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demo2.py
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demo2.py
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##############################################
# Demo file
# python demo2.py --- will use video
# python demo2.py image --- will use images
#############################################
import numpy as np
import cv2
import pybgs as bgs
import sys
import glob
print("OpenCV Version: {}".format(cv2.__version__))
def is_cv2():
return check_opencv_version("2.")
def is_cv3():
return check_opencv_version("3.")
def is_lower_or_equals_cv347():
[major, minor, revision] = str(cv2.__version__).split('.')
return int(major) == 3 and int(minor) <= 4 and int(revision) <= 7
def is_cv4():
return check_opencv_version("4.")
def check_opencv_version(major):
return cv2.__version__.startswith(major)
## bgslibrary algorithms
algorithms=[]
algorithms.append(bgs.FrameDifference())
algorithms.append(bgs.StaticFrameDifference())
algorithms.append(bgs.WeightedMovingMean())
algorithms.append(bgs.WeightedMovingVariance())
algorithms.append(bgs.AdaptiveBackgroundLearning())
algorithms.append(bgs.AdaptiveSelectiveBackgroundLearning())
algorithms.append(bgs.MixtureOfGaussianV2())
algorithms.append(bgs.PixelBasedAdaptiveSegmenter())
algorithms.append(bgs.SigmaDelta())
algorithms.append(bgs.SuBSENSE())
algorithms.append(bgs.LOBSTER())
algorithms.append(bgs.PAWCS())
algorithms.append(bgs.TwoPoints())
algorithms.append(bgs.ViBe())
algorithms.append(bgs.CodeBook())
algorithms.append(bgs.FuzzySugenoIntegral())
algorithms.append(bgs.FuzzyChoquetIntegral())
algorithms.append(bgs.LBSimpleGaussian())
algorithms.append(bgs.LBFuzzyGaussian())
algorithms.append(bgs.LBMixtureOfGaussians())
algorithms.append(bgs.LBAdaptiveSOM())
algorithms.append(bgs.LBFuzzyAdaptiveSOM())
algorithms.append(bgs.VuMeter())
algorithms.append(bgs.KDE())
algorithms.append(bgs.IndependentMultimodal())
if is_cv2():
algorithms.append(bgs.MixtureOfGaussianV1()) # if opencv 2.x
algorithms.append(bgs.GMG()) # if opencv 2.x
if not is_cv2():
algorithms.append(bgs.KNN()) # if opencv > 2
if is_cv2() or is_cv3():
algorithms.append(bgs.DPAdaptiveMedian())
algorithms.append(bgs.DPGrimsonGMM())
algorithms.append(bgs.DPZivkovicAGMM())
algorithms.append(bgs.DPMean())
algorithms.append(bgs.DPWrenGA())
algorithms.append(bgs.DPPratiMediod())
algorithms.append(bgs.DPEigenbackground())
algorithms.append(bgs.DPTexture())
algorithms.append(bgs.T2FGMM_UM())
algorithms.append(bgs.T2FGMM_UV())
algorithms.append(bgs.T2FMRF_UM())
algorithms.append(bgs.T2FMRF_UV())
algorithms.append(bgs.MultiCue())
if is_cv2() or is_lower_or_equals_cv347():
algorithms.append(bgs.LBP_MRF())
algorithms.append(bgs.MultiLayer())
# check if we want to use the images
image = False
if (len(sys.argv) == 2):
if(sys.argv[1] == "image"):
image = True
img_folder = "dataset/frames"
img_array = sorted(glob.iglob(img_folder + '/*.png'))
video_file = "dataset/video.avi"
print("Number of available algorithms: ", len(algorithms))
for algorithm in algorithms:
print("Running ", algorithm.__class__)
if(image):
# loop x times as files in our folder
for x in range(0, len(img_array)):
# we can loop now through our array of images
img_path = img_array[x]
# read file into open cv and apply to algorithm to generate background model
img = cv2.imread(img_path)
img_output = algorithm.apply(img)
img_bgmodel = algorithm.getBackgroundModel()
# show images in python imshow window
cv2.imshow('image', img)
cv2.imshow('img_output', img_output)
cv2.imshow('img_bgmodel', img_bgmodel)
# we need waitKey otherwise it wont display the image
if 0xFF & cv2.waitKey(10) == 27:
break
# Comment out to save images to bg and fg folder
#img_bg = img_path.replace(img_folder, "output/bg")
#img_fg = img_path.replace(img_folder, "output/fg")
#cv2.imwrite(img_bg, img_bgmodel)
#cv2.imwrite(img_fg, img_output)
print("Frames left: " + str(len(img_array)-x))
else:
capture = cv2.VideoCapture(video_file)
while not capture.isOpened():
capture = cv2.VideoCapture(video_file)
cv2.waitKey(1000)
print("Wait for the header")
#pos_frame = capture.get(cv2.cv.CV_CAP_PROP_POS_FRAMES)
#pos_frame = capture.get(cv2.CV_CAP_PROP_POS_FRAMES)
pos_frame = capture.get(1)
while True:
flag, frame = capture.read()
if flag:
cv2.imshow('video', frame)
#pos_frame = capture.get(cv2.cv.CV_CAP_PROP_POS_FRAMES)
#pos_frame = capture.get(cv2.CV_CAP_PROP_POS_FRAMES)
pos_frame = capture.get(1)
#print str(pos_frame)+" frames"
img_output = algorithm.apply(frame)
img_bgmodel = algorithm.getBackgroundModel()
cv2.imshow('img_output', img_output)
cv2.imshow('img_bgmodel', img_bgmodel)
else:
#capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, pos_frame-1)
#capture.set(cv2.CV_CAP_PROP_POS_FRAMES, pos_frame-1)
#capture.set(1, pos_frame-1)
#print "Frame is not ready"
cv2.waitKey(1000)
break
if 0xFF & cv2.waitKey(10) == 27:
break
#if capture.get(cv2.cv.CV_CAP_PROP_POS_FRAMES) == capture.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT):
#if capture.get(cv2.CV_CAP_PROP_POS_FRAMES) == capture.get(cv2.CV_CAP_PROP_FRAME_COUNT):
#if capture.get(1) == capture.get(cv2.CV_CAP_PROP_FRAME_COUNT):
#break
print("Finished")
cv2.destroyAllWindows()