# BASELINE
import time as t
import picamera
import picamera.array
import cv2
import Constants
import numpy as np
import io
from Filter import Filter as f
camera = picamera.PiCamera()
camera.resolution = (640,480)
camera.framerate = 60
capture = picamera.array.PiRGBArray(camera, size=(640,480))
filt = f(cv2.imread('img/balloon.jpg'), consts=Constants.VIDEOS_CONTOUR_FILTER_CONSTANTS_1, display=False)
camera.start_preview()
t.sleep(5)
camera.stop_preview()
out = ""
time = 0
numFrames = 0
controlFrames = 500
testCount = 3
wantedTestTypes = [1,3]
filtering = True
outputDir = "Notes.txt"
def outputs():
else:
# When the pool is starved, wait a while for it to refill
# print "WAITING!"
waits += 1
waitDelay = execTime + 0.08 #Tries to wait for how long the last run was plus a small buffer. Should not run multiple times, 1 time for one thread
t.sleep(waitDelay)
# pass
with picamera.PiCamera() as camera:
pool = [ImageProcessor() for i in range(threadCount)]
camera.resolution = (640, 480)
camera.framerate = 30
camera.start_preview()
filt = f(cv2.imread('img/balloon.jpg'),
consts=Constants.VIDEOS_CONTOUR_FILTER_CONSTANTS_1,
display=False)
t.sleep(2)
tot1 = cv2.getTickCount()
try:
camera.capture_sequence(streams(), use_video_port=True)
except:
print frame
tot2 = cv2.getTickCount()
total = (tot2 - tot1) / cv2.getTickFrequency() # ACCURATE
cv2.destroyAllWindows()
camera.close()
output = "\nTOTAL FILTER TIME:\t " + str(
time) + "\n\nAVERAGE TIME PER FRAME:\t " + str(
time / numFrames) + "\n\nAVERAGE FRAMES PER SECOND:\t " + str(
# cv2.THRESH_BINARY
# cv2.THRESH_BINARY + cv2.THRESH_OTSU
filterTypeNum = 2 # Number to use for filtering listed below
# 1 = Adaptive
# 2 = RGB
# 3 = Binary
size = 35 # Size of box to average for adaptive thresholds
c = 2 # Number to subtract result of adaptive threshold from
approx = cv2.CHAIN_APPROX_SIMPLE # Contour mappings: Map to shapes or dont map at all listed below
# cv2.CHAIN_APPROX_NONE
# cv2.CHAIN_APPROX_SIMPLE
for item in pics:
filt = f(
cv2.imread(item), consts=consts, display=True
) # Creates a filter object with the image, constants and displayability
filt2 = f(cv2.imread(item), consts=consts2,
display=True) # Another filter for second attempt
#grayimg = cv2.cvtColor(img.image, cv2.COLOR_BGR2GRAY)
#ret, thresh = cv2.threshold(grayimg,127,255,0)
#blur = cv2.GaussianBlur(grayimg,(5,5),0)
img = si(
item
) # Creates a savable image (an image that is saveable to the hard drive)
im = cv2.imread(
item) # Reads the same image again, 'im' is the original image
# blur = filt.blur() # blurs the image using the filter object
blur = im
img.showRaw("original") # shows the image with title 'original'
cv2.waitKey(0) # waits 0 milliseconds (forever) for a key to be pressed
'minMean' : 0.0,
'maxMean' : 255.0,
'minVerticies' : 40,
'maxVerticies' : 10200,
'minAngle' : -360.0,
'maxAngle' : 360.0,
'minRatioWidthtoSize' : 0.05,
'maxRatioWidthtoSize' : 0.4,
'minRatioHeighttoSize' : 0.033,
'maxRatioHeighttoSize' : 0.4,
'tolerance' : 24 # Number of above conditions to be met for successful contour observation
}
cap = cv2.VideoCapture(0)
while(cap.isOpened()):
ret, frame = cap.read()
if ret==True:
e1 = cv2.getTickCount() # Starttime
coolFrame = np.copy(frame) # Copies the frame
filt = f(frame, consts=consts, display=False) # Filter Object
ret3, filtered, imagey, contours, h = filt.getContours(cv2.CHAIN_APPROX_SIMPLE, 113, 255, cv2.THRESH_BINARY)#+cv2.THRESH_OTSU)
coolImage = filt.run(coolFrame)
cv2.imshow('frame',coolImage)
if cv2.waitKey(1) & 0xFF == ord('q'):
break # Quits when you press q
e2 = cv2.getTickCount()
time = (e2 - e1) / cv2.getTickFrequency()
print time
else:
break
cap.release()
cv2.destroyAllWindows()
'maxVerticies': 10200,
'minAngle': -360.0,
'maxAngle': 360.0,
'minRatioWidthtoSize': 0.05,
'maxRatioWidthtoSize': 0.4,
'minRatioHeighttoSize': 0.033,
'maxRatioHeighttoSize': 0.4,
'tolerance':
24 # Number of above conditions to be met for successful contour observation
}
cap = cv2.VideoCapture(0)
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
e1 = cv2.getTickCount() # Starttime
coolFrame = np.copy(frame) # Copies the frame
filt = f(frame, consts=consts, display=False) # Filter Object
ret3, filtered, imagey, contours, h = filt.getContours(
cv2.CHAIN_APPROX_SIMPLE, 113, 255,
cv2.THRESH_BINARY) #+cv2.THRESH_OTSU)
coolImage = filt.run(coolFrame)
cv2.imshow('frame', coolImage)
if cv2.waitKey(1) & 0xFF == ord('q'):
break # Quits when you press q
e2 = cv2.getTickCount()
time = (e2 - e1) / cv2.getTickFrequency()
print time
else:
break
cap.release()
cv2.destroyAllWindows()
# cv2.THRESH_BINARY
# cv2.THRESH_BINARY + cv2.THRESH_OTSU
filterTypeNum = 2 # Number to use for filtering listed below
# 1 = Adaptive
# 2 = RGB
# 3 = Binary
size = 35 # Size of box to average for adaptive thresholds
c = 2 # Number to subtract result of adaptive threshold from
approx = cv2.CHAIN_APPROX_SIMPLE # Contour mappings: Map to shapes or dont map at all listed below
# cv2.CHAIN_APPROX_NONE
# cv2.CHAIN_APPROX_SIMPLE
for item in pics:
filt = f(cv2.imread(item),consts=consts, display=True) # Creates a filter object with the image, constants and displayability
filt2 = f(cv2.imread(item), consts=consts2, display=True) # Another filter for second attempt
#grayimg = cv2.cvtColor(img.image, cv2.COLOR_BGR2GRAY)
#ret, thresh = cv2.threshold(grayimg,127,255,0)
#blur = cv2.GaussianBlur(grayimg,(5,5),0)
img = si(item) # Creates a savable image (an image that is saveable to the hard drive)
im = cv2.imread(item) # Reads the same image again, 'im' is the original image
# blur = filt.blur() # blurs the image using the filter object
blur = im
img.showRaw("original") # shows the image with title 'original'
cv2.waitKey(0) # waits 0 milliseconds (forever) for a key to be pressed
img.image = blur # sets the savable image to the blurred image
img.showRaw("blurred") # shows the blurred iamge with title 'blurred'
cv2.waitKey(0)
if filterTypeNum == 1: # If using adaptive thresholding
filtered, imagey, contours, h = filt.adaptiveGet(approx, filterHigh, size, c, filterType) # gets the filtered binary image, the original image, the contours, and the hierarchy from the filter object