chore: slightly refine the codebase

app.py

@@ -44,8 +44,8 @@ data_examples = [
 #################### interface logic ####################
 
 # Define components first
-eye_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target eye-close ratio")
+eye_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target eyes-open ratio")
-lip_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target lip-close ratio")
+lip_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target lip-open ratio")
 retargeting_input_image = gr.Image(type="numpy")
 output_image = gr.Image(type="numpy")
 output_image_paste_back = gr.Image(type="numpy")
@@ -56,7 +56,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.HTML(load_description(title_md))
     gr.Markdown(load_description("assets/gradio_description_upload.md"))
     with gr.Row():
-        with gr.Accordion(open=True, label="Reference Portrait"):
+        with gr.Accordion(open=True, label="Source Portrait"):
             image_input = gr.Image(type="filepath")
         with gr.Accordion(open=True, label="Driving Video"):
             video_input = gr.Video()
@@ -64,7 +64,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Row():
         with gr.Accordion(open=True, label="Animation Options"):
             with gr.Row():
-                flag_relative_input = gr.Checkbox(value=True, label="relative pose")
+                flag_relative_input = gr.Checkbox(value=True, label="relative motion")
                 flag_do_crop_input = gr.Checkbox(value=True, label="do crop")
                 flag_remap_input = gr.Checkbox(value=True, label="paste-back")
     with gr.Row():

assets/gradio_description_animation.md

@@ -1,4 +1,4 @@
-<span style="font-size: 1.2em;">🔥 To animate the reference portrait with the driving video, please follow these steps:</span>
+<span style="font-size: 1.2em;">🔥 To animate the source portrait with the driving video, please follow these steps:</span>
 <div style="font-size: 1.2em; margin-left: 20px;">
     1. Specify the options in the <strong>Animation Options</strong> section. We recommend checking the <strong>do crop</strong> option when facial areas occupy a relatively small portion of your image.
 </div>

assets/gradio_description_retargeting.md

@@ -1 +1 @@
-<span style="font-size: 1.2em;">🔥 To change the target eye-close and lip-close ratio of the reference portrait, please drag the sliders and then click the <strong>🚗 Retargeting</strong> button. The result would be shown in the middle block. You can try running it multiple times. <strong>😊 Set both ratios to 0.8 to see what's going on!</strong> </span>
+<span style="font-size: 1.2em;">🔥 To change the target eyes-open and lip-open ratio of the source portrait, please drag the sliders and then click the <strong>🚗 Retargeting</strong> button. The result would be shown in the middle block. You can try running it multiple times. <strong>😊 Set both ratios to 0.8 to see what's going on!</strong> </span>

assets/gradio_description_upload.md

@@ -1,4 +1,2 @@
 ## 🤗 This is the official gradio demo for **LivePortrait**.
-### Guidance for the gradio page:
+<div style="font-size: 1.2em;">Please upload or use the webcam to get a source portrait to the <strong>Source Portrait</strong> field and a driving video to the <strong>Driving Video</strong> field.</div>
-<div style="font-size: 1.2em;">Please upload or use the webcam to get a reference portrait to the <strong>Reference Portrait</strong> field and a driving video to the <strong>Driving Video</strong> field.</div>
-

src/config/argument_config.py

@@ -14,7 +14,7 @@ from .base_config import PrintableConfig, make_abs_path
 @dataclass(repr=False)  # use repr from PrintableConfig
 class ArgumentConfig(PrintableConfig):
     ########## input arguments ##########
-    source_image: Annotated[str, tyro.conf.arg(aliases=["-s"])] = make_abs_path('../../assets/examples/source/s6.jpg')  # path to the reference portrait
+    source_image: Annotated[str, tyro.conf.arg(aliases=["-s"])] = make_abs_path('../../assets/examples/source/s6.jpg')  # path to the source portrait
     driving_info:  Annotated[str, tyro.conf.arg(aliases=["-d"])] = make_abs_path('../../assets/examples/driving/d0.mp4')  # path to driving video or template (.pkl format)
     output_dir: Annotated[str, tyro.conf.arg(aliases=["-o"])] = 'animations/'  # directory to save output video
     #####################################
@@ -25,9 +25,9 @@ class ArgumentConfig(PrintableConfig):
     flag_eye_retargeting: bool = False
     flag_lip_retargeting: bool = False
     flag_stitching: bool = True  # we recommend setting it to True!
-    flag_relative: bool = True  # whether to use relative pose
+    flag_relative: bool = True  # whether to use relative motion
     flag_pasteback: bool = True  # whether to paste-back/stitch the animated face cropping from the face-cropping space to the original image space
-    flag_do_crop: bool = True  # whether to crop the reference portrait to the face-cropping space
+    flag_do_crop: bool = True  # whether to crop the source portrait to the face-cropping space
     flag_do_rot: bool = True  # whether to conduct the rotation when flag_do_crop is True
     #########################################
 

src/config/inference_config.py

@@ -28,7 +28,7 @@ class InferenceConfig(PrintableConfig):
     flag_lip_retargeting: bool = False
     flag_stitching: bool = True  # we recommend setting it to True!
 
-    flag_relative: bool = True  # whether to use relative pose
+    flag_relative: bool = True  # whether to use relative motion
     anchor_frame: int = 0  # set this value if find_best_frame is True
 
     input_shape: Tuple[int, int] = (256, 256)  # input shape
@@ -45,5 +45,5 @@ class InferenceConfig(PrintableConfig):
     ref_shape_n: int = 2
 
     device_id: int = 0
-    flag_do_crop: bool = False  # whether to crop the reference portrait to the face-cropping space
+    flag_do_crop: bool = False  # whether to crop the source portrait to the face-cropping space
     flag_do_rot: bool = True  # whether to conduct the rotation when flag_do_crop is True

src/gradio_pipeline.py

@@ -64,7 +64,7 @@ class GradioPipeline(LivePortraitPipeline):
             gr.Info("Run successfully!", duration=2)
             return video_path, video_path_concat,
         else:
-            raise gr.Error("The input reference portrait or driving video hasn't been prepared yet 💥!", duration=5)
+            raise gr.Error("The input source portrait or driving video hasn't been prepared yet 💥!", duration=5)
 
     def execute_image(self, input_eye_ratio: float, input_lip_ratio: float):
         """ for single image retargeting
@@ -74,12 +74,12 @@ class GradioPipeline(LivePortraitPipeline):
         elif self.f_s_user is None:
             if self.start_prepare:
                 raise gr.Error(
-                    "The reference portrait is under processing 💥! Please wait for a second.", 
+                    "The source portrait is under processing 💥! Please wait for a second.",
                     duration=5
                 )
             else:
                 raise gr.Error(
-                    "The reference portrait hasn't been prepared yet 💥! Please scroll to the top of the page to upload.", 
+                    "The source portrait hasn't been prepared yet 💥! Please scroll to the top of the page to upload.",
                     duration=5
                 )
         else:
@@ -107,7 +107,7 @@ class GradioPipeline(LivePortraitPipeline):
             gr.Info("Upload successfully!", duration=2)
             self.start_prepare = True
             inference_cfg = self.live_portrait_wrapper.cfg
-            ######## process reference portrait ########
+            ######## process source portrait ########
             img_rgb = load_img_online(input_image_path, mode='rgb', max_dim=1280, n=16)
             log(f"Load source image from {input_image_path}.")
             crop_info = self.cropper.crop_single_image(img_rgb)

src/live_portrait_pipeline.py

@@ -40,7 +40,7 @@ class LivePortraitPipeline(object):
 
     def execute(self, args: ArgumentConfig):
         inference_cfg = self.live_portrait_wrapper.cfg # for convenience
-        ######## process reference portrait ########
+        ######## process source portrait ########
         img_rgb = load_image_rgb(args.source_image)
         img_rgb = resize_to_limit(img_rgb, inference_cfg.ref_max_shape, inference_cfg.ref_shape_n)
         log(f"Load source image from {args.source_image}")

src/live_portrait_wrapper.py

@@ -10,12 +10,12 @@ import cv2
 import torch
 import yaml
 
-from src.utils.timer import Timer
+from .utils.timer import Timer
-from src.utils.helper import load_model, concat_feat
+from .utils.helper import load_model, concat_feat
-from src.utils.camera import headpose_pred_to_degree, get_rotation_matrix
+from .utils.camera import headpose_pred_to_degree, get_rotation_matrix
 from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio
-from src.config.inference_config import InferenceConfig
+from .config.inference_config import InferenceConfig
-from src.utils.rprint import rlog as log
+from .utils.rprint import rlog as log
 
 
 class LivePortraitWrapper(object):

src/utils/helper.py

@@ -6,17 +6,14 @@ utility functions and classes to handle feature extraction and model loading
 
 import os
 import os.path as osp
-import cv2
 import torch
-from rich.console import Console
 from collections import OrderedDict
 
-from src.modules.spade_generator import SPADEDecoder
+from ..modules.spade_generator import SPADEDecoder
-from src.modules.warping_network import WarpingNetwork
+from ..modules.warping_network import WarpingNetwork
-from src.modules.motion_extractor import MotionExtractor
+from ..modules.motion_extractor import MotionExtractor
-from src.modules.appearance_feature_extractor import AppearanceFeatureExtractor
+from ..modules.appearance_feature_extractor import AppearanceFeatureExtractor
-from src.modules.stitching_retargeting_network import StitchingRetargetingNetwork
+from ..modules.stitching_retargeting_network import StitchingRetargetingNetwork
-from .rprint import rlog as log
 
 
 def suffix(filename):
@@ -45,6 +42,7 @@ def is_video(file_path):
         return True
     return False
 
+
 def is_template(file_path):
     if file_path.endswith(".pkl"):
         return True
@@ -149,8 +147,8 @@ def calculate_transformation(config, s_kp_info, t_0_kp_info, t_i_kp_info, R_s, R
     new_scale = s_kp_info['scale'] * (t_i_kp_info['scale'] / t_0_kp_info['scale'])
     return new_rotation, new_expression, new_translation, new_scale
 
+
 def load_description(fp):
     with open(fp, 'r', encoding='utf-8') as f:
         content = f.read()
     return content
-