AdroitAnandAI

    def preprocess_output(self, outputs, image):
        '''
        Before feeding the output of this model to the next model,
        you might have to preprocess the output. This function is where you can do that.
        '''
        # https://docs.openvinotoolkit.org/latest/omz_models_intel_facial_landmarks_35_adas_0002_description_facial_landmarks_35_adas_0002.html

        h, w = image.shape[0:2]
        paddingConstant = 10

AdroitAnandAI

    def preprocess_output(self, output, head_position):
        '''
        Before feeding the output of this model to the next model,
        you might have to preprocess the output. This function is where you can do that.
        '''
        roll = head_position[2]
        gaze_vector = output / cv2.norm(output)

        cosValue = math.cos(roll * math.pi / 180.0)
        sinValue = math.sin(roll * math.pi / 180.0)

AdroitAnandAI

    def moveWithGaze(self, x, y):

        # To bound the x, y coordinates within screen.dimensions.
        x = max(min(x, self.x_max), self.x_min)
        y = max(min(y, self.y_max), self.y_min)

        # To compute the x, y coordinates in the screen.
        x_cord = self.w * (1 - (x - self.x_min) / (self.x_max - self.x_min))
        y_cord = self.h * (y - self.y_min) / (self.y_max - self.y_min)

AdroitAnandAI

from googletrans import Translator

translator = Translator()
result = translator.translate('description in any language', dest='en')

AdroitAnandAI

LDA_df = pd.DataFrame(v,columns=df_topic_keywords.T.columns)
NMF_df = pd.DataFrame(d,columns=df_topic_keywords1.T.columns)

LDA_normalized = normalize(LDA_df)
NMF_normalized = normalize(NMF_df)


LDANMF = pd.concat([NMF_normalized,LDA_normalized],axis=1)

dominant_topic = np.argmax(LDANMF.values, axis=1)

AdroitAnandAI

vectorizer = CountVectorizer(analyzer='word',       
                             min_df=3,                        # minimum required occurences of a word 
                             stop_words='english',             # remove stop words
                             lowercase=True,                   # convert all words to lowercase
                             token_pattern='[a-zA-Z0-9]{3,}',  # num chars > 3
                             max_features=5000,             # max number of unique words. Build a vocabulary that only consider the top max_features ordered by term frequency across the corpus
                            )

data_vectorized = vectorizer.fit_transform(data['description'])

AdroitAnandAI

result = []
for idx in artClassifier.index: 
    
    if (artClassifier['confidence'][idx] > 0.05):
        result.append([artClassifier['id'][idx], str(artClassifier['category'][idx])[6:-2].replace("/", "^")])
        
    elif (urlClassifier['confidence'][idx] > 0.4):
        result.append([urlClassifier['id'][idx], str(urlClassifier['category'][idx])[6:-2].replace("/", "^")])
    else:

AdroitAnandAI

# from gensim.models import Word2Vec
from gensim.models.word2vec import Word2Vec
from gensim.models import KeyedVectors

model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)

AdroitAnandAI

onlineNode = find_by_attr(root, 'Online Services')
onlineNode.name = 'Online'

family = find_by_attr(root, 'Family and Relationships')
family.name = 'Family Relationships'

career = find_by_attr(root, 'Career & Job')
career.name = 'Profession'

AdroitAnandAI

# To compute the confidence score of category prediction based on the numerical distribution of values in the list
def computeConfidence(similarityList):
    
    similarScores = set(similarityList) 
    highest = max(similarScores)
    
    similarScores.remove(highest)
    if (len(similarScores) == 0):
        return 0