def main2D_3D_2D_LowResolution():
'''1. read origin obj and image'''
objPath = '../../github/vrn-07231340/obj/trump-12.obj'
objLines, vLines, fLines = readObj(objPath)
imgPath = '../../github/vrn-07231340/examples/scaled/trump-12.jpg'
img = cv2.imread(imgPath)
'''2. origin 2D landmark --> origin 3D landmark'''
''' BUT ONE 2D LANDMARK --> MORE THAN ONE 3D LANDMARK'''
''' HOW TO CHOOSE THE BEST ONE ?'''
''' OPTION 1: 选Z最大的3D landmark'''
'''2.1 origin 2D landmark [x, y]'''
originLandmark2DList = getLandmark2D(img)
'''2.2 origin 3D landmark list [[[x, y], [(line1, vLine1), (line2, vLine2)]], ... ]'''
originLandmark3DList = getLandmark3D(originLandmark2DList, vLines)
'''2.3 OPTION 1: 选Z最大的3D landmark'''
originLandmark3DList = optionChooseZMax(originLandmark3DList)
'''3. nod'''
nodAngle = 15
nodCenterMode = 'maxY'
nodedObjLines = nod(objPath, nodAngle, nodCenterMode)
'''4. noded 3D landmark list'''
'''nodedLandmark3DList: [[x0, y0], [x0, y0, z0], [x1, y1], [x1, y1, z1], (line, vLine)]'''
'''len(nodedLandmark3DList): number of landmarks both on 2D and 3D'''
'''[x0, y0]: original landmark on 2D img'''
'''[x0, y0, z0]: original landmark on 3D model'''
'''[x1, y1]: noded landmark on 2D img'''
'''[x1, y1, z1]: noded landmark on 3D model'''
'''(line, vLine): the line(th) noded vLine'''
nodedVLines = objLines2vLines(nodedObjLines)
nodedLandmark3DList = getNodedLandmark3D(nodedVLines, originLandmark3DList)
'''5. noded 3D landmark --> noded 2D landmark'''
nodedLandmark2D = []
for nodedLandmark3D in nodedLandmark3DList:
nodedLandmark2D.append(nodedLandmark3D[2])
nodedLandmark2D = addEdgeLandmark(nodedLandmark2D, img)
'''6. delaunay triangle for (origin landmarks which are both on 2D and 3D) + (eight edge points)'''
originLandmark2D = []
for nodedLandmark3D in nodedLandmark3DList:
originLandmark2D.append((nodedLandmark3D[0][0], nodedLandmark3D[0][1]))
originLandmark2D = addEdgeLandmark(originLandmark2D, img)
assert len(originLandmark2D) == len(nodedLandmark2D)
triList = generateTriList(originLandmark2D, img)
'''7. warp'''
imgMorph = morph_modify_for_2D3D2D_low_resolution(originLandmark2D,
nodedLandmark2D, img,
triList)
imshow('imgMorph', imgMorph)
cv2.imwrite('./noded.jpg', imgMorph)
def main2D_3D_2D_TurnFace():
'''1. read img and obj'''
imgPath = '../../github/vrn-07231340/examples/scaled/trump-12.jpg'
img = cv2.imread(imgPath)
objPath = '../../github/vrn-07231340/obj/trump-12.obj'
objLines, vLines, fLines = readObj(objPath)
'''2. origin 2D landmark --> origin 3D landmark'''
''' BUT ONE 2D LANDMARK --> MORE THAN ONE 3D LANDMARK'''
''' HOW TO CHOOSE THE BEST ONE ?'''
''' OPTION 1: 选Z最大的3D landmark'''
'''2.1 origin 2D landmark [x, y]'''
originLandmark2DList = getLandmark2D(img)
'''2.2 origin 3D landmark list [[[x, y], [(line1, vLine1), (line2, vLine2)]], ... ]'''
originLandmark3DList = getLandmark3D(originLandmark2DList, vLines)
'''2.3 OPTION 1: 选Z最大的3D landmark'''
originLandmark3DList = optionChooseZMax(originLandmark3DList)
'''3. turn'''
turnAngle = 15
turnCenterMode = 'maxY'
turnDirection = 'right'
turnObjLines = turn(objPath, turnAngle, turnDirection, turnCenterMode)
# projection(turnObjLines, imgPath)
'''4. turn 3D landmark list'''
'''turnLandmark3DList: [[x0, y0], [x0, y0, z0], [x1, y1], [x1, y1, z1], (line, vLine)]'''
'''len(turnLandmark3DList): number of landmarks both on 2D and 3D'''
'''[x0, y0]: original landmark on 2D img'''
'''[x0, y0, z0]: original landmark on 3D model'''
'''[x1, y1]: turn landmark on 2D img'''
'''[x1, y1, z1]: turn landmark on 3D model'''
'''(line, vLine): the line(th) turn vLine'''
turnVLines = objLines2vLines(turnObjLines)
turnLandmark3DList = getTurnLandmark3D(turnVLines, originLandmark3DList)
'''5. turn 3D landmark --> turn 2D landmark'''
turnLandmark2D = []
for turnLandmark3D in turnLandmark3DList:
turnLandmark2D.append(turnLandmark3D[2])
'''6. origin 2D landmarks'''
originLandmark2D = []
for turnLandmark3D in turnLandmark3DList:
originLandmark2D.append((turnLandmark3D[0][0], turnLandmark3D[0][1]))
assert len(originLandmark2D) == len(turnLandmark2D)
print len(turnLandmark2D)
def main2D_3D_2D_LR_Hair():
'''1. read origin obj and image'''
objPath = '../../github/vrn-07231340/obj/trump-12.obj'
objLines, vLines, fLines = readObj(objPath)
imgPath = '../../github/vrn-07231340/examples/scaled/trump-12.jpg'
img = cv2.imread(imgPath)
imgHRPath = '../../github/vrn-07231340/examples/trump-12.jpg'
imgHR = cv2.imread(imgHRPath)
'''2. origin 2D landmark --> origin 3D landmark'''
''' BUT ONE 2D LANDMARK --> MORE THAN ONE 3D LANDMARK'''
''' HOW TO CHOOSE THE BEST ONE ?'''
''' OPTION 1: 选Z最大的3D landmark'''
'''2.1 origin 2D landmark [x, y]'''
originLandmark2DList = getLandmark2D(img)
'''2.2 origin 3D landmark list [[[x, y], [(line1, vLine1), (line2, vLine2)]], ... ]'''
originLandmark3DList = getLandmark3D(originLandmark2DList, vLines)
'''2.3 OPTION 1: 选Z最大的3D landmark'''
originLandmark3DList = optionChooseZMax(originLandmark3DList)
'''3. nod'''
nodAngle = 15
nodCenterMode = 'maxY'
nodObjLines = nod(objPath, nodAngle, nodCenterMode)
'''4. nod 3D landmark list'''
'''nodLandmark3DList: [[x0, y0], [x0, y0, z0], [x1, y1], [x1, y1, z1], (line, vLine)]'''
'''len(nodLandmark3DList): number of landmarks both on 2D and 3D'''
'''[x0, y0]: original landmark on 2D img'''
'''[x0, y0, z0]: original landmark on 3D model'''
'''[x1, y1]: nod landmark on 2D img'''
'''[x1, y1, z1]: nod landmark on 3D model'''
'''(line, vLine): the line(th) nod vLine'''
nodVLines = objLines2vLines(nodObjLines)
nodLandmark3DList = getNodLandmark3D(nodVLines, originLandmark3DList)
'''5. nod 3D landmark --> nod 2D landmark'''
nodLandmark2D = []
for nodLandmark3D in nodLandmark3DList:
nodLandmark2D.append(nodLandmark3D[2])
'''6. origin 2D landmarks'''
originLandmark2D = []
for nodLandmark3D in nodLandmark3DList:
originLandmark2D.append((nodLandmark3D[0][0], nodLandmark3D[0][1]))
assert len(originLandmark2D) == len(nodLandmark2D)
'''7. face 2D landmarks + hair 2D landmarks'''
# hairLandmark2DList, nodHairLandmark2DList = main2D_3D_2D_ManuallySelectHair()
hairLandmark2DList, nodHairLandmark2DList, edgePoints, ellipseVerts, outerEllipseVerts = main2D_3D_2D_LR_MeshHair(
)
assert len(hairLandmark2DList) == len(nodHairLandmark2DList)
'''7.1 origin face 2D landmarks + origin hair 2D landmarks'''
originLandmark2D = originLandmark2D + hairLandmark2DList
'''7.2 nod face 2D landmarks + nod hair 2D landmarks'''
nodLandmark2D = nodLandmark2D + nodHairLandmark2DList
'''7.3 save origin and nod 2D landmarks txt'''
# originLandmark2DTxtPath = './originLandmark2D.txt'
# originLandmark2DTxt = open(originLandmark2DTxtPath, 'w')
# for i in originLandmark2D:
# originLandmark2DTxt.write('{} {}\n'.format(i[0], i[1]))
# originLandmark2DTxt.close()
# nodLandmark2DTxtPath = './nodLandmark2D.txt'
# nodLandmark2DTxt = open(nodLandmark2DTxtPath, 'w')
# for i in nodLandmark2D:
# nodLandmark2DTxt.write('{} {}\n'.format(i[0], i[1]))
# nodLandmark2DTxt.close()
'''8. add edge points'''
'''8.1 OPTION1: 8 IMAGE EDGE POINTS'''
''' 如果在加头发关键点之前加8个edge points，第7个edge point（图像最上面那个）会被往下warp一点'''
''' 如果在加头发关键点之后加8个edge points，第7个edge point经过warp后仍在原处，形成一个小三角'''
# originLandmark2D = addEdgeLandmark(originLandmark2D, img)
# nodLandmark2D = addEdgeLandmark(nodLandmark2D, img)
'''8.2 OPTION2: OUTER ELLIPSE'''
# a = copy.copy(originLandmark2D)
originLandmark2D = originLandmark2D + edgePoints
nodLandmark2D = nodLandmark2D + edgePoints
# drawPointsOnImg(originLandmark2D, img, 'b')
# drawPointsOnImg(a, img, 'r')
# drawPointsOnImg(nodLandmark2D, img, 'g')
# drawPointsOnImg(ellipseVerts, img, 'g', cover=True)
# drawPointsOnImg(outerEllipseVerts, img, 'r', cover=True)
# drawPointsOnImg(originLandmark2D, img, 'g', cover=True)
# drawPointsOnImg(edgePoints, img, 'b', radius=3)
# drawPointsOnImg(nodLandmark2D, img, 'r')
'''9. delaunay triangle for [origin landmarks (face + hair)] + (eight edge points)'''
# triList = generateTriList(originLandmark2D, img)
# '''9.1 save tri txt'''
# triTxtPath = './nodTri.txt'
# triTxt = open(triTxtPath, 'w')
# for i in triList:
# triTxt.write('{}\n'.format(i))
# triTxt.close()
'''10. warp LR'''
# imgMorph = morph_modify_for_2D3D2D_low_resolution(
# originLandmark2D, nodLandmark2D, img, triList)
# imshow('imgMorph', imgMorph)
# cv2.imwrite('./nod.jpg', imgMorph)
# return originLandmark2D, nodLandmark2D, triList
'''LR to HR'''
originLandmark2DHR = LR2HR(img, imgHR, originLandmark2D)
for (x, y), (xmin, ymin) in zip(originLandmark2DHR, originLandmark2D):
if float(x) >= 640 or float(y) >= 640:
print(int(xmin), int(ymin))
cv2.circle(img, (int(xmin), int(ymin)), 3, (0, 255, 0), -1)
imshow('img', img)
nodLandmark2DHR = LR2HR(img, imgHR, nodLandmark2D)
'''8. add 8 egde points'''
# nodLandmark2DHR = addEdgeLandmark(nodLandmark2DHR, imgHR)
# originLandmark2DHR = addEdgeLandmark(originLandmark2DHR, imgHR)
# drawPointsOnImg(originLandmark2DHR, imgHR, 'g')
# drawPointsOnImg(nodLandmark2DHR, imgHR, 'r')
'''8.1 remove >= 640'''
originLandmark2DHR = [(i[0], i[1]) for i in originLandmark2DHR
if i[0] < imgHR.shape[0] and i[1] < imgHR.shape[1]]
nodLandmark2DHR = [(i[0], i[1]) for i in nodLandmark2DHR
if i[0] < imgHR.shape[0] and i[1] < imgHR.shape[1]]
'''9. delaunay triangle for [origin landmarks (face + hair)] + (edge points)'''
triList = generateTriList(originLandmark2DHR, imgHR)
'''9.1 save tri txt'''
triTxtPath = './nodTri.txt'
triTxt = open(triTxtPath, 'w')
for i in triList:
triTxt.write('{}\n'.format(i))
triTxt.close()
'''10. warp HR'''
imgMorph = morph_modify_for_2D3D2D_low_resolution(originLandmark2DHR,
nodLandmark2DHR, imgHR,
triList)
# imshow('imgMorph', imgMorph)
# cv2.imwrite('./nod.jpg', imgMorph)
return originLandmark2DHR, nodLandmark2DHR, triList
def mainTurnHR():
'''1. read img LR and HR'''
imgLRPath = '../../github/vrn-07231340/examples/scaled/trump_12.jpg'
imgLR = cv2.imread(imgLRPath)
imgHRPath = '../../github/vrn-07231340/examples/trump_12.png'
imgHR = cv2.imread(imgHRPath)
objPath = '../../github/vrn-07231340/obj/trump_12.obj'
'''2. img LR --> HR'''
zRatio = 3
objLinesHR, vLinesHR, landmarkLR, landmarkHR = lr2hr3DModel(
imgLR, imgHR, zRatio, objPath)
'''3. HR: origin 2D landmark --> origin 3D landmark'''
originLandmark3DList = getLandmark3DHR(landmarkHR, vLinesHR)
originLandmark3DList = optionChooseZMax(originLandmark3DList)
faceLandmarkVlines = []
for (_, _, (_, vLine)) in originLandmark3DList:
faceLandmarkVlines.append(vLine)
'''4. HR: collar detection'''
_, collarPoint = collarDetection(imgHR, minSize=1200, offset=15)
'''5. HR: hair detection'''
originHairPoints = hairDetection(imgHR, minSize=20000)
'''5.1 hair points 不应该全部都在一个平面上（minZ=200），他们应该在一个曲面上'''
''' 取XZ平面上face landmark的三个极点，画一个圆'''
''' 除了face landmark以外的所有点 的 z，都落到圆上'''
'''5.1.1 face landmark上XZ平面的三个极点'''
faceLandmarkOnXZ = []
for faceLandmarkVline in faceLandmarkVlines:
v, x, y, z, r, g, b = faceLandmarkVline.split()
faceLandmarkOnXZ.append((float(x), float(z)))
faceLandmarkOnXZMinX = min(faceLandmarkOnXZ, key=lambda x: x[0])
faceLandmarkOnXZMaxX = max(faceLandmarkOnXZ, key=lambda x: x[0])
faceLandmarkOnXZMaxZ = max(faceLandmarkOnXZ, key=lambda x: x[1])
'''5.1.2 三点构成一个圆'''
circleCenterXOnXZ, circleCenterZOnXZ, radiusOnXZ = threePoints2Circle(
faceLandmarkOnXZMinX, faceLandmarkOnXZMaxX, faceLandmarkOnXZMaxZ)
circleCenterZOnXZ = circleCenterZOnXZ-70
'''5.1.3 把这个圆扩大，圆心不动，半径扩大至hair points的极点'''
k1 = abs(circleCenterXOnXZ-max(originHairPoints, key=lambda x: x[0])[0])
k2 = abs(circleCenterXOnXZ-min(originHairPoints, key=lambda x: x[0])[0])
radiusOnXZ = max([k1, k2])+5
rr, cc = circle_perimeter(int(circleCenterXOnXZ), int(
circleCenterZOnXZ), int(radiusOnXZ))
circleOnXZ = np.dstack([rr, cc])[0]
'''5.1.4 hair points 的 z 落在圆上'''
hairPointsVlines = []
for (x, y) in originHairPoints:
maxZ = 0
for (x0, z0) in circleOnXZ:
if int(x) == int(x0):
if z0 > maxZ:
maxZ = z0
hairVline = '{} {} {} {} {} {} {}'.format('v', x, y, maxZ, 0, 0, 0)
hairPointsVlines.append(hairVline)
maxZ = 0
assert len(hairPointsVlines) == len(originHairPoints)
'''6. HR: face landmarks + hair points'''
originVlines = faceLandmarkVlines+hairPointsVlines
'''7. HR: turn face and hair'''
turnAngle = 10
turnDirection = ['left', 'right']
turnCenterMode = 'maxY'
leftTurnVLines = turnHR(originVlines, turnAngle,
turnDirection[0], turnCenterMode)
rightTurnVLines = turnHR(originVlines, turnAngle,
turnDirection[1], turnCenterMode)
assert len(originVlines) == len(leftTurnVLines) == len(rightTurnVLines)
'''8. HR: turn 3D --> 2D'''
originPointsHR = []
leftTurnPointsHR = []
rightTurnPointsHR = []
for originVline, leftTurnVLine, rightTurnVLine in zip(originVlines, leftTurnVLines, rightTurnVLines):
_, x, y, _, _, _, _ = originVline.split()
originPointsHR.append((float(x), float(y)))
_, x, y, _, _, _, _ = leftTurnVLine.split()
leftTurnPointsHR.append((float(x), float(y)))
_, x, y, _, _, _, _ = rightTurnVLine.split()
rightTurnPointsHR.append((float(x), float(y)))
assert len(originPointsHR) == len(
leftTurnPointsHR) == len(rightTurnPointsHR)
# drawPointsOnImg(originPointsHR, imgHR, 'g')
# drawPointsOnImg(turnPointsHR, imgHR, 'r')
''''9. inner ellipse'''
left = min(originPointsHR, key=lambda x: x[0])[0]
right = max(originPointsHR, key=lambda x: x[0])[0]
up = min(originPointsHR, key=lambda x: x[1])[1]
bottom = max(originPointsHR, key=lambda x: x[1])[1]
innerEllipseCenterX = int((left+right)/2.0)
innerEllipseCenterY = int((up+bottom)/2.0)
innerEllipseSemiX = int((right-left)/2.0)
innerEllipseSemiY = int((bottom-up)/2.0)
rr, cc = ellipse_perimeter(innerEllipseCenterX, innerEllipseCenterY,
innerEllipseSemiY, innerEllipseSemiX, orientation=np.pi*1.5)
innerEllipseVerts = np.dstack([rr, cc])[0]
'''10. outer ellipse'''
'''10.1 ratio = outer ellipse / inner ellipse'''
''' 椭圆心和衣领的线段 和 椭圆的交点'''
minDistance = np.inf
for pt in innerEllipseVerts:
distance = twoPointsDistance(pt, collarPoint)
if distance < minDistance:
minDistance = distance
ratio = twoPointsDistance((innerEllipseCenterX, innerEllipseCenterY), collarPoint) / \
twoPointsDistance(
(innerEllipseCenterX, innerEllipseCenterY), pt)
'''10.2 outer ellipse'''
outerEllipseSemiX = int(ratio*innerEllipseSemiX)
outerEllipseSemiY = int(ratio*innerEllipseSemiY)
rr, cc = ellipse_perimeter(innerEllipseCenterX, innerEllipseCenterY,
outerEllipseSemiY, outerEllipseSemiX, orientation=np.pi*1.5)
outerEllipseVerts = np.dstack([rr, cc])[0]
# drawPointsOnImg(outerEllipseVerts, imgHR, 'g')
'''11. edge points on outer ellipse'''
edgePoints = edgePointsOnOuterEllipse(outerEllipseVerts)
'''12. final origin and turn points'''
originPointsHRFinal = originPointsHR+edgePoints
leftTurnPointsHRFinal = leftTurnPointsHR+edgePoints
rightTurnPointsHRFinal = rightTurnPointsHR+edgePoints
'''13. tri.txt'''
triList = generateTriList(
originPointsHRFinal, imgHR, triTxtPath='./turnTri.txt')
'''14. warp'''
# imgMorph = morph_modify_for_2D3D2D_low_resolution(
# originPointsHRFinal, turnPointsHRFinal, imgHR, triList)
# imshow('imgMorph', imgMorph)
# cv2.imwrite('./imgMorph.png', imgMorph)
return originPointsHRFinal, leftTurnPointsHRFinal, rightTurnPointsHRFinal, triList
def main2D_3D_2D_LR_Hair():
'''1. read origin obj and image'''
objPath = '../../github/vrn-07231340/obj/trump-12.obj'
objLines, vLines, fLines = readObj(objPath)
imgPath = '../../github/vrn-07231340/examples/scaled/trump-12.jpg'
img = cv2.imread(imgPath)
'''2. origin 2D landmark --> origin 3D landmark'''
''' BUT ONE 2D LANDMARK --> MORE THAN ONE 3D LANDMARK'''
''' HOW TO CHOOSE THE BEST ONE ?'''
''' OPTION 1: 选Z最大的3D landmark'''
'''2.1 origin 2D landmark [x, y]'''
originLandmark2DList = getLandmark2D(img)
'''2.2 origin 3D landmark list [[[x, y], [(line1, vLine1), (line2, vLine2)]], ... ]'''
originLandmark3DList = getLandmark3D(originLandmark2DList, vLines)
'''2.3 OPTION 1: 选Z最大的3D landmark'''
originLandmark3DList = optionChooseZMax(originLandmark3DList)
'''3. nod'''
nodAngle = 15
nodCenterMode = 'maxY'
nodObjLines = nod(objPath, nodAngle, nodCenterMode)
'''4. nod 3D landmark list'''
'''nodLandmark3DList: [[x0, y0], [x0, y0, z0], [x1, y1], [x1, y1, z1], (line, vLine)]'''
'''len(nodLandmark3DList): number of landmarks both on 2D and 3D'''
'''[x0, y0]: original landmark on 2D img'''
'''[x0, y0, z0]: original landmark on 3D model'''
'''[x1, y1]: nod landmark on 2D img'''
'''[x1, y1, z1]: nod landmark on 3D model'''
'''(line, vLine): the line(th) nod vLine'''
nodVLines = objLines2vLines(nodObjLines)
nodLandmark3DList = getNodLandmark3D(nodVLines, originLandmark3DList)
'''5. nod 3D landmark --> nod 2D landmark + 8 edge points'''
nodLandmark2D = []
for nodLandmark3D in nodLandmark3DList:
nodLandmark2D.append(nodLandmark3D[2])
nodLandmark2D = addEdgeLandmark(nodLandmark2D, img)
'''6. origin 2D landmarks + 8 edge points'''
originLandmark2D = []
for nodLandmark3D in nodLandmark3DList:
originLandmark2D.append((nodLandmark3D[0][0], nodLandmark3D[0][1]))
originLandmark2D = addEdgeLandmark(originLandmark2D, img)
assert len(originLandmark2D) == len(nodLandmark2D)
'''7. face 2D landmarks + hair 2D landmarks'''
# hairLandmark2DList, nodHairLandmark2DList = main2D_3D_2D_ManuallySelectHair()
hairLandmark2DList, nodHairLandmark2DList = main2D_3D_2D_LR_MeshHair()
'''7.1 origin face 2D landmarks + origin hair 2D landmarks'''
originLandmark2D = originLandmark2D+hairLandmark2DList
# drawPointsOnImg(originLandmark2D, img, 'g')
'''7.2 nod face 2D landmarks + nod hair 2D landmarks'''
nodLandmark2D = nodLandmark2D+nodHairLandmark2DList
# drawPointsOnImg(nodLandmark2D, img, 'r')
'''7.3 save origin and nod 2D landmarks txt'''
originLandmark2DTxtPath = './originLandmark2D.txt'
originLandmark2DTxt = open(originLandmark2DTxtPath, 'w')
for i in originLandmark2D:
originLandmark2DTxt.write('{} {}\n'.format(i[0], i[1]))
originLandmark2DTxt.close()
nodLandmark2DTxtPath = './nodLandmark2D.txt'
nodLandmark2DTxt = open(nodLandmark2DTxtPath, 'w')
for i in nodLandmark2D:
nodLandmark2DTxt.write('{} {}\n'.format(i[0], i[1]))
nodLandmark2DTxt.close()
'''8. delaunay triangle for [origin landmarks (face + hair)] + (eight edge points)'''
triList = generateTriList(originLandmark2D, img)
# print triList
'''8.1 save tri txt'''
triTxtPath = './nodTri.txt'
triTxt = open(triTxtPath, 'w')
for i in triList:
triTxt.write('{}\n'.format(i))
triTxt.close()
'''9. warp'''
imgMorph = morph_modify_for_2D3D2D_low_resolution(
originLandmark2D, nodLandmark2D, img, triList)
# imshow('imgMorph', imgMorph)
cv2.imwrite('./nod.jpg', imgMorph)
def mainTurnHRHairOnEllipsoid():
'''1. read img LR and HR'''
imgLRPath = '../../github/vrn-07231340/examples/scaled/trump_12.jpg'
imgLR = cv2.imread(imgLRPath)
imgHRPath = '../../github/vrn-07231340/examples/trump_12.png'
imgHR = cv2.imread(imgHRPath)
objPath = '../../github/vrn-07231340/obj/trump_12.obj'
'''2. img LR --> HR'''
zRatio = 3
objLinesHR, vLinesHR, landmarkLR, landmarkHR = lr2hr3DModel(
imgLR, imgHR, zRatio, objPath)
'''3. HR: origin 2D landmark --> origin 3D landmark'''
originLandmark3DList = getLandmark3DHR(landmarkHR, vLinesHR)
originLandmark3DList = optionChooseZMax(originLandmark3DList)
faceLandmarkVlines = []
for (_, _, (_, vLine)) in originLandmark3DList:
faceLandmarkVlines.append(vLine)
'''4. HR: collar detection'''
_, collarPoint = collarDetection(imgHR, minSize=1200, offset=15)
'''5. HR: hair detection'''
originHairPoints = hairDetection(imgHR, minSize=20000)
'''5.1 hair vLines z = -1'''
hairPointsVlines = []
for (x, y) in originHairPoints:
hairVline = '{} {} {} {} {} {} {}'.format('v', x, y, -1, 0, 0, 0)
hairPointsVlines.append(hairVline)
'''6. HR: origin vLines = face landmark vLines + hair vLines'''
originVlines = faceLandmarkVlines+hairPointsVlines
'''7. HR: origin points'''
originPointsHR = []
for originVline in originVlines:
_, x, y, _, _, _, _ = originVline.split()
originPointsHR.append((float(x), float(y)))
'''8. inner ellipse'''
left = min(originPointsHR, key=lambda x: x[0])[0]
right = max(originPointsHR, key=lambda x: x[0])[0]
up = min(originPointsHR, key=lambda x: x[1])[1]
bottom = max(originPointsHR, key=lambda x: x[1])[1]
innerEllipseCenterX = int((left+right)/2.0)
innerEllipseCenterY = int((up+bottom)/2.0)
innerEllipseSemiX = int((right-left)/2.0)
innerEllipseSemiY = int((bottom-up)/2.0)
rr, cc = ellipse_perimeter(innerEllipseCenterX, innerEllipseCenterY,
innerEllipseSemiY, innerEllipseSemiX, orientation=np.pi*1.5)
innerEllipseVerts = np.dstack([rr, cc])[0]
# drawPointsOnImg(innerEllipseVerts, imgHR, 'g')
'''9. outer ellipse'''
'''9.1 ratio = outer ellipse / inner ellipse'''
''' 椭圆心和衣领的线段 和 椭圆的交点'''
minDistance = np.inf
for pt in innerEllipseVerts:
distance = twoPointsDistance(pt, collarPoint)
if distance < minDistance:
minDistance = distance
ratio = twoPointsDistance((innerEllipseCenterX, innerEllipseCenterY), collarPoint) / \
twoPointsDistance(
(innerEllipseCenterX, innerEllipseCenterY), pt)
'''9.2 outer ellipse'''
outerEllipseSemiX = int(ratio*innerEllipseSemiX)
outerEllipseSemiY = int(ratio*innerEllipseSemiY)
rr, cc = ellipse_perimeter(innerEllipseCenterX, innerEllipseCenterY,
outerEllipseSemiY, outerEllipseSemiX, orientation=np.pi*1.5)
outerEllipseVerts = np.dstack([rr, cc])[0]
# drawPointsOnImg(outerEllipseVerts, imgHR, 'r')
'''10. head ellipoid (outer ellipse)'''
'''!!! F I N A L L Y !!!'''
maxminDict = maxminXYZ(vLinesHR)
maxZ = maxminDict['maxZCoord'][2]
minZ = maxminDict['minZCoord'][2]
ellipsoidHeadVLines, ellipsoidHeadXY, ellipsoidHeadXZ, ellipsoidHeadYZ, ellipsoidHeadXYZ = ellipsoidHead(
outerEllipseSemiX, outerEllipseSemiY, maxZ - minZ, innerEllipseCenterX, innerEllipseCenterY, minZ)
# drawPointsOnImg(ellipsoidHeadXY, imgHR, 'b')
# drawPointsOnImg(ellipsoidHeadXZ, imgHR, 'r')
# drawPointsOnImg(ellipsoidHeadYZ, imgHR, 'g')
'''11. hair vLines: z = -1 --> z on ellipsoid'''
newHairPointsVlines = []
for hairPointsVline in hairPointsVlines:
v, x, y, z, r, g, b = hairPointsVline.split()
maxZ = 0
for (x0, y0, z0) in ellipsoidHeadXYZ:
if int(float(x)) == int(float(x0)) and int(float(y)) == int(float(y0)) and float(z0) > 0:
# print int(float(x0)), int(float(y0)), int(float(z0))
if int(float(z0)) > int(float(z)) and int(float(z0)) > maxZ:
maxZ = int(float(z0))
print maxZ
newHairPointsVline = '{} {} {} {} {} {} {}'.format(
v, x, y, maxZ, r, g, b)
newHairPointsVlines.append(newHairPointsVline)
# print hairPointsVlines
# print newHairPointsVlines
assert len(hairPointsVlines) == len(newHairPointsVlines)
'''12. HR: origin vLines = face landmark vLines + new hair vLines'''
originVlines = faceLandmarkVlines+newHairPointsVlines
'''13. HR: turn face and hair'''
turnAngle = 10
turnDirection = ['left', 'right']
turnCenterMode = 'maxY'
leftTurnVLines = turnHR(originVlines, turnAngle,
turnDirection[0], turnCenterMode)
rightTurnVLines = turnHR(originVlines, turnAngle,
turnDirection[1], turnCenterMode)
assert len(originVlines) == len(leftTurnVLines) == len(rightTurnVLines)
'''14. HR: turn 3D --> 2D'''
originPointsHR = []
leftTurnPointsHR = []
rightTurnPointsHR = []
for originVline, leftTurnVLine, rightTurnVLine in zip(originVlines, leftTurnVLines, rightTurnVLines):
_, x, y, _, _, _, _ = originVline.split()
originPointsHR.append((float(x), float(y)))
_, x, y, _, _, _, _ = leftTurnVLine.split()
leftTurnPointsHR.append((float(x), float(y)))
_, x, y, _, _, _, _ = rightTurnVLine.split()
rightTurnPointsHR.append((float(x), float(y)))
assert len(originPointsHR) == len(
leftTurnPointsHR) == len(rightTurnPointsHR)
'''15. edge points on outer ellipse'''
edgePoints = edgePointsOnOuterEllipse(outerEllipseVerts)
'''16. final origin and turn points'''
originPointsHRFinal = originPointsHR+edgePoints
leftTurnPointsHRFinal = leftTurnPointsHR+edgePoints
rightTurnPointsHRFinal = rightTurnPointsHR+edgePoints
'''17. tri.txt'''
triList = generateTriList(
originPointsHRFinal, imgHR, triTxtPath='./turnTri.txt')
return originPointsHRFinal, leftTurnPointsHRFinal, rightTurnPointsHRFinal, triList
def mainNodHR():
'''1. read img LR and HR'''
imgLRPath = '../../github/vrn-07231340/examples/scaled/trump-12.jpg'
imgLR = cv2.imread(imgLRPath)
imgHRPath = '../../github/vrn-07231340/examples/trump-12.jpg'
imgHR = cv2.imread(imgHRPath)
objPath = '../../github/vrn-07231340/obj/trump-12.obj'
'''2. img LR --> HR'''
zRatio = 3
objLinesHR, vLinesHR, landmarkLR, landmarkHR = lr2hr3DModel(
imgLR, imgHR, zRatio, objPath)
'''3. HR: origin 2D landmark --> origin 3D landmark'''
originLandmark3DList = getLandmark3DHR(landmarkHR, vLinesHR)
originLandmark3DList = optionChooseZMax(originLandmark3DList)
faceLandmarkVlines = []
for (_, _, (_, vLine)) in originLandmark3DList:
faceLandmarkVlines.append(vLine)
'''4. HR: collar detection'''
_, collarPoint = collarDetection(imgHR, minSize=1200, offset=15)
'''5. HR: hair detection'''
originHairPoints = hairDetection(imgHR, minSize=20000)
# minZ = maxminXYZ(objLinesHR)['minZCoord'][2]
minZ = 180
hairPointsVlines = []
for (x, y) in originHairPoints:
hairVline = '{} {} {} {} {} {} {}'.format('v', x, y, minZ, 0, 0, 0)
hairPointsVlines.append(hairVline)
'''6. HR: face landmarks + hair points'''
originVlines = faceLandmarkVlines + hairPointsVlines
'''7. HR: nod face and hair'''
nodAngle = 15
nodCenterMode = 'maxY'
nodVlines = nodHR(originVlines, nodAngle, nodCenterMode)
assert len(originVlines) == len(nodVlines)
'''8. HR: nod 3D --> 2D'''
originPointsHR = []
nodPointsHR = []
for originVline, nodVline in zip(originVlines, nodVlines):
_, x, y, _, _, _, _ = originVline.split()
originPointsHR.append((float(x), float(y)))
_, x, y, _, _, _, _ = nodVline.split()
nodPointsHR.append((float(x), float(y)))
assert len(originPointsHR) == len(nodPointsHR)
# drawPointsOnImg(originPointsHR, imgHR, 'g', radius=2)
# drawPointsOnImg(nodPointsHR, imgHR, 'r', radius=2)
''''9. inner ellipse'''
left = min(originPointsHR, key=lambda x: x[0])[0]
right = max(originPointsHR, key=lambda x: x[0])[0]
up = min(originPointsHR, key=lambda x: x[1])[1]
bottom = max(originPointsHR, key=lambda x: x[1])[1]
innerEllipseCenterX = int((left + right) / 2.0)
innerEllipseCenterY = int((up + bottom) / 2.0)
innerEllipseSemiX = int((right - left) / 2.0)
innerEllipseSemiY = int((bottom - up) / 2.0)
rr, cc = ellipse_perimeter(innerEllipseCenterX,
innerEllipseCenterY,
innerEllipseSemiY,
innerEllipseSemiX,
orientation=np.pi * 1.5)
innerEllipseVerts = np.dstack([rr, cc])[0]
'''10. outer ellipse'''
'''10.1 ratio = outer ellipse / inner ellipse'''
''' 椭圆心和衣领的线段 和 椭圆的交点'''
minDistance = np.inf
for pt in innerEllipseVerts:
distance = twoPointsDistance(pt, collarPoint)
if distance < minDistance:
minDistance = distance
ratio = twoPointsDistance((innerEllipseCenterX, innerEllipseCenterY), collarPoint) / \
twoPointsDistance(
(innerEllipseCenterX, innerEllipseCenterY), pt)
'''10.2 outer ellipse'''
outerEllipseSemiX = int(ratio * innerEllipseSemiX)
outerEllipseSemiY = int(ratio * innerEllipseSemiY)
rr, cc = ellipse_perimeter(innerEllipseCenterX,
innerEllipseCenterY,
outerEllipseSemiY,
outerEllipseSemiX,
orientation=np.pi * 1.5)
outerEllipseVerts = np.dstack([rr, cc])[0]
'''11. edge points on outer ellipse'''
edgePoints = edgePointsOnOuterEllipse(outerEllipseVerts)
'''12. final origin and nod points'''
originPointsHRFinal = originPointsHR + edgePoints
nodPointsHRFinal = nodPointsHR + edgePoints
assert len(originPointsHRFinal) == len(nodPointsHRFinal)
'''13. tri.txt'''
triList = generateTriList(originPointsHRFinal,
imgHR,
triTxtPath='./nodTri.txt')
'''14. warp'''
imgMorph = morph_modify_for_2D3D2D_low_resolution(originPointsHRFinal,
nodPointsHRFinal, imgHR,
triList)
imshow('imgMorph', imgMorph)
cv2.imwrite('./imgMorph.png', imgMorph)
return originPointsHRFinal, nodPointsHRFinal, triList
def main2D_3D_2D_HR_NodHair():
'''1. read origin obj and image'''
objPath = '../../github/vrn-07231340/obj/trump-12.obj'
objLines, vLines, fLines = readObj(objPath)
imgLRPath = '../../github/vrn-07231340/examples/scaled/trump-12.jpg'
imgLR = cv2.imread(imgLRPath)
imgHRPath = '../../github/vrn-07231340/examples/trump-12.jpg'
imgHR = cv2.imread(imgHRPath)
'''2. origin LR 2D landmark --> origin 3D landmark'''
'''2.1 origin LR 2D landmark list [x, y]'''
originLandmark2DLRList = getLandmark2D(imgLR)
'''2.2 origin 3D landmark list [[[x, y], [(line1, vLine1), (line2, vLine2)]], ... ]'''
originLandmark3DList = getLandmark3D(originLandmark2DLRList, vLines)
'''2.3 OPTION 1: 相同(x, y)的情况下，选z最大的3D landmark'''
originLandmark3DList = optionChooseZMax(originLandmark3DList)
'''3. 3D model nod'''
nodAngle = 10
nodCenterMode = 'maxY'
nodObjLines = nod(objPath, nodAngle, nodCenterMode)
'''4. nod 3D landmark list'''
nodVLines = objLines2vLines(nodObjLines)
nodLandmark3DList = getNodLandmark3D(nodVLines, originLandmark3DList)
'''5. nod 3D landmark --> nod LR 2D landmark'''
nodLandmark2DLR = [i[2] for i in nodLandmark3DList]
'''6. origin 2D landmark'''
originLandmark2DLR = [(i[0][0], i[0][1]) for i in nodLandmark3DList]
assert len(originLandmark2DLR) == len(nodLandmark2DLR)
'''7. LR --> HR: origin and nod face landmark'''
originLandmark2DHR = lr2hr(imgLR, imgHR, originLandmark2DLR)
nodLandmark2DHR = lr2hr(imgLR, imgHR, nodLandmark2DLR)
'''8. HR: collar detection'''
_, collarPoint = collarDetection(imgHR, minSize=1200, offset=15)
'''9. HR: hair detection'''
originHairPoints = hairDetection(imgHR, minSize=20000)
'''10. HR: nod hair points'''
'''??? nod center ???'''
nodAngleHair = 18
# drawPointsOnImg(originHairPoints, imgHR, 'r', radius=3)
# nodHairPoints = nodHairHR(
# objLines, nodAngleHair, nodCenterMode, originHairPoints, minZ=100.0)
nodHairPoints = nodHairHR_m(
nodAngleHair, collarPoint, originHairPoints, minZ=100.0)
# for (x, y), (i, j) in zip(nodHairPoints, nodHairPoints_m):
# if x == i and y == j:
# pass
# else:
# print 'yes'
'''10. HR: all origin points and nod points'''
originPoints = originLandmark2DHR+originHairPoints
nodPoints = nodLandmark2DHR+nodHairPoints
assert len(originPoints) == len(nodPoints)
# drawPointsOnImg(originPoints, imgHR, 'g', radius=2)
'''11. inner ellipse'''
left = min(originPoints, key=lambda x: x[0])[0]
right = max(originPoints, key=lambda x: x[0])[0]
up = min(originPoints, key=lambda x: x[1])[1]
bottom = max(originPoints, key=lambda x: x[1])[1]
innerEllipseCenterX = int((left+right)/2.0)
innerEllipseCenterY = int((up+bottom)/2.0)
innerEllipseSemiX = int((right-left)/2.0)
innerEllipseSemiY = int((bottom-up)/2.0)
rr, cc = ellipse_perimeter(innerEllipseCenterX, innerEllipseCenterY,
innerEllipseSemiY, innerEllipseSemiX, orientation=np.pi*1.5)
innerEllipseVerts = np.dstack([rr, cc])[0]
'''12. outer ellipse'''
'''12.1 ratio = outer ellipse / inner ellipse'''
''' 椭圆心和衣领的线段 和 椭圆的交点'''
minDistance = np.inf
for pt in innerEllipseVerts:
distance = twoPointsDistance(pt, collarPoint)
if distance < minDistance:
minDistance = distance
ratio = twoPointsDistance((innerEllipseCenterX, innerEllipseCenterY), collarPoint) / \
twoPointsDistance(
(innerEllipseCenterX, innerEllipseCenterY), pt)
'''12.2 outer ellipse'''
outerEllipseSemiX = int(ratio*innerEllipseSemiX)
outerEllipseSemiY = int(ratio*innerEllipseSemiY)
rr, cc = ellipse_perimeter(innerEllipseCenterX, innerEllipseCenterY,
outerEllipseSemiY, outerEllipseSemiX, orientation=np.pi*1.5)
outerEllipseVerts = np.dstack([rr, cc])[0]
'''13. edge points on outer ellipse'''
edgePoints = edgePointsOnOuterEllipse(outerEllipseVerts)
'''14. final origin and nod HR points '''
originPointsFinal = originPoints+edgePoints
nodPointsFinal = nodPoints+edgePoints
assert len(originPointsFinal) == len(nodPointsFinal)
drawPointsOnImg(originPointsFinal, imgHR, 'b', radius=2)
drawPointsOnImg(originPoints, imgHR, 'r', radius=2)
drawPointsOnImg(nodPointsFinal, imgHR, 'g', radius=2)
'''15. tri.txt'''
triList = generateTriList(originPointsFinal, imgHR,
triTxtPath='./nodTri.txt')
'''16. warp'''
imgMorph = morph_modify_for_2D3D2D_low_resolution(
originPointsFinal, nodPointsFinal, imgHR, triList)
imshow('imgMorph', imgMorph)
cv2.imwrite('./imgMorph_{}2.png'.format(nodAngleHair), imgMorph)
return originPointsFinal, nodPointsFinal, triList
