def generate(model, input_args):
encoder, decoder, latent_avg, manipulator = model
original = input_args['original']
input_size = [1024, 1024]
output = original.copy()
cropped, n_faces, quad = face_detection.align(original)
for k, v in editor.edits.items():
layer_index, channel_index, sense = v
conv_name = editor.idx_dict[layer_index]
manipulator.edits[conv_name][channel_index] = input_args[k]*sense
for i in range(n_faces):
# We already did the first one above
if i > 0:
cropped, _, quad = face_detection.align(original, face_index=i)
transformed_crop = editor.run(encoder, decoder, latent_avg, cropped)
output = face_detection.composite_images(quad, transformed_crop, output)
return output
def test_face_detection_multi_face():
image_in = Image.open("test_data/two-face.jpg")
aligned_image_1, n_faces_1, quad_1 = face_detection.align(image_in,
face_index=0,
output_size=256)
aligned_image_2, n_faces_2, quad_2 = face_detection.align(image_in,
face_index=1,
output_size=256)
assert aligned_image_1.size == aligned_image_2.size == (256, 256)
assert n_faces_1 == n_faces_2 == 2
assert (quad_1 != quad_2).all()
def test_composite_different_face():
"""Don't get the same image back"""
image_in = Image.open("test_data/two-face.jpg")
output = image_in.copy()
aligned_image_1, n_faces_1, quad_1 = face_detection.align(image_in,
face_index=0,
output_size=1024)
aligned_image_2, n_faces_2, quad_2 = face_detection.align(image_in,
face_index=1,
output_size=1024)
composited = face_detection.composite_images(quad_1, aligned_image_2,
output)
composited.thumbnail((128, 128))
image_in.thumbnail((128, 128))
assert not np.allclose(np.array(composited), np.array(image_in), atol=100)
def test_face_detection_one_face():
image_in = Image.open("test_data/face-ok.jpg")
aligned_image, n_faces, quad = face_detection.align(image_in,
face_index=0,
output_size=256)
assert aligned_image.size == (256, 256)
assert n_faces == 1
assert quad.shape == (4, 2)
def test_composite():
"""Get the orginal back when compositing the same face"""
image_in = Image.open("test_data/face-ok.jpg")
output = image_in.copy()
aligned_image, n_faces, quad = face_detection.align(image_in,
face_index=0,
output_size=1024)
composited = face_detection.composite_images(quad, aligned_image, output)
composited.thumbnail((128, 128))
image_in.thumbnail((128, 128))
assert np.allclose(np.array(composited), np.array(image_in), atol=100)