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segmentation.py
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segmentation.py
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'''
Description: Segmentation class.
-> Obtains 16x16 blocks of the raw video and computes motion vectors.
-> videoData class used to inherit block accessor
#----------------------------------------------------------------------------------------------------------------#
Class functions:
#----------------------------------------------------------------------------------------------------------------#
Notes:
'''
import cv2
import numpy as np
from videoData import videoData
ROW = 0
COL = 1
R_CHANNEL = 0
G_CHANNEL = 1
B_CHANNEL = 2
class segmentation(videoData):
#------------------------------ Constructor ------------------------------#
def __init__(self, vidData, k):
self.__blockSize = 16
self.__vidData = vidData
self.__searchWin = k
self.globalMotion = [0,0]
# def findGlobalMotion(self, frame, prevFrame):
# iIndices = range(2*self.__blockSize, 5*self.__blockSize, self.__blockSize)
# jIndices = range(0, self.__vidData.getWidth(), self.__blockSize)
# motionVectors = np.zeros((len(iIndices)*len(jIndices), 2))
# height = self.__vidData.getHeight()
# width = self.__vidData.getWidth()
# k = self.__searchWin
# blockCounter = 0
# for i in iIndices:
# for j in jIndices:
# block = frame[i:i+self.__blockSize, j:j+self.__blockSize]
# topLeft = [max((i-k), 0), max((j-k),0)]
# bottomRight = [min(i+k+self.__blockSize-1, height-1), min(j+k+self.__blockSize-1, width-1)]
# # print topLeft, bottomRight
# searchSpace = prevFrame[topLeft[ROW]:bottomRight[ROW]+1, topLeft[COL]:bottomRight[COL]+1] # +1 so that bottomRight is included too
# dx, dy = self.computeMotionVectorPyramid(searchSpace, block, [i-topLeft[ROW], j-topLeft[COL]])
# motionVectors[blockCounter] = dx, dy
# blockCounter += 1
# glblMotion = stats.mode(motionVectors)
# self.globalMotion = [glblMotion.mode[0][ROW], glblMotion.mode[0][COL]]
# return self.globalMotion
def segmentBlocksInFrame(self, frame, prevFrame, frameNumber, SAD_Thresh):
#shiftdx, shiftdy = self.findGlobalMotion(frame, prevFrame)
# print shiftdx, shiftdy
#prevFrame[15:-15,15:-15] = prevFrame[15+shiftdx: -15+shiftdx , 15+shiftdy: -15+shiftdy]
# cv2.imshow('prevframe', np.uint8(prevFrame))
# cv2.waitKey(1)
#------------- Indices to traverse in the rows and cols ---------------#
iIndices = list(range(0, self.__vidData.getHeight(), self.__blockSize))
jIndices = list(range(0, self.__vidData.getWidth(), self.__blockSize))
#------ Handle the boundary cases by overlapping blocks, i.e.,---------#
#---------shifting the last index to up/left appropriately-------------#
iIndices[-1] = self.__vidData.getHeight() - self.__blockSize
jIndices[-1] = self.__vidData.getWidth() - self.__blockSize
height = self.__vidData.getHeight()
width = self.__vidData.getWidth()
k = self.__searchWin
# motionVectors = np.zeros((len(iIndices)*len(jIndices), 2))
foregroundCount = 0
blockCounter = 0
for i in iIndices:
for j in jIndices:
block = frame[i:i+self.__blockSize, j:j+self.__blockSize]
topLeft = [max((i-k), 0), max((j-k),0)]
bottomRight = [min(i+k+self.__blockSize-1, height-1), min(j+k+self.__blockSize-1, width-1)]
# print topLeft, bottomRight
searchSpace = prevFrame[topLeft[ROW]:bottomRight[ROW]+1, topLeft[COL]:bottomRight[COL]+1] # +1 so that bottomRight is included too
SADval, bgBlock = self.SAD_Check(searchSpace, block, [i-topLeft[ROW], j-topLeft[COL]], SAD_Thresh)
dx, dy = [0, 0]
if bgBlock == False:
dx, dy = self.computeMotionVectorPyramid(searchSpace, block, [i-topLeft[ROW], j-topLeft[COL]])
if dx == 0 and dy == 0:
self.setLabel(frameNumber, blockCounter, 0) # Backgrounds because of extremely low dx and dy
else:
# print i,j, SADval, (dx, dy)
self.setLabel(frameNumber, blockCounter, 1) # Foreground
foregroundCount += 1
# cv2.rectangle(frame, (j, i), (j + 16, i + 16), (0, 255, 0), 2)
# cv2.imshow('searchSpace', np.uint8(searchSpace))
# cv2.waitKey(0)
else:
self.setLabel(frameNumber, blockCounter, 0) # Backgrounds because of low SAD at the center
# motionVectors[blockCounter] = dx, dy
# if i == 336 and j==192:
# cv2.imshow('searchSpace', np.uint8(searchSpace))
# cv2.waitKey(0)
# exit(0)
blockCounter += 1
# cv2.imshow('frame', np.uint8(frame))
# cv2.waitKey(1)
# if cv2.waitKey(1) & 0xFF == ord('h'):
# cv2.destroyAllWindows()
# cv2.imshow('frame', np.uint8(frame))
# cv2.waitKey(0)
# np.savetxt('motionVectors.txt', motionVectors, fmt='%d')
# exit(0)
# fig = plt
# fig.plot(motionVectors[:,0], motionVectors[:,1], 'b*')
# fig.grid()
# fig.show()
return foregroundCount
def SAD_Check(self, searchSpace, block, blockTopLeft, SAD_Thresh):
i = blockTopLeft[ROW]
j = blockTopLeft[COL]
blockSize = block.shape[ROW] # or COL; same thing
localNeighbor = searchSpace[i:i+blockSize, j:j+blockSize]
SADval = np.sum(np.sum(np.abs(localNeighbor-block)))
if SADval<SAD_Thresh: # Mostly a background
return SADval, True
else:
return SADval, False
def computeMotionVector(self, searchSpace, block, blockTopLeft):
rows = searchSpace.shape[0]
cols = searchSpace.shape[1]
blockSize = block.shape[ROW] # or COL; same thing
SADvals = np.zeros((rows-blockSize+1, cols-blockSize+1))
for i in range(0, SADvals.shape[0]):
for j in range(0, SADvals.shape[1]):
localNeighbor = searchSpace[i:i+blockSize, j:j+blockSize]
SADvals[i][j] = np.sum(np.sum(np.abs(localNeighbor-block)))
minSAD_pos = np.where(SADvals == SADvals.min())
minSAD_TopLeft = [minSAD_pos[ROW].tolist()[0], minSAD_pos[COL].tolist()[0]]
# print 'blockTopLeft', blockTopLeft, '\t minSAD_TopLeft', minSAD_TopLeft, 'minSAD_pos', minSAD_pos
# np.savetxt('SADvals.txt', SADvals, fmt='%d')
dx = minSAD_TopLeft[1]-blockTopLeft[1]
dy = minSAD_TopLeft[0]-blockTopLeft[0]
return dx, dy
def computeMotionVectorPyramid(self, searchSpace, block, blockTopLeft):
# -----------Level 3------------ #
blockLevel3 = block[0:-1:4, 0:-1:4]
searchSpaceLevel3 = searchSpace[0:-1:4, 0:-1:4]
blockTopLeftLevel3 = [blockTopLeft[ROW]/4, blockTopLeft[COL]/4]
dxLevel3, dyLevel3 = self.computeMotionVector(searchSpaceLevel3, blockLevel3, blockTopLeftLevel3)
# -----------Level 2------------ #
dxLevel2, dyLevel2 = dxLevel3*2, dyLevel3*2
searchSpaceLevel2 = searchSpace[0:-1:2, 0:-1:2]
blockLevel2 = block[0:-1:2, 0:-1:2]
n = block.shape[ROW]/2
blockTopLeftLevel2 = [blockTopLeft[ROW]/2, blockTopLeft[COL]/2]
refinedSearchSpaceTopLeft = [max(blockTopLeftLevel2[ROW]-1, 0), max(blockTopLeftLevel2[COL]-1, 0)]
refinedSearchSpaceBotRight = [blockTopLeftLevel2[ROW]+n, blockTopLeftLevel2[COL]+n]
refinedSearchSpace = searchSpaceLevel2[int(refinedSearchSpaceTopLeft[ROW]):int(refinedSearchSpaceBotRight[ROW]+1), int(refinedSearchSpaceTopLeft[COL]):int(refinedSearchSpaceBotRight[COL]+1)]
refinedBlockTopLeft = [blockTopLeftLevel2[ROW]-refinedSearchSpaceTopLeft[ROW], blockTopLeftLevel2[COL]-refinedSearchSpaceTopLeft[COL]]
dxRefined, dyRefined = self.computeMotionVector(refinedSearchSpace, blockLevel2, refinedBlockTopLeft)
dxLevel2 += dxRefined
dyLevel2 += dyRefined
# -----------Level 1------------ #
dxLevel1, dyLevel1 = dxLevel2*2, dyLevel2*2
k = self.__searchWin
n = block.shape[ROW]
refinedSearchSpaceTopLeft = [max(blockTopLeft[ROW]-1, 0), max(blockTopLeft[COL]-1, 0)]
refinedSearchSpaceBotRight = [blockTopLeft[ROW]+n+1, blockTopLeft[COL]+n+1]
refinedSearchSpace = searchSpace[refinedSearchSpaceTopLeft[ROW]:refinedSearchSpaceBotRight[ROW]+1, refinedSearchSpaceTopLeft[COL]:refinedSearchSpaceBotRight[COL]+1]
refinedBlockTopLeft = [blockTopLeft[ROW]-refinedSearchSpaceTopLeft[ROW], blockTopLeft[COL]-refinedSearchSpaceTopLeft[COL]]
dxRefined, dyRefined = self.computeMotionVector(refinedSearchSpace, block, refinedBlockTopLeft)
dx = dxLevel1+dxRefined
dy = dxLevel1+dyRefined
return dx, dy
# Can't use cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) since frame is of size (3, rows, cols)
# and not (rows, cols, 3) as needed in cvtColor. So using the formula for YUV to RGB
# Source: http://www.pcmag.com/encyclopedia/term/55166/yuv-rgb-conversion-formulas
def YfromRGB(self,frame):
bgr = np.empty((frame.shape[1], frame.shape[2], frame.shape[0]), dtype = 'uint8')
bgr[:,:,2] = frame[0,:,:]
bgr[:,:,1] = frame[1,:,:]
bgr[:,:,0] = frame[2,:,:]
# hue = np.empty((hsv.shape[0], hsv.shape[1],hsv.shape[2]), dtype = 'uint8')
gray = 255-cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
# gray = 0.299*frame[R_CHANNEL, :, :] + 0.587*frame[G_CHANNEL, :, :] + 0.114*frame[B_CHANNEL, :, :]
gray = cv2.equalizeHist(gray)
# cv2.normalize(gray, gray, alpha=50,beta=60, norm_type=cv2.NORM_MINMAX)
return np.int16(gray)
def SfromRGB(self,frame):
hsv = np.empty((frame.shape[1], frame.shape[2], frame.shape[0]), dtype = 'uint8')
# print frame.shape
hsv[:,:,2] = frame[0,:,:]
hsv[:,:,1] = frame[1,:,:]
hsv[:,:,0] = frame[2,:,:]
# hue = np.empty((hsv.shape[0], hsv.shape[1],hsv.shape[2]), dtype = 'uint8')
saturation = cv2.cvtColor(hsv, cv2.COLOR_BGR2HSV)[:,:,1]
saturation = cv2.equalizeHist(saturation)
return np.int16(saturation)
def HfromRGB(self,frame):
hsv = np.empty((frame.shape[1], frame.shape[2], frame.shape[0]), dtype = 'uint8')
# print frame.shape
hsv[:,:,2] = frame[0,:,:]
hsv[:,:,1] = frame[1,:,:]
hsv[:,:,0] = frame[2,:,:]
# hue = np.empty((hsv.shape[0], hsv.shape[1],hsv.shape[2]), dtype = 'uint8')
hue = cv2.cvtColor(hsv, cv2.COLOR_BGR2HSV)[:,:,0]
hue = cv2.equalizeHist(hue)
return np.int16(hue)
def setLabel(self, frameNumber, blockCounter, label):
r = int(blockCounter/60)
c = int(blockCounter%60)
frameNumber = int(frameNumber)
# print blockCounter, r, c
self.__vidData.blockLabels[frameNumber][2*r][2*c] = label
self.__vidData.blockLabels[frameNumber][2*r][2*c+1] = label
self.__vidData.blockLabels[frameNumber][2*r+1][2*c] = label
self.__vidData.blockLabels[frameNumber][2*r+1][2*c+1] = label