增加:用户自定义手势
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Datasets/one.npz
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156
demo.py
156
demo.py
@ -12,12 +12,15 @@ import mediapipe as mp
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import torch
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import torch.nn as nn
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import numpy as np
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import shutil
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from os.path import exists
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from os import mkdir
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from pathlib import Path
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from torch.utils.data import DataLoader, TensorDataset
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class CNN(nn.Module):
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def __init__(self):
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def __init__(self, m):
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super(CNN, self).__init__()
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self.out_label = []
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self.conv1 = nn.Sequential(
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@ -37,13 +40,17 @@ class CNN(nn.Module):
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nn.MaxPool2d(3),
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)
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self.med = nn.Linear(32 * 7 * 1, 500)
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self.out = nn.Linear(500, 10) # fully connected layer, output 10 classes
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self.med2 = nn.Linear(1*21*3, 100)
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self.med3 = nn.Linear(100, 500)
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self.out = nn.Linear(500, m) # fully connected layer, output 10 classes
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = x.view(x.size(0), -1) # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
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x = self.med(x)
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# x = self.med2(x)
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# x = self.med3(x)
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output = self.out(x)
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return output
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@ -104,19 +111,20 @@ class HandDetector:
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:param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
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:return: 像素格式的手部关节位置列表；手部边界框
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"""
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x_list = []
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y_list = []
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one_data = np.zeros([21, 3])
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bbox_info = []
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self.lmList = []
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h, w, c = img.shape
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if self.results.multi_hand_landmarks:
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my_hand = self.results.multi_hand_landmarks[hand_no]
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for _, lm in enumerate(my_hand.landmark):
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for i, lm in enumerate(my_hand.landmark):
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px, py = int(lm.x * w), int(lm.y * h)
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x_list.append(px)
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y_list.append(py)
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self.lmList.append([lm.x, lm.y, lm.z])
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one_data[i] = np.array([lm.x, lm.y, lm.z])
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if draw:
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cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
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x_min, x_max = min(x_list), max(x_list)
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@ -131,83 +139,52 @@ class HandDetector:
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(bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
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(0, 255, 0), 2)
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return self.lmList, bbox_info
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def fingers_up(self):
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"""
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查找列表中打开并返回的手指数。会分别考虑左手和右手
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:return: 竖起手指的列表
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"""
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fingers = []
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if self.results.multi_hand_landmarks:
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my_hand_type = self.hand_type()
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# Thumb
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if my_hand_type == "Right":
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if self.lmList[self.tipIds[0]][0] > self.lmList[self.tipIds[0] - 1][0]:
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fingers.append(1)
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else:
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fingers.append(0)
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else:
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if self.lmList[self.tipIds[0]][0] < self.lmList[self.tipIds[0] - 1][0]:
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fingers.append(1)
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else:
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fingers.append(0)
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# 4 Fingers
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for i in range(1, 5):
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if self.lmList[self.tipIds[i]][1] < self.lmList[self.tipIds[i] - 2][1]:
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fingers.append(1)
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else:
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fingers.append(0)
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return fingers
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return one_data, (h, w), self.lmList, bbox_info
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def hand_type(self):
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"""
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检查传入的手部是左还是右
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:return: "Right" 或 "Left"
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检查传入的手部 是左还是右
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:return: 1 或 0
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"""
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if self.results.multi_hand_landmarks:
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if self.lmList[17][0] < self.lmList[5][0]:
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return "Right"
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return 1
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else:
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return "Left"
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return 0
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class Main:
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def __init__(self):
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self.EPOCH = 50
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self.BATCH_SIZE = 5
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class AI:
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def __init__(self, datasets_dir):
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self.EPOCH = 20
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self.BATCH_SIZE = 2
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self.LR = 10e-5
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self.DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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self.camera.set(3, 1280)
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self.camera.set(4, 720)
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self.datasets_dir = "Datasets"
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self.datasets_dir = datasets_dir
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self.train_loader = None
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self.m = 0
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self.out_label = [] # CNN网络输出后数字标签转和字符串标签的映射关系
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self.detector = None
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def load_datasets(self):
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train_data = []
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train_label = []
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self.m = 0
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for file in Path(self.datasets_dir).rglob("*.npz"):
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data = np.load(str(file))
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train_data.append(data["data"])
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label_number = np.ones(len(data["data"]))*len(self.out_label)
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label_number = np.ones(len(data["data"])) * len(self.out_label)
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train_label.append(label_number)
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self.out_label.append(data["label"])
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self.m += 1
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train_data = torch.Tensor(np.concatenate(train_data, axis=0))
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train_data = train_data.unsqueeze(1)
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train_label = torch.tensor(np.concatenate(train_label, axis=0)).long()
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dataset = TensorDataset(train_data, train_label)
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self.train_loader = DataLoader(dataset, batch_size=self.BATCH_SIZE, shuffle=True)
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return self.m
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def train_cnn(self):
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cnn = CNN().to(self.DEVICE)
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cnn = CNN(self.m).to(self.DEVICE)
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optimizer = torch.optim.Adam(cnn.parameters(), self.LR) # optimize all cnn parameters
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loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
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@ -236,14 +213,80 @@ class Main:
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torch.save(cnn, 'CNN.pkl')
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print("训练结束")
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class Main:
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def __init__(self):
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self.camera = None
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self.detector = HandDetector()
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self.default_datasets = "Datasets"
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def make_datasets(self, datasets_dir="default", n=100):
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if datasets_dir == "default":
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return
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if exists(datasets_dir):
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shutil.rmtree(datasets_dir)
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mkdir(datasets_dir)
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if self.camera is None:
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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self.camera.set(3, 1280)
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self.camera.set(4, 720)
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label = input("label:")
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while not label == "":
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data = np.zeros([n, 21, 3])
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shape_list = np.zeros([n, 2], dtype=np.int16)
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hand_type = np.zeros(n, dtype=np.int8)
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zero_data = np.zeros([21, 3])
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count = 0
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cv2.startWindowThread()
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while True:
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frame, img = self.camera.read()
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img = self.detector.find_hands(img)
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result, shape, _, bbox = self.detector.find_position(img)
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if result.all() != zero_data.all(): # 假设矩阵不为0，即捕捉到手部时
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data[count] = result
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hand_type[count] = self.detector.hand_type()
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shape_list[count] = np.array(shape)
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count += 1
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cv2.putText(img, str("{}/{}".format(count, n)), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 255, 0), 3)
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cv2.imshow("camera", img)
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key = cv2.waitKey(100)
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if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
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break
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elif key == 27:
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break
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elif count == n - 1:
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break
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cv2.destroyAllWindows()
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open(datasets_dir + "/" + label + ".npz", "w")
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np.savez(datasets_dir + "/" + label + ".npz", label=label, data=data,
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handtype=hand_type, shape=shape_list)
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label = input("label:")
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def train(self, datasets_dir="default"):
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if datasets_dir == "default":
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datasets_dir = self.default_datasets
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ai = AI(datasets_dir)
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ai.load_datasets()
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ai.train_cnn()
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def gesture_recognition(self):
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if self.camera is None:
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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self.camera.set(3, 1280)
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self.camera.set(4, 720)
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self.detector = HandDetector()
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cnn = torch.load("CNN.pkl")
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out_label = cnn.out_label
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while True:
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frame, img = self.camera.read()
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img = self.detector.find_hands(img)
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lm_list, bbox = self.detector.find_position(img)
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_, _, lm_list, bbox = self.detector.find_position(img)
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if lm_list:
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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@ -265,7 +308,8 @@ class Main:
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if __name__ == '__main__':
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Solution = Main()
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Solution.load_datasets()
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Solution.train_cnn()
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Solution.gesture_recognition()
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solution = Main()
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my_datasets_dir = "test"
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solution.make_datasets(my_datasets_dir, 200)
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solution.train(my_datasets_dir)
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solution.gesture_recognition()
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