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录制手势.py
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录制手势.py
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import cv2
import numpy as np
from keras.models import load_model
from training import Training
import os
from keras import backend
import time
import random
class Gesture():
def __init__(self, train_path, predict_path, gesture, train_model):
self.blurValue = 5
self.bgSubThreshold = 36
self.train_path = train_path
self.predict_path = predict_path
self.threshold = 60
self.gesture = gesture
self.train_model = train_model
self.skinkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
self.x1 = 380
self.y1 = 60
self.x2 = 640
self.y2 = 350
def collect_gesture(self, capture, ges, photo_num):
photo_num = photo_num
vedeo = False
predict = False
count = 0
# 读取默认摄像头
cap = cv2.VideoCapture(capture)
# 设置捕捉模式
cap.set(10, 200)
# 背景减法创建及初始化
bgModel = cv2.createBackgroundSubtractorMOG2(0, self.bgSubThreshold)
while True:
# 读取视频帧
ret, frame = cap.read()
# 镜像转换
frame = cv2.flip(frame, 1)
cv2.imshow('Original', frame)
# 双边滤波
frame = cv2.bilateralFilter(frame, 5, 50,100)
# 绘制矩形，第一个为左上角坐标(x,y），第二个为右下角坐标
# rec = cv2.rectangle(frame, (220, 50), (450, 300), (255, 0, 0), 2)
rec = cv2.rectangle(frame, (self.x1, self.y1), (self.x2, self.y2), (255, 0, 0), 2)
# 定义roi区域，第一个为y的取值，第2个为x的取值
# frame = frame[50:300, 220:450]
frame = frame[self.y1:self.y2, self.x1:self.x2]
cv2.imshow('bilateralFilter', frame)
# 背景减法运动检测
bg = bgModel.apply(frame, learningRate=0)
# 显示背景减法的窗口
cv2.imshow('bg', bg)
# 图像边缘处理--腐蚀
fgmask = cv2.erode(bg, self.skinkernel, iterations=1)
# 显示边缘处理后的图像
cv2.imshow('erode', fgmask)
# 将原始图像与背景减法+腐蚀处理后的蒙版做"与"操作
bitwise_and = cv2.bitwise_and(frame, frame, mask=fgmask)
# 显示与操作后的图像
cv2.imshow('bitwise_and', bitwise_and)
# 灰度处理
gray = cv2.cvtColor(bitwise_and, cv2.COLOR_BGR2GRAY)
# 高斯滤波
blur = cv2.GaussianBlur(gray, (self.blurValue, self.blurValue), 2)
cv2.imshow('GaussianBlur', blur)
# 使用自适应阈值分割(adaptiveThreshold)
thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
cv2.imshow('thresh', thresh)
Ges = cv2.resize(thresh, (100, 100))
# 图像的阈值处理(采用ostu)
# _, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# cv2.imshow('threshold1', thresh)
if predict == True:
# img = cv2.resize(thresh, (100, 100))
img = np.array(Ges).reshape(-1, 100, 100, 1)/255
prediction = p_model.predict(img)
final_prediction = [result.argmax() for result in prediction][0]
ges_type = self.gesture[final_prediction]
# print(ges_type)
cv2.putText(rec, ges_type, (self.x1, self.y1), fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=1, thickness=2, color=(0, 0, 255))
# cv2.putText(rec, ges_type, (150, 220), fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=1, thickness=3, color=(0, 0, 255))
cv2.imshow('Original', rec)
if vedeo is True and count < photo_num:
# 录制训练集
cv2.imencode('.jpg', Ges)[1].tofile(self.train_path + '{}_{}.jpg'.format(str(random.randrange(1000, 100000)),str(ges)))
count += 1
print(count)
elif count == photo_num:
print('{}张测试集手势录制完毕，3秒后录制此手势测试集，共{}张'.format(photo_num, int(photo_num*0.43)))
time.sleep(3)
count += 1
elif vedeo is True and photo_num < count < int(photo_num*1.43):
cv2.imencode('.jpg', Ges)[1].tofile(self.predict_path + '{}_{}.jpg'.format(str(random.randrange(1000, 100000)),str(ges)))
count += 1
print(count)
elif vedeo is True and count >= int(photo_num*1.43):
vedeo = False
ges += 1
if ges < len(self.gesture):
print('此手势录制完成，按l录制下一个手势')
else:
print('手势录制结束, 按t进行训练')
k = cv2.waitKey(10)
if k == 27:
break
elif k == ord('l'): # 录制手势
vedeo = True
count = 0
elif k == ord('p'): # 预测手势
predict = True
while True:
model_name = input('请输入模型的名字:\n')
if model_name == 'exit':
break
if model_name in os.listdir('./'):
print('正在加载{}模型'.format(model_name))
p_model = load_model(model_name)
break
else:
print('模型名字输入错误，请重新输入，或输入exit退出')
elif k == ord('b'):
bgModel = cv2.createBackgroundSubtractorMOG2(0, self.bgSubThreshold)
print('背景重置完成')
elif k == ord('t'):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
train = Training(batch_size=32, epochs=5, categories=len(self.gesture), train_folder=self.train_path,
test_folder=self.predict_path, model_name=self.train_model, type=self.gesture)
train.train()
backend.clear_session()
print(f'{self.train_model}模型训练结束')
if __name__ == '__main__':
# 要训练的手势类型
# Gesturetype = input('请输入训练手势(用逗号隔开)：\n')
# if Gesturetype == "none":
# Gesturetype = ['666', 'yech', 'stop', 'punch', 'OK']
# else:
# Gesturetype = Gesturetype.split(',')
Gesturetype = ['666', 'yech', 'stop', 'punch', 'OK']
train_path = 'Gesture_train/'
pridect_path = 'Gesture_predict/'
# # 训练集路径
# train_path = 'train_test/'
# # 测试集路径
# pridect_path = 'predict_test/'
for path in [train_path, pridect_path]:
if not os.path.exists(path):
os.mkdir(path)
print(f'训练手势有：{Gesturetype}')
# 模型保存命名
# train_model = input('请输入训练模型名：\n')
train_model = 'Gesture.h5'
# 初始化手势识别类
Ges = Gesture(train_path, pridect_path, Gesturetype, train_model)
# 单个手势要录制的数量
num = 500
# 训练手势类别计数器
x = 0
# 调用启动函数
Ges.collect_gesture(capture=0, ges=x, photo_num=num)