def morph(name, src_img, src_points, dest_img, dest_points, width=500, height=600, num_frames=20, fps=10,
out_frames=None, plot=False, background='black'):
"""
Create a morph sequence from source to destination image
:param src_img: ndarray source image
:param src_points: source image array of x,y face points
:param dest_img: ndarray destination image
:param dest_points: destination image array of x,y face points
:param video: facemorpher.videoer.Video object
"""
size = (height, width)
stall_frames = np.clip(int(fps*0.15), 1, fps) # Show first & last longer
plt = plotter.Plotter(plot, num_images=num_frames, out_folder=out_frames)
num_frames -= (stall_frames * 2) # No need to process src and dest image
plt.plot_one(src_img)
# Produce morph frames!
percent = np.linspace(1, 0, num=num_frames)
points = locator.weighted_average_points(src_points, dest_points, percent[8])
src_face = warper.warp_image(src_img, src_points, points, size)
end_face = warper.warp_image(dest_img, dest_points, points, size)
average_face = blender.weighted_average(src_face, end_face, percent[8])
if background in ('transparent', 'average'):
mask = blender.mask_from_points(average_face.shape[:2], points)
average_face = np.dstack((average_face, mask))
if background == 'average':
average_background = blender.weighted_average(src_img, dest_img, percent[8])
average_face = blender.overlay_image(average_face, mask, average_background)
#plt.plot_one(average_face)
#name = src_img[-11:-4]+"_"+dest_img[-11:-4]+".png"
plt.save(average_face,name)
def morph(src_img, src_points, dest_img, dest_points,
video, width=500, height=600, num_frames=20, fps=10,
out_frames=None, out_video=None, alpha=False, plot=False):
"""
Create a morph sequence from source to destination image
:param src_img: ndarray source image
:param src_img: source image array of x,y face points
:param dest_img: ndarray destination image
:param dest_img: destination image array of x,y face points
:param video: facemorpher.videoer.Video object
"""
size = (height, width)
stall_frames = np.clip(int(fps*0.15), 1, fps) # Show first & last longer
plt = plotter.Plotter(plot, num_images=num_frames, folder=out_frames)
num_frames -= (stall_frames * 2) # No need to process src and dest image
plt.plot_one(src_img)
video.write(src_img, stall_frames)
# Produce morph frames!
for percent in np.linspace(1, 0, num=num_frames):
points = locator.weighted_average_points(src_points, dest_points, percent)
src_face = warper.warp_image(src_img, src_points, points, size)
end_face = warper.warp_image(dest_img, dest_points, points, size)
average_face = blender.weighted_average(src_face, end_face, percent)
# Comment to remove background
#average_face = alpha_image(average_face, points) if alpha else average_face
plt.plot_one(average_face, 'save')
video.write(average_face)
plt.plot_one(dest_img)
video.write(dest_img, stall_frames)
plt.show()
def morph(src_img, src_points, dest_img, dest_points,
video, width=500, height=600, num_frames=20, fps=10,
out_frames=None, out_video=None, alpha=False, plot=False):
"""
Create a morph sequence from source to destination image
:param src_img: ndarray source image
:param src_img: source image array of x,y face points
:param dest_img: ndarray destination image
:param dest_img: destination image array of x,y face points
:param video: facemorpher.videoer.Video object
"""
size = (height, width)
stall_frames = np.clip(int(fps*0.15), 1, fps) # Show first & last longer
plt = plotter.Plotter(plot, num_images=num_frames, folder=out_frames)
num_frames -= (stall_frames * 2) # No need to process src and dest image
plt.plot_one(src_img)
video.write(src_img, stall_frames)
# Produce morph frames!
for percent in np.linspace(1, 0, num=num_frames):
points = locator.weighted_average_points(src_points, dest_points, percent)
src_face = warper.warp_image(src_img, src_points, points, size)
end_face = warper.warp_image(dest_img, dest_points, points, size)
average_face = blender.weighted_average(src_face, end_face, percent)
average_face = alpha_image(average_face, points) if alpha else average_face
plt.plot_one(average_face, 'save')
video.write(average_face)
plt.plot_one(dest_img)
video.write(dest_img, stall_frames)
plt.show()
def test_local():
from functools import partial
import cv2
import scipy.misc
import locator
import aligner
from matplotlib import pyplot as plt
# Load source image
face_points_func = partial(locator.face_points, '../data')
base_path = '../females/Screenshot 2015-03-04 17.11.12.png'
src_path = '../females/BlDmB5QCYAAY8iw.jpg'
src_img = cv2.imread(src_path)
# Define control points for warps
src_points = face_points_func(src_path)
base_img = cv2.imread(base_path)
base_points = face_points_func(base_path)
size = (600, 500)
src_img, src_points = aligner.resize_align(src_img, src_points, size)
base_img, base_points = aligner.resize_align(base_img, base_points, size)
result_points = locator.weighted_average_points(src_points, base_points,
0.2)
# Perform transform
dst_img1 = warp_image(src_img, src_points, result_points, size)
dst_img2 = warp_image(base_img, base_points, result_points, size)
import blender
ave = blender.weighted_average(dst_img1, dst_img2, 0.6)
mask = blender.mask_from_points(size, result_points)
blended_img = blender.poisson_blend(dst_img1, dst_img2, mask)
plt.subplot(2, 2, 1)
plt.imshow(ave)
plt.subplot(2, 2, 2)
plt.imshow(dst_img1)
plt.subplot(2, 2, 3)
plt.imshow(dst_img2)
plt.subplot(2, 2, 4)
plt.imshow(blended_img)
plt.show()
def test_local(): from functools import partial import scipy.ndimage import scipy.misc import locator import aligner from matplotlib import pyplot as plt # Load source image face_points_func = partial(locator.face_points, '../data') base_path = '../females/Screenshot 2015-03-04 17.11.12.png' src_path = '../females/BlDmB5QCYAAY8iw.jpg' src_img = scipy.ndimage.imread(src_path)[:, :, :3] # Define control points for warps src_points = face_points_func(src_path) base_img = scipy.ndimage.imread(base_path)[:, :, :3] base_points = face_points_func(base_path) size = (600, 500) src_img, src_points = aligner.resize_align(src_img, src_points, size) base_img, base_points = aligner.resize_align(base_img, base_points, size) result_points = locator.weighted_average_points(src_points, base_points, 0.2) # Perform transform dst_img1 = warp_image(src_img, src_points, result_points, size) dst_img2 = warp_image(base_img, base_points, result_points, size) print 'blending' import blender ave = blender.weighted_average(dst_img1, dst_img2, 0.6) mask = blender.mask_from_points(size, result_points) blended_img = blender.poisson_blend(dst_img1, dst_img2, mask) plt.subplot(2, 2, 1) plt.imshow(ave) plt.subplot(2, 2, 2) plt.imshow(dst_img1) plt.subplot(2, 2, 3) plt.imshow(dst_img2) plt.subplot(2, 2, 4) plt.imshow(blended_img) plt.show()