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leafiber 2022-05-28 21:38:34 +08:00
parent 1fa9906377
commit 84e8c23fe3
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demo.py
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@ -1,13 +1,10 @@
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
""" """
信号设计课程小组设计
CODE >>> SINCE IN CAIXYPROMISE. @ by: Leaf
STRIVE FOR EXCELLENT. @ date: 2022-05-28
CONSTANTLY STRIVING FOR SELF-IMPROVEMENT.
@ by: caixy
@ date: 2021-10-1
""" """
import cv2 import cv2
@ -20,37 +17,38 @@ class HandDetector:
如查找方式，许多手指向上或两个手指之间的距离。而且提供找到的手的边界框信息。 如查找方式，许多手指向上或两个手指之间的距离。而且提供找到的手的边界框信息。
""" """
def __init__(self, mode=False, maxHands=2, detectionCon=0.5, minTrackCon=0.5): def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
""" """
:param mode: 在静态模式下，对每个图像进行检测 :param mode: 在静态模式下，对每个图像进行检测
:param maxHands: 要检测的最大手数 :param max_hands: 要检测的最大手数
:param detectionCon: 最小检测置信度 :param detection_con: 最小检测置信度
:param minTrackCon: 最小跟踪置信度 :param min_track_con: 最小跟踪置信度
""" """
self.results = None
self.mode = mode self.mode = mode
self.maxHands = maxHands self.max_hands = max_hands
self.modelComplex = False self.modelComplex = False
self.detectionCon = detectionCon self.detection_con = detection_con
self.minTrackCon = minTrackCon self.min_track_con = min_track_con
# 初始化手部识别模型 # 初始化手部的识别模型
self.mpHands = mp.solutions.hands self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelComplex, self.hands = self.mpHands.Hands(self.mode, self.max_hands, self.modelComplex,
self.detectionCon, self.minTrackCon) self.detection_con, self.min_track_con)
self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器 self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器
self.tipIds = [4, 8, 12, 16, 20] # 指尖列表 self.tipIds = [4, 8, 12, 16, 20] # 指尖列表
self.fingers = [] self.fingers = []
self.lmList = [] self.lmList = []
def findHands(self, img, draw=True): def find_hands(self, img, draw=True):
""" """
从图像(BRG)中找到手部。 从图像(BRG)中找到手部。
:param img: 用于查找手的图像。 :param img: 用于查找手的图像。
:param draw: 在图像上绘制输出的标志。 :param draw: 在图像上绘制输出的标志。
:return: 带或不带图形的图像 :return: 带或不带图形的图像
""" """
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 将传入的图像由BGR模式转标准的Opencv模式——RGB模式， img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 将传入的图像由BGR模式转标准的Opencv模式——RGB模式，
self.results = self.hands.process(imgRGB) self.results = self.hands.process(img_rgb)
if self.results.multi_hand_landmarks: if self.results.multi_hand_landmarks:
for handLms in self.results.multi_hand_landmarks: for handLms in self.results.multi_hand_landmarks:
@ -59,55 +57,53 @@ class HandDetector:
self.mpHands.HAND_CONNECTIONS) self.mpHands.HAND_CONNECTIONS)
return img return img
def findPosition(self, img, handNo=0, draw=True): def find_position(self, img, hand_no=0, draw=True):
""" """
查找单手的地标并将其放入列表中像素格式。还可以返回手部周围的边界框。 查找单手的地标并将其放入列表中像素格式。还可以返回手部的周围的边界框。
:param img: 要查找的主图像 :param img: 要查找的主图像
:param handNo: 如果检测到多只手，则为手部id :param hand_no: 如果检测到多只手，则为手部id
:param draw: 在图像上绘制输出的标志。(默认绘制矩形框) :param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
:return: 像素格式的手部关节位置列表；手部边界框 :return: 像素格式的手部关节位置列表；手部边界框
""" """
xList = [] x_list = []
yList = [] y_list = []
bbox = [] bbox_info = []
bboxInfo = []
self.lmList = [] self.lmList = []
if self.results.multi_hand_landmarks: if self.results.multi_hand_landmarks:
myHand = self.results.multi_hand_landmarks[handNo] my_hand = self.results.multi_hand_landmarks[hand_no]
for id, lm in enumerate(myHand.landmark): for _, lm in enumerate(my_hand.landmark):
h, w, c = img.shape h, w, c = img.shape
px, py = int(lm.x * w), int(lm.y * h) px, py = int(lm.x * w), int(lm.y * h)
xList.append(px) x_list.append(px)
yList.append(py) y_list.append(py)
self.lmList.append([px, py]) self.lmList.append([px, py])
if draw: if draw:
cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED) cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
xmin, xmax = min(xList), max(xList) x_min, x_max = min(x_list), max(x_list)
ymin, ymax = min(yList), max(yList) y_min, y_max = min(y_list), max(y_list)
boxW, boxH = xmax - xmin, ymax - ymin box_w, box_h = x_max - x_min, y_max - y_min
bbox = xmin, ymin, boxW, boxH bbox = x_min, y_min, box_w, box_h
cx, cy = bbox[0] + (bbox[2] // 2), \ cx, cy = bbox[0] + (bbox[2] // 2), bbox[1] + (bbox[3] // 2)
bbox[1] + (bbox[3] // 2) bbox_info = {"id": hand_no, "bbox": bbox, "center": (cx, cy)}
bboxInfo = {"id": id, "bbox": bbox, "center": (cx, cy)}
if draw: if draw:
cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20), cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
(bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20), (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
(0, 255, 0), 2) (0, 255, 0), 2)
return self.lmList, bboxInfo return self.lmList, bbox_info
def fingersUp(self): def fingers_up(self):
""" """
查找列表中打开并返回的手指数。会分别考虑左手和右手 查找列表中打开并返回的手指数。会分别考虑左手和右手
：return：竖起手指的列表 :return: 竖起手指的列表
""" """
fingers = []
if self.results.multi_hand_landmarks: if self.results.multi_hand_landmarks:
myHandType = self.handType() my_hand_type = self.hand_type()
fingers = []
# Thumb # Thumb
if myHandType == "Right": if my_hand_type == "Right":
if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]: if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
fingers.append(1) fingers.append(1)
else: else:
@ -118,17 +114,17 @@ class HandDetector:
else: else:
fingers.append(0) fingers.append(0)
# 4 Fingers # 4 Fingers
for id in range(1, 5): for i in range(1, 5):
if self.lmList[self.tipIds[id]][1] < self.lmList[self.tipIds[id] - 2][1]: if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
fingers.append(1) fingers.append(1)
else: else:
fingers.append(0) fingers.append(0)
return fingers return fingers
def handType(self): def hand_type(self):
""" """
检查传入的手部是左还是右 检查传入的手部是左还是右
：return: "Right" 或 "Left" :return: "Right" 或 "Left"
""" """
if self.results.multi_hand_landmarks: if self.results.multi_hand_landmarks:
if self.lmList[17][0] < self.lmList[5][0]: if self.lmList[17][0] < self.lmList[5][0]:
@ -139,21 +135,21 @@ class HandDetector:
class Main: class Main:
def __init__(self): def __init__(self):
self.detector = None
self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
self.camera.set(3, 1280) self.camera.set(3, 1280)
self.camera.set(4, 720) self.camera.set(4, 720)
def Gesture_recognition(self): def gesture_recognition(self):
fps = cv2.CAP_PROP_FPS
self.detector = HandDetector() self.detector = HandDetector()
while True: while True:
frame, img = self.camera.read() frame, img = self.camera.read()
img = self.detector.findHands(img) img = self.detector.find_hands(img)
lmList, bbox = self.detector.findPosition(img) lm_list, bbox = self.detector.find_position(img)
if lmList: if lm_list:
x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1] x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
x1, x2, x3, x4, x5 = self.detector.fingersUp() x1, x2, x3, x4, x5 = self.detector.fingers_up()
if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0): if (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
@ -179,8 +175,8 @@ class Main:
elif x1 and (x2 == 0, x3 == 0, x4 == 0, x5 == 0): elif x1 and (x2 == 0, x3 == 0, x4 == 0, x5 == 0):
cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
(0, 0, 255), 3) (0, 0, 255), 3)
cv2.imshow("camera", img)
cv2.imshow("camera", img)
key = cv2.waitKey(1) key = cv2.waitKey(1)
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1: if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
break break
@ -188,7 +184,6 @@ class Main:
break break
if __name__ == '__main__': if __name__ == '__main__':
Solution = Main() Solution = Main()
Solution.Gesture_recognition() Solution.gesture_recognition()