gmyrianthous

import json

import base64
import asyncio
import pyaudio
import websockets

SAMPLE_RATE=16000
FRAMES_PER_BUFFER = 3200
API_KEY = '<your AssemblyAI Key goes here>'

petebankhead

/**
 * QuPath script to threshold a single channel of an image.
 * This can also be useful to convert a binary image into QuPath annotations.
 *
 * First written for https://forum.image.sc/t/rendering-wsi-as-overlay-on-top-of-another-wsi/52629/24?u=petebankhead
 *
 * @author Pete Bankhead
 */

int channel = 0                              // 0-based index for the channel to threshold

mjesusugarte

from nonconformist.cp import IcpRegressor
from nonconformist.cp import IcpClassifier
from nonconformist.nc import NcFactory
import lightgbm as lgbm
# Create the underlying model
model = lgbm.LGBMRegressor()
# Default nonconformity measure
nc = NcFactory.create_nc(model)
# Inductive conformal regressor
icp = IcpRegressor(nc)

rodydavis

name: CI
on:
  push:
    branches:
      - master
  pull_request:
    branches:
      - master
jobs:
  build_web:

SuperShinyEyes

def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor:
    '''Calculate F1 score. Can work with gpu tensors
    
    The original implmentation is written by Michal Haltuf on Kaggle.
    
    Returns
    -------
    torch.Tensor
        `ndim` == 1. 0 <= val <= 1
    

you359

import keras
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input, decode_predictions
from keras.preprocessing import image
import keras.backend as K

import tensorflow as tf
from tensorflow.python.framework import ops

import numpy as np

didacroyo

# from the guide https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
# and from other resources found, trying to achieve a good classifier based on Inveption V3 pre-trained netfork

from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
from keras.callbacks import ModelCheckpoint

Svidro

Scripts mostly taken from Pete, and also from the forums. For easy access and reference.

TOC

Alignment: Several scripts to assist in alignment as of M9. Store scripts make files using the Affine transformation while
TransferObjects uses the stored file in the Affine folder with the current image name to move objects into it.
The final result is that one set of files can be generated for the transforms, and those transforms can be accessed to move objects
back and forth between the images.

Change annotations into Cell objects.groovy - Converts annotations into PathCellObjects, which allows certian functions to work within

Svidro

Scripts mostly taken from Pete, and also from the forums.  For easy access and reference.  
Covers both Selecting, which is necessary to run most commands like cell detection, and collecting objects into a variable
for processing.  The latter is more efficient when you do not need to run a command like cell detection.

TOC 

A Selection guide.groovy - not actually a script, just a collection of useful tips.

Access top level objects.groovy - Creates a list of all objects at the "top" of the hierarchy. This list is dynamic.

Svidro

Collections of scripts harvested mainly from Pete, but also picked up from the forums

TOC

Annotation subtraction example.groovy - An example of creating and subtracing ROIs through scripting

Create annotation of fixed size.groovy - see https://petebankhead.github.io/qupath/scripting/2018/03/09/script-create-fixed-size-region.html

Create object based on Viewer position.groovy - Creates an object at the Viewer position. In this case a rectangle. Useful if you want
to create the exact same size object multiple times and then move it to an area of interest for subsampling.