def adjustBlenAlpha(*args):
global points1, points2, dt, imNorm1, imNorm2
alpha = float(args[0]) / 10
pointsMorph = (1 - alpha) * points1 + alpha * points2
imOut1 = fbc.warpImage(imNorm1, points1, pointsMorph.tolist(), dt)
imOut2 = fbc.warpImage(imNorm2, points2, pointsMorph.tolist(), dt)
imMorph = (1 - alpha) * imOut1 + alpha * imOut2
cv2.imshow("Morphed Face", imMorph)
def morphImages(alpha, imgNorm1, postions1, imgNorm2, postions2, dt):
pointsMorph = (1.0 - alpha) * points1 + alpha * points2
imOut1 = fbc.warpImage(imgNorm1, points1, pointsMorph, dt)
imOut2 = fbc.warpImage(imgNorm2, points2, pointsMorph, dt)
imMorph = (1 - alpha) * imOut1 + alpha * imOut2
imgShow = np.zeros((480, 480 * 3, 3), dtype=float)
imgShow = cv2.hconcat([imgNorm1, imgNorm2, imMorph])
cv2.imshow(winName, imgShow)
pointsAvg = pointsAvg + (points/(1.0*numImages))
# 添加边界点
points = np.concatenate((points, boundaryPts), axis=0)
pointsNorm.append(points)
imagesNorm.append(img)
pointsAvg = np.concatenate((pointsAvg, boundaryPts), axis=0)
# 计算delaunay三角形
rect = (0, 0, w, h)
dt = fbc.calculateDelaunayTriangles(rect, pointsAvg)
# 输出图像
output = np.zeros((h, w, 3), dtype=np.float)
for i in range(0, numImages):
imWarp = fbc.warpImage(imagesNorm[i], pointsNorm[i], pointsAvg.tolist(), dt)
output = output + imWarp
output = output/(1.0*numImages)
cv2.imshow("Result", output)
cv2.waitKey(0)
# Find forehead points appendForeheadPoints(points1) appendForeheadPoints(points2) # Find Delaunay Triangulation sizeImg1 = img1.shape rect = (0, 0, sizeImg1[1], sizeImg1[0]) dt = fbc.calculateDelaunayTriangles(rect, points1) # Convert image for warping img1 = np.float32(img1)/255.0 img2 = np.float32(img2)/255.0 # Warp wrinkle image to face image img1Warped = np.copy(img2) img1Warped = fbc.warpImage(img1, img1Warped, points1, points2, dt, useOutputImageSize=True) img1Warped = np.uint8(img1Warped*255.0) img2 = np.uint8(img2*255.0) # Calculate face mask for seamless cloning mask = getFaceMask(img2.shape[0:2], points2) # Seamlessly clone the wrinkle image onto original face r1 = cv2.boundingRect(np.float32(points2)) # Bounding rectangle r1 has 4 elements: left(x1), top(y1), width(w), height(h) center1X = r1[0] + int(r1[2]/2.0) center1Y = r1[1] + int(r1[3]/2.0) center1 = (center1X, center1Y) clonedOutput = cv2.seamlessClone(img1Warped, img2, mask, center1, cv2.MIXED_CLONE) # Blurring face mask to alpha blend to hide seams
pointsAvg = np.concatenate((pointsAvg, boundaryPoints), axis=0)
# Calculate Delaunay triangulation.
rect = (0, 0, w, h)
dt = fbc.calculateDelaunayTriangles(rect, pointsAvg)
# Start animation.
alpha = 0
increaseAlpha = True
while True:
# Compute landmark points based on morphing parameter alpha
pointsMorph = (1 - alpha) * points1 + alpha * points2
# Warp images such that normalized points line up with morphed points.
imOut1 = fbc.warpImage(imNorm1, points1, pointsMorph.tolist(), dt)
imOut2 = fbc.warpImage(imNorm2, points2, pointsMorph.tolist(), dt)
# Blend warped images based on morphing parameter alpha
imMorph = (1 - alpha) * imOut1 + alpha * imOut2
# Keep animating by ensuring alpha stays between 0 and 1.
if (alpha <= 0 and not increaseAlpha):
increaseAlpha = True
if (alpha >= 1 and increaseAlpha):
increaseAlpha = False
if increaseAlpha:
alpha += 0.025
else:
alpha -= 0.025