anujonthemove

CAP_PROP_POS_MSEC       =0, //!< Current position of the video file in milliseconds.
CAP_PROP_POS_FRAMES     =1, //!< 0-based index of the frame to be decoded/captured next.
CAP_PROP_POS_AVI_RATIO  =2, //!< Relative position of the video file: 0=start of the film, 1=end of the film.
CAP_PROP_FRAME_WIDTH    =3, //!< Width of the frames in the video stream.
CAP_PROP_FRAME_HEIGHT   =4, //!< Height of the frames in the video stream.
CAP_PROP_FPS            =5, //!< Frame rate.
CAP_PROP_FOURCC         =6, //!< 4-character code of codec. see VideoWriter::fourcc .
CAP_PROP_FRAME_COUNT    =7, //!< Number of frames in the video file.
CAP_PROP_FORMAT         =8, //!< Format of the %Mat objects returned by VideoCapture::retrieve().
CAP_PROP_MODE           =9, //!< Backend-specific value indicating the current capture mode.

georgekrax

Emoji	Purpose	MD Markup	Prefix
📄	Generic message	:page_facing_up:
📐	Improve the format / structure of the code / files	:triangular_ruler:	[IMPROVE]:
⚡	Improve performance	:zap:	[IMPROVE]:
🚀	Improve something (anything)	:rocket:	[IMPROVE]:
📝	Write docs	:memo:	[PROD]:
💡	New idea

fclesio

def get_classification_report(y_test, y_pred):
    '''Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format'''
    from sklearn import metrics
    report = metrics.classification_report(y_test, y_pred, output_dict=True)
    df_classification_report = pd.DataFrame(report).transpose()
    df_classification_report = df_classification_report.sort_values(by=['f1-score'], ascending=False)
    return df_classification_report

TheSalarKhan

import numpy as np
import cv2


class BackGroundSubtractor:
	# When constructing background subtractor, we
	# take in two arguments:
	# 1) alpha: The background learning factor, its value should
	# be between 0 and 1. The higher the value, the more quickly
	# your program learns the changes in the background. Therefore, 

DominicBreuker

import tensorflow as tf

x = tf.Variable(2, name='x', dtype=tf.float32)
log_x = tf.log(x)
log_x_squared = tf.square(log_x)

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(log_x_squared)

init = tf.initialize_all_variables()

prhbrt