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Commit 2ebcbee7 authored by Petteri Laitinen's avatar Petteri Laitinen
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testejä eriytetty pois pääkoodista. 

Roskis-kansio poistettu.
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.gitlab-ci.yml
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...@@ -5,9 +5,15 @@ before_script: ...@@ -5,9 +5,15 @@ before_script:
- pip install -r requirements.txt - pip install -r requirements.txt
stages: stages:
- kehitystesti - perustesti
- toinentesti
kokeilutesti: kokeilutesti:
stage: kehitystesti stage: perustesti
script: script:
- python bayesian_adapted_gmm_for_background_extraction.py - python bayesian_adapted_gmm_for_background_extraction.py
laajatesti:
stage: toinentesti
script:
- python test.py
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.vs/bayesian_adapted_gmm_for_background_extraction/v16/.suo
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.vs/bayesian_adapted_gmm_for_background_extraction/v16/Browse.VC.db
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.vs/slnx.sqlite
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Roskis/GMM.py deleted 100644 → 0
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import numpy as np
import cv2
class GMM():
def __init__(self,prior,windowlength,gaussians):
"""
Initialize the Gaussian mixture model.
Parameters
----------
prior : float
a priori probability of background.
windowlength : int
how many frames are used in te start of the video to calculate gaussians
gaussians : int
number of gaussians used
"""
self.prior = prior
self.windowlength=windowlength
self.gaussians=gaussians
self.weights=np.ones(gaussians)/gaussians
self.averages=np.ones(gaussians)
self.variances=np.ones(gaussians)
class Gaussian():
def __init__(self, numberofgaussians):
"""
Initialize
"""
pass
print(GMM(0.1,100,3).weights)
Roskis/bayesian_adapted_gmm_for_background_extraction.sln deleted 100644 → 0
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Roskis/bayesian_adapted_gmm_for_background_extraction.vcxproj deleted 100644 → 0
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− 119
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Roskis/bayesian_adapted_gmm_for_background_extraction.vcxproj.filters deleted 100644 → 0
+ 0
− 33
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Roskis/main_code.py deleted 100644 → 0
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## First some code to test
import os
import cv2
import numpy as np
print(os.getcwd())
#execfile('ci_test_program.py')
## Then the actual class
class BackgroundSubtractorBayes():
def __init__(self,prior,windowlength,gaussians):
"""
Initialize the Gaussian mixture model.
Parameters
----------
prior : float
a priori probability of background.
windowlength : int
how many frames are used in te start of the video to calculate gaussians
gaussians : int
number of gaussians used
"""
self.prior = prior
self.windowlength=windowlength
self.gaussians=gaussians
self.weights=np.ones(gaussians)/gaussians
self.averages=np.ones(gaussians)
self.variances=np.ones(gaussians)
self.core=cv2.createBackgroundSubtractorMOG2()
self.framelista=[]
self.generation=0
def apply(self,InputFrame,OtherIndex):
if self.prior<1 and self.prior>0.6:
## The adapted algorithm comes here
#OutputFrame=InputFrame
OutputFrame = self.core.apply(InputFrame)
framelista.append(freimi(OtherIndex,InputFrame))
temporarylista=[]
if generation>1:
for i in range(0,2,1):
temporarylista.append(self.framelista[generation-i].frame)
median = np.median(self.temporarylista, axis=0).astype(dtype=np.uint8)
generation=generation+1
OutputFrame=median
else:
## if the probabilities for both background and foregraound are around 50%,
## use the normal Mixture of Gaussians.
# This will give gray frames only because subtractor is initialized for every frame
#backSub = cv2.createBackgroundSubtractorMOG2()
#OutputFrame=backSub.apply(InputFrame)
OutputFrame = self.core.apply(InputFrame)
#framelista.append([self.generation, freimi(OtherIndex,InputFrame)])
#median = np.median(frames, axis=0).astype(dtype=np.uint8)
return OutputFrame
class freimi():
def __init__(self,numero,frame):
"""
Initialize the Gaussian mixture model.
Parameters
----------
numero : int
frame number
frame : NEEDS TO BE DEFINED matrix with integer values 0...255 ?
the actual frame
frb : int
"""
self.numero=numero
self.frame=frame
koeBayes=BackgroundSubtractorBayes(0.61,10,3)
cap = cv2.VideoCapture('test_video_1.mp4')
frame_idx = cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1
takaperinlista=[]
etuperinlista=[]
# voitaneen poistaa?
#AltSub = cv2.createBackgroundSubtractorMOG2()
if 1>0:
while(cap.isOpened()):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#g2= BackgroundSubtractorBayes.apply(gray,0.7)
#g3=AltSub.apply(gray)
#g4=koeBayes.core.apply(gray)
g5=koeBayes.apply(gray)
etuperinlista.append(freimi('etuperin',int(cap.get(cv2.CAP_PROP_POS_FRAMES)),g5))
cv2.imshow('frame',g5)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
while cap.isOpened() and frame_idx >= 0:
# Set the current frame position to start:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
ret, frame = cap.read()
if ret is True:
# Display the frame:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#g2= BackgroundSubtractorBayes.apply(gray,0.7)
#g3=AltSub.apply(gray)
g4=koeBayes.core.apply(gray)
g5=koeBayes.apply(gray)
cv2.imshow('frame',g5)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Reduce the index to read next frame:
frame_idx = frame_idx - 1
#print("Next index: '{}'".format(frame_idx))
cap.release()
cv2.destroyAllWindows()
print('Terve')
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