def main():
directories.clearFolder(directories.test)
print("Running GrabCut...")
for img, bbox, filename in ImagesAndBBoxes:
#for img, bbox, filename in [ImagesAndBBoxes[0], ImagesAndBBoxes[5]]:
bbox = map(int, bbox)
bbox = tuple(bbox)
#mask = openCVGrabCut(img, bbox)
mask = myGrabCut(img, bbox, filename)
#result = cv2.Image(mask)
print("Finished one image.")
directories.saveArrayAsImage(directories.output + filename + ".bmp", mask)
print("GrabCut is finished :D")
def main():
directories.clearFolder(directories.test)
print("Running GrabCut...")
for img, bbox, filename in ImagesAndBBoxes:
#for img, bbox, filename in [ImagesAndBBoxes[0], ImagesAndBBoxes[5]]:
bbox = map(int, bbox)
bbox = tuple(bbox)
#mask = openCVGrabCut(img, bbox)
mask = myGrabCut(img, bbox, filename)
#result = cv2.Image(mask)
print("Finished one image.")
directories.saveArrayAsImage(directories.output + filename + ".bmp",
mask)
print("GrabCut is finished :D")
def main():
directories.clearFolder(directories.test)
print("Running GrabCut...")
for img, bbox, filename in ImagesAndBBoxes:
bbox = map(int, bbox)
bbox = tuple(bbox)
step = 10
img = sp.misc.imresize(img, float(1)/step)
bbox = [x/step for x in bbox]
#mask = calcMaskUsingOpenCVGrabCut(img, bbox)
mask = calcMaskUsingMyGrabCut(img, bbox, filename)
#result = cv2.Image(mask)
print("Finished one image.")
directories.saveArrayAsImage(directories.output + filename + ".bmp", mask)
print("GrabCut is finished :D")
def calcMaskUsingMyGrabCut(img, bbox, filename):
ftype = ".jpg"
trimap = initTrimapFromBBox(img, bbox)
pmask = np.zeros(trimap.shape)
mask = np.copy(trimap)
mask += 1
binaryEdges, binaryCapacities, edges = initBinaryEdges(img)
iteration = 0
while differenceBetweenTwoMasks(pmask, mask) > .01 or iteration < 3:
print "Difference between masks", differenceBetweenTwoMasks(pmask, mask)
print("Beginning " + filename + " on iteration " + str(iteration))
pmask = np.copy(mask)
fgObs = img[mask != 0]
bgObs = img[mask == 0]
print("Making GMMs...")
fgProb = np.zeros(mask.shape)
if len(fgObs) >= 0:
for _ in range(settings.numGMMs):
fgProb += fitGMM(fgObs).score(np.asarray(img).reshape(-1, 3)).reshape(mask.shape)
bgProb = np.zeros(mask.shape)
if len(bgObs) > 0:
for _ in range(settings.numGMMs):
bgProb += fitGMM(bgObs).score(np.asarray(img).reshape(-1, 3)).reshape(mask.shape)
#Visualize the image mapped to best components of one gaussian mixture model or the other
fgProb = normalizeArr(fgProb)
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "fgProb" + ftype, fgProb)
bgProb = normalizeArr(bgProb)
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "bgProb" + ftype, bgProb)
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "edges" + ftype, edges)
binaryEdgesArr = np.zeros(mask.shape)
for edge, cap in zip(binaryEdges, binaryCapacities):
binaryEdgesArr[edge[0]] += cap
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "y" + ftype, binaryEdgesArr)
#Create edges to source and sink
unaryTerm = fgProb - bgProb
unaryTerm = gaussianBlur(unaryTerm, sigma=3) #Blur the unary term to remove high frequency noise
unaryTerm = normalizeArr(unaryTerm, shift=True)
unaryTerm += settings.bias
"""
print("Binary edges range from " + str(min(binaryCapacities)) + " to " + str(max(binaryCapacities)) + "\nwith median of " + str(np.median(binaryCapacities)) + " and average of " + str(np.average(binaryCapacities)))
print("Unary edges range from " + str(np.min(unaryTerm)) + " to " + str(np.max(unaryTerm)) + "\nwith median of " + str(np.median(unaryTerm)) + " and average of " + str(np.average(unaryTerm)))
"""
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "unaryTerm" + ftype, unaryTerm)
REALLY_BIG_CAPACITY = 10000
unaryTerm[trimap == -1] = -REALLY_BIG_CAPACITY
unaryTerm[trimap == 1] = REALLY_BIG_CAPACITY
assert np.min(unaryTerm) < 0
assert np.max(unaryTerm) > 0
unaryEdges = []
unaryCapacities = []
for x in range(mask.shape[1]):
for y in range(mask.shape[0]):
uterm = unaryTerm[y, x]
if uterm > 0:
unaryEdges.append(("source", (y,x)))
unaryCapacities.append(uterm)
elif uterm < 0:
unaryEdges.append(((y,x), "sink"))
unaryCapacities.append(-uterm)
vertexList = [(y, x) for x in range(mask.shape[1]) for y in range(mask.shape[0])]
assert max(binaryCapacities + unaryCapacities) <= REALLY_BIG_CAPACITY
assert min(binaryCapacities + unaryCapacities) >= 0
cuts = minCuts(vertexList, binaryEdges + unaryEdges, binaryCapacities + unaryCapacities)
if len(cuts) == 0:
print("No cuts found! Trying again anyway....")
print("")
mask[...] = 0
mask[unaryTerm > .00001] = 1
else:
cut = cuts[0]
print(cut)
mask[...] = 0 #zero out the mask
for vertexIndex in cut[0]:
if vertexIndex < len(vertexList):
pt = vertexList[vertexIndex]
mask[pt] = 1
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "z" + ftype, mask)
print("")
iteration += 1
mask = removeAllButLargestComponent(mask)
return mask
def calcMaskUsingMyGrabCut(img, bbox, filename):
trimap = initTrimapFromBBox(img, bbox)
pmask = np.zeros(trimap.shape)
mask = np.copy(trimap)
mask += 1
binaryEdges, binaryCapacities = initBinaryEdges(img)
iteration = 0
while differenceBetweenTwoMasks(pmask, mask) > .005 or iteration < 3:
print(differenceBetweenTwoMasks(pmask, mask))
pmask = np.copy(mask)
print("Beginning " + filename + " on iteration " + str(iteration))
fgObs = img[mask == 1]
bgObs = img[mask == 0]
allObs = np.asarray(img).reshape(-1, 3)
"""
#Plot the point clouds
#plt.scatter(x=allObs[::100, 2], y=allObs[::100, 0])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
step = 10000
colorList = [0] * len(fgObs) + [1] * len(bgObs)
colorArr = np.zeros((len(allObs), 3))
colorArr[:, 0] = 255 * np.asarray(colorList)
colorArr[:, 1] = 255 * (1 - np.asarray(colorList))
colorArr[:, 2] = 255
ax.scatter(allObs[::step, 0], allObs[::step, 1], allObs[::step, 2], c=colorArr)
plt.show()
exit()
"""
numComponents = 10
covType = 'diag'
print("Making GMMs...")
if len(allObs) >= numComponents:
#Make BG Components
allGMM = sklearn.mixture.DPGMM(n_components=numComponents, alpha=10, covariance_type=covType, random_state=None, thresh=0.001, min_covar=0.001, n_iter=10, params='wmc', init_params='wmc')
allGMM.fit(allObs)
allComponents = allGMM.predict(np.reshape(img, (-1, 3)))
allComponents = np.reshape(allComponents, mask.shape)
agProb = allGMM.score(allObs).reshape(mask.shape)
agLogProb, agResponsibilities = allGMM.eval(allObs)
agLogProb = agLogProb.reshape(mask.shape)
agResponsibilities = agResponsibilities.reshape((mask.shape[0], mask.shape[1], -1))
if len(fgObs) >= numComponents:
#Make FG Components
fgGMM = sklearn.mixture.DPGMM(n_components=numComponents, alpha=.01, covariance_type=covType, random_state=None, thresh=0.001, min_covar=None, n_iter=10, params='wmc', init_params='wmc')
fgGMM.fit(fgObs)
fgComponents = fgGMM.predict(np.reshape(img, (-1, 3)))
fgComponents = np.reshape(fgComponents, mask.shape)
fgProb = fgGMM.score(np.asarray(img).reshape(-1, 3)).reshape(mask.shape)
#fgProb /= allProb
if len(bgObs) >= numComponents:
#Make BG Components
bgGMM = sklearn.mixture.DPGMM(n_components=numComponents, alpha=.01, covariance_type=covType, random_state=None, thresh=0.001, min_covar=0.001, n_iter=10, params='wmc', init_params='wmc')
bgGMM.fit(bgObs)
bgComponents = bgGMM.predict(np.reshape(img, (-1, 3)))
bgComponents = np.reshape(bgComponents, mask.shape)
bgProb = bgGMM.score(np.asarray(img).reshape(-1, 3)).reshape(mask.shape)
#bgProb /= allProb
print("We found " + str(np.max(fgComponents) + 1) + " foreground components, and " + str(np.max(bgComponents) + 1) + " background components")
#Visualize the image mapped to best components of one gaussian mixture model or the other
agProb = normalizeArr(agProb)
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "agProb" + ".bmp", agProb)
fgProb = normalizeArr(fgProb)
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "fgProb" + ".bmp", fgProb)
bgProb = normalizeArr(bgProb)
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "bgProb" + ".bmp", bgProb)
binaryEdgesArr = np.zeros(mask.shape)
for edge, cap in zip(binaryEdges, binaryCapacities):
binaryEdgesArr[edge[0]] += cap
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "y" + ".bmp", binaryEdgesArr)
#Create edges to source and sink
fgWeight = 1#float(len(fgObs)) / len(allObs)
bgWeight = 1#float(len(bgObs)) / len(allObs)
unaryTerm = fgWeight * fgProb - bgWeight * bgProb
unaryTerm -= np.median(unaryTerm) * .5 #This is the strength of the normalization
print("Binary edges range from " + str(min(binaryCapacities)) + " to " + str(max(binaryCapacities)) + "\nwith median of " + str(np.median(binaryCapacities)) + " and average of " + str(np.average(binaryCapacities)))
print("Unary edges range from " + str(np.min(unaryTerm)) + " to " + str(np.max(unaryTerm)) + "\nwith median of " + str(np.median(unaryTerm)) + " and average of " + str(np.average(unaryTerm)))
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "unaryTerm" + ".bmp", unaryTerm)
REALLY_BIG_CAPACITY = 10000
unaryTerm[trimap == -1] = -REALLY_BIG_CAPACITY
unaryTerm[trimap == 1] = REALLY_BIG_CAPACITY
assert np.min(unaryTerm) < 0
assert np.max(unaryTerm) > 0
unaryEdges = []
unaryCapacities = []
for x in range(mask.shape[1]):
for y in range(mask.shape[0]):
uterm = unaryTerm[y, x]
if uterm > 0:
unaryEdges.append(("source", (y,x)))
unaryCapacities.append(uterm)
elif uterm < 0:
unaryEdges.append(((y,x), "sink"))
unaryCapacities.append(-uterm)
vertexList = [(y, x) for x in range(mask.shape[1]) for y in range(mask.shape[0])]
assert max(binaryCapacities + unaryCapacities) <= REALLY_BIG_CAPACITY
assert min(binaryCapacities + unaryCapacities) >= 0
cuts = minCuts(vertexList, binaryEdges + unaryEdges, binaryCapacities + unaryCapacities)
if len(cuts) == 0:
print("No cuts found! Trying again anyway....")
print("")
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "z" + ".bmp", mask)
continue
cut = cuts[0]
print(cut)
mask[...] = 0 #zero out the mask
for vertexIndex in cut[0]:
if vertexIndex < len(vertexList):
pt = vertexList[vertexIndex]
mask[pt] = 1
directories.saveArrayAsImage(directories.test + filename + "-" + str(iteration) + "z" + ".bmp", mask)
print("")
iteration += 1
return mask
def calcMaskUsingMyGrabCut(img, bbox, filename):
ftype = ".jpg"
trimap = initTrimapFromBBox(img, bbox)
pmask = np.zeros(trimap.shape)
mask = np.copy(trimap)
mask += 1
binaryEdges, binaryCapacities, edges = initBinaryEdges(img)
iteration = 0
while differenceBetweenTwoMasks(pmask, mask) > .01 or iteration < 3:
print "Difference between masks", differenceBetweenTwoMasks(
pmask, mask)
print("Beginning " + filename + " on iteration " + str(iteration))
pmask = np.copy(mask)
fgObs = img[mask != 0]
bgObs = img[mask == 0]
print("Making GMMs...")
fgProb = np.zeros(mask.shape)
if len(fgObs) >= 0:
for _ in range(settings.numGMMs):
fgProb += fitGMM(fgObs).score(np.asarray(img).reshape(
-1, 3)).reshape(mask.shape)
bgProb = np.zeros(mask.shape)
if len(bgObs) > 0:
for _ in range(settings.numGMMs):
bgProb += fitGMM(bgObs).score(np.asarray(img).reshape(
-1, 3)).reshape(mask.shape)
#Visualize the image mapped to best components of one gaussian mixture model or the other
fgProb = normalizeArr(fgProb)
directories.saveArrayAsImage(
directories.test + filename + "-" + str(iteration) + "fgProb" +
ftype, fgProb)
bgProb = normalizeArr(bgProb)
directories.saveArrayAsImage(
directories.test + filename + "-" + str(iteration) + "bgProb" +
ftype, bgProb)
directories.saveArrayAsImage(
directories.test + filename + "-" + str(iteration) + "edges" +
ftype, edges)
binaryEdgesArr = np.zeros(mask.shape)
for edge, cap in zip(binaryEdges, binaryCapacities):
binaryEdgesArr[edge[0]] += cap
directories.saveArrayAsImage(
directories.test + filename + "-" + str(iteration) + "y" + ftype,
binaryEdgesArr)
#Create edges to source and sink
unaryTerm = fgProb - bgProb
unaryTerm = gaussianBlur(
unaryTerm,
sigma=3) #Blur the unary term to remove high frequency noise
unaryTerm = normalizeArr(unaryTerm, shift=True)
unaryTerm += settings.bias
"""
print("Binary edges range from " + str(min(binaryCapacities)) + " to " + str(max(binaryCapacities)) + "\nwith median of " + str(np.median(binaryCapacities)) + " and average of " + str(np.average(binaryCapacities)))
print("Unary edges range from " + str(np.min(unaryTerm)) + " to " + str(np.max(unaryTerm)) + "\nwith median of " + str(np.median(unaryTerm)) + " and average of " + str(np.average(unaryTerm)))
"""
directories.saveArrayAsImage(
directories.test + filename + "-" + str(iteration) + "unaryTerm" +
ftype, unaryTerm)
REALLY_BIG_CAPACITY = 10000
unaryTerm[trimap == -1] = -REALLY_BIG_CAPACITY
unaryTerm[trimap == 1] = REALLY_BIG_CAPACITY
assert np.min(unaryTerm) < 0
assert np.max(unaryTerm) > 0
unaryEdges = []
unaryCapacities = []
for x in range(mask.shape[1]):
for y in range(mask.shape[0]):
uterm = unaryTerm[y, x]
if uterm > 0:
unaryEdges.append(("source", (y, x)))
unaryCapacities.append(uterm)
elif uterm < 0:
unaryEdges.append(((y, x), "sink"))
unaryCapacities.append(-uterm)
vertexList = [(y, x) for x in range(mask.shape[1])
for y in range(mask.shape[0])]
assert max(binaryCapacities + unaryCapacities) <= REALLY_BIG_CAPACITY
assert min(binaryCapacities + unaryCapacities) >= 0
cuts = minCuts(vertexList, binaryEdges + unaryEdges,
binaryCapacities + unaryCapacities)
if len(cuts) == 0:
print("No cuts found! Trying again anyway....")
print("")
mask[...] = 0
mask[unaryTerm > .00001] = 1
else:
cut = cuts[0]
print(cut)
mask[...] = 0 #zero out the mask
for vertexIndex in cut[0]:
if vertexIndex < len(vertexList):
pt = vertexList[vertexIndex]
mask[pt] = 1
directories.saveArrayAsImage(
directories.test + filename + "-" + str(iteration) + "z" + ftype,
mask)
print("")
iteration += 1
mask = removeAllButLargestComponent(mask)
return mask
