Create gradio_pipeline_stf.py

src/gradio_pipeline_stf.py

@@ -0,0 +1,132 @@
+# coding: utf-8
+
+"""
+Pipeline for gradio
+"""
+import gradio as gr
+from .config.argument_config import ArgumentConfig
+from .live_portrait_pipeline import LivePortraitPipeline
+from .utils.io import load_img_online
+from .utils.rprint import rlog as log
+from .utils.crop import prepare_paste_back, paste_back
+# from .utils.camera import get_rotation_matrix
+
+from .utils.video import merge_audio_video
+
+
+def update_args(args, user_args):
+    """update the args according to user inputs
+    """
+    for k, v in user_args.items():
+        if hasattr(args, k):
+            setattr(args, k, v)
+    return args
+
+class GradioPipeline(LivePortraitPipeline):
+
+    def __init__(self, inference_cfg, crop_cfg, args: ArgumentConfig):
+        super().__init__(inference_cfg, crop_cfg)
+        # self.live_portrait_wrapper = self.live_portrait_wrapper
+        self.args = args
+
+    def execute_video(
+        self,
+        input_image_path,
+        input_video_path,
+        flag_relative_input,
+        flag_do_crop_input,
+        flag_remap_input
+        audio_path=None,
+    ):
+        """ for video driven potrait animation
+        """
+        if input_image_path is not None and input_video_path is not None:
+            args_user = {
+                'source_image': input_image_path,
+                'driving_info': input_video_path,
+                'flag_relative': flag_relative_input,
+                'flag_do_crop': flag_do_crop_input,
+                'flag_pasteback': flag_remap_input,
+            }
+            # update config from user input
+            self.args = update_args(self.args, args_user)
+            self.live_portrait_wrapper.update_config(self.args.__dict__)
+            self.cropper.update_config(self.args.__dict__)
+            # video driven animation
+            video_path, video_path_concat = self.execute(self.args)
+            # gr.Info("Run successfully!", duration=2)
+            
+            #return video_path, video_path_concat,
+
+
+             if audio_path is not None:
+                merged_video_path = video_path[:-3]+"_audio.mp4"
+                merge_audio_video(video_path, audio_path, merged_video_path)
+                video_path = merged_video_path
+
+            gr.Info("Run successfully!", duration=2)
+            if video_path.endswith(".jpg"):
+                #return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), output_path, gr.update(visible=True), output_path_concat, gr.update(visible=True)
+                return video_path, video_path_concat
+            else:
+                #return output_path, gr.update(visible=True), output_path_concat, gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+                return video_path, video_path_concat
+            
+        else:
+            raise gr.Error("Please upload the source portrait and driving video 🤗🤗🤗", duration=5)
+
+    def execute_image(self, input_eye_ratio: float, input_lip_ratio: float, input_image, flag_do_crop = True):
+        """ for single image retargeting
+        """
+        # disposable feature
+        f_s_user, x_s_user, source_lmk_user, crop_M_c2o, mask_ori, img_rgb = \
+        self.prepare_retargeting(input_image, flag_do_crop)
+
+        if input_eye_ratio is None or input_lip_ratio is None:
+            raise gr.Error("Invalid ratio input 💥!", duration=5)
+        else:
+            x_s_user = x_s_user.to("cuda")
+            f_s_user = f_s_user.to("cuda")
+            # ∆_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i)
+            combined_eye_ratio_tensor = self.live_portrait_wrapper.calc_combined_eye_ratio([[input_eye_ratio]], source_lmk_user)
+            eyes_delta = self.live_portrait_wrapper.retarget_eye(x_s_user, combined_eye_ratio_tensor)
+            # ∆_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i)
+            combined_lip_ratio_tensor = self.live_portrait_wrapper.calc_combined_lip_ratio([[input_lip_ratio]], source_lmk_user)
+            lip_delta = self.live_portrait_wrapper.retarget_lip(x_s_user, combined_lip_ratio_tensor)
+            num_kp = x_s_user.shape[1]
+            # default: use x_s
+            x_d_new = x_s_user + eyes_delta.reshape(-1, num_kp, 3) + lip_delta.reshape(-1, num_kp, 3)
+            # D(W(f_s; x_s, x′_d))
+            out = self.live_portrait_wrapper.warp_decode(f_s_user, x_s_user, x_d_new)
+            out = self.live_portrait_wrapper.parse_output(out['out'])[0]
+            out_to_ori_blend = paste_back(out, crop_M_c2o, img_rgb, mask_ori)
+            # gr.Info("Run successfully!", duration=2)
+            return out, out_to_ori_blend
+
+
+    def prepare_retargeting(self, input_image, flag_do_crop = True):
+        """ for single image retargeting
+        """
+        if input_image is not None:
+            # gr.Info("Upload successfully!", duration=2)
+            inference_cfg = self.live_portrait_wrapper.cfg
+            ######## process source portrait ########
+            img_rgb = load_img_online(input_image, mode='rgb', max_dim=1280, n=1) # n=1 means do not trim the pixels
+            log(f"Load source image from {input_image}.")
+            crop_info = self.cropper.crop_single_image(img_rgb)
+            if flag_do_crop:
+                I_s = self.live_portrait_wrapper.prepare_source(crop_info['img_crop_256x256'])
+            else:
+                I_s = self.live_portrait_wrapper.prepare_source(img_rgb)
+            x_s_info = self.live_portrait_wrapper.get_kp_info(I_s)
+            # R_s = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll'])
+            ############################################
+            f_s_user = self.live_portrait_wrapper.extract_feature_3d(I_s)
+            x_s_user = self.live_portrait_wrapper.transform_keypoint(x_s_info)
+            source_lmk_user = crop_info['lmk_crop']
+            crop_M_c2o = crop_info['M_c2o']
+            mask_ori = prepare_paste_back(inference_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
+            return f_s_user, x_s_user, source_lmk_user, crop_M_c2o, mask_ori, img_rgb
+        else:
+            # when press the clear button, go here
+            raise gr.Error("Please upload a source portrait as the retargeting input 🤗🤗🤗", duration=5)