parthdesai93

/* requires AWS creds to be updated.
 * if they aren't, update using AWS.config.update() method before instatiing the client. 
 * 
 * import this module where you instantiate the client, and simply pass this module as the connection class. 
 *
 * eg:
 * const client = new Client({
 *   node,
 *   Connection: AwsConnector
 * });

radames

import cv2

cap = cv2.VideoCapture(0)
cap.set(3, 640)  # WIDTH
cap.set(4, 480)  # HEIGHT

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")

while(True):

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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mike-lawrence

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# This code creates 2 sub-processes: one that continually polls a webcam and labels each resulting image with a timestamp, and another that saves these images to file. The process that saves the images to file doesn't save all the images, but instead listens to the main process for time windows that it should save. This permits one to save only certain time windows (toss images that fall outside this time window) and avoids the capture start up lag that occurs if you simply try to poll the webcam during the time window of interest (this lag can be several tenths of a second!). Converting the images to string then pickling before queueing them was necessary because opencv's iplimage format doesn't like to be put in a queue directly.
########

import multiprocessing
import cv
import cPickle
import time

queue_to_cam_writer = multiprocessing.Queue()