TensorFlow2.X结合OpenCV 实现手势识别功能

使用Tensorflow 构建卷积神经网络，训练手势识别模型，使用opencv DNN 模块加载模型实时手势识别
效果如下：

先显示下部分数据集图片（0到9的表示，感觉很怪）

构建模型进行训练

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,layers,optimizers,Sequential,metrics
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
import os 
import pathlib
import random
import matplotlib.pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
def read_data(path):
 path_root = pathlib.Path(path)
 # print(path_root)
 # for item in path_root.iterdir():
 #  print(item)
 image_paths = list(path_root.glob('*/*'))
 image_paths = [str(path) for path in image_paths]
 random.shuffle(image_paths)
 image_count = len(image_paths)
 # print(image_count)
 # print(image_paths[:10])
 label_names = sorted(item.name for item in path_root.glob('*/') if item.is_dir())
 # print(label_names)
 label_name_index = dict((name, index) for index, name in enumerate(label_names))
 # print(label_name_index)
 image_labels = [label_name_index[pathlib.Path(path).parent.name] for path in image_paths]
 # print("First 10 labels indices: ", image_labels[:10])
 return image_paths,image_labels,image_count
def preprocess_image(image):
 image = tf.image.decode_jpeg(image, channels=3)
 image = tf.image.resize(image, [100, 100])
 image /= 255.0 # normalize to [0,1] range
 # image = tf.reshape(image,[100*100*3])
 return image
def load_and_preprocess_image(path,label):
 image = tf.io.read_file(path)
 return preprocess_image(image),label
def creat_dataset(image_paths,image_labels,bitch_size):
 db = tf.data.Dataset.from_tensor_slices((image_paths, image_labels))
 dataset = db.map(load_and_preprocess_image).batch(bitch_size) 
 return dataset
def train_model(train_data,test_data):
 #构建模型
 network = keras.Sequential([
   keras.layers.Conv2D(32,kernel_size=[5,5],padding="same",activation=tf.nn.relu),
   keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
   keras.layers.Conv2D(64,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
   keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
   keras.layers.Conv2D(64,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
   keras.layers.Flatten(),
   keras.layers.Dense(512,activation='relu'),
   keras.layers.Dropout(0.5),
   keras.layers.Dense(128,activation='relu'),
   keras.layers.Dense(10)])
 network.build(input_shape=(None,100,100,3))
 network.summary()
 network.compile(optimizer=optimizers.SGD(lr=0.001),
   loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
   metrics=['accuracy']
 )
 #模型训练
 network.fit(train_data, epochs = 100,validation_data=test_data,validation_freq=2) 
 network.evaluate(test_data)
 tf.saved_model.save(network,'D:\\code\\PYTHON\\gesture_recognition\\model\\')
 print("保存模型成功")
 # Convert Keras model to ConcreteFunction
 full_model = tf.function(lambda x: network(x))
 full_model = full_model.get_concrete_function(
 tf.TensorSpec(network.inputs[0].shape, network.inputs[0].dtype))
 # Get frozen ConcreteFunction
 frozen_func = convert_variables_to_constants_v2(full_model)
 frozen_func.graph.as_graph_def()

 layers = [op.name for op in frozen_func.graph.get_operations()]
 print("-" * 50)
 print("Frozen model layers: ")
 for layer in layers:
  print(layer)

 print("-" * 50)
 print("Frozen model inputs: ")
 print(frozen_func.inputs)
 print("Frozen model outputs: ")
 print(frozen_func.outputs)

 # Save frozen graph from frozen ConcreteFunction to hard drive
 tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
   logdir="D:\\code\\PYTHON\\gesture_recognition\\model\\frozen_model\\",
   name="frozen_graph.pb",
   as_text=False)
 print("模型转换完成，训练结束")


if __name__ == "__main__":
 print(tf.__version__)
 train_path = 'D:\\code\\PYTHON\\gesture_recognition\\Dataset'
 test_path = 'D:\\code\\PYTHON\\gesture_recognition\\testdata' 
 image_paths,image_labels,_ = read_data(train_path)
 train_data = creat_dataset(image_paths,image_labels,16)
 image_paths,image_labels,_ = read_data(test_path)
 test_data = creat_dataset(image_paths,image_labels,16)
 train_model(train_data,test_data)

OpenCV加载模型，实时检测

这里为了简化检测使用了ROI。

import cv2
from cv2 import dnn
import numpy as np
print(cv2.__version__)
class_name = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
net = dnn.readNetFromTensorflow('D:\\code\\PYTHON\\gesture_recognition\\model\\frozen_model\\frozen_graph.pb')
cap = cv2.VideoCapture(0)
i = 0
while True:
 _,frame= cap.read() 
 src_image = frame
 cv2.rectangle(src_image, (300, 100),(600, 400), (0, 255, 0), 1, 4)
 frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
 pic = frame[100:400,300:600]
 cv2.imshow("pic1", pic)
 # print(pic.shape)
 pic = cv2.resize(pic,(100,100))
 blob = cv2.dnn.blobFromImage(pic,  
        scalefactor=1.0/225.,
        size=(100, 100),
        mean=(0, 0, 0),
        swapRB=False,
        crop=False)
 # blob = np.transpose(blob, (0,2,3,1))       
 net.setInput(blob)
 out = net.forward()
 out = out.flatten()

 classId = np.argmax(out)
 # print("classId",classId)
 print("预测结果为：",class_name[classId])
 src_image = cv2.putText(src_image,str(classId),(300,100), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,0,255),2,4)
 # cv.putText(img, text, org, fontFace, fontScale, fontcolor, thickness, lineType)
 cv2.imshow("pic",src_image)
 if cv2.waitKey(10) == ord('0'):
  break

小结

这里本质上还是一个图像分类任务。而且，样本数量较少。优化的时候需要做数据增强，还需要防止过拟合。

到此这篇关于TensorFlow2.X结合OpenCV 实现手势识别功能的文章就介绍到这了,更多相关TensorFlow OpenCV 手势识别内容请搜索三水点靠木以前的文章或继续浏览下面的相关文章希望大家以后多多支持三水点靠木！