From 84e8c23fe3b3a41731f3e3c9a063654994be26a2 Mon Sep 17 00:00:00 2001 From: leafiber Date: Sat, 28 May 2022 21:38:34 +0800 Subject: [PATCH] =?UTF-8?q?=E9=87=8D=E6=96=B0=E6=A0=BC=E5=BC=8F=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- demo.py | 107 +++++++++++++++++++++++++++----------------------------- 1 file changed, 51 insertions(+), 56 deletions(-) diff --git a/demo.py b/demo.py index ee1cdfd..a411694 100644 --- a/demo.py +++ b/demo.py @@ -1,13 +1,10 @@ # -*- coding:utf-8 -*- """ +信号设计课程小组设计 -CODE >>> SINCE IN CAIXYPROMISE. -STRIVE FOR EXCELLENT. -CONSTANTLY STRIVING FOR SELF-IMPROVEMENT. -@ by: caixy -@ date: 2021-10-1 - +@ by: Leaf +@ date: 2022-05-28 """ 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。 """ - 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 maxHands: 要检测的最大手数 - :param detectionCon: 最小检测置信度 - :param minTrackCon: 最小跟踪置信度 + :param max_hands: 要检测的最大手数 + :param detection_con: 最小检测置信度 + :param min_track_con: 最小跟踪置信度 """ + self.results = None self.mode = mode - self.maxHands = maxHands + self.max_hands = max_hands self.modelComplex = False - self.detectionCon = detectionCon - self.minTrackCon = minTrackCon + self.detection_con = detection_con + 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.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelComplex, - self.detectionCon, self.minTrackCon) + self.hands = self.mpHands.Hands(self.mode, self.max_hands, self.modelComplex, + self.detection_con, self.min_track_con) self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器 self.tipIds = [4, 8, 12, 16, 20] # 指尖列表 self.fingers = [] self.lmList = [] - def findHands(self, img, draw=True): + def find_hands(self, img, draw=True): """ 从图像(BRG)中找到手部。 :param img: 用于查找手的图像。 :param draw: 在图像上绘制输出的标志。 :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， - self.results = self.hands.process(imgRGB) + 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(img_rgb) if self.results.multi_hand_landmarks: for handLms in self.results.multi_hand_landmarks: @@ -59,55 +57,53 @@ class HandDetector: self.mpHands.HAND_CONNECTIONS) 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 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) :return: 像素格式的手部关节位置列表；手部边界框 """ - xList = [] - yList = [] - bbox = [] - bboxInfo = [] + x_list = [] + y_list = [] + bbox_info = [] self.lmList = [] if self.results.multi_hand_landmarks: - myHand = self.results.multi_hand_landmarks[handNo] - for id, lm in enumerate(myHand.landmark): + my_hand = self.results.multi_hand_landmarks[hand_no] + for _, lm in enumerate(my_hand.landmark): h, w, c = img.shape px, py = int(lm.x * w), int(lm.y * h) - xList.append(px) - yList.append(py) + x_list.append(px) + y_list.append(py) self.lmList.append([px, py]) if draw: cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED) - xmin, xmax = min(xList), max(xList) - ymin, ymax = min(yList), max(yList) - boxW, boxH = xmax - xmin, ymax - ymin - bbox = xmin, ymin, boxW, boxH - cx, cy = bbox[0] + (bbox[2] // 2), \ - bbox[1] + (bbox[3] // 2) - bboxInfo = {"id": id, "bbox": bbox, "center": (cx, cy)} + x_min, x_max = min(x_list), max(x_list) + y_min, y_max = min(y_list), max(y_list) + box_w, box_h = x_max - x_min, y_max - y_min + bbox = x_min, y_min, box_w, box_h + cx, cy = bbox[0] + (bbox[2] // 2), bbox[1] + (bbox[3] // 2) + bbox_info = {"id": hand_no, "bbox": bbox, "center": (cx, cy)} if draw: cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20), (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20), (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 - ：return：竖起手指的列表 + :return: 竖起手指的列表 """ + fingers = [] if self.results.multi_hand_landmarks: - myHandType = self.handType() - fingers = [] + my_hand_type = self.hand_type() # Thumb - if myHandType == "Right": + if my_hand_type == "Right": if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]: fingers.append(1) else: @@ -118,17 +114,17 @@ class HandDetector: else: fingers.append(0) # 4 Fingers - for id in range(1, 5): - if self.lmList[self.tipIds[id]][1] < self.lmList[self.tipIds[id] - 2][1]: + for i in range(1, 5): + if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]: fingers.append(1) else: fingers.append(0) return fingers - def handType(self): + def hand_type(self): """ 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.lmList[17][0] < self.lmList[5][0]: @@ -139,21 +135,21 @@ class HandDetector: class Main: def __init__(self): + self.detector = None self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW) self.camera.set(3, 1280) self.camera.set(4, 720) - def Gesture_recognition(self): - fps = cv2.CAP_PROP_FPS + def gesture_recognition(self): self.detector = HandDetector() while True: frame, img = self.camera.read() - img = self.detector.findHands(img) - lmList, bbox = self.detector.findPosition(img) + img = self.detector.find_hands(img) + lm_list, bbox = self.detector.find_position(img) - if lmList: + if lm_list: 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): 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): cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) - cv2.imshow("camera", img) + cv2.imshow("camera", img) key = cv2.waitKey(1) if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1: break @@ -188,7 +184,6 @@ class Main: break - if __name__ == '__main__': Solution = Main() - Solution.Gesture_recognition() + Solution.gesture_recognition()