2

我的形象，

在此处输入图像描述

我试图从手部图像中获得相当准确的手掌并使用 mediapipe(mp) 库来做到这一点。但结果是：

在此处输入图像描述

mp计算并绘制绿色笔划和红点，同时也给出红点的坐标。所以我可以使用给定的红色圆圈坐标来获取蓝色圆圈的坐标。但仅仅获得“准确”的手掌是不够的。我需要得到黑点的坐标。以下是我的代码：

import cv2
import mediapipe as mp
import utils

mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands




def makeCircle(img,circle_y,circle_x,radius):
    image_height, image_width, _ = img.shape
    y = int(circle_y * image_height)
    x = int(circle_x * image_width)
    circle_coordinates = (x,y)
    color = (255, 0, 0)
    thickness = 2
    return cv2.circle(img,circle_coordinates,radius,color,thickness)



# For static images:
IMAGE_FILES = [
    "C:/Users/USER/workspace/palm/images/sample1.png",
    "C:/Users/USER/workspace/palm/images/sample2.png",
    "C:/Users/USER/workspace/palm/images/sample3.png",
    "C:/Users/USER/workspace/palm/images/sample4.png"
]

with mp_hands.Hands(
    static_image_mode=True,
    max_num_hands=2,
    min_detection_confidence=0.5) as hands:
  for idx, file in enumerate(IMAGE_FILES):
    # Read an image, flip it around y-axis for correct handedness output (see
    # above).
    image = cv2.flip(cv2.imread(file), 1)
    image = utils.remove_bground(image)


    # resize image's height 600 fixing the ratio
    image = utils.ResizeWithAspectRatio(image,height=600)

    # Convert the BGR image to RGB before processing.
    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    print("results:",results)
    # Print handedness and draw hand landmarks on the image.
    print('Handedness:', results.multi_handedness)
    if not results.multi_hand_landmarks:
      continue
    image_height, image_width, _ = image.shape
    annotated_image = image.copy()
    for hand_landmarks in results.multi_hand_landmarks:
      print('hand_landmarks:', hand_landmarks)
      print(
          f'Index finger tip coordinates: (',
          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'
      )


      circle_coordinates = [
        (
            (hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_MCP].y + hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_PIP].y)/2,
            (hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_MCP].x + hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_PIP].x)/2
        ),
        (
            (hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_MCP].y + hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_PIP].y)/2,
            (hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_MCP].x + hand_landmarks.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_PIP].x)/2
        ),
        (
            (hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_MCP].y + hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_PIP].y)/2,
            (hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_MCP].x + hand_landmarks.landmark[mp_hands.HandLandmark.RING_FINGER_PIP].x)/2
        ),
        (
        (hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP].y + hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_PIP].y)/2,
        (hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP].x + hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_PIP].x)/2
        ),
        (
            hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].y,
            hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].x
        ),
        (
            hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_CMC].y,
            hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_CMC].x
        ),
        (
            hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_MCP].y,
            hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_MCP].x
        ),
      ]

      for (circle_y,circle_x) in circle_coordinates:
          annotated_image = makeCircle(annotated_image,circle_y,circle_x,10)

      palm_center_y = (hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_CMC].y +
        hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP].y)/2
      palm_center_x = (hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_CMC].x +
        hand_landmarks.landmark[mp_hands.HandLandmark.PINKY_MCP].x)/2
      #
      palm_range = 100

      # annotated_image = makeCircle(annotated_image, palm_center_y,palm_center_x,palm_range)


      mp_drawing.draw_landmarks(
          annotated_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
      hand_landmark_x = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width
      hand_landmark_y = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height


    cv2.imwrite(
        '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
    cv2.imshow("img" + str(idx),cv2.flip(annotated_image,1))
    cv2.waitKey(0)

如何获取第一手图像中点的坐标？

4

1 回答 1

1

也许你可以使用cv2.convexHullandcv2.convexityDefects找到那些点。我给你留下一个我认为它可能有用的视频教程：Python - OpenCV

于 2021-08-29T16:32:28.350 回答