def RandomWalkMetHastings(img, AS, fMap, sigma, N):
imH, imW = img.shape
#Split array to quickly go through pixels
pUsed = utils.nonNan(AS)
values, points = utils.seperateArray(AS, pUsed)
for i in range(values.size):
y, x = points[i]
yPrev = y = int(y)
xPrev = x = int(x)
for j in range(N):
xProp = utils.walkIndex(xPrev, imW - 1, sigma)
yProp = utils.walkIndex(yPrev, imH - 1, sigma)
# Ratio of new point compared to previous on feature map
α = min((fMap[yProp][xProp]) / (fMap[yPrev][xPrev]), 1)
# Random int between 1 and 0
r = random.uniform(0, 1)
# Check proposal
if r < α:
# Check if point is used
if math.isnan(AS[yProp][xProp]):
yPrev = yProp
xPrev = xProp
AS[y][x] = np.nan
AS[yPrev][xPrev] = img[yPrev][xPrev]
nearestAS = utils.nInterp2D(pUsed, AS)
#nearestAS = AS
return nearestAS
def MetHastings(img, pixels, fMap, N):
imH, imW = img.shape
#Define AS value array and Feature Map
#fMap = np.zeros((imH, imW))
AS = np.empty((imH, imW))
AS[:] = np.nan
#Pixels sampled
pCount = 0
#Set initial Met Hastings position
rX = random.randint(0, imW - 1)
rY = random.randint(0, imH - 1)
nX = rX
nY = rY
n = img[rY][rX]
AS[rY][rX] = n
pCount += 1
#Determine if the iteration succeeded in finding a good candidate
accept = False
#Loop through other pixels
while pCount < pixels:
accept = False
for i in range(N):
# Random x and y Values
rX = random.randint(0, imW - 1)
rY = random.randint(0, imH - 1)
# Ratio of new point compared to previous on feature map
α = min((fMap[rY][rX]) / (fMap[nY][nX]), 1)
# Random int between 1 and 0
r = random.uniform(0, 1)
if r < α:
# Check if pixel is used
if math.isnan(AS[rY][rX]):
nX = rX
nY = rY
n = img[rY][rX]
accept = True
if accept:
# Check if pixel is used
if math.isnan(AS[nY][nX]):
AS[nY][nX] = n
pCount += 1
nearestAS = utils.nInterp2D(pixels, AS)
#nearestAS = AS
return nearestAS
def uniformS(img, nPixels):
imH, imW = img.shape
if(nPixels > imH * imW):
nPixels = imH * imW
US = np.empty((imH, imW))
US[:] = np.nan
US = utils.uniformSpread(img, nPixels, US)
uniformNearest = utils.nInterp2D(nPixels, US)
return uniformNearest
def randomAS(img, ROI, pixels, roiPort):
imH, imW = img.shape
AS = np.empty((imH, imW))
AS[:] = np.nan
if pixels > imH * imW:
print("Error: Pixel allocation is too large")
pixels = imH * imW
# Round ROI pixels
newROIP = round(pixels * roiPort)
totROI = 0
#Total ROI pixels
for r in ROI:
x, y, w, h = r
totROI += w * h
if newROIP > totROI:
newROIP = totROI
pCount = 0
for r in ROI:
x, y, w, h = r
#Portion of total ROI
ROIPort = (w * h) / (totROI)
nPixels = newROIP * ROIPort
while pCount < nPixels:
rX = random.randint(x, x + w - 1)
rY = random.randint(y, y + h - 1)
if math.isnan(AS[rY][rX]):
AS[rY][rX] = img[rY][rX]
pCount += 1
while pCount < pixels:
rX = random.randint(0, imW - 1)
rY = random.randint(0, imH - 1)
if math.isnan(AS[rY][rX]):
AS[rY][rX] = img[rY][rX]
pCount += 1
nearestAS = utils.nInterp2D(pixels, AS)
return nearestAS
def uniformAS(img, ROI, nPixels, rPort):
imH, imW = img.shape
if(nPixels > imH * imW):
nPixels = imH * imW
AS = np.empty((imH, imW))
AS[:] = np.nan
#Can be an input
bPort = 1 - rPort
rPixels = round(nPixels * rPort)
bPixels = round(nPixels * bPort)
#Pixels Remaining
pRem = 0
#Total pixels in ROI
roiSum = 0
for r in ROI:
x, y, w, h = r
roiSum += ((w + 1) * (h + 1))
for r in ROI:
x, y, w, h = r
roiPort = ((w + 1) * (h + 1))/roiSum
roiPixels = round(roiPort * rPixels)
#Add on the remainding pixels
roiPixels += pRem
AS[y:y+h, x:x+w] = utils.uniformSpread(img[y:y+h, x:x+w], roiPixels, AS[y:y+h, x:x+w])
pRem = roiPixels - utils.nonNan(AS[y:y+h, x:x+w])
bPixels += pRem
AS = utils.uniformSpread(img, bPixels, AS)
pUsed = utils.nonNan(AS)
uniformNearest = utils.nInterp2D(pUsed, AS)
#uniformNearest = AS
return uniformNearest
def randomS(img, pixels):
imH, imW = img.shape
RS = np.empty((imH, imW))
RS[:] = np.nan
if pixels > imH * imW:
print("Error: Pixel allocation is too large")
pixels = imH * imW
pCount = 0
while pCount < pixels:
rX = random.randint(0, imW - 1)
rY = random.randint(0, imH - 1)
if math.isnan(RS[rY][rX]):
RS[rY][rX] = img[rY][rX]
pCount += 1
nearestRS = utils.nInterp2D(pixels, RS)
return nearestRS