def __init__(self):
# If video source is USB device for testing, we will use the vs and cap variables below.
#self.vs = WebcamVideoStream(src=0).start() # so we want to read video in as a stream now so we can
self.capture = cv2.VideoCapture(0)
# CHANGE MILES
# 0 for drone,
# 1 for webcam in the case of Miles computer
# If video source is drone, we will use the code below.
self.fourcc = cv2.cv.CV_FOURCC(*'XVID')
self.out = cv2.VideoWriter('output.mov', self.fourcc, 20.0, (640, 480))
# So we want to open application for video driver first, and then run file.
# Currently the webcam video stream class does not work for video capture, therefore we
# need to stick to cv2.VideoCapture() until WebcamVideoStream can be optimized for working.
# Instantiate objects
self.filters = Filters() # Filters for filtering the file.
self.motionDetection = MotionDetection(
) # MotionDetection for grabbing motion.
self.cascadeDetection = Cascading(
) # Cascading for feature recognition.
self.blurDetection = DetectBlur(
150) # 100 would be the value to be used for fine tuning.
self.destroyWindows = WindowDestruction()
time.sleep(0.25) # Allow camera a few miliseconds for booting up.
self.firstFrame = None # First frame is a variable to be used for motion tracking, firstFrame is the frame being compared for motion change.
# Initiate toggles.
self.motionTime = False
self.cascadeTime = False
self.blurDetectionTime = False
# Initialize external variables.
self.numFrames = 0
self.ts = time.time()
import cv2
import numpy as np
import time
import datetime
from Cascading import Cascading
from Detect_Blur import DetectBlur
blurDetection = DetectBlur(140)
cascade = Cascading()
#capture = cv2.VideoCapture(0) # Load video feed, we can also load a video from the directory if we wanted too, all we would do is place the path to the directory in here instead of the 0.
capture = cv2.VideoCapture('testFlight.MP4') # Loading a actual video.
# Will need to change directory for future testing, unless files are in same directory path.
fgbg = cv2.BackgroundSubtractorMOG2() # The foreground for objects we want, we will subtract those from the actual background.
# Define size of video for reading in
size = (int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)))
# Define FPS
fps = 20
#fourcc = cv2.cv.CV_FOURCC(*'XVID')
fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v')
#vout = cv2.VideoWriter()
#success = vout.open('output.mov', fourcc, fps, size, True)
#out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
ts = time.time()
counter = 0
while True:
ret, frame = capture.read() # Load the file into our boolean, then asssign it to a frame object.
# For this program I'm testing the use of thresholding by applying different filters
# and seeing how easy it is to detect corners and objects within the camera frame.
ap = argparse.ArgumentParser()
ap.add_argument("-c",
"--conf",
required=True,
help="path to the json configuration file")
args = vars(ap.parse_args())
vs = WebcamVideoStream(
src=0).start() # so we want to read video in as a stream now so we can
#cap = cv2.VideoCapture(0)
filters = Filters()
cascades = Cascading()
blurDetection = DetectBlur(120)
imgCmpr = ImageCompression()
conf = json.load(open(args["conf"])) # Load the json file.
client = None
# check to see if the Dropbox should be used
if conf["use_dropbox"]:
# connect to dropbox and start the session authorization process
flow = DropboxOAuth2FlowNoRedirect(conf["dropbox_key"],
conf["dropbox_secret"])
print "[INFO] Authorize this application: {}".format(flow.start())
authCode = raw_input("Enter auth code here: ").strip()
# finish the authorization and grab the Dropbox client
(accessToken, userID) = flow.finish(authCode)
