Update app.py

app.py

@@ -1,12 +1,5 @@
 import gradio as gr
-import cv2
 from gradio_webrtc import WebRTC
-import mediapipe as mp
-
-# 初始化 MediaPipe Hands
-mp_hands = mp.solutions.hands
-mp_drawing = mp.solutions.drawing_utils
-hands = mp_hands.Hands(min_detection_confidence=0.3, min_tracking_confidence=0.3)  # 降低置信度提升速度
 
 # WebRTC 配置
 rtc_configuration = {
@@ -14,30 +7,6 @@ rtc_configuration = {
     "iceTransportPolicy": "relay"
 }
 
-# 手势检测函数
-def detection(image, conf_threshold=0.5):
-    """
-    使用 MediaPipe Hands 进行手势检测。
-    """
-    # 将图像从 BGR 转换为 RGB（MediaPipe 需要 RGB 格式）
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    # 将图像大小缩小到一个较小的尺寸，降低计算负担
-    image = cv2.resize(image, (640, 480))
-
-    # 使用 MediaPipe Hands 处理图像
-    results = hands.process(image_rgb)
-
-    # 如果检测到手，绘制手部关键点
-    if results.multi_hand_landmarks:
-        for hand_landmarks in results.multi_hand_landmarks:
-            mp_drawing.draw_landmarks(
-                image, hand_landmarks, mp_hands.HAND_CONNECTIONS
-            )
-
-    # 返回带注释的图像
-    return image
-
 # Gradio 界面
 css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
          .my-column {display: flex !important; justify-content: center !important; align-items: center !important;}"""
@@ -46,31 +15,22 @@ with gr.Blocks(css=css) as demo:
     gr.HTML(
         """
         <h1 style='text-align: center'>
-        Hand Gesture Detection with MediaPipe (Powered by WebRTC ⚡️)
+        WebRTC Stream Test
         </h1>
         """
     )
     gr.HTML(
         """
         <h3 style='text-align: center'>
-        <a href='https://mediapipe.dev/'>MediaPipe Hands</a>
+        WebRTC without MediaPipe
         </h3>
         """
     )
     with gr.Column(elem_classes=["my-column"]):
         with gr.Group(elem_classes=["my-group"]):
             image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
-            conf_threshold = gr.Slider(
-                label="Confidence Threshold",
-                minimum=0.0,
-                maximum=1.0,
-                step=0.05,
-                value=0.5,
-            )
 
-        image.stream(
-            fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10
-        )
+        image.stream(fn=lambda x: x, inputs=[image], outputs=[image], time_limit=10)
 
 if __name__ == "__main__":
     demo.launch()