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()
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()
from compressImages import ImageCompression
# 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)
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.
class CameraSession():
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()
def main(self):
counter = 0 # Counter for gathering image files for blur detection. Counter increments number of pictures for filename.
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
# CHANGE MILES
#frame = self.vs.read()
ret, frame = self.capture.read()
# If the WebcamVideoStream object has a frame grabbed, lets perform the basic required operations for all the features.
# CHANGE MILES
if (ret):
#if (self.vs.grabbed):
#self.motionDetection.refreshFirstFrame(frame)
grayFrame = cv2.cvtColor(
frame, cv2.COLOR_BGR2GRAY) # Grayscale the image
grayGausFrame = cv2.GaussianBlur(
grayFrame, (21, 21), 0
) # Second, we want to apply a guassian blur to reduce noise.
# originally our dimensions for gaussian blur is 21 by 21
#commandImage = str(input('Enter Image Function to perform:')) # String input variable for checking.
if (cv2.waitKey(3) &
0xFF == ord('m')): # Initiate motion tracking feature.
print "You initiated the motion tracking featurette"
# Toggle switch
if (self.motionTime != True):
self.motionTime = True
else:
self.motionTime = False
print "Motion tracking is turned off."
# Cascade checking.
elif (cv2.waitKey(3) &
0xFF == ord('c')): # Initiate cascade detection feature.
print "You initiated the cascade image recogntion feature"
# Toggle on off the cascading featurette.
if (self.cascadeTime != True):
self.cascadeTime = True
else:
self.cascadeTime = False
print "Cascading is turned off."
elif (cv2.waitKey(3)
& 0xFF == ord('b')): # Initiate blur detection feature.
print "You initiated the blur detection featurette."
if (self.blurDetectionTime != True):
self.blurDetectionTime = True
else:
self.blurDetectionTime = False
print "Blur detection is turned off."
# May want to sub categorize the 'r' 'u' & 'd' keys.
elif (cv2.waitKey(5) & 0xFF == ord('r')
): # Refresh the first frame for motion tracking.
print "You pressed refresh"
self.motionDetection.refreshFirstFrame(frame)
continue
elif (cv2.waitKey(5)
& 0xFF == ord('i')): # Increase motion tracking thresh.
print(
"You increased the motion detection thresh value to " +
str(self.motionDetection.cornerDetectionThresh))
self.motionDetection.increaseCornerDetectionThresh(100)
continue
elif (cv2.waitKey(5)
& 0xFF == ord('d')): # Decrease motion tracking thresh.
print(
"You decreased the motion detection thresh value to " +
str(self.motionDetection.cornerDetectionThresh))
self.motionDetection.decreaseCornerDetectionThresh(100)
continue
elif (cv2.waitKey(5)
& 0xFF == ord('q')): # Increase blur detection thresh.
newBlurThresh = (75 + self.blurDetection.getBlurThresh())
self.blurDetection.setBlurThresh(newBlurThresh)
print("You increased blur detection thresh to " +
str(self.blurDetection.getBlurThresh()))
continue
elif (cv2.waitKey(5)
& 0xFF == ord('w')): # Decrease blur detection thresh.
newBlurThresh = (self.blurDetection.getBlurThresh() - 75)
self.blurDetection.setBlurThresh(newBlurThresh)
print("You decreased blur detection thresh to " +
str(self.blurDetection.getBlurThresh()))
continue
# Alas, apart of the motion tracking, if our first frame is set to none, we will set it up ourselves.
elif self.motionDetection.firstFrame is None:
self.motionDetection.refreshFirstFrame(frame)
continue
''' End of object featurette toggling via keyboard keys. '''
# Toggle checks
if (self.motionTime):
cnts, frameDelta = self.motionDetection.showMotion(
grayFrame)
for c in cnts:
# if the contour is too small, ignore it
# In respect to fine tuning, it looks like around 500 is optimal for testing in a room like environment.
# A change occurs if we want to notice someone walking in, or motion in a undetected scene, then we jump the gun to 4500+.
if cv2.contourArea(
c
) < self.motionDetection.cornerDetectionThresh: # Value to fine tune.
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h),
(255, 0, 0), 2)
elif (self.cascadeTime
): # If we chose to perform haarcascading
self.cascadeDetection.faceCascadeDetectionOfImage(frame)
elif (self.blurDetectionTime):
# We grayscaled it so we can apply the laplacian variance on the grayscaled frame.
fm = self.blurDetection.variance_of_laplacian(
grayFrame
) # Return an integer value depicting the varaiance factor of blur detection.
# This will be the value that is outputted to the screen.
#if (fm > ((self.blurDetection.getBlurThresh()/2)+self.blurDetection.getBlurThresh())):
if (fm > (self.blurDetection.getBlurThresh() * 5)):
# So basically we set the conditional to have a limited range of blurDetectionThresh
# We specify it to be 2 times the threshValue to save it.
#myDirname = os.path.normpath("C:/Users/M4l2l_es/Dropbox/Ali_and_miless_awesome_vision_stuff/FeatureDetection/NoneBlurDetectedPictures/")
st = datetime.datetime.fromtimestamp(
self.ts).strftime('_TS:%Y-%m-%d::%H:%M:%S')
# CHANGE MILES
#myDirName = os.path.normpath("NoneBlurDetectedPictures/" + str(counter) + st + ".png") # os.path.normpath will set the directory path for any opreating system.
myDirName = str(counter) + "Image" + ".jpg"
cv2.imwrite(myDirName, frame)
counter = counter + 1
text = "" # will have to get rid of text variable later below and beside.
cv2.putText(frame, "{}: {:.2f}".format(text, fm), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 3)
# Outside of toggle checks lets render the frames
cv2.imshow("Frame", frame)
self.out.write(
frame) # Writing frame, so outputting frame to file.
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
# CHANGE MILES
#self.vs.stop()
self.capture.release()
cv2.destroyAllWindows()
self.destroyWindows.windowDestroyer(1)
class CameraSession():
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()
def main(self):
counter = 0 # Counter for gathering image files for blur detection. Counter increments number of pictures for filename.
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
# CHANGE MILES
#frame = self.vs.read()
ret, frame = self.capture.read()
# If the WebcamVideoStream object has a frame grabbed, lets perform the basic required operations for all the features.
# CHANGE MILES
if (ret):
#if (self.vs.grabbed):
#self.motionDetection.refreshFirstFrame(frame)
grayFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Grayscale the image
grayGausFrame = cv2.GaussianBlur(grayFrame, (21, 21), 0) # Second, we want to apply a guassian blur to reduce noise.
# originally our dimensions for gaussian blur is 21 by 21
#commandImage = str(input('Enter Image Function to perform:')) # String input variable for checking.
if (cv2.waitKey(3) & 0xFF == ord('m')): # Initiate motion tracking feature.
print "You initiated the motion tracking featurette"
# Toggle switch
if (self.motionTime != True):
self.motionTime = True
else:
self.motionTime = False
print "Motion tracking is turned off."
# Cascade checking.
elif (cv2.waitKey(3) & 0xFF == ord('c')): # Initiate cascade detection feature.
print "You initiated the cascade image recogntion feature"
# Toggle on off the cascading featurette.
if (self.cascadeTime != True):
self.cascadeTime = True
else:
self.cascadeTime = False
print "Cascading is turned off."
elif (cv2.waitKey(3) & 0xFF == ord('b')): # Initiate blur detection feature.
print "You initiated the blur detection featurette."
if (self.blurDetectionTime != True):
self.blurDetectionTime = True
else:
self.blurDetectionTime = False
print "Blur detection is turned off."
# May want to sub categorize the 'r' 'u' & 'd' keys.
elif (cv2.waitKey(5) & 0xFF == ord('r')): # Refresh the first frame for motion tracking.
print "You pressed refresh"
self.motionDetection.refreshFirstFrame(frame)
continue
elif (cv2.waitKey(5) & 0xFF == ord('i')): # Increase motion tracking thresh.
print("You increased the motion detection thresh value to " + str(self.motionDetection.cornerDetectionThresh))
self.motionDetection.increaseCornerDetectionThresh(100)
continue
elif (cv2.waitKey(5) & 0xFF == ord('d')): # Decrease motion tracking thresh.
print("You decreased the motion detection thresh value to " + str(self.motionDetection.cornerDetectionThresh))
self.motionDetection.decreaseCornerDetectionThresh(100)
continue
elif (cv2.waitKey(5) & 0xFF == ord('q')): # Increase blur detection thresh.
newBlurThresh = (75 + self.blurDetection.getBlurThresh())
self.blurDetection.setBlurThresh(newBlurThresh)
print("You increased blur detection thresh to " + str(self.blurDetection.getBlurThresh()))
continue
elif (cv2.waitKey(5) & 0xFF == ord('w')): # Decrease blur detection thresh.
newBlurThresh = (self.blurDetection.getBlurThresh() - 75)
self.blurDetection.setBlurThresh(newBlurThresh)
print("You decreased blur detection thresh to " + str(self.blurDetection.getBlurThresh()))
continue
# Alas, apart of the motion tracking, if our first frame is set to none, we will set it up ourselves.
elif self.motionDetection.firstFrame is None:
self.motionDetection.refreshFirstFrame(frame)
continue
''' End of object featurette toggling via keyboard keys. '''
# Toggle checks
if (self.motionTime):
cnts, frameDelta = self.motionDetection.showMotion(grayFrame)
for c in cnts:
# if the contour is too small, ignore it
# In respect to fine tuning, it looks like around 500 is optimal for testing in a room like environment.
# A change occurs if we want to notice someone walking in, or motion in a undetected scene, then we jump the gun to 4500+.
if cv2.contourArea(c) < self.motionDetection.cornerDetectionThresh: # Value to fine tune.
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
elif (self.cascadeTime): # If we chose to perform haarcascading
self.cascadeDetection.faceCascadeDetectionOfImage(frame)
elif (self.blurDetectionTime):
# We grayscaled it so we can apply the laplacian variance on the grayscaled frame.
fm = self.blurDetection.variance_of_laplacian(grayFrame) # Return an integer value depicting the varaiance factor of blur detection.
# This will be the value that is outputted to the screen.
#if (fm > ((self.blurDetection.getBlurThresh()/2)+self.blurDetection.getBlurThresh())):
if (fm > (self.blurDetection.getBlurThresh()*5)):
# So basically we set the conditional to have a limited range of blurDetectionThresh
# We specify it to be 2 times the threshValue to save it.
#myDirname = os.path.normpath("C:/Users/M4l2l_es/Dropbox/Ali_and_miless_awesome_vision_stuff/FeatureDetection/NoneBlurDetectedPictures/")
st = datetime.datetime.fromtimestamp(self.ts).strftime('_TS:%Y-%m-%d::%H:%M:%S')
# CHANGE MILES
#myDirName = os.path.normpath("NoneBlurDetectedPictures/" + str(counter) + st + ".png") # os.path.normpath will set the directory path for any opreating system.
myDirName = str(counter) + "Image" + ".jpg"
cv2.imwrite(myDirName, frame)
counter = counter + 1
text = "" # will have to get rid of text variable later below and beside.
cv2.putText(frame, "{}: {:.2f}".format(text, fm), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 3)
# Outside of toggle checks lets render the frames
cv2.imshow("Frame", frame)
self.out.write(frame) # Writing frame, so outputting frame to file.
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
# CHANGE MILES
#self.vs.stop()
self.capture.release()
cv2.destroyAllWindows()
self.destroyWindows.windowDestroyer(1)
# 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)
