def lr2hr3DModel(imgLR, imgHR, zRatio, objPath):
'''1. read LR and HR img'''
# 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. face landmark detection on LR and HR img'''
landmarkLR = getLandmark2D(imgLR)
landmarkHR = getLandmark2D(imgHR)
landmarkLR1 = np.float32([[landmarkLR[0][0], landmarkLR[0][1]],
[landmarkLR[8][0], landmarkLR[8][1]],
[landmarkLR[16][0], landmarkLR[16][1]]])
landmarkHR1 = np.float32([[landmarkHR[0][0], landmarkHR[0][1]],
[landmarkHR[8][0], landmarkHR[8][1]],
[landmarkHR[16][0], landmarkHR[16][1]]])
landmarkLR2 = np.float32([[landmarkLR[27][0], landmarkLR[27][1]],
[landmarkLR[48][0], landmarkLR[48][1]],
[landmarkLR[54][0], landmarkLR[54][1]]])
landmarkHR2 = np.float32([[landmarkHR[27][0], landmarkHR[27][1]],
[landmarkHR[48][0], landmarkHR[48][1]],
[landmarkHR[54][0], landmarkHR[54][1]]])
landmarkLR3 = np.float32([[landmarkLR[36][0], landmarkLR[36][1]],
[landmarkLR[45][0], landmarkLR[45][1]],
[landmarkLR[33][0], landmarkLR[33][1]]])
landmarkHR3 = np.float32([[landmarkHR[36][0], landmarkHR[36][1]],
[landmarkHR[45][0], landmarkHR[45][1]],
[landmarkHR[33][0], landmarkHR[33][1]]])
'''3. affine transform'''
M1 = cv2.getAffineTransform(landmarkLR1, landmarkHR1)
M2 = cv2.getAffineTransform(landmarkLR2, landmarkHR2)
M3 = cv2.getAffineTransform(landmarkLR3, landmarkHR3)
M = (M1 + M2 + M3) / 3
objLines, _, _ = readObj(objPath)
newObjLines = []
newVLines = []
for objLine in objLines:
if objLine.split()[0] == 'v':
v, x, y, z, r, g, b = objLine.split()
l = np.array([float(x), float(y), 1])
dst = np.dot(M, l.T)
newX = float(dst[0])
newY = float(dst[1])
newZ = float(z) * zRatio
objLine = '{} {} {} {} {} {} {}'.format(v, newX, newY, newZ, r, g,
b)
newVLines.append(objLine)
newObjLines.append(objLine)
saveObjFile('HR', newObjLines, objPath)
landmarkHR = []
for landmark in landmarkLR:
l = np.array([landmark[0], landmark[1], 1])
dst = np.dot(M, l.T)
landmarkHR.append((dst[0], dst[1]))
return newObjLines, newVLines, landmarkLR, landmarkHR
def mainLR2HR():
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)
srcLandmarkLR = getLandmark2D(imgLR)
lr2hr(imgLR, imgHR, srcLandmarkLR)
def lr2hr(imgLR, imgHR, srcLandmarkLR):
'''1. read LR and HR img'''
# 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. face landmark detection on LR and HR img'''
landmarkLR = getLandmark2D(imgLR)
landmarkHR = getLandmark2D(imgHR)
landmarkLR1 = np.float32([[landmarkLR[0][0], landmarkLR[0][1]],
[landmarkLR[8][0], landmarkLR[8][1]],
[landmarkLR[16][0], landmarkLR[16][1]]])
landmarkHR1 = np.float32([[landmarkHR[0][0], landmarkHR[0][1]],
[landmarkHR[8][0], landmarkHR[8][1]],
[landmarkHR[16][0], landmarkHR[16][1]]])
landmarkLR2 = np.float32([[landmarkLR[27][0], landmarkLR[27][1]],
[landmarkLR[48][0], landmarkLR[48][1]],
[landmarkLR[54][0], landmarkLR[54][1]]])
landmarkHR2 = np.float32([[landmarkHR[27][0], landmarkHR[27][1]],
[landmarkHR[48][0], landmarkHR[48][1]],
[landmarkHR[54][0], landmarkHR[54][1]]])
landmarkLR3 = np.float32([[landmarkLR[36][0], landmarkLR[36][1]],
[landmarkLR[45][0], landmarkLR[45][1]],
[landmarkLR[33][0], landmarkLR[33][1]]])
landmarkHR3 = np.float32([[landmarkHR[36][0], landmarkHR[36][1]],
[landmarkHR[45][0], landmarkHR[45][1]],
[landmarkHR[33][0], landmarkHR[33][1]]])
'''3. affine transform'''
M1 = cv2.getAffineTransform(landmarkLR1, landmarkHR1)
M2 = cv2.getAffineTransform(landmarkLR2, landmarkHR2)
M3 = cv2.getAffineTransform(landmarkLR3, landmarkHR3)
M = (M1 + M2 + M3) / 3
landmarkHR = []
for landmark in srcLandmarkLR:
l = np.array([landmark[0], landmark[1], 1])
dst = np.dot(M, l.T)
landmarkHR.append((dst[0], dst[1]))
# print landmarkHR
# drawPointsOnImg(landmarkHR, imgHR, 'r')
return landmarkHR
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_MeshHair():
'''1. read obj and img'''
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. 2D face landmark'''
oriFaceLandmark2DList = getLandmark2D(img)
'''3. create source hair region mesh'''
gridSize = 15
rows, cols = img.shape[0], img.shape[1]
src_rows = np.linspace(0, rows, gridSize) # 10 rows
src_cols = np.linspace(0, cols, gridSize) # 20 columns
src_rows, src_cols = np.meshgrid(src_rows, src_cols)
src = np.dstack([src_cols.flat, src_rows.flat])[0]
'''3.1 create head region ellipse'''
'''!!!手动修改了椭圆中心点，因为川普本来就是侧脸!!!'''
ellipseParam = {
'ellipseCenterX': oriFaceLandmark2DList[ELLIPSE_CENTER][1],
'ellipseCenterY': oriFaceLandmark2DList[ELLIPSE_CENTER][0] - 10,
'ellipseSemiX': 70,
'ellipseSemiY': 90,
'orientation': np.pi * 1.5
}
rr, cc = ellipse_perimeter(ellipseParam['ellipseCenterY'],
ellipseParam['ellipseCenterX'],
ellipseParam['ellipseSemiY'],
ellipseParam['ellipseSemiX'],
orientation=ellipseParam['orientation'])
'''3.2 mesh points in ellipse'''
ellipseVerts = np.dstack([rr, cc])[0]
'''3.3 add outer ellipse edge points'''
edgePoints, outerEllipseVerts = addOuterEllipseEdgeLandmark(
ellipseParam, img)
# drawPointsOnImg(ellipseVerts, img, 'g', cover=True)
# drawPointsOnImg(edgePoints, img, 'r')
mask = points_in_poly(src, ellipseVerts)
pointsInEllipseList = []
indexInEllipseList = []
for i, (s, m) in enumerate(zip(src, mask)):
if m == True:
pointsInEllipseList.append(s)
indexInEllipseList.append(i)
assert len(pointsInEllipseList) == len(indexInEllipseList)
'''3.3 mesh points in face region'''
faceVerts = []
for FACE_CONTOUR_INDICE in FACE_CONTOUR_INDICES:
faceVerts.append([
oriFaceLandmark2DList[FACE_CONTOUR_INDICE][0],
oriFaceLandmark2DList[FACE_CONTOUR_INDICE][1]
])
mask = points_in_poly(src, faceVerts)
pointsInFaceList = []
indexInFaceList = []
for i, (s, m) in enumerate(zip(src, mask)):
if m == True:
pointsInFaceList.append(s)
indexInFaceList.append(i)
assert len(pointsInFaceList) == len(indexInFaceList)
'''3.4 mesh points between (face region) and (ellipse)'''
for i, ((pointInEllipseX, pointInEllipseY), indexInEllise) in enumerate(
zip(pointsInEllipseList, indexInEllipseList)):
for (pointInFaceX, pointInFaceY) in pointsInFaceList:
if pointInEllipseX == pointInFaceX and pointInEllipseY == pointInFaceY:
pointsInEllipseList[i] = np.array([-1, -1])
indexInEllipseList[i] = -1
pointsInHairList = []
for (pointInEllipseX, pointInEllipseY) in pointsInEllipseList:
if pointInEllipseX != -1 and pointInEllipseY != -1:
pointsInHairList.append([pointInEllipseX, pointInEllipseY])
indexInHairList = [x for x in indexInEllipseList if x != -1]
assert len(pointsInHairList) == len(indexInHairList)
'''3.5 mesh points above minY'''
minY = minYInLandmark(oriFaceLandmark2DList)
hairMeshPointsInEllipseList = []
hairMeshIndexInEllipseList = []
for (s, i) in zip(pointsInHairList, indexInHairList):
srcY = s[1]
srcX = s[0]
if srcY < minY and srcY >= 0:
hairMeshPointsInEllipseList.append((srcX, srcY))
hairMeshIndexInEllipseList.append(i)
assert len(hairMeshPointsInEllipseList) == len(hairMeshIndexInEllipseList)
# drawPointsOnImg(hairMeshPointsInEllipseList, img, 'r')
'''4. 2D hair mesh points --> 3D hair mesh points'''
'''4.1 3D hair landmark list: (x, y, z)'''
# maxminDict = maxminXYZ(objLines)
# minZ = float(maxminDict['maxZCoord'][2])
minZ = 60.0
hairLandmark3DList = []
for hairLandmark2D in hairMeshPointsInEllipseList:
hairLandmark3D = (float(hairLandmark2D[0]), float(hairLandmark2D[1]),
minZ)
hairLandmark3DList.append(hairLandmark3D)
'''4.2 hair landmark color list: (r, g, b)'''
colorList = []
for hairLandmark2D in hairMeshPointsInEllipseList:
r = img[int(hairLandmark2D[0]), int(hairLandmark2D[1]), 2] / 255.0
g = img[int(hairLandmark2D[0]), int(hairLandmark2D[1]), 1] / 255.0
b = img[int(hairLandmark2D[0]), int(hairLandmark2D[1]), 0] / 255.0
color = (r, g, b)
colorList.append(color)
'''4.3 3D vLines'''
hairVLines = []
for (x, y, z), (r, g, b) in zip(hairLandmark3DList, colorList):
hairVLine = 'v {} {} {} {} {} {}'.format(x, y, z, r, g, b)
hairVLines.append(hairVLine)
'''4.4 write vLines txt'''
hairVLinesTxtPath = './hairVLines.txt'
hairVLinesTxt = open(hairVLinesTxtPath, 'w')
for hairVLine in hairVLines:
hairVLinesTxt.write(hairVLine + '\n')
hairVLinesTxt.close()
'''5. nod hair'''
nodAngle = 15
nodCenterMode = 'maxY'
nodHairVLines = nodHair(objPath, nodAngle, nodCenterMode, hairVLines,
hairVLinesTxtPath)
'''6. 2D nod hair landmark'''
nodHairLandmark2DList = []
for nodHairVLine in nodHairVLines:
_, x, y, _, _, _, _ = nodHairVLine.split()
nodHairLandmark2DList.append((float(x), float(y)))
assert len(hairMeshPointsInEllipseList) == len(nodHairLandmark2DList)
# print hairMeshPointsInEllipseList
# print nodHairLandmark2DList
return hairMeshPointsInEllipseList, nodHairLandmark2DList, edgePoints, ellipseVerts, outerEllipseVerts
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 mainMeshWarp():
'''1. read image and load new landmarks (nod)'''
image = cv2.imread('../../github/vrn-07231340/examples/trump-12.jpg')
imageTmp = copy.deepcopy(image)
rows, cols = image.shape[0], image.shape[1]
originLandmark2D = getLandmark2D(image)
targetLandmark2DPath = '../../data/talkingphoto/IMG_2294/IMG_2294_26.png.txt'
targetLandmark2D = readPoints(targetLandmark2DPath)[:68]
triTxtPath = './meshTri_640_640(trump-12).txt'
'''2. 新建source mesh'''
gridSize = 50
src_rows = np.linspace(0, rows, gridSize) # 10 行
src_cols = np.linspace(0, cols, gridSize) # 20 列
src_rows, src_cols = np.meshgrid(src_rows, src_cols)
src = np.dstack([src_cols.flat, src_rows.flat])[0]
'''!!! IMPORTANT DEEPCOPY!!!'''
dst = copy.deepcopy(src)
'''3. SKIMAGE画一个椭圆, 并且得到椭圆内所有的网格点'''
rr, cc = ellipse_perimeter(originLandmark2D[ELLIPSE_CENTER][1],
originLandmark2D[ELLIPSE_CENTER][0], 200, 260, orientation=30)
tmp = rr
rr = cc
cc = tmp
# print rr.shape, cc.shape
ellipseVerts = np.dstack([rr, cc])[0] # 椭圆边长上所有点
# print ellipseVerts
mask = points_in_poly(src, ellipseVerts)
pointsInEllipseList = []
indexInEllipseList = []
for i, (s, m) in enumerate(zip(src, mask)):
if m == True:
pointsInEllipseList.append(s)
indexInEllipseList.append(i)
# print len(indexInEllipseList)
# x=pointsInEllipseList[i][1], y=pointsInEllipseList[i][0]
pointsInEllipseArray = np.asarray(pointsInEllipseList)
'''swap collums of pointsInEllipseList'''
# x=pointsInEllipseList[i][0], y=pointsInEllipseList[i][1]
# pointsInEllipseArray[:, [0, 1]] = pointsInEllipseArray[:, [1, 0]]
# image[cc, rr] = 255 # draw ellipse perimeter on image
drawPointsOnImg(pointsInEllipseArray, imageTmp, 'r')
'''4. compute delta (68) of each new landmark X(Y) and old landmark X(Y)'''
deltaAllLandmarksList = []
for i in range(len(targetLandmark2D)):
deltaAllLandmarksList.append(
[targetLandmark2D[i][0]-originLandmark2D[i][0], targetLandmark2D[i][1]-originLandmark2D[i][1]])
# deltaAllLandmarksArray = np.asarray(deltaAllLandmarksList)
# print deltaAllLandmarksList
'''5. compute delta of each point in ellipse'''
radius = 5*rows/float(gridSize)
targetPointsInEllipseList = []
for meshPoint in pointsInEllipseArray:
_, _, landmarksInSmallGridArray, deltaInSmallGridArray = getLandmarksInSmallGrid(
meshPoint, originLandmark2D, deltaAllLandmarksList, radius=radius)
'''画smallGrid以及其内的所有landmarks的delta'''
# imageTmp = copy.deepcopy(image)
# cv2.circle(imageTmp, (int(meshPoint[0]),
# int(meshPoint[1])), int(radius), (0, 255, 0), 2)
# if landmarksInSmallGridArray.shape[0] != 0:
# for (deltaX, deltaY), (landmarkX, landmarkY) in zip(deltaInSmallGridArray, landmarksInSmallGridArray):
# cv2.line(imageTmp, (int(landmarkX), int(landmarkY)), (int(
# landmarkX+deltaX), int(landmarkY+deltaY)), (255, 0, 0), 2)
# cv2.circle(imageTmp, (int(landmarkX+deltaX),
# int(landmarkY+deltaY)), 2, (0, 0, 255), -1)
# cv2.imshow('imgTmp', imageTmp)
# cv2.waitKey(50)
deltaXOfMeshPoint = 0
deltaYOfMeshPoint = 0
if deltaInSmallGridArray.shape[0] != 0:
for i, (deltaInSmallGrid, landmarkInSmallGrid) in enumerate(zip(deltaInSmallGridArray, landmarksInSmallGridArray)):
deltaInSmallGridX = deltaInSmallGrid[0]
deltaInSmallGridY = deltaInSmallGrid[1]
deltaXOfMeshPoint = deltaXOfMeshPoint + deltaInSmallGridX / \
twoPointsDistance(meshPoint, landmarkInSmallGrid)
deltaYOfMeshPoint = deltaYOfMeshPoint + deltaInSmallGridY / \
twoPointsDistance(meshPoint, landmarkInSmallGrid)
targetMeshPointX = meshPoint[0]+deltaXOfMeshPoint
targetMeshPointY = meshPoint[1]+deltaYOfMeshPoint
'''画每一个meshPoint的起点和终点'''
# cv2.line(imageTmp, (int(meshPoint[0]), int(meshPoint[1])),
# (int(targetMeshPointX), int(targetMeshPointY)), (0, 255, 0), 2)
# cv2.circle(imageTmp, (int(meshPoint[0]), int(meshPoint[1])),
# 3, (0, 255, 0), -1)
# cv2.circle(imageTmp, (int(targetMeshPointX), int(targetMeshPointY)),
# 2, (0, 0, 255), -1)
# cv2.imshow('imageTmp', imageTmp)
# cv2.waitKey(0)
targetPointsInEllipseList.append([targetMeshPointX, targetMeshPointY])
targetPointsInEllipseArray = np.asarray(targetPointsInEllipseList)
# drawPointsOnImg(pointsInEllipseArray, imageTmp, 'b')
# drawPointsOnImg(targetPointsInEllipseArray, imageTmp, 'r')
'''6. compute final target mesh'''
# print targetPointsInEllipseArray.shape
drawPointsOnImg(targetPointsInEllipseArray, imageTmp, 'b')
# targetPointsInEllipseArray[:, [0, 1]
# ] = targetPointsInEllipseArray[:, [1, 0]]
dst[indexInEllipseList] = targetPointsInEllipseArray
'''draw src and dst mesh on image'''
# drawPointsOnImg(src, imageTmp, 'g')
# drawPointsOnImg(dst, imageTmp, 'b')
cv2.imwrite('./tmp.png', imageTmp)
'''7. PiecewiseAffineTransform from skimage'''
# tform = PiecewiseAffineTransform()
# tform.estimate(dst, src)
# out = warp(image, tform)
'''8. imshow and imwrite'''
# imshow('out_pat', out)
# cv2.imwrite('./out_pat.png', out*255)
# cv2.imwrite('./ori.jpg', image)
# # fig, ax = plt.subplots()
# # ax.imshow(out)
# # ax.scatter(pointsInEllipseArray[:, 0], pointsInEllipseArray[:, 1],
# # marker='+', color='b', s=5)
# # ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.r')
# # plt.show()
'''7. mesh warp wirtten by meself'''
'''draw dst triangle'''
imgTmp = copy.copy(image)
triTxt = open(triTxtPath, 'r')
# for line in triTxt:
# a, b, c = line.split()
# a = int(a)
# b = int(b)
# c = int(c)
# z1 = (int(dst[a][0]), int(dst[a][1]))
# z2 = (int(dst[b][0]), int(dst[b][1]))
# z3 = (int(dst[c][0]), int(dst[c][1]))
# cv2.line(imgTmp, z1, z2, (255, 255, 255), 1)
# cv2.line(imgTmp, z2, z3, (255, 255, 255), 1)
# cv2.line(imgTmp, z3, z1, (255, 255, 255), 1)
# cv2.imshow('tmp', imgTmp)
# cv2.waitKey(1)
imgMorph = morph_modify_for_meshwarp(src, dst, image, triTxtPath)
'''8. imshow and imwrite'''
imshow('imgMorph', imgMorph)
cv2.imwrite('./out_wo.png', imgMorph)
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 mainMeshWarp():
'''1. read image and load new landmarks (nod)'''
tmp = cv2.imread('./tmp.png')
image = cv2.imread('../../github/vrn-07231340/examples/trump-12.jpg')
rows, cols = image.shape[0], image.shape[1]
originLandmark2D = getLandmark2D(image)
targetLandmark2DPath = '../../data/talkingphoto/IMG_2294/IMG_2294_26.png.txt'
targetLandmark2D = readPoints(targetLandmark2DPath)[:68]
'''2. 新建source mesh'''
gridSize = 50
src_rows = np.linspace(0, rows, 10) # 10 行
src_cols = np.linspace(0, cols, gridSize) # 20 列
src_rows, src_cols = np.meshgrid(src_rows, src_cols)
src = np.dstack([src_cols.flat, src_rows.flat])[0]
'''!!! IMPORTANT DEEPCOPY!!!'''
dst = copy.deepcopy(src)
'''3. SKIMAGE画一个椭圆, 并且得到椭圆内所有的网格点'''
rr, cc = ellipse_perimeter(originLandmark2D[NOSE_CENTER][1],
originLandmark2D[NOSE_CENTER][0], 180, 300, orientation=30)
# print rr.shape, cc.shape
ellipseVerts = np.dstack([rr, cc])[0] # 椭圆边长上所有点
mask = points_in_poly(src, ellipseVerts)
pointsInEllipseList = []
indexInEllipseList = []
for i, (s, m) in enumerate(zip(src, mask)):
if m == True:
pointsInEllipseList.append(s)
indexInEllipseList.append(i)
# print len(indexInEllipseList)
# x=pointsInEllipseList[i][1], y=pointsInEllipseList[i][0]
pointsInEllipseArray = np.asarray(pointsInEllipseList)
'''swap collums of pointsInEllipseList'''
# x=pointsInEllipseList[i][0], y=pointsInEllipseList[i][1]
# pointsInEllipseArray[:, [0, 1]] = pointsInEllipseArray[:, [1, 0]]
image[rr, cc] = 255 # draw ellipse perimeter on image
# drawPointsOnImg(pointsInEllipseArray, image, 'r')
'''4. compute delta (68) of each new landmark X(Y) and old landmark X(Y)'''
deltaAllLandmarksList = []
for i in range(len(targetLandmark2D)):
deltaAllLandmarksList.append(
[targetLandmark2D[i][0]-originLandmark2D[i][0], targetLandmark2D[i][1]-originLandmark2D[i][1]])
# deltaAllLandmarksArray = np.asarray(deltaAllLandmarksList)
# print deltaAllLandmarksList
'''5. compute delta of each point in ellipse'''
radius = 5*rows/float(gridSize)
targetPointsInEllipseList = []
for meshPoint in pointsInEllipseArray:
_, _, landmarksInSmallGridArray, deltaInSmallGridArray = getLandmarksInSmallGrid(
meshPoint, originLandmark2D, deltaAllLandmarksList, radius=radius)
'''画smallGrid以及其内的所有landmarks的delta'''
# imageTmp = copy.deepcopy(image)
# cv2.circle(imageTmp, (int(meshPoint[0]),
# int(meshPoint[1])), int(radius), (0, 255, 0), 2)
# if landmarksInSmallGridArray.shape[0] != 0:
# for (deltaX, deltaY), (landmarkX, landmarkY) in zip(deltaInSmallGridArray, landmarksInSmallGridArray):
# cv2.line(imageTmp, (int(landmarkX), int(landmarkY)), (int(
# landmarkX+deltaX), int(landmarkY+deltaY)), (255, 0, 0), 2)
# cv2.circle(imageTmp, (int(landmarkX+deltaX),
# int(landmarkY+deltaY)), 2, (0, 0, 255), -1)
# cv2.imshow('imgTmp', imageTmp)
# cv2.waitKey(0)
deltaXOfMeshPoint = 0
deltaYOfMeshPoint = 0
if deltaInSmallGridArray.shape[0] != 0:
for i, (deltaInSmallGrid, landmarkInSmallGrid) in enumerate(zip(deltaInSmallGridArray, landmarksInSmallGridArray)):
deltaInSmallGridX = deltaInSmallGrid[0]
deltaInSmallGridY = deltaInSmallGrid[1]
deltaXOfMeshPoint = deltaXOfMeshPoint + deltaInSmallGridX / \
twoPointsDistance(meshPoint, landmarkInSmallGrid)
deltaYOfMeshPoint = deltaYOfMeshPoint + deltaInSmallGridY / \
twoPointsDistance(meshPoint, landmarkInSmallGrid)
targetMeshPointX = meshPoint[0]+deltaXOfMeshPoint
targetMeshPointY = meshPoint[1]+deltaYOfMeshPoint
'''画每一个meshPoint的起点和终点'''
# imageTmp = copy.deepcopy(tmp)
# cv2.line(imageTmp, (int(meshPoint[0]), int(meshPoint[1])),
# (int(targetMeshPointX), int(targetMeshPointY)), (0, 255, 0), 2)
# cv2.circle(imageTmp, (int(meshPoint[0]), int(meshPoint[1])),
# 3, (0, 255, 0), -1)
# cv2.circle(imageTmp, (int(targetMeshPointX), int(targetMeshPointY)),
# 2, (0, 0, 255), -1)
# cv2.imshow('imageTmp', imageTmp)
# cv2.waitKey(0)
targetPointsInEllipseList.append([targetMeshPointX, targetMeshPointY])
targetPointsInEllipseArray = np.asarray(targetPointsInEllipseList)
# drawPointsOnImg(pointsInEllipseArray, image, 'b')
# drawPointsOnImg(targetPointsInEllipseArray, image, 'r')
'''6. compute final target mesh'''
print targetPointsInEllipseArray.shape
drawPointsOnImg(targetPointsInEllipseArray, image, 'g')
# targetPointsInEllipseArray[:, [0, 1]
# ] = targetPointsInEllipseArray[:, [1, 0]]
# dst[indexInEllipseList] = targetPointsInEllipseArray
# dst[:, [0, 1]] = dst[:, [1, 0]]
'''draw src and dst mesh on image'''
drawPointsOnImg(src, image, 'b')
drawPointsOnImg(dst, image, 'r')
cv2.imwrite('./tmp.png', image)
'''7. PiecewiseAffineTransform'''
tform = PiecewiseAffineTransform()
# for i, s, d in zip(indexInEllipseList, src, dst):
# print src[i], dst[i]
tform.estimate(src, dst)
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
out = warp(image, tform)
cv2.imwrite('./out.jpg', out*255)
cv2.imwrite('./ori.jpg', image)
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