def match_ann(fileName):
js = json.loads(open(fileName).read())
for items in js['people']:
pose = items["pose_keypoints_2d"]
handRight = items["hand_right_keypoints_2d"]
handLeft = items["hand_left_keypoints_2d"]
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 > 12:
if LeftConfidence > 12 or LeftConfidence < 2:
# normalizing bodyKeyPoints
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)
# normalizing RightHandKeyPoints
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])
# normalizing LeftHandKeyPoints
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:
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
]
# combining points
posePoints = []
for x in range(18):
posePoints.append(poseResults[x])
for x in range(30, 38):
posePoints.append(poseResults[x])
results = handRightResults + handLeftResults + posePoints
# extracting data from db
connection = sqlite3.connect("data\\db\\main_dataset.db")
crsr = connection.cursor()
# extracting x and y points
sql = 'SELECT Rx1,Ry1'
for x in range(2, 22):
sql = sql + ',Rx' + str(x) + ',Ry' + str(x)
for x in range(1, 22):
sql = sql + ',Lx' + str(x) + ',Ly' + str(x)
for x in range(1, 14):
sql = sql + ',Px' + str(x) + ',Py' + str(x)
sql = sql + ' FROM poseDataset 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 poseDataset 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([results])))
C = np.argmax(y_pred)
result = le.inverse_transform([C])
return result[0]
else:
return "no confidence"
else:
return "no confidence"
def populate_words():
"""
Delete all records from database
"""
connection = sqlite3.connect("data\\db\\main_dataset.db")
crsr = connection.cursor()
sql_command = "DELETE FROM poseDataset;"
crsr.execute(sql_command)
connection.commit()
connection.close()
print("records deleted, new records will be entered now")
folders = []
files = []
fileNames = []
for entry in os.scandir('data\\datasets\\words_dataset'):
if entry.is_dir():
folders.append(entry.path)
for entry1 in os.scandir(entry.path):
if entry1.is_dir():
folders.append(entry1.path)
for entry2 in os.scandir(entry1.path):
if entry2.is_dir():
folders.append(entry2.path)
elif entry2.is_file():
if os.path.splitext(entry2)[1] == ".json":
files.append(entry2.path)
fileNames.append(entry2.name)
elif entry1.is_file():
if os.path.splitext(entry1)[1] == ".json":
files.append(entry1.path)
fileNames.append(entry1.name)
elif entry.is_file():
if os.path.splitext(entry)[1] == ".json":
fileNames.append(entry1.name)
# connecting to the database
connection = sqlite3.connect("data\\db\\main_dataset.db")
# cursor
crsr = connection.cursor()
count = 0
for file in files:
js = json.loads(open(files[count]).read())
parent = (os.path.dirname(files[count])).split('\\')
parentName = "'" + parent[len(parent) - 2] + "'"
count += 1
for items in js['people']:
pose = items["pose_keypoints_2d"]
handRight = items["hand_right_keypoints_2d"]
handLeft = items["hand_left_keypoints_2d"]
# extract confidence points
LeftConfPoints = helper.confidencePoints(handLeft)
# add all confidence points
LeftConfidence = helper.confidence(LeftConfPoints)
pose_points = helper.removePoints(pose)
#posePoints = helper.join_points(pose_points)
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 > 2:
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))
LeftResult, Points = scale.scalePoints(hand_left_points, distance)
handLeftResults, handLeftPoints = norm.move_to_wrist(
LeftResult, poseResults[14], poseResults[15])
else:
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,
]
# SQL command to insert the data in the table
sql_command = "INSERT INTO poseDataset VALUES (NULL, " + str(
handRightResults[0]) + ", " + str(handRightResults[1]) + "," + str(
handRightResults[2]
) + "," + str(handRightResults[3]) + "," + str(
handRightResults[4]
) + "," + str(handRightResults[5]) + "," + str(
handRightResults[6]
) + "," + str(handRightResults[7]) + "," + str(
handRightResults[8]) + "," + str(
handRightResults[9]) + "," + str(
handRightResults[10]) + "," + str(
handRightResults[11]) + "," + str(
handRightResults[12]
) + "," + str(handRightResults[13]) + "," + str(
handRightResults[14]
) + "," + str(handRightResults[15]) + "," + str(
handRightResults[16]
) + "," + str(handRightResults[17]) + "," + str(
handRightResults[18]
) + "," + str(handRightResults[19]) + "," + str(
handRightResults[20]
) + "," + str(handRightResults[21]) + "," + str(
handRightResults[22]
) + "," + str(handRightResults[23]) + "," + str(
handRightResults[24]
) + "," + str(handRightResults[25]) + "," + str(
handRightResults[26]
) + "," + str(handRightResults[27]) + "," + str(
handRightResults[28]
) + "," + str(handRightResults[29]) + "," + str(
handRightResults[30]
) + "," + str(handRightResults[31]) + "," + str(
handRightResults[32]
) + "," + str(handRightResults[33]) + "," + str(
handRightResults[34]
) + "," + str(handRightResults[35]) + "," + str(
handRightResults[36]
) + "," + str(handRightResults[37]) + "," + str(
handRightResults[38]
) + "," + str(handRightResults[39]) + "," + str(
handRightResults[40]
) + "," + str(handRightResults[41]) + "," + str(
handLeftResults[0]
) + ", " + str(handLeftResults[1]) + "," + str(
handLeftResults[2]
) + "," + str(handLeftResults[3]) + "," + str(
handLeftResults[4]
) + "," + str(handLeftResults[5]) + "," + str(
handLeftResults[6]
) + "," + str(handLeftResults[7]) + "," + str(
handLeftResults[8]
) + "," + str(handLeftResults[9]) + "," + str(
handLeftResults[10]
) + "," + str(handLeftResults[11]) + "," + str(
handLeftResults[12]
) + "," + str(handLeftResults[13]) + "," + str(
handLeftResults[14]
) + "," + str(handLeftResults[15]) + "," + str(
handLeftResults[16]
) + "," + str(handLeftResults[17]) + "," + str(
handLeftResults[18]
) + "," + str(handLeftResults[19]) + "," + str(
handLeftResults[20]
) + "," + str(handLeftResults[21]) + "," + str(
handLeftResults[22]
) + "," + str(handLeftResults[23]) + "," + str(
handLeftResults[24]
) + "," + str(handLeftResults[25]) + "," + str(
handLeftResults[26]
) + "," + str(handLeftResults[27]) + "," + str(
handLeftResults[28]
) + "," + str(handLeftResults[29]) + "," + str(
handLeftResults[30]
) + "," + str(handLeftResults[31]) + "," + str(
handLeftResults[32]
) + "," + str(handLeftResults[33]) + "," + str(
handLeftResults[34]
) + "," + str(handLeftResults[35]) + "," + str(
handLeftResults[36]
) + "," + str(handLeftResults[37]) + "," + str(
handLeftResults[38]
) + "," + str(handLeftResults[39]) + "," + str(
handLeftResults[40]
) + "," + str(handLeftResults[41]) + "," + str(
poseResults[0]
) + ", " + str(poseResults[1]) + "," + str(
poseResults[2]
) + "," + str(poseResults[3]) + "," + str(
poseResults[4]
) + "," + str(poseResults[5]) + "," + str(
poseResults[6]
) + "," + str(poseResults[7]) + "," + str(
poseResults[8]
) + "," + str(poseResults[9]) + "," + str(
poseResults[10]
) + "," + str(poseResults[11]) + "," + str(
poseResults[12]
) + "," + str(poseResults[13]) + "," + str(
poseResults[14]
) + "," + str(poseResults[15]) + "," + str(
poseResults[16]
) + "," + str(poseResults[17]) + "," + str(
poseResults[30]
) + "," + str(poseResults[31]) + "," + str(
poseResults[32]) + "," + str(
poseResults[33]) + "," + str(
poseResults[34]) + "," + str(
poseResults[35]) + "," + str(
poseResults[36]) + "," + str(
poseResults[37]
) + "," + parentName + ");"
crsr.execute(sql_command)
connection.commit()
connection.close()
def populate_db():
"""
Delete all records from database
"""
connection = sqlite3.connect("data\\db\\main_dataset.db")
crsr = connection.cursor()
sql_command = "DELETE FROM rightHandDataset;"
crsr.execute(sql_command)
connection.commit()
connection.close()
folders = []
files = []
fileNames = []
for entry in os.scandir('data\\datasets\\alphabets_dataset'):
if entry.is_dir():
folders.append(entry.path)
for entry1 in os.scandir(entry.path):
if entry1.is_dir():
folders.append(entry1.path)
for entry2 in os.scandir(entry1.path):
if entry2.is_dir():
folders.append(entry2.path)
elif entry2.is_file():
if os.path.splitext(entry2)[1] == ".json":
files.append(entry2.path)
fileNames.append(entry2.name)
elif entry1.is_file():
if os.path.splitext(entry1)[1] == ".json":
files.append(entry1.path)
fileNames.append(entry1.name)
elif entry.is_file():
if os.path.splitext(entry)[1] == ".json":
fileNames.append(entry1.name)
# connecting to the database
connection = sqlite3.connect("data\\db\\main_dataset.db")
# cursor
crsr = connection.cursor()
count = 0
for file in files:
js = json.loads(open(files[count]).read())
parent = (os.path.dirname(files[count])).split('\\')
parentName = "'" + parent[len(parent) - 2] + "'"
count += 1
for items in js['people']:
handRight = items["hand_right_keypoints_2d"]
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.scalePoints(handPoints, distance)
handRightResults, handRightPoints = move.centerPoints(Result)
# SQL command to insert the data in the table
sql_command = "INSERT INTO rightHandDataset VALUES (NULL, " + str(
handRightResults[0]) + ", " + str(handRightResults[1]) + "," + str(
handRightResults[2]
) + "," + str(handRightResults[3]) + "," + str(
handRightResults[4]
) + "," + str(handRightResults[5]) + "," + str(
handRightResults[6]
) + "," + str(handRightResults[7]) + "," + str(
handRightResults[8]) + "," + str(
handRightResults[9]) + "," + str(
handRightResults[10]) + "," + str(
handRightResults[11]) + "," + str(
handRightResults[12]
) + "," + str(handRightResults[13]) + "," + str(
handRightResults[14]
) + "," + str(handRightResults[15]) + "," + str(
handRightResults[16]
) + "," + str(handRightResults[17]) + "," + str(
handRightResults[18]
) + "," + str(handRightResults[19]) + "," + str(
handRightResults[20]
) + "," + str(handRightResults[21]) + "," + str(
handRightResults[22]
) + "," + str(handRightResults[23]) + "," + str(
handRightResults[24]
) + "," + str(handRightResults[25]) + "," + str(
handRightResults[26]
) + "," + str(handRightResults[27]) + "," + str(
handRightResults[28]
) + "," + str(handRightResults[29]) + "," + str(
handRightResults[30]
) + "," + str(handRightResults[31]) + "," + str(
handRightResults[32]
) + "," + str(handRightResults[33]) + "," + str(
handRightResults[34]
) + "," + str(handRightResults[35]) + "," + str(
handRightResults[36]
) + "," + str(handRightResults[37]) + "," + str(
handRightResults[38]) + "," + str(
handRightResults[39]) + "," + str(
handRightResults[40]) + "," + str(
handRightResults[41]
) + "," + parentName + ");"
crsr.execute(sql_command)
connection.commit()
connection.close()
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"