mp.solutions.drawing_utils

mp.solutions.drawing_utils是一个Python模块，用于在MediaPipe中绘制图形和标注。它提供了一些函数，如draw_landmarks()和draw_detection()，可以方便地在图像上绘制关键点和检测结果。

相关问题

import cv2 import mediapipe as mp mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles mp_holistic = mp.solutions.holistic # For webcam input: filepath='F:\\video000\\ce.mp4' cap = cv2.VideoCapture(filepath) with mp_holistic.Holistic( min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic: while cap.isOpened(): success, image = cap.read() if not success: print("Ignoring empty camera frame.") # If loading a video, use 'break' instead of 'continue'. break # To improve performance, optionally mark the image as not writeable to # pass by reference. image.flags.writeable = False image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = holistic.process(image) # Draw landmark annotation on the image. image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) mp_drawing.draw_landmarks( image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS, landmark_drawing_spec=None, connection_drawing_spec=mp_drawing_styles .get_default_face_mesh_contours_style()) mp_drawing.draw_landmarks( image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, landmark_drawing_spec=mp_drawing_styles .get_default_pose_landmarks_style()) # Flip the image horizontally for a selfie-view display. cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1)) if cv2.waitKey(5) & 0xFF == 27: break cap.release() 在这段代码中引入注意力机制，使人体步态特征更好的提取

在这段代码中，要引入注意力机制以提取人体步态特征，可以考虑在Holistic模型的输出结果中使用注意力机制进行加权融合，以强化对关键信息的关注和捕捉。具体操作如下：

1. 在Holistic模型的process()函数中，设置use_attention=True，以启用注意力机制。

2. 在Holistic模型的输出结果中，选择关键的特征点或特征向量，并使用注意力机制进行加权融合，以得到最终的人体步态特征表示。

3. 对特征表示进行进一步的处理和分类，以实现步态识别等应用。

下面是一个示例代码，用于在Holistic模型中引入注意力机制：

import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_holistic = mp.solutions.holistic

# For webcam input:
filepath = 'F:\\video000\\ce.mp4'
cap = cv2.VideoCapture(filepath)
with mp_holistic.Holistic(
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5,
    use_attention=True) as holistic:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      break
      
    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = holistic.process(image)
    
    # Extract the key feature points or vectors from the output results.
    pose_landmarks = results.pose_landmarks.landmark
    face_landmarks = results.face_landmarks.landmark
    left_hand_landmarks = results.left_hand_landmarks.landmark
    right_hand_landmarks = results.right_hand_landmarks.landmark
    
    # Apply attention mechanism to the key feature points or vectors.
    pose_attention = apply_attention(pose_landmarks)
    face_attention = apply_attention(face_landmarks)
    left_hand_attention = apply_attention(left_hand_landmarks)
    right_hand_attention = apply_attention(right_hand_landmarks)
    
    # Combine the attention-weighted feature vectors to form the final gait feature.
    gait_feature = np.concatenate([pose_attention, face_attention, left_hand_attention, right_hand_attention])
    
    # Further process and classify the gait feature to achieve gait recognition.
    ...
    
    # Draw landmark annotation on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    mp_drawing.draw_landmarks(
        image,
        results.face_landmarks,
        mp_holistic.FACEMESH_CONTOURS,
        landmark_drawing_spec=None,
        connection_drawing_spec=mp_drawing_styles
        .get_default_face_mesh_contours_style())
    mp_drawing.draw_landmarks(
        image,
        results.pose_landmarks,
        mp_holistic.POSE_CONNECTIONS,
        landmark_drawing_spec=mp_drawing_styles
        .get_default_pose_landmarks_style())
    
    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()

其中，apply_attention()函数用于对输入的特征点或特征向量应用注意力机制，可以根据具体需求选择不同的注意力模型和参数。注意力机制的加权融合可以使用numpy库中的矩阵乘法或加法等运算实现。

mp_drawing = mp.solutions.drawing_utils DrawingSpec_point = mp_drawing.DrawingSpec((0, 255, 0), 1, 1) DrawingSpec_line = mp_drawing.DrawingSpec((0, 0, 255), 1, 1)

这段代码是使用Mediapipe库绘制图形的相关设置。`mp_drawing`是Mediapipe库的绘图工具，`DrawingSpec_point`和`DrawingSpec_line`是绘制点和线的规格参数。

`DrawingSpec_point`用于定义绘制点的规格，它包含三个参数：颜色值`(0, 255, 0)`表示点的颜色为绿色，线宽为1，点的半径也为1。

`DrawingSpec_line`用于定义绘制线的规格，它包含三个参数：颜色值`(0, 0, 255)`表示线的颜色为蓝色，线宽为1，线的类型也为1。

这些设置可以根据实际需求进行调整，用于在图像上绘制不同样式的点和线。