def affine_example():
# set from points to corners of im1
m, n = im1.shape[:2]
fp = np.array([[0, m, m, 0], [0, 0, n, n], [1, 1, 1, 1]])
# first triangle
tp2 = tp[:, :3]
fp2 = fp[:, :3]
# compute H
H = homography.Haffine_from_points(tp2, fp2)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
# alpha for triangle
alpha = warp.alpha_for_triangle(tp2, im2.shape[0], im2.shape[1])
im3 = (1 - alpha) * im2 + alpha * im1_t
# second triangle
tp2 = tp[:, [0, 2, 3]]
fp2 = fp[:, [0, 2, 3]]
# compute H
H = homography.Haffine_from_points(tp2, fp2)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
# alpha for triangle
alpha = warp.alpha_for_triangle(tp2, im2.shape[0], im2.shape[1])
im4 = (1 - alpha) * im3 + alpha * im1_t
figure()
gray()
imshow(im4)
axis('equal')
axis('off')
show()
def image_in_image(im1, im2, tp):
""" Put im1 in im2 with an affine transformation
such that corners are as close to tp as possible.
tp are homogeneous and counter-clockwise from top left. """
# points to warp from
m, n = im1.shape[:2]
fp = array([[0, m, m, 0], [0, 0, n, n], [1, 1, 1, 1]])
#first triangle
tp2 = tp[:, :3]
fp2 = fp[:, :3]
# compute affine transform and apply
H = homography.Haffine_from_points(tp2, fp2)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
# alpha for triangle
alpha = alpha_for_triangle(tp2, im2.shape[0], im2.shape[1])
im3 = (1 - alpha) * im2 + alpha * im1_t
#second triangle
tp2 = tp[:, [0, 2, 3]]
fp2 = fp[:, [0, 2, 3]]
# compute H
H = homography.Haffine_from_points(tp2, fp2)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
# alpha for triangle
alpha = alpha_for_triangle(tp2, im2.shape[0], im2.shape[1])
return (1 - alpha) * im3 + alpha * im1_t
def pw_affine(fromim,toim,fp,tp,tri):
""" Warp triangular patches from an image.
fromim = image to warp
toim = destination image
fp = from points in hom. coordinates
tp = to points in hom. coordinates
tri = triangulation. """
im = toim.copy()
# check if image is grayscale or color
is_color = len(fromim.shape) == 3
# create image to warp to (needed if iterate colors)
im_t = zeros(im.shape, 'uint8')
for t in tri:
# compute affine transformation
H = homography.Haffine_from_points(tp[:,t],fp[:,t])
if is_color:
for col in range(fromim.shape[2]):
im_t[:,:,col] = ndimage.affine_transform(
fromim[:,:,col],H[:2,:2],(H[0,2],H[1,2]),im.shape[:2])
else:
im_t = ndimage.affine_transform(
fromim,H[:2,:2],(H[0,2],H[1,2]),im.shape[:2])
# alpha for triangle
alpha = alpha_for_triangle(tp[:,t],im.shape[0],im.shape[1])
# add triangle to image
im[alpha>0] = im_t[alpha>0]
return im
def image_in_image(im1, im2, tp):
""" Put im1 in im2 with an affine transformation
such that corners are as close to tp as possible.
tp are homogeneous and counter-clockwise from top left. """
# points to warp from
m, n = im1.shape[:2]
fp = np.array([[0, m, m, 0], [0, 0, n, n], [1, 1, 1, 1]])
# compute affine transform and apply
H = homography.Haffine_from_points(tp, fp)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
alpha = (im1_t > 0)
return (1 - alpha) * im2 + alpha * im1_t
def image_in_image(im1, im2, tp):
""" 使用仿射变换将 im1 放置在 im2 上，使 im1 图像的角和 tp 尽可能的靠近
tp 是齐次表示的，并且是按照从左上角逆时针计算的 """
# 扭曲的点
m, n = im1.shape[:2]
fp = array([[0, m, m, 0], [0, 0, n, n], [1, 1, 1, 1]])
# 计算仿射变换，并且将其应用于图像 im1
H = homography.Haffine_from_points(tp, fp)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
alpha = (im1_t > 0)
return (1 - alpha) * im2 + alpha * im1_t
def pw_affine(fromim, toim, fp, tp, tri):
""" 从一幅图像中扭曲矩形图像块
fromim= 将要扭曲的图像
toim= 目标图像
fp= 齐次坐标表示下，扭曲前的点
tp= 齐次坐标表示下，扭曲后的点
tri= 三角剖分 """
im = toim.copy()
# 检查图像是灰度图像还是彩色图象
is_color = len(fromim.shape) == 3
# 创建扭曲后的图像（如果需要对彩色图像的每个颜色通道进行迭代操作，那么有必要这样做）
im_t = zeros(im.shape, 'uint8')
for t in tri:
# 计算仿射变换
H = homography.Haffine_from_points(tp[:, t], fp[:, t])
if is_color:
for col in range(fromim.shape[2]):
im_t[:, :,
col] = ndimage.affine_transform(fromim[:, :,
col], H[:2, :2],
(H[0, 2], H[1, 2]),
im.shape[:2])
else:
im_t = ndimage.affine_transform(fromim, H[:2, :2],
(H[0, 2], H[1, 2]), im.shape[:2])
# 三角形的 alpha
alpha = alpha_for_triangle(tp[:, t], im.shape[0], im.shape[1])
# 将三角形加入到图像中
im[alpha > 0] = im_t[alpha > 0]
return im
subplot(142)
axis('off')
imshow(im2)
subplot(143)
axis('off')
imshow(im3)
# tp = array([[1650,1980,1980,1650],[1515,1510,2015,2015],[1,1,1,1]])
# set from points to corners of im1
m, n = im1.shape[:2]
fp = array([[0, m, m, 0], [0, 0, n, n], [1, 1, 1, 1]])
# first triangle
tp2 = tp[:, :3]
fp2 = fp[:, :3]
# compute H
H = homography.Haffine_from_points(tp2, fp2)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
# alpha for triangle
alpha = warp.alpha_for_triangle(tp2, im2.shape[0], im2.shape[1])
im3 = (1 - alpha) * im2 + alpha * im1_t
# second triangle
tp2 = tp[:, [0, 2, 3]]
fp2 = fp[:, [0, 2, 3]]
# compute H
H = homography.Haffine_from_points(tp2, fp2)
im1_t = ndimage.affine_transform(im1, H[:2, :2], (H[0, 2], H[1, 2]),
im2.shape[:2])
# alpha for triangle
alpha = warp.alpha_for_triangle(tp2, im2.shape[0], im2.shape[1])
im4 = (1 - alpha) * im3 + alpha * im1_t
import homography import numpy fp = numpy.array([[0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]]).T tp = numpy.array([[0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]]).T print homography.H_from_points(fp, tp) fp = numpy.array([[0, 0, 1], [1, 0, 1], [0, 1, 1]]).T tp = numpy.array([[0, 0, 1], [1, 0, 1], [0, 1, 1]]).T print homography.Haffine_from_points(fp, tp)
