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 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 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,
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 averager(imgpaths, resultFolder, width=500, height=600, alpha=False, blur_edges=False, useDb=False):
#startTime = time.time()
size = (height, width)
images = []
point_set = []
if useDb:
#con = sqlite3.connect(os.path.join(alignedFolder, 'points.sqlite'), detect_types=sqlite3.PARSE_DECLTYPES)
with con:
cur = con.cursor()
for path in imgpaths:
img = scipy.ndimage.imread(path)[..., :3]
if img is not None:
filename = os.path.basename(path)
data = cur.execute('SELECT points FROM entries WHERE name=?', ([filename])).fetchone()
if data:
images.append(img)
point_set.append(convert_array(data[0]))
else:
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):
#print '{0} of {1}'.format(i+1, num_images)
result_images += warper.warp_image(images[i], point_set[i],
ave_points, size, np.float32)
# Store all images
result_image = np.uint8(result_images / (i+1))
# mpimg.imsave(os.path.join(resultFolder, filenames[i]), result_image)
mpimg.imsave(os.path.join(resultFolder, "{:05d}.jpg".format(i)), result_image)
# result_image = np.uint8(result_images / num_images)
#result_image = np.uint8(result_images)
#mpimg.imsave(os.path.join(resultFolder, "result_raw.jpg"), result_image)
result_image = np.uint8(result_images / num_images)
mpimg.imsave(os.path.join(resultFolder, "result.jpg"), result_image)