TensorFlow2.X结合结合OpenCV 实现手势识别功能 实现手势识别功能 主要介绍了TensorFlow2.X结合OpenCV 实现手势识别功能,本文通过实例代码给大家介绍的非常详细，对大家 的学习或工作具有一定的参考借鉴价值，需要的朋友可以参考下 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 数据集地址 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 容请搜索我们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我们！