def change_avatar(token):
try:
vprint("Processes: ", app.processes)
vprint("Predictors: ", app.predictors)
IMG_SIZE = 256
ava_f = request.files['avatar']
if ava_f is not None:
ava_g = ava_f.read()
ava_np = np.fromstring(ava_g, np.uint8)
ava_h = cv2.imdecode(ava_np, cv2.IMREAD_COLOR)
if token in app.processes:
predictor = app.processes[token]['predictor']
if ava_h.ndim == 2:
ava_h = np.tile(ava_h[..., None], [1, 1, 3])
ava_h = ava_h[..., :3][..., ::-1]
ava_h = resize(ava_h, (IMG_SIZE, IMG_SIZE))
vprint('set_source_image')
predictor.set_source_image(ava_h)
vprint('finished set_source_image')
vprint('reset_frames')
predictor.reset_frames()
vprint('finished reset_frames')
return avatar_response(status=afy_flask_avatar_status.SUCCESS)
return avatar_response(status=afy_flask_avatar_status.NO_PREDICTOR,
error="Predictor not available")
return avatar_response(
status=afy_flask_avatar_status.INPUT_IMAGE_ERROR,
error="Invalid image / image corrupted")
except Exception as e:
if app.verbose:
traceback.print_exc()
return avatar_response(error=str(e))
def is_new_frame_better(source, driving, predictor):
global avatar_kp
global display_string
if avatar_kp is None:
display_string = "No face detected in avatar."
return False
if predictor.get_start_frame() is None:
display_string = "No frame to compare to."
return True
driving_smaller = resize(driving, (128, 128))[..., :3]
new_kp = predictor.get_frame_kp(driving)
if new_kp is not None:
new_norm = (np.abs(avatar_kp - new_kp)**2).sum()
old_norm = (np.abs(avatar_kp -
predictor.get_start_frame_kp())**2).sum()
out_string = "{0} : {1}".format(int(new_norm * 100),
int(old_norm * 100))
display_string = out_string
log(out_string)
return new_norm < old_norm
else:
display_string = "No face found!"
return False
def load_stylegan_avatar():
url = "https://thispersondoesnotexist.com/image"
r = requests.get(url, headers={'User-Agent': "My User Agent 1.0"}).content
image = np.frombuffer(r, np.uint8)
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = resize(image, (IMG_SIZE, IMG_SIZE))
return image
def load_stylegan_avatar():
img_id = np.random.randint(0, 50000)
print('select waifu {}'.format(img_id))
url = "http://thiswaifudoesnotexist.net/v2/example-{}.jpg".format(img_id)
r = requests.get(url, headers={'User-Agent': "My User Agent 1.0"}).content
image = np.frombuffer(r, np.uint8)
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = resize(image, (IMG_SIZE, IMG_SIZE))
return image
def load_images(IMG_SIZE=256):
avatars = []
filenames = []
images_list = sorted(glob.glob(f'{opt.avatars}/*'))
for i, f in enumerate(images_list):
if f.endswith('.jpg') or f.endswith('.jpeg') or f.endswith('.png'):
img = cv2.imread(f)
if img.ndim == 2:
img = np.tile(img[..., None], [1, 1, 3])
img = img[..., :3][..., ::-1]
img = resize(img, (IMG_SIZE, IMG_SIZE))
avatars.append(img)
filenames.append(f)
return avatars, filenames
def predict(token):
try:
vprint("Processes: ", app.processes)
vprint("Predictors: ", app.predictors)
IMG_SIZE = 256
frame_proportion = 0.9
frame_offset_x = 0
frame_offset_y = 0
img = request.files['image']
if img is not None:
img_g = img.read()
img_np = np.fromstring(img_g, np.uint8)
frame = cv2.imdecode(img_np, cv2.IMREAD_COLOR)
if token in app.processes:
predictor = app.processes[token]['predictor']
frame = frame[..., ::-1]
frame_orig = frame.copy()
frame, lrudwh = crop(frame,
p=frame_proportion,
offset_x=frame_offset_x,
offset_y=frame_offset_y)
frame_lrudwh = lrudwh
frame = resize(frame, (IMG_SIZE, IMG_SIZE))[..., :3]
out = predictor.predict(frame)
if out is not None:
out = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
_, out = cv2.imencode('.jpg', out)
out = out.tobytes()
out = base64.b64encode(out).decode("utf-8")
return predict_response(
status=afy_flask_predict_status.SUCCESS, image=out)
return predict_response(
status=afy_flask_predict_status.SUCCESS)
return predict_response(
status=afy_flask_predict_status.NO_PREDICTOR,
error="Predictor not available")
return predict_response(
status=afy_flask_predict_status.INPUT_IMAGE_ERROR,
error="Invalid image / image corrupted")
except Exception as e:
if app.verbose:
traceback.print_exc()
return predict_response(error=str(e))
tt.tic()
ret, frame = cap.read()
if not ret:
log("Can't receive frame (stream end?). Exiting ...")
break
frame = frame[..., ::-1]
frame_orig = frame.copy()
frame, lrudwh = crop(frame,
p=frame_proportion,
offset_x=frame_offset_x,
offset_y=frame_offset_y)
frame_lrudwh = lrudwh
frame = resize(frame, (IMG_SIZE, IMG_SIZE))[..., :3]
if find_keyframe:
if is_new_frame_better(avatar, frame, predictor):
log("Taking new frame!")
green_overlay = True
predictor.reset_frames()
timing['preproc'] = tt.toc()
if passthrough:
out = frame
elif is_calibrated:
tt.tic()
out = predictor.predict(frame)
if out is None:
