npinto

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

npinto

import cv2
import cv2.cv as cv


def detect(img, cascade_fn='haarcascades/haarcascade_frontalface_alt.xml',
           scaleFactor=1.3, minNeighbors=4, minSize=(20, 20),
           flags=cv.CV_HAAR_SCALE_IMAGE):

    cascade = cv2.CascadeClassifier(cascade_fn)
    rects = cascade.detectMultiScale(img, scaleFactor=scaleFactor,

npinto

#!/bin/bash
# 
# script formats bonnie output and calls gnuplot to create a png graph
# of various bonnie parameters.
#
# feed script bonnie++ output - it will attempt to find the last line of bonnie output
# which is all it really cares about anyway
#
# eg: Using uid:65534, gid:65534.
# Writing a byte at a time...done

npinto

package trainableSegmentation.metrics;

/**
 *
 * License: GPL
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License 2
 * as published by the Free Software Foundation.
 *

npinto

#!/bin/bash

watch -n 10 'curl https://data.mtgox.com/api/2/BTCUSD/money/ticker | tee data/ticker.$(date +"%Y-%m-%d_%R:%S.%N").json | python -mjson.tool; for f in data/*.json; do gzip $f; done;'

npinto

    UIImageOrientation imageOrientation;
    if (UserDefaults.usingFrontCamera) {
        switch (curDeviceOrientation) {
            case UIDeviceOrientationLandscapeLeft:
                imageOrientation = UIImageOrientationDown;
                break;
            case UIDeviceOrientationLandscapeRight:
                imageOrientation = UIImageOrientationUp;
                break;
            case UIDeviceOrientationPortraitUpsideDown:

npinto

	UIDeviceOrientation curDeviceOrientation = [[UIDevice currentDevice] orientation];
	
	enum {
		PHOTOS_EXIF_0ROW_TOP_0COL_LEFT			= 1, //   1  =  0th row is at the top, and 0th column is on the left (THE DEFAULT).
		PHOTOS_EXIF_0ROW_TOP_0COL_RIGHT			= 2, //   2  =  0th row is at the top, and 0th column is on the right.  
		PHOTOS_EXIF_0ROW_BOTTOM_0COL_RIGHT      = 3, //   3  =  0th row is at the bottom, and 0th column is on the right.  
		PHOTOS_EXIF_0ROW_BOTTOM_0COL_LEFT       = 4, //   4  =  0th row is at the bottom, and 0th column is on the left.  
		PHOTOS_EXIF_0ROW_LEFT_0COL_TOP          = 5, //   5  =  0th row is on the left, and 0th column is the top.  
		PHOTOS_EXIF_0ROW_RIGHT_0COL_TOP         = 6, //   6  =  0th row is on the right, and 0th column is the top.  
		PHOTOS_EXIF_0ROW_RIGHT_0COL_BOTTOM      = 7, //   7  =  0th row is on the right, and 0th column is the bottom.  

npinto

NSString* jsonPath = [[NSBundle mainBundle] pathForResource:@"arr_fb" ofType:@"json"];
NSData* jsonData = [NSData dataWithContentsOfFile:jsonPath];
JSONDecoder* decoder = [[JSONDecoder alloc] initWithParseOptions:JKParseOptionNone];
NSDictionary* json = [decoder objectWithData:jsonData];
NSArray* shape = [json objectForKey:@"shape"];
NSUInteger fbn_ = [[shape objectAtIndex:0] intValue];

npinto

import os
from os import path
__location__ = path.realpath(path.join(os.getcwd(), path.dirname(__file__)))

npinto

import time
import gc
import numpy as np


def theano_memory_hack(func_exp, local_vars, 
                       input_exps=('input',),
                       msize_best=None,
                       msize_start=1024, msize_factor=2,
                       verbose=False):