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 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 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 {} images'.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,
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()