victormurcia

def estimate_n_speakers_nmf(features, max_speakers, min_speakers):
    """
    Estimate number of speakers using NMF reconstruction error and stability analysis.
    """
    reconstruction_errors = []
    stability_scores = []
    
    # Run NMF multiple times for each number of components
    for n in range(min_speakers, max_speakers + 1):
        errors = []

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def diarize_audio(audio_path, method='nmf', min_segment_duration=1.0, max_speakers=8, min_speakers=1):
    """
    Diarization workflow with automatic speaker count estimation.
    
    Parameters:
    -----------
    audio_path : str
        Path to the audio file
    method : str
        Diarization method ('nmf' or 'ica')

victormurcia

from Bio import Entrez
from Bio.Medline import parse
from io import StringIO
import pandas as pd

def fetch_pubmed_data(search_term, email, retmax=100):
    """
    Fetches data from PubMed related to a specific search term.

    Parameters:

victormurcia

def makePokemonFromPokedex(ver,nPokemon):
  #Loop over nPokemons to get the descritptions and generate images for each
  #poke_id = 1
  for poke_id in range(1, nPokemon+1, 1):
    #print(poke_id)
    #Specify which Pokemon we want to query using its ID number
    pokemon = pypokedex.get(dex=poke_id)
    #print(pokemon)

    #This is the name of the Pokemon we are querying

victormurcia

remove_safety = False
num_images = 4

if remove_safety:
  negative_prompt = None
else:
  negative_prompt = "nude, naked"

images = pipe(
    prompt,

victormurcia

import mediapy as media
import torch
from diffusers import StableDiffusionPipeline

device = "cuda"

if model_id.startswith("stabilityai/"):
  model_revision = "fp16"
else:
  model_revision = None

victormurcia

def objectTracker2(facePath, eyePath):
    
    faceCascade = cv2.CascadeClassifier(facePath)
    eyeCascade  = cv2.CascadeClassifier(eyePath)
    cv2.namedWindow("preview")
    vc = cv2.VideoCapture(0)
    
    while True: # try to get the first frame
        rval, frame = vc.read()
        # Capture frame-by-frame

victormurcia

def objectTracker1(cascPath):
    
    faceCascade = cv2.CascadeClassifier(cascPath)
    
    cv2.namedWindow("preview")
    vc = cv2.VideoCapture(0)
    
    while True: # try to get the first frame
        rval, frame = vc.read()
        # Capture frame-by-frame

victormurcia

def calc_angle():
    #Get line coordinates
    x1 = cs.points[0][0];y1 = cs.points[0][1]
    x2 = cs.points[1][0];y2 = cs.points[1][1]
    x3 = cs.points[2][0];y3 = cs.points[2][1]
    x4 = cs.points[3][0];y4 = cs.points[3][1]

    #Use coordinates to get components of vectors
    vx1 = (x1-x2);vy1 = (y1-y2)
    vx2 = (x4-x3);vy2 = (y4-y3)

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class StoreCoordinates:
    def __init__(self):
        self.points = []
        
    def select_point(self,event,x,y,flags,param):
        if event == cv2.EVENT_LBUTTONDOWN:
            # Display the clicked coordinates on shell
            print(x, ' ', y)
                
            # displaying the coordinates on the image window