案例介绍
基于mediapipe实现方块的虚拟拖拽。环境使用python3.8.
代码示例
"""
这个案例 展示了 摄像头的视频流
"""
import math
import cv2 # pip install opencv-python
import numpy as np
# mdeiapipe 不能使用conda装 只能用pip装 装之前最好换一下pip源
# 导入mediapipe：https://google.github.io/mediapipe/solutions/hands
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(
model_complexity=0,
min_detection_confidence=0.5,
min_tracking_confidence=0.5)
# 获取摄像头视频流
cap = cv2.VideoCapture(0)
# 界面方块的参数
square_x = 100
square_y = 100
square_width = 100
# 获取画面的宽高,用于恢复手指在图片上的坐标
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# 方块初始数组
x = 100
y = 100
w = 200
h = 200
L1 = 0
L2 = 0
on_square = False
square_color = (0, 255, 0)
while True:
# 读取每一帧 ret(bool) : 代表是否打开成功摄像头 frame （numpy.ndarray） ： 单帧的图像
# tips：opencv的读取是BGR的顺序，很多算法是RGB，所以需要转化。
ret, frame = cap.read()
# print(type(frame))
# print(type(ret))
if not ret:
print("无法打开摄像头")
continue
# print(ret)
# 对图像进行处理，镜像一下，围绕y轴
frame = cv2.flip(frame, 1)
frame.flags.writeable = False
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 识别
results = hands.process(frame)
frame.flags.writeable = True
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# 判断是否出现手
if results.multi_hand_landmarks:
# 解析便利每一双手
for hand_landmarks in results.multi_hand_landmarks:
# 绘制21个关键点
mp_drawing.draw_landmarks(
frame,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
"""
print(hand_landmarks)
每个关键点的解析
landmark {
x: 0.18473060429096222
y: 0.058572977781295776
z: -0.10718432068824768
}
"""
# 21 个关键点的x,y坐标列表
x_list = []
y_list = []
for landmark in hand_landmarks.landmark:
x_list.append(landmark.x)
y_list.append(landmark.y)
# 输出一下长度，21 就识别全了
# print(len(x_list))
# 获取食指指尖坐标，坐标位置查看：https://google.github.io/mediapipe/solutions/hands
index_finger_x = int(x_list[8] * width)
index_finger_y = int(y_list[8] * height)
# 食指尖画圆
cv2.circle(frame, (index_finger_x, index_finger_y), 20, (255, 0, 255), -1)
# 获取中指坐标
middle_finger_x = int(x_list[12] * width)
middle_finger_y = int(y_list[12] * height)
# 计算两指距离
# finger_distance =math.sqrt( (middle_finger_x - index_finger_x)**2 + (middle_finger_y-index_finger_y)**2)
finger_distance = math.hypot((middle_finger_x - index_finger_x), (middle_finger_y - index_finger_y))
# 判断食指指尖在不在方块上
if finger_distance < 60:
# X坐标范围 Y坐标范围
if (index_finger_x > x and index_finger_x < (x + w)) and (
index_finger_y > y and index_finger_y < (y + h)):
if on_square == False:
print('在')
L1 = index_finger_x - x
L2 = index_finger_y - y
square_color = (255, 0, 255)
on_square = True
else:
print('不在')
else:
# 解除
on_square = False
square_color = (0, 255, 0)
# 更新坐标
if on_square:
x = index_finger_x - L1
y = index_finger_y - L2
# 画一个正方形，需要实心
# cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),-1)
# 半透明处理
overlay = frame.copy()
cv2.rectangle(frame, (x, y), (x + w, y + h), square_color, -1)
frame = cv2.addWeighted(overlay, 0.5, frame, 1 - 0.5, 0)
# 看一下距离
# print(finger_distance)
# 此时图片是BGR 不是RGB -1 代表实心 255 = b 0 = g 0 =r 所以是蓝色方块
# cv2.rectangle(frame, (square_x, square_y), (square_x + square_width, square_y + square_width), (255, 0, 0), -1)
# 显示
cv2.imshow('Virtual drag', frame)
# 退出条件 esc 退出
if cv2.waitKey(10) & 0xFF == 27:
break
cap.release()
cv2.destroyAllWindows()
关键点解析
半透明方块
# 半透明处理
overlay = frame.copy()
cv2.rectangle(frame, (x, y), (x + w, y + h), square_color, -1)
frame = cv2.addWeighted(overlay, 0.5, frame, 1 - 0.5, 0)
