code stringlengths 17 6.64M |
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def init_devices(device_type=None):
if (device_type is None):
device_type = 'cpu'
num_cores = 4
if (device_type == 'gpu'):
num_GPU = 1
num_CPU = 1
else:
num_CPU = 1
num_GPU = 0
config = tf.ConfigProto(intra_op_parallelism_threads=num_cores, inter_op_parallel... |
def reporthook(block_num, block_size, total_size):
read_so_far = (block_num * block_size)
if (total_size > 0):
percent = ((read_so_far * 100.0) / total_size)
s = ('\r%5.1f%% %*d / %d' % (percent, len(str(total_size)), read_so_far, total_size))
sys.stderr.write(s)
if (read_so_fa... |
def download_glove(data_dir_path=None):
if (data_dir_path is None):
data_dir_path = 'very_large_data'
glove_model_path = (((data_dir_path + '/glove.6B.') + str(GLOVE_EMBEDDING_SIZE)) + 'd.txt')
if (not os.path.exists(glove_model_path)):
glove_zip = (data_dir_path + '/glove.6B.zip')
... |
def load_glove(data_dir_path=None):
if (data_dir_path is None):
data_dir_path = 'very_large_data'
download_glove(data_dir_path)
_word2em = {}
glove_model_path = (((data_dir_path + '/glove.6B.') + str(GLOVE_EMBEDDING_SIZE)) + 'd.txt')
file = open(glove_model_path, mode='rt', encoding='utf8'... |
def glove_zero_emb():
return np.zeros(shape=GLOVE_EMBEDDING_SIZE)
|
class Glove(object):
word2em = None
GLOVE_EMBEDDING_SIZE = GLOVE_EMBEDDING_SIZE
def __init__(self):
self.word2em = load_glove()
|
def in_white_list(_word):
for char in _word:
if (char in WHITELIST):
return True
return False
|
def absTokenizer1(regex, abstracts):
'\n above abstractTokenizer returns all the word but sentence structured.\n '
stopWords = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(regex)
tokened = [tokenizer.tokenize(abstract) for abstract in abstracts]
print('Abstracts are tokenized.... |
def df2model(path_to_json):
data = json2list(path_to_json)
df_to_model = pd.DataFrame({'Abstracts': absTokenizer1('\\w+', data['abstracts']), 'Titles': data['titles']})
df_to_model.columns = ['input_text', 'target_text']
for i in range(len(df_to_model)):
df_to_model.iloc[(i, 0)] = ' '.join(df_... |
def fit_text(X, Y, input_seq_max_length=None, target_seq_max_length=None):
if (input_seq_max_length is None):
input_seq_max_length = MAX_INPUT_SEQ_LENGTH
if (target_seq_max_length is None):
target_seq_max_length = MAX_TARGET_SEQ_LENGTH
input_counter = Counter()
target_counter = Counter... |
def getCategoryVocab(df_raw):
df_raw = pd.read_csv(df_raw)
category_vocab = [item for sublist in (category.split(' ') for category in df_raw['Category']) for item in sublist]
return category_vocab
|
def getCategoryVocabByYear(df_raw):
df_raw = pd.read_csv(df_raw)
category_year_vocab = []
pair = []
for i in range(len(df_raw)):
splitted = df_raw.iloc[(i, (- 1))].split(' ')
for splits in splitted:
pair.append(splits)
pair.append(df_raw.iloc[(i, (- 2))])
... |
def countCategoryVocabByYear(category_vocab_by_year):
count = collections.defaultdict(dict)
i = 0
for pair in category_vocab_by_year:
try:
count[str(pair[1])][pair[0]] = 0
except Exception as e:
if (e is IndexError):
print('This exception is not poss... |
def countCategories(category_vocab, k):
countCat = collections.defaultdict(int)
for cat in category_vocab:
countCat[cat] += 1
return dict(islice(collections.OrderedDict(countCat).items(), k))
|
def populars(df_csv_raw, k=10):
df_raw = pd.read_csv(df_csv_raw)
category_vocab = getCategoryVocab(df_csv_raw)
topk = countCategories(category_vocab, 10)
print('TOP 10 CATEGORY BY POPULARITY')
for (key, value) in topk.items():
print(' {}: {}'.format(key, value))
|
def popularsbar(df_csv_raw, k):
df_raw = pd.read_csv(df_csv_raw)
category_vocab = getCategoryVocab(df_csv_raw)
topk = countCategories(category_vocab, k)
cat_dir = '../data/categories.json'
with open(cat_dir) as json_file:
categories = json.load(json_file)
legends = []
for a in list... |
def get(seed=0, pc_valid=0.1):
data = {}
taskcla = []
size = [3, 32, 32]
if (not os.path.isdir(file_dir)):
os.makedirs(file_dir)
mean = [(x / 255) for x in [125.3, 123.0, 113.9]]
std = [(x / 255) for x in [63.0, 62.1, 66.7]]
dat = {}
dat['train'] = datasets.CIFA... |
def cifar100_superclass_python(task_order, group=5, validation=False, val_ratio=0.05, flat=False, one_hot=True, seed=0):
CIFAR100_LABELS_LIST = ['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', '... |
def imshow(img):
npimg = img
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
|
def get(seed=1, fixed_order=False, pc_valid=0.05):
data = {}
taskcla = []
size = [3, 32, 32]
idata = np.arange(5)
print('Task order =', idata)
if (not os.path.isdir('./data/Five_data/binary_mixture_5_Data/')):
os.makedirs('./data/Five_data/binary_mixture_5_Data')
for (n, idx) i... |
class FashionMNIST(datasets.MNIST):
'`Fashion MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ Dataset.\n '
urls = ['http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz', 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz', 'ht... |
class TrafficSigns(torch.utils.data.Dataset):
"`German Traffic Signs <http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset>`_ Dataset.\n\n Args:\n root (string): Root directory of dataset where directory ``Traffic signs`` exists.\n split (string): One of {'train', 'test'}.\n trans... |
class Facescrub(torch.utils.data.Dataset):
"Subset of the Facescrub cropped from the official Megaface challenge page: http://megaface.cs.washington.edu/participate/challenge.html, resized to 38x38\n\n Args:\n root (string): Root directory of dataset where directory ``Traffic signs`` exists.\n sp... |
class notMNIST(torch.utils.data.Dataset):
"The notMNIST dataset is a image recognition dataset of font glypyhs for the letters A through J useful with simple neural networks. It is quite similar to the classic MNIST dataset of handwritten digits 0 through 9.\n\n Args:\n root (string): Root directory of ... |
def get(seed=0, fixed_order=False, pc_valid=0.1):
data = {}
taskcla = []
size = [1, 28, 28]
nperm = 10
seeds = np.array(list(range(nperm)), dtype=int)
if (not fixed_order):
seeds = shuffle(seeds, random_state=seed)
if (not os.path.isdir(pmnist_dir)):
os.makedirs(pmnist_dir)... |
class LanguageIdentification(object):
def __init__(self, language):
self.language = language
if (self.language == 'spa-eng'):
print('Downloading pretrained model. It will take time according to model size and your internet speed')
self.tokenizer = AutoTokenizer.from_pretra... |
class POS(object):
def __init__(self, language):
self.language = language
if (self.language == 'spa-eng'):
print('Downloading pretrained model. It will take time according to model size and your internet speed')
self.tokenizer = AutoTokenizer.from_pretrained('sagorsarker/c... |
class NER(object):
def __init__(self, language):
self.language = language
if (self.language == 'spa-eng'):
print('Downloading pretrained model. It will take time according to model size and your internet speed')
self.tokenizer = AutoTokenizer.from_pretrained('sagorsarker/c... |
class SentimentAnalysis(object):
def __init__(self, language):
if (language == 'spa-eng'):
self.tokenizer = AutoTokenizer.from_pretrained('sagorsarker/codeswitch-spaeng-sentiment-analysis-lince')
self.model = AutoModelForSequenceClassification.from_pretrained('sagorsarker/codeswit... |
class Config(object):
'Base configuration class. For custom configurations, create a\n sub-class that inherits from this one and override properties\n that need to be changed.\n '
NAME = None
GPU_COUNT = 1
IMAGES_PER_GPU = 1
STEPS_PER_EPOCH = 1000
VALIDATION_STEPS = 10
BACKBONE = ... |
class ParallelModel(KM.Model):
'Subclasses the standard Keras Model and adds multi-GPU support.\n It works by creating a copy of the model on each GPU. Then it slices\n the inputs and sends a slice to each copy of the model, and then\n merges the outputs together and applies the loss on the combined\n ... |
class Config(object):
'Base configuration class. For custom configurations, create a\n sub-class that inherits from this one and override properties\n that need to be changed.\n '
NAME = None
GPU_COUNT = 1
IMAGES_PER_GPU = 1
STEPS_PER_EPOCH = 500
VALIDATION_STEPS = 10
BACKBONE = '... |
class ParallelModel(KM.Model):
'Subclasses the standard Keras Model and adds multi-GPU support.\n It works by creating a copy of the model on each GPU. Then it slices\n the inputs and sends a slice to each copy of the model, and then\n merges the outputs together and applies the loss on the combined\n ... |
def _parse_requirements(file_path):
pip_ver = pkg_resources.get_distribution('pip').version
pip_version = list(map(int, pip_ver.split('.')[:2]))
if (pip_version >= [6, 0]):
raw = pip.req.parse_requirements(file_path, session=pip.download.PipSession())
else:
raw = pip.req.parse_requirem... |
def create_root(file_prefix, width, height, depth):
root = ET.Element('annotations')
ET.SubElement(root, 'folder').text = 'images'
ET.SubElement(root, 'filename').text = '{}'.format(file_prefix)
ET.SubElement(root, 'path').text = (output_images_dir + '{}'.format(file_prefix))
source = ET.SubElemen... |
def create_object_annotation(root, table_list, table_information_list):
length_table_list = len(table_list)
print('length_table_list==>', length_table_list)
for i in range(length_table_list):
obj = ET.SubElement(root, 'object')
ET.SubElement(obj, 'name').text = 'table'
ET.SubElemen... |
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.removePoint... |
def signal_handler(signal, frame):
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')
... |
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))
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], [... |
@eel.expose
def capture_alphabet_dataset(sec):
global remfileNames
'\n ----------------------Start OpenPoseDemo.exe----------------------\n --render_pose 0 --display 0\n '
os.chdir('bin\\openpose')
print('Starting OpenPose')
subprocess.Popen('bin\\OpenPoseDemo.exe --hand --write_json ..... |
@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):
label = a.strip()
print(label)
"\n traverse 'dataset' folder ,\n find subfolder matching 'label' ,\n create folder with timestamp in matched folder , \n and copy everything from 'Keypoints_temp' to created folder\n "
for entry in os.scandir('data\\datasets\... |
@eel.expose
def db_train():
retrain.re_train(1)
|
def signal_handler(signal, frame):
shutil.rmtree('Keypoints', ignore_errors=True, onerror=handleRemoveReadonly)
print('All done')
sys.exit(0)
|
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))
func(path)
else:
raise Exception
|
def json_files(Dir):
folders = []
files = []
fileNames = []
for entry in os.scandir(Dir):
if entry.is_dir():
folders.append(entry.path)
for entry1 in os.scandir(entry.path):
if entry1.is_dir():
folders.append(entry1.path)
... |
def removePoints(handRight):
handRightResults = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 3):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 3):
handRightY.append(handRight[x])
for x in range(len(handRightX)):
handRightResul... |
def getCoordPoints(handRight):
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 3):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 3):
handRightY.append(handRight[x])
for x in range(len(handRightX)):
handRightPoin... |
def confidencePoints(handRight):
handRightC = []
for x in range(2, len(handRight), 3):
handRightC.append(handRight[x])
return handRightC
|
def confidence(handRight):
sum = handRight[0]
for x in range(1, len(handRight)):
sum += handRight[x]
return sum
|
def seperate_points(handRight):
handRightResults = []
handRightX = []
handRightY = []
for x in range(len(handRight)):
handRightX.append(handRight[x][0])
handRightY.append(handRight[x][1])
for x in range(len(handRight)):
handRightResults.append(handRightX[x])
handRig... |
def join_points(handRight):
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
for x in range(len(handRightX)):
handRightPoints.... |
def isolatePoints(handRight):
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
minX = min(handRightX, key=fl... |
def centerPoints(handRight):
refX = 150
refY = 150
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
... |
def dummy_centerPoints(handRight):
refX = 600
refY = 600
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x]... |
def movePoints(handRight, addX, addY):
refX = (handRight[0] + addX)
refY = (handRight[1] + addY)
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2)... |
def moveBothHands(handRight, handLeft, addX, addY):
refX = (handRight[0] + addX)
refY = (handRight[1] + addY)
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(h... |
def move_to_wrist(handRight, wristX, wristY):
refX = wristX
refY = wristY
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.ap... |
def scaleBody(handRight, distance):
ref = 200
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
scale = (... |
def moveBody(handRight):
refX = 1000
refY = 400
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
p1 ... |
def dummyMoveBody(handRight):
refX = 400
refY = 200
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
... |
def dummyScaleBody(handRight, distance):
ref = 500
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
scal... |
def plot_skeleton(fileName, background, isMove, isScale):
js = json.loads(open(fileName).read())
for items in js['people']:
handRight = items['hand_right_keypoints_2d']
handCoord = helper.getCoordPoints(handRight)
handPoints = helper.removePoints(handRight)
p1 = [handPoints[0], handPoints[... |
def plot_points(points, background):
handRight = points
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
for x in range(len(handRi... |
def plot_db():
ret_frame = []
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'
connection = sqlite3.connect('db\\main_datase... |
def plot_db_label(label):
ret_frame = []
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'
connection = sqlite3.connect('db\\... |
def plot_dataset(handRightPoints, color):
ret_frame = []
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]]
colors = [[0, 0, 130], [0, 0, 175], [0, 0, 210], [0, 0, ... |
def save_old_dataset(handRightPoints, color, name):
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]]
colors = [[0, 0, 130], [0, 0, 175], [0, 0, 210], [0, 0, 250], [0,... |
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], [... |
def plotPoseDataset():
POSE_PAIRS = [[1, 0], [1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7], [1, 8], [0, 9], [9, 11], [0, 10], [10, 12]]
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], ... |
def rotate(point, angle, center_point=(0, 0)):
'Rotates a point around center_point(origin by default)\n Angle is in degrees.\n Rotation is counter-clockwise\n '
angle_rad = radians((angle % 360))
new_point = ((point[0] - center_point[0]), (point[1] - center_point[1]))
new_point = (((new_poin... |
def rotate_file(fileName):
js = json.loads(open(fileName).read())
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] ... |
def rotate_points(points, angle):
coordPoints = helper.join_points(points)
newPoints = [coordPoints[0]]
for x in range(1, len(coordPoints)):
newPoints.append(rotate(coordPoints[x], angle, coordPoints[0]))
return newPoints
|
def rotate_line(origin, point, angle):
'\n Rotate a point counterclockwise by a given angle around a given origin.\n\n The angle should be given in radians.\n '
(ox, oy) = origin
(px, py) = point
qx = ((ox + (math.cos(angle) * (px - ox))) - (math.sin(angle) * (py - oy)))
qy = ((oy + (math... |
def scalePoints(handRight, distance):
ref = 50
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
scale = ... |
def dummy_scalePoints(handRight, distance):
ref = 200
handRightResults = []
handRightPoints = []
handRightX = []
handRightY = []
for x in range(0, len(handRight), 2):
handRightX.append(handRight[x])
for x in range(1, len(handRight), 2):
handRightY.append(handRight[x])
s... |
def synthesize(angle):
'\n extracting data from db\n '
connection = sqlite3.connect('data\\db\\main_dataset.db')
crsr = connection.cursor()
sql = 'SELECT x1,y1'
for x in range(2, 22):
sql = ((((sql + ',x') + str(x)) + ',y') + str(x))
sql = (sql + ' FROM rightHandDataset WHERE 1')... |
def synthesize_multiple(angle1, angle2):
'\n extracting data from db\n '
connection = sqlite3.connect('..\\..\\data\\db\\main_dataset.db')
crsr = connection.cursor()
sql = 'SELECT x1,y1'
for x in range(2, 22):
sql = ((((sql + ',x') + str(x)) + ',y') + str(x))
sql = (sql + ' FROM ... |
def re_train(mode):
if (mode == 0):
dbh.create_table()
dbh.populate_db()
synth.synthesize(20)
alphabet_model.train_alphabets()
if (mode == 1):
dbh.create_pose_table()
dbh.populate_words()
word_model.train_words()
|
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)
LeftConfPoin... |
def signal_handler(signal, frame):
shutil.rmtree('Keypoints', ignore_errors=True, onerror=handleRemoveReadonly)
shutil.rmtree('gui\\Learn_images', ignore_errors=True, onerror=handleRemoveReadonly)
os.system('taskkill /f /im OpenPoseDemo.exe')
print('All done')
sys.exit(0)
|
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))
func(path)
else:
raise Exception
|
@eel.expose
def skip_Sign():
global skip_sign
skip_sign = True
print('skip_sign')
|
@eel.expose
def openposelearn():
'\n Starting OpenPoseDemo.exe\n and storing json files to temporary folder [Keypoints]\n '
print('Starting OpenPose')
os.chdir('bin\\openpose')
subprocess.Popen('bin\\OpenPoseDemo.exe --hand --write_json ..\\..\\Keypoints --net_resolution 128x128 --number_p... |
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], [... |
@eel.expose
def learning():
global skip_sign
'\n storing json files to temporary folder [Keypoints]\n Creating temp folder and initializing with zero padded json file\n '
dirName = 'Keypoints'
fileName = 'PSL\\000000000000_keypoints.json'
try:
os.mkdir(dirName)
shutil.copy... |
def on_close(page, sockets):
print(page, 'closed')
print('Still have sockets open to', sockets)
|
def signal_handler(signal, frame):
shutil.rmtree('Keypoints', ignore_errors=True, onerror=handleRemoveReadonly)
os.system('taskkill /f /im OpenPoseDemo.exe')
print('All done')
sys.exit(0)
|
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))
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], [... |
@eel.expose
def exit_openpose():
os.system('taskkill /f /im OpenPoseDemo.exe')
|
@eel.expose
def openpose():
'\n Starting OpenPoseDemo.exe\n and storing json files to temporary folder [Keypoints]\n '
print('Starting OpenPose')
os.chdir('bin\\openpose')
subprocess.Popen('bin\\OpenPoseDemo.exe --hand --write_json ..\\..\\Keypoints --net_resolution 128x128 --number_people... |
@eel.expose
def match(speech, mode):
global label, lastLabel
'\n Load each .json file from Keypoints folder and\n predict the label\n '
for entry in os.scandir('Keypoints'):
if entry.is_file():
if (os.path.splitext(entry)[1] == '.json'):
filePlotName = entry.na... |
def test_file1_method1():
x = 5
y = 6
assert ((x + 1) == y), 'test failed'
assert (x == y), 'test failed'
|
def test_file1_method2():
x = 5
y = 6
assert ((x + 1) == y), 'test failed'
|
def call_html():
import IPython
display(IPython.core.display.HTML('\n <script src="/static/components/requirejs/require.js"></script>\n <script>\n requirejs.config({\n paths: {\n base: \'/static/base\',\n "d3": "https://cdnjs.cloudflare.com/ajax/libs... |
def call_html():
import IPython
display(IPython.core.display.HTML('\n <script src="/static/components/requirejs/require.js"></script>\n <script>\n requirejs.config({\n paths: {\n base: \'/static/base\',\n "d3": "https://cdnjs.cloudflare.com/ajax/libs... |
def branch2num(branch, init_root=0):
num = [init_root]
for b in branch:
if (b == 'L'):
num.append(((num[(- 1)] * 2) + 1))
if (b == 'R'):
num.append(((num[(- 1)] * 2) + 2))
return num
|
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