551695],"pose_keypoints_3d":[],"face_keypoints_3d":[],"hand_left_keypoints_3d":[],"hand_right_keypoints_3d":[]}]}

USL/data/datasets/words_dataset/good/1564405243.754477/000000000003_keypoints.json

@@ -0,0 +1 @@
+{"version":1.3,"people":[{"person_id":[-1],"pose_keypoints_2d":[721.414,182.979,0.867081,751.724,325.449,0.871441,609.619,325.136,0.881691,508.211,518.39,0.935375,650.166,467.452,0.821585,863.293,325.435,0.839334,954.844,508.059,0.666606,985.036,609.323,0.265601,741.376,690.65,0.77274,629.959,690.704,0.756111,629.759,924.259,0.116461,0,0,0,832.92,690.516,0.763362,903.764,934.315,0.120046,0,0,0,691.358,132.749,0.892817,761.776,142.581,0.911846,680.666,162.976,0.564581,792.495,183.247,0.859701,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"face_keypoints_2d":[],"hand_left_keypoints_2d":[1013.74,575.291,0.0220301,1004.74,599.273,0.0190662,1021.98,648.735,0.0117144,1021.98,650.234,0.0101789,962.025,665.972,0.00444836,1022.73,617.259,0.0208003,986.756,638.243,0.0164368,988.255,641.241,0.00942788,1066.94,638.992,0.00545067,1012.24,625.503,0.0216027,988.255,636.744,0.0190285,1057.95,656.979,0.0118552,1014.48,680.96,0.00392548,1015.23,628.5,0.0217476,988.255,635.245,0.0171161,988.255,638.243,0.0126671,1006.99,670.468,0.00784819,1056.45,627.002,0.0173792,988.255,635.995,0.0149033,1009.24,673.466,0.0142477,988.255,639.742,0.00976102],"hand_right_keypoints_2d":[654.578,480.266,0.622895,674.594,447.44,0.680358,690.607,415.415,0.803538,709.021,389.795,0.797276,710.622,368.979,0.851946,721.831,440.235,0.822731,754.656,433.83,0.791122,740.245,429.827,0.470852,726.635,431.428,0.432797,727.435,465.054,0.779448,756.258,453.845,0.868299,741.846,451.443,0.669628,726.635,454.646,0.548467,729.036,487.472,0.809326,752.255,474.662,0.87167,741.846,473.06,0.720811,726.635,477.864,0.590732,729.837,505.085,0.759022,746.65,497.079,0.73606,739.445,493.877,0.742723,730.638,496.278,0.63147],"pose_keypoints_3d":[],"face_keypoints_3d":[],"hand_left_keypoints_3d":[],"hand_right_keypoints_3d":[]}]}

USL/data/datasets/words_dataset/good/1564405243.754477/000000000004_keypoints.json

@@ -0,0 +1 @@
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USL/data/datasets/words_dataset/good/1564405243.754477/000000000005_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000006_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000007_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000008_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000009_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000011_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000012_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000013_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000014_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000015_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000016_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000017_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000018_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000019_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000020_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000021_keypoints.json

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USL/data/datasets/words_dataset/good/1564405243.754477/000000000022_keypoints.json

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