modify load avatar

human/human_context.py

@@ -9,7 +9,7 @@ from .audio_mal_handler import AudioMalHandler
 from .human_render import HumanRender
 from nlp import PunctuationSplit, DouBao
 from tts import TTSEdge, TTSAudioSplitHandle, TTSEdgeHttp
-from utils import load_avatar, get_device, object_stop
+from utils import load_avatar, get_device, object_stop, load_avatar_from_processed
 
 logger = logging.getLogger(__name__)
 current_file_path = os.path.dirname(os.path.abspath(__file__))
@@ -35,9 +35,11 @@ class HumanContext:
 
         self._device = get_device()
         print(f'device:{self._device}')
-        base_path = os.path.join(current_file_path, '..', 'face')
-        logger.info(f'_create_recognizer init, path:{base_path}')
-        full_images, face_frames, coord_frames = load_avatar(base_path, self._image_size, self._device)
+        base_path = os.path.join(current_file_path, '..')
+        logger.info(f'base path:{base_path}')
+        # full_images, face_frames, coord_frames = load_avatar(base_path, self._image_size, self._device)
+        full_images, face_frames, coord_frames = load_avatar_from_processed(base_path,
+                                                                            'wav2lip_avatar1')
         self._frame_list_cycle = full_images
         self._face_list_cycle = face_frames
         self._coord_list_cycle = coord_frames

test/test_mzzsfy_tts.py

@@ -25,7 +25,7 @@ def download_tts(url):
 
 def __create_bytes_stream(byte_stream):
     stream, sample_rate = sf.read(byte_stream)  # [T*sample_rate,] float64
-    print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
+    # print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
     stream = stream.astype(np.float32)
 
     if stream.ndim > 1:

tts/tts_edge_http.py

@@ -79,15 +79,15 @@ class TTSEdgeHttp(TTSBase):
 
     def __create_bytes_stream(self, byte_stream):
         stream, sample_rate = sf.read(byte_stream)  # [T*sample_rate,] float64
-        print(f'[INFO]tts audio stream {sample_rate}: {stream.shape}')
+        logging.info(f'tts audio stream {sample_rate}: {stream.shape}')
         stream = stream.astype(np.float32)
 
         if stream.ndim > 1:
-            print(f'[WARN] audio has {stream.shape[1]} channels, only use the first.')
+            logging.warning(f'audio has {stream.shape[1]} channels, only use the first.')
             stream = stream[:, 0]
 
         if sample_rate != self._handle.sample_rate and stream.shape[0] > 0:
-            print(f'[WARN] audio sample rate is {sample_rate}, resampling into {self._handle.sample_rate}.')
+            logging.warning(f'audio sample rate is {sample_rate}, resampling into {self._handle.sample_rate}.')
             stream = resampy.resample(x=stream, sr_orig=sample_rate, sr_new=self._handle.sample_rate)
 
         return stream

utils/__init__.py

@@ -4,4 +4,5 @@ from .async_task_queue import AsyncTaskQueue
 from .sync_queue import SyncQueue
 from .utils import mirror_index, load_model, get_device, load_avatar, config_logging
 from .utils import read_image, object_stop
+from .utils import load_avatar_from_processed
 from .audio_utils import melspectrogram, save_wav

utils/utils.py

@@ -1,6 +1,8 @@
 #encoding = utf8
+import glob
 import logging
 import os
+import pickle
 
 import cv2
 import numpy as np
@@ -33,7 +35,6 @@ def read_images(img_list):
     frames = []
     print('reading images...')
     for img_path in tqdm(img_list):
-        print(f'read image path:{img_path}')
         # frame = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
         frame = Image.open(img_path)
         frame = np.array(frame)
@@ -185,6 +186,30 @@ def load_avatar(path, img_size, device):
     return full_list_cycle, face_frames, coord_frames
 
 
+def load_avatar_from_processed(base_path, avatar_name):
+    avatar_path = os.path.join(base_path, 'data', 'avatars', avatar_name)
+    print(f'load avatar from processed:{avatar_path}')
+    coord_path = os.path.join(avatar_path, 'coords.pkl')
+    print(f'load avatar_path from processed:{avatar_path}')
+    face_image_path = os.path.join(avatar_path, 'face_imgs')
+    print(f'load face_image_path from processed:{face_image_path}')
+    full_image_path = os.path.join(avatar_path, 'full_imgs')
+    print(f'load full_image_path from processed:{full_image_path}')
+
+    with open(coord_path, 'rb') as f:
+        coord_list_frames = pickle.load(f)
+
+    face_image_list = glob.glob(os.path.join(face_image_path, '*.[jpJP][pnPN]*[gG]'))
+    face_image_list = sorted(face_image_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+    face_list_cycle = read_images(face_image_list)
+
+    full_image_list = glob.glob(os.path.join(full_image_path, '*.[jpJP][pnPN]*[gG]'))
+    full_image_list = sorted(full_image_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
+    frame_list_cycle = read_images(full_image_list)
+
+    return frame_list_cycle, face_list_cycle, coord_list_frames
+
+
 def config_logging(file_name: str, console_level: int = logging.INFO, file_level: int = logging.DEBUG):
     file_handler = logging.FileHandler(file_name, mode='a', encoding="utf8")
     file_handler.setFormatter(logging.Formatter(