def get_mixs(name1, name2, points=46):
img1 = align(name1)
img2 = align(name2)
z1 = encode(img1)
z2 = encode(img2)
imgs, _ = mix_range(z1, z2, points)
return imgs
def get_mixs(name1, name2, points=46):
img1 = align(name1)
img2 = align(name2)
z1 = encode(img1)
z2 = encode(img2)
imgs, _ = mix_range(z1, z2, points)
return imgs
def encode(input_path):
img = Image.open(input_path)
img = align_face.align(input_path)
img = np.reshape(img, [1,256,256,3])
t = time.time()
eps = model.encode(img)
print("Encoding latency {} sec/img".format(time.time() - t))
return eps
def get_encodings(model, image, get_box=False):
face, box = af.align(image)
emb = []
if len(face) > 1:
for f in face:
f = cv2.resize(f, dsize=(96, 96), interpolation=cv2.INTER_CUBIC)
#f = f[...,::-1]
f = np.array(
[np.around(np.transpose(f, [2, 0, 1]) / 255.0, decimals=12)])
emb.append(model.predict_on_batch(f))
else:
face = cv2.resize(face[0],
dsize=(96, 96),
interpolation=cv2.INTER_CUBIC)
face = np.array(
[np.around(np.transpose(face, [2, 0, 1]) / 255.0, decimals=12)])
emb.append(model.predict_on_batch(face))
if get_box:
return emb, box
return emb
def align_image(input_path, output_path):
img = align_face.align(input_path)
img = Image.fromarray(img).convert('RGB')
img.save(output_path)
def get_manipulations(name, typ, points=46, scale=1.0):
img = align(name)
z = encode(img)
imgs, _ = manipulate_range(z, typ, points, scale)
return imgs
def get_manipulations(name, typ, points=46, scale=1.0):
img = align(name)
z = encode(img)
imgs, _ = manipulate_range(z, typ, points, scale)
return imgs