def averager(imgpaths,
dest_filename=None,
width=500,
height=600,
background='black',
blur_edges=False,
out_filename='result.png',
plot=False):
size = (height, width)
images = []
point_set = []
for path in imgpaths:
img, points = load_image_points(path, size)
if img is not None:
images.append(img)
point_set.append(points)
if len(images) == 0:
raise FileNotFoundError('Could not find any valid work.' +
' Supported formats are .jpg, .png, .jpeg')
if dest_filename is not None:
dest_img, dest_points = load_image_points(dest_filename, size)
if dest_img is None or dest_points is None:
raise Exception('No face or detected face points in dest img: ' +
dest_filename)
else:
dest_img = np.zeros(images[0].shape, np.uint8)
dest_points = locator.average_points(point_set)
num_images = len(images)
result_images = np.zeros(images[0].shape, np.float32)
for i in range(num_images):
result_images += warper.warp_image(images[i], point_set[i],
dest_points, size, np.float32)
result_image = np.uint8(result_images / num_images)
face_indexes = np.nonzero(result_image)
dest_img[face_indexes] = result_image[face_indexes]
mask = blender.mask_from_points(size, dest_points)
if blur_edges:
blur_radius = 10
mask = cv2.blur(mask, (blur_radius, blur_radius))
if background in ('transparent', 'average'):
dest_img = np.dstack((dest_img, mask))
if background == 'average':
average_background = locator.average_points(images)
dest_img = blender.overlay_image(dest_img, mask,
average_background)
print('Averaged {} work'.format(num_images))
plt = plotter.Plotter(plot, num_images=1, out_filename=out_filename)
plt.save(dest_img)
plt.plot_one(dest_img)
plt.show()
def averager(imgpaths, width=500, height=600, alpha=False,
blur_edges=False, out_filename='result.png', plot=False):
size = (height, width)
images = []
point_set = []
for path in imgpaths:
img, points = load_image_points(path, size)
if img is not None:
images.append(img)
point_set.append(points)
ave_points = locator.average_points(point_set)
num_images = len(images)
result_images = np.zeros(images[0].shape, np.float32)
for i in xrange(num_images):
result_images += warper.warp_image(images[i], point_set[i],
ave_points, size, np.float32)
result_image = np.uint8(result_images / num_images)
mask = blender.mask_from_points(size, ave_points)
if blur_edges:
blur_radius = 10
mask = cv2.blur(mask, (blur_radius, blur_radius))
if alpha:
result_image = np.dstack((result_image, mask))
mpimg.imsave(out_filename, result_image)
if plot:
plt.axis('off')
plt.imshow(result_image)
plt.show()
def averager(imgpaths, width=500, height=600, alpha=False,
blur_edges=False, out_filename='result.png', plot=False):
size = (height, width)
images = []
point_set = []
for path in imgpaths:
img, points = load_image_points(path, size)
if img is not None:
images.append(img)
point_set.append(points)
ave_points = locator.average_points(point_set)
num_images = len(images)
result_images = np.zeros(images[0].shape, np.float32)
for i in range(num_images):
result_images += warper.warp_image(images[i], point_set[i],
ave_points, size, np.float32)
result_image = np.uint8(result_images / num_images)
mask = blender.mask_from_points(size, ave_points)
if blur_edges:
blur_radius = 10
mask = cv2.blur(mask, (blur_radius, blur_radius))
if alpha:
result_image = np.dstack((result_image, mask))
mpimg.imsave(out_filename, result_image)
if plot:
plt.axis('off')
plt.imshow(result_image)
plt.show()
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 averager(imgpaths,
dest_filename=None,
width=500,
height=600,
alpha=False,
blur_edges=False,
out_filename='result.png',
plot=False):
size = (height, width)
images = []
point_set = []
for path in imgpaths:
img, points = load_image_points(path, size)
if img is not None:
images.append(img)
point_set.append(points)
if len(images) == 0:
raise FileNotFoundError('Could not find any valid images.' +
' Supported formats are .jpg, .png, .jpeg')
if dest_filename is not None:
dest_img, dest_points = load_image_points(dest_filename, size)
if dest_img is None or dest_points is None:
raise Exception('No face or detected face points in dest img: ' +
dest_filename)
else:
dest_img = np.zeros(images[0].shape, np.uint8)
dest_points = locator.average_points(point_set)
num_images = len(images)
result_images = np.zeros(images[0].shape, np.float32)
for i in range(num_images):
result_images += warper.warp_image(images[i], point_set[i],
dest_points, size, np.float32)
result_image = np.uint8(result_images / num_images)
face_indexes = np.nonzero(result_image)
dest_img[face_indexes] = result_image[face_indexes]
mask = blender.mask_from_points(size, dest_points)
if blur_edges:
blur_radius = 10
mask = cv2.blur(mask, (blur_radius, blur_radius))
if alpha:
dest_img = np.dstack((dest_img, mask))
mpimg.imsave(out_filename, dest_img)
if plot:
plt.axis('off')
plt.imshow(dest_img)
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()
def alpha_image(img, points): mask = blender.mask_from_points(img.shape[:2], points) return np.dstack((img, mask))
def alpha_image(img, points):
mask = blender.mask_from_points(img.shape[:2], points)
return np.dstack((img, mask))
