对话界面可以显示对话文字内容;

GPU的启动以及训练，可以手动选择GPU

对话界面可以显示对话文字内容;
GPU的启动以及训练，可以手动选择GPU
冯杨
Commit 82f9fc646db10b525799c67769383a9e8679a9f7 82f9fc64 1 parent 5f386786
Showing 6 changed files with 295 additions and 18 deletions
app.py
lipreal.py
wav2lip/face_detection/__init__.py
wav2lip/face_detection/api.py
wav2lip/genavatar.py
web/webrtcapi.html
--- a/app.py
View file @82f9fc6
+++ b/app.py
View file @82f9fc6
@@ -407,6 +407,7 @@ if __name__ == '__main__':
     # parser.add_argument('--EMOTION', type=str, default='default')
     parser.add_argument('--model', type=str, default='ernerf') #musetalk wav2lip
+    parser.add_argument('--gpu', type=int, default=0, help="指定使用的GPU编号，例如0表示第一张GPU，1表示第二张GPU")
     parser.add_argument('--transport', type=str, default='rtcpush') #rtmp webrtc rtcpush
     parser.add_argument('--push_url', type=str, default='http://localhost:1985/rtc/v1/whip/?app=live&stream=livestream') #rtmp://localhost/live/livestream
@@ -445,7 +446,7 @@ if __name__ == '__main__':
     elif opt.model == 'wav2lip':
         from lipreal import LipReal,load_model,load_avatar,warm_up
         logger.info(opt)
-        model = load_model("./models/wav2lip.pth")
+        model = load_model("./models/wav2lip.pth", opt.gpu)
         avatar = load_avatar(opt.avatar_id)
         warm_up(opt.batch_size,model,256)
         # for k in range(opt.max_session):
--- a/lipreal.py
View file @82f9fc6
+++ b/lipreal.py
View file @82f9fc6
@@ -44,8 +44,25 @@ from basereal import BaseReal
 from tqdm import tqdm
 from logger import logger
-device = "cuda" if torch.cuda.is_available() else ("mps" if (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()) else "cpu")
-print('Using {} for inference.'.format(device))
+# 根据命令行参数选择GPU设备
+def get_device(gpu_id=0):
+    if torch.cuda.is_available():
+        if torch.cuda.device_count() > gpu_id:
+            torch.cuda.set_device(gpu_id)
+            return f"cuda:{gpu_id}"
+        else:
+            available_gpus = torch.cuda.device_count()
+            print(f"指定的GPU {gpu_id} 不可用，可用GPU数量为 {available_gpus}，使用默认设备 0")
+            torch.cuda.set_device(0)
+            return "cuda:0"
+    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+        return "mps"
+    else:
+        return "cpu"
+
+# 全局变量，将在load_model和其他函数中使用
+device = None
+print('Device will be set when model is loaded.')
 def _load(checkpoint_path):
 	if device == 'cuda':
@@ -55,7 +72,11 @@ def _load(checkpoint_path):
 								map_location=lambda storage, loc: storage)
 	return checkpoint
-def load_model(path):
+def load_model(path, gpu_id=0):
+	global device
+	device = get_device(gpu_id)
+	logger.info("Using {} for inference.".format(device))
+	
 	model = Wav2Lip()
 	logger.info("Load checkpoint from: {}".format(path))
 	checkpoint = _load(path)
--- a/wav2lip/face_detection/__init__.py
View file @82f9fc6
+++ b/wav2lip/face_detection/__init__.py
View file @82f9fc6
@@ -4,4 +4,4 @@ __author__ = """Adrian Bulat"""
 __email__ = 'adrian.bulat@nottingham.ac.uk'
 __version__ = '1.0.1'
-from .api import FaceAlignment, LandmarksType, NetworkSize
+from .api import FaceAlignment, LandmarksType, NetworkSize, ImageStyle
--- a/wav2lip/face_detection/api.py
View file @82f9fc6
+++ b/wav2lip/face_detection/api.py
View file @82f9fc6
@@ -5,11 +5,18 @@ from torch.utils.model_zoo import load_url
 from enum import Enum
 import numpy as np
 import cv2
+from .detection.core import FaceDetector
+
 try:
     import urllib.request as request_file
-except BaseException:
+except ImportError:
     import urllib as request_file
+try:
+    import dlib
+except ImportError:
+    dlib = None
+
 from .models import FAN, ResNetDepth
 from .utils import *
@@ -27,6 +34,20 @@ class LandmarksType(Enum):
     _3D = 3
+class ImageStyle(Enum):
+    """Enum class defining different image styles for face detection optimization.
+    
+    ``REALISTIC`` - Real human faces, standard detection parameters
+    ``ANIME`` - Anime/cartoon style faces, optimized for 2D illustrations
+    ``ANCIENT`` - Ancient/traditional art style, enhanced for classical paintings
+    ``AUTO`` - Automatic style detection based on image characteristics
+    """
+    REALISTIC = 1
+    ANIME = 2
+    ANCIENT = 3
+    AUTO = 4
+
+
 class NetworkSize(Enum):
     # TINY = 1
     # SMALL = 2
@@ -43,14 +64,65 @@ class NetworkSize(Enum):
 ROOT = os.path.dirname(os.path.abspath(__file__))
+
+def detect_image_style(image):
+    """Automatically detect image style based on visual characteristics.
+    
+    Args:
+        image: Input image as numpy array
+        
+    Returns:
+        ImageStyle: Detected style enum
+    """
+    # Convert to grayscale for analysis
+    if len(image.shape) == 3:
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    else:
+        gray = image
+    
+    # Calculate edge density (anime/cartoon images typically have more defined edges)
+    edges = cv2.Canny(gray, 50, 150)
+    edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1])
+    
+    # Calculate color saturation (anime images often have higher saturation)
+    if len(image.shape) == 3:
+        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        saturation_mean = np.mean(hsv[:, :, 1])
+    else:
+        saturation_mean = 0
+    
+    # Calculate texture complexity
+    laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
+    
+    # Style classification logic
+    if edge_density > 0.15 and saturation_mean > 100:
+        return ImageStyle.ANIME
+    elif laplacian_var < 100 and saturation_mean < 80:
+        return ImageStyle.ANCIENT
+    else:
+        return ImageStyle.REALISTIC
+
+
 class FaceAlignment:
     def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
-                 device='cuda', flip_input=False, face_detector='sfd', verbose=False):
+                 device='cuda', flip_input=False, face_detector='sfd', verbose=False,
+                 image_style=ImageStyle.AUTO, confidence_threshold=None):
         self.device = device
         self.flip_input = flip_input
         self.landmarks_type = landmarks_type
         self.verbose = verbose
-
+        self.image_style = image_style
+        
+        # Style-specific confidence thresholds
+        self.style_thresholds = {
+            ImageStyle.REALISTIC: 0.5,
+            ImageStyle.ANIME: 0.3,      # Lower threshold for anime faces
+            ImageStyle.ANCIENT: 0.25,   # Even lower for ancient art
+            ImageStyle.AUTO: 0.4        # Balanced default
+        }
+        
+        self.confidence_threshold = confidence_threshold or self.style_thresholds.get(image_style, 0.4)
+        
         network_size = int(network_size)
         if 'cuda' in device:
@@ -61,19 +133,75 @@ class FaceAlignment:
                                           globals(), locals(), [face_detector], 0)
         self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose)
+    def preprocess_image_by_style(self, image, style):
+        """Apply style-specific preprocessing to improve detection.
+        
+        Args:
+            image: Input image
+            style: ImageStyle enum
+            
+        Returns:
+            Preprocessed image
+        """
+        processed = image.copy()
+        
+        if style == ImageStyle.ANIME:
+            # Enhance edges for anime/cartoon faces
+            kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
+            processed = cv2.filter2D(processed, -1, kernel)
+            # Increase contrast
+            processed = cv2.convertScaleAbs(processed, alpha=1.2, beta=10)
+            
+        elif style == ImageStyle.ANCIENT:
+            # Enhance contrast and reduce noise for ancient art
+            processed = cv2.convertScaleAbs(processed, alpha=1.3, beta=15)
+            # Apply slight gaussian blur to reduce texture noise
+            processed = cv2.GaussianBlur(processed, (3, 3), 0.5)
+            
+        return processed
+    
     def get_detections_for_batch(self, images):
-        images = images[..., ::-1]
-        detected_faces = self.face_detector.detect_from_batch(images.copy())
+        # Auto-detect style if needed
+        if self.image_style == ImageStyle.AUTO and len(images) > 0:
+            detected_style = detect_image_style(images[0])
+            current_threshold = self.style_thresholds[detected_style]
+            if self.verbose:
+                print(f"Auto-detected style: {detected_style.name}, using threshold: {current_threshold}")
+        else:
+            detected_style = self.image_style
+            current_threshold = self.confidence_threshold
+        
+        # Apply style-specific preprocessing
+        processed_images = []
+        for img in images:
+            processed = self.preprocess_image_by_style(img, detected_style)
+            processed_images.append(processed)
+        
+        # Convert color format
+        processed_images = np.array(processed_images)
+        processed_images = processed_images[..., ::-1]
+        
+        # Detect faces with original method
+        detected_faces = self.face_detector.detect_from_batch(processed_images.copy())
         results = []
         for i, d in enumerate(detected_faces):
             if len(d) == 0:
                 results.append(None)
                 continue
-            d = d[0]
-            d = np.clip(d, 0, None)
-            x1, y1, x2, y2 = map(int, d[:-1])
+            # Filter by style-specific confidence threshold
+            valid_detections = [det for det in d if len(det) > 4 and det[-1] > current_threshold]
+            
+            if len(valid_detections) == 0:
+                results.append(None)
+                continue
+                
+            # Use the detection with highest confidence
+            best_detection = max(valid_detections, key=lambda x: x[-1])
+            best_detection = np.clip(best_detection, 0, None)
+            
+            x1, y1, x2, y2 = map(int, best_detection[:-1])
             results.append((x1, y1, x2, y2))
         return results
--- a/wav2lip/genavatar.py
View file @82f9fc6
+++ b/wav2lip/genavatar.py
View file @82f9fc6
@@ -19,10 +19,21 @@ parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0],
 					help='Padding (top, bottom, left, right). Please adjust to include chin at least')
 parser.add_argument('--face_det_batch_size', type=int, 
 					help='Batch size for face detection', default=16)
+parser.add_argument('--gpu_id', type=int, default=0, 
+					help='GPU device ID to use (default: 0)')
+parser.add_argument('--image_style', type=str, default='auto', 
+					choices=['auto', 'realistic', 'anime', 'ancient'],
+					help='Image style for face detection optimization (default: auto)')
+parser.add_argument('--confidence_threshold', type=float, default=None,
+					help='Custom confidence threshold for face detection (overrides style defaults)')
 args = parser.parse_args()
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-print('Using {} for inference.'.format(device))
+if torch.cuda.is_available():
+    device = f'cuda:{args.gpu_id}'
+    print(f'Using GPU {args.gpu_id} for inference.')
+else:
+    device = 'cpu'
+    print('CUDA not available, using CPU for inference.')
 def osmakedirs(path_list):
     for path in path_list:
@@ -60,8 +71,24 @@ def get_smoothened_boxes(boxes, T):
 	return boxes
 def face_detect(images):
-	detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, 
-											flip_input=False, device=device)
+	# Convert style string to enum
+	style_map = {
+		'auto': face_detection.ImageStyle.AUTO,
+		'realistic': face_detection.ImageStyle.REALISTIC,
+		'anime': face_detection.ImageStyle.ANIME,
+		'ancient': face_detection.ImageStyle.ANCIENT
+	}
+	
+	image_style = style_map.get(args.image_style, face_detection.ImageStyle.AUTO)
+	
+	detector = face_detection.FaceAlignment(
+		face_detection.LandmarksType._2D, 
+		flip_input=False, 
+		device=device,
+		image_style=image_style,
+		confidence_threshold=args.confidence_threshold,
+		verbose=True
+	)
 	batch_size = args.face_det_batch_size
--- a/web/webrtcapi.html
View file @82f9fc6
+++ b/web/webrtcapi.html
View file @82f9fc6
@@ -221,6 +221,52 @@
         border-radius: 8px;
         box-shadow: 0 1px 3px rgba(0,0,0,0.05);
     }
+    
+    /* 聊天消息样式 */
+    #chatOverlay .message {
+        display: flex;
+        margin-bottom: 10px;
+        max-width: 100%;
+    }
+    
+    #chatOverlay .message.right {
+        justify-content: flex-end;
+    }
+    
+    #chatOverlay .message.left {
+        justify-content: flex-start;
+    }
+    
+    #chatOverlay .avatar {
+        width: 30px;
+        height: 30px;
+        border-radius: 50%;
+        margin: 0 5px;
+    }
+    
+    #chatOverlay .text-container {
+        background-color: rgba(255,255,255,0.9);
+        border-radius: 10px;
+        padding: 8px 12px;
+        max-width: 70%;
+        color: #333;
+    }
+    
+    #chatOverlay .message.right .text-container {
+        background-color: #4285f4;
+        color: white;
+    }
+    
+    #chatOverlay .time {
+        font-size: 10px;
+        color: #888;
+        margin-top: 4px;
+        text-align: right;
+    }
+    
+    #chatOverlay .message.right .time {
+        color: rgba(255,255,255,0.8);
+    }
     </style>
 </head>
 <body>
@@ -281,6 +327,10 @@
         <audio id="audio" autoplay="true"></audio>
         <video id="video" autoplay="true" playsinline="true"></video>
     </div>
+    <!-- 聊天消息显示区域 -->
+    <div id="chatOverlay" style="position: absolute; bottom: 20px; right: 20px; width: 300px; max-height: 400px; overflow-y: auto; background-color: rgba(0,0,0,0.7); border-radius: 10px; padding: 10px; color: white; z-index: 1005;">
+        <!-- 消息将在这里动态添加 -->
+    </div>
 </div>
 <script src="client.js"></script>
@@ -554,12 +604,18 @@
           function addMessage(text, type = "right") {
             const chatOverlay = document.getElementById("chatOverlay");
+            if (!chatOverlay) {
+                console.error('聊天显示区域不存在');
+                return;
+            }
+            
             const messageDiv = document.createElement("div");
             messageDiv.classList.add("message", type);
             const avatar = document.createElement("img");
             avatar.classList.add("avatar");
-            avatar.src = type === "right" ? "images/avatar-right.png" : "images/avatar-left.png";
+            // 使用默认头像，如果图片不存在不会报错
+            avatar.src = type === "right" ? "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'%3E%3Ccircle cx='12' cy='12' r='12' fill='%234285f4'/%3E%3Cpath d='M12 6a3 3 0 1 0 0 6 3 3 0 0 0 0-6zm0 8c-2.67 0-8 1.34-8 4v2h16v-2c0-2.66-5.33-4-8-4z' fill='white'/%3E%3C/svg%3E" : "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'%3E%3Ccircle cx='12' cy='12' r='12' fill='%23999'/%3E%3Cpath d='M12 6a3 3 0 1 0 0 6 3 3 0 0 0 0-6zm0 8c-2.67 0-8 1.34-8 4v2h16v-2c0-2.66-5.33-4-8-4z' fill='white'/%3E%3C/svg%3E";
             const textContainer = document.createElement("div");
             textContainer.classList.add("text-container");
@@ -582,6 +638,9 @@
             // 自动滚动到底部
             chatOverlay.scrollTop = chatOverlay.scrollHeight;
+            
+            // 显示聊天区域（如果之前是隐藏的）
+            chatOverlay.style.display = 'block';
           }
           ws.onmessage = function(e) {
@@ -605,6 +664,47 @@
                        }else if (messageData.Data.Key == "text") {
                                 var reply = messageData.Data.Value;
                                 addMessage(reply, "left");
+                                
+                                // 将text类型消息推送到服务器，由数字人服务通过TTS合成语音并播放
+                                fetch('/human', {
+                                    body: JSON.stringify({
+                                        text: reply,
+                                        type: 'echo',
+                                        interrupt: true,
+                                        sessionid: parseInt(document.getElementById('sessionid').value),
+                                    }),
+                                    headers: {
+                                        'Content-Type': 'application/json'
+                                    },
+                                    method: 'POST'
+                                });
+                                
+                                // 如果是纯文本消息且没有音频，则使用浏览器的语音合成API进行本地语音合成
+                                // if (!messageData.Data.HttpValue && window.speechSynthesis) {
+                                //     console.log('使用本地语音合成播放文本:', reply);
+                                //     var utterance = new SpeechSynthesisUtterance(reply);
+                                //     utterance.lang = 'zh-CN'; // 设置语言为中文
+                                //     utterance.rate = 1.0;     // 设置语速
+                                //     utterance.pitch = 1.0;    // 设置音高
+                                //     utterance.volume = 1.0;   // 设置音量
+                                //     speechSynthesis.speak(utterance);
+                                // }
+                              }else if (messageData.Data.Key == "plaintext") {
+                                // 处理纯文本消息类型
+                                var textContent = messageData.Data.Value;
+                                console.log('收到纯文本消息:', textContent);
+                                addMessage(textContent, "left");
+                                
+                                // 使用浏览器的语音合成API进行本地语音合成
+                                if (window.speechSynthesis) {
+                                    console.log('使用本地语音合成播放文本:', textContent);
+                                    var utterance = new SpeechSynthesisUtterance(textContent);
+                                    utterance.lang = 'zh-CN'; // 设置语言为中文
+                                    utterance.rate = 1.0;     // 设置语速
+                                    utterance.pitch = 1.0;    // 设置音高
+                                    utterance.volume = 1.0;   // 设置音量
+                                    speechSynthesis.speak(utterance);
+                                }
                               }  
                }