Python基于keras训练实现微笑识别的示例详解
程序员文章站
2022-06-09 19:38:20
目录一、数据预处理二、训练模型创建模型训练模型训练结果三、预测效果四、源代码pretreatment.pytrain.pypredict.py一、数据预处理实验数据来自genki4k提取含有完整人脸的...
一、数据预处理
实验数据来自genki4k
提取含有完整人脸的图片
def init_file(): num = 0 bar = tqdm(os.listdir(read_path)) for file_name in bar: bar.desc = "预处理图片: " # a图片的全路径 img_path = (read_path + "/" + file_name) # 读入的图片的路径中含非英文 img = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), cv2.imread_unchanged) # 获取图片的宽高 img_shape = img.shape img_height = img_shape[0] img_width = img_shape[1] # 用来存储生成的单张人脸的路径 # dlib检测 dets = detector(img, 1) for k, d in enumerate(dets): if len(dets) > 1: continue num += 1 # 计算矩形大小 # (x,y), (宽度width, 高度height) # pos_start = tuple([d.left(), d.top()]) # pos_end = tuple([d.right(), d.bottom()]) # 计算矩形框大小 height = d.bottom() - d.top() width = d.right() - d.left() # 根据人脸大小生成空的图像 img_blank = np.zeros((height, width, 3), np.uint8) for i in range(height): if d.top() + i >= img_height: # 防止越界 continue for j in range(width): if d.left() + j >= img_width: # 防止越界 continue img_blank[i][j] = img[d.top() + i][d.left() + j] img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.inter_cubic) # 保存图片 cv2.imencode('.jpg', img_blank)[1].tofile(save_path + "/" + "file" + str(num) + ".jpg") logging.info("一共", len(os.listdir(read_path)), "个样本") logging.info("有效样本", num)
二、训练模型
创建模型
# 创建网络 def create_model(): model = models.sequential() model.add(layers.conv2d(32, (3, 3), activation='relu', input_shape=(150, 150, 3))) model.add(layers.maxpooling2d((2, 2))) model.add(layers.conv2d(64, (3, 3), activation='relu')) model.add(layers.maxpooling2d((2, 2))) model.add(layers.conv2d(128, (3, 3), activation='relu')) model.add(layers.maxpooling2d((2, 2))) model.add(layers.conv2d(128, (3, 3), activation='relu')) model.add(layers.maxpooling2d((2, 2))) model.add(layers.flatten()) model.add(layers.dropout(0.5)) model.add(layers.dense(512, activation='relu')) model.add(layers.dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer=optimizers.rmsprop(lr=1e-4), metrics=['acc']) return model
训练模型
# 训练模型 def train_model(model): # 归一化处理 train_datagen = imagedatagenerator( rescale=1. / 255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=true, ) test_datagen = imagedatagenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( # this is the target directory train_dir, # all images will be resized to 150x150 target_size=(150, 150), batch_size=32, # since we use binary_crossentropy loss, we need binary labels class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_dir, target_size=(150, 150), batch_size=32, class_mode='binary') history = model.fit_generator( train_generator, steps_per_epoch=60, epochs=12, validation_data=validation_generator, validation_steps=30) # 保存模型 save_path = "../output/model" if not os.path.exists(save_path): os.makedirs(save_path) model.save(save_path + "/smiledetect.h5") return history
训练结果
准确率
丢失率
训练过程
三、预测
通过读取摄像头内容进行预测
def rec(img): gray = cv2.cvtcolor(img, cv2.color_bgr2gray) dets = detector(gray, 1) if dets is not none: for face in dets: left = face.left() top = face.top() right = face.right() bottom = face.bottom() cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2) img1 = cv2.resize(img[top:bottom, left:right], dsize=(150, 150)) img1 = cv2.cvtcolor(img1, cv2.color_bgr2rgb) img1 = np.array(img1) / 255. img_tensor = img1.reshape(-1, 150, 150, 3) prediction = model.predict(img_tensor) if prediction[0][0] > 0.5: result = 'unsmile' else: result = 'smile' cv2.puttext(img, result, (left, top), font, 2, (0, 255, 0), 2, cv2.line_aa) cv2.imshow('video', img) while video.isopened(): res, img_rd = video.read() if not res: break rec(img_rd) if cv2.waitkey(1) & 0xff == ord('q'): break
效果
四、源代码
pretreatment.py
import dlib # 人脸识别的库dlib import numpy as np # 数据处理的库numpy import cv2 # 图像处理的库opencv import os import shutil from tqdm import tqdm import logging # dlib预测器 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor('../resources/shape_predictor_68_face_landmarks.dat') # 原图片路径 read_path = "../resources/genki4k/files" # 提取人脸存储路径 save_path = "../output/genki4k/files" if not os.path.exists(save_path): os.makedirs(save_path) # 新的数据集 data_dir = '../resources/data' if not os.path.exists(data_dir): os.makedirs(data_dir) # 训练集 train_dir = data_dir + "/train" if not os.path.exists(train_dir): os.makedirs(train_dir) # 验证集 validation_dir = os.path.join(data_dir, 'validation') if not os.path.exists(validation_dir): os.makedirs(validation_dir) # 测试集 test_dir = os.path.join(data_dir, 'test') if not os.path.exists(test_dir): os.makedirs(test_dir) # 初始化训练数据 def init_data(file_list): # 如果不存在文件夹则新建 for file_path in file_list: if not os.path.exists(file_path): os.makedirs(file_path) # 存在则清空里面所有数据 else: for i in os.listdir(file_path): path = os.path.join(file_path, i) if os.path.isfile(path): os.remove(path) def init_file(): num = 0 bar = tqdm(os.listdir(read_path)) for file_name in bar: bar.desc = "预处理图片: " # a图片的全路径 img_path = (read_path + "/" + file_name) # 读入的图片的路径中含非英文 img = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), cv2.imread_unchanged) # 获取图片的宽高 img_shape = img.shape img_height = img_shape[0] img_width = img_shape[1] # 用来存储生成的单张人脸的路径 # dlib检测 dets = detector(img, 1) for k, d in enumerate(dets): if len(dets) > 1: continue num += 1 # 计算矩形大小 # (x,y), (宽度width, 高度height) # pos_start = tuple([d.left(), d.top()]) # pos_end = tuple([d.right(), d.bottom()]) # 计算矩形框大小 height = d.bottom() - d.top() width = d.right() - d.left() # 根据人脸大小生成空的图像 img_blank = np.zeros((height, width, 3), np.uint8) for i in range(height): if d.top() + i >= img_height: # 防止越界 continue for j in range(width): if d.left() + j >= img_width: # 防止越界 continue img_blank[i][j] = img[d.top() + i][d.left() + j] img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.inter_cubic) # 保存图片 cv2.imencode('.jpg', img_blank)[1].tofile(save_path + "/" + "file" + str(num) + ".jpg") logging.info("一共", len(os.listdir(read_path)), "个样本") logging.info("有效样本", num) # 划分数据集 def divide_data(file_path, message, begin, end): files = ['file{}.jpg'.format(i) for i in range(begin, end)] bar = tqdm(files) bar.desc = message for file in bar: src = os.path.join(save_path, file) dst = os.path.join(file_path, file) shutil.copyfile(src, dst) if __name__ == "__main__": init_file() positive_train_dir = os.path.join(train_dir, 'smile') negative_train_dir = os.path.join(train_dir, 'unsmile') positive_validation_dir = os.path.join(validation_dir, 'smile') negative_validation_dir = os.path.join(validation_dir, 'unsmile') positive_test_dir = os.path.join(test_dir, 'smile') negative_test_dir = os.path.join(test_dir, 'unsmile') file_list = [positive_train_dir, positive_validation_dir, positive_test_dir, negative_train_dir, negative_validation_dir, negative_test_dir] init_data(file_list) divide_data(positive_train_dir, "划分训练集正样本", 1, 1001) divide_data(negative_train_dir, "划分训练集负样本", 2200, 3200) divide_data(positive_validation_dir, "划分验证集正样本", 1000, 1500) divide_data(negative_validation_dir, "划分验证集负样本", 3000, 3500) divide_data(positive_test_dir, "划分测试集正样本", 1500, 2000) divide_data(negative_test_dir, "划分测试集负样本", 2800, 3500)
train.py
import os from keras import layers from keras import models from tensorflow import optimizers import matplotlib.pyplot as plt from keras.preprocessing.image import imagedatagenerator train_dir = "../resources/data/train" validation_dir = "../resources/data/validation" # 创建网络 def create_model(): model = models.sequential() model.add(layers.conv2d(32, (3, 3), activation='relu', input_shape=(150, 150, 3))) model.add(layers.maxpooling2d((2, 2))) model.add(layers.conv2d(64, (3, 3), activation='relu')) model.add(layers.maxpooling2d((2, 2))) model.add(layers.conv2d(128, (3, 3), activation='relu')) model.add(layers.maxpooling2d((2, 2))) model.add(layers.conv2d(128, (3, 3), activation='relu')) model.add(layers.maxpooling2d((2, 2))) model.add(layers.flatten()) model.add(layers.dropout(0.5)) model.add(layers.dense(512, activation='relu')) model.add(layers.dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer=optimizers.rmsprop(lr=1e-4), metrics=['acc']) return model # 训练模型 def train_model(model): # 归一化处理 train_datagen = imagedatagenerator( rescale=1. / 255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=true, ) test_datagen = imagedatagenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( # this is the target directory train_dir, # all images will be resized to 150x150 target_size=(150, 150), batch_size=32, # since we use binary_crossentropy loss, we need binary labels class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_dir, target_size=(150, 150), batch_size=32, class_mode='binary') history = model.fit_generator( train_generator, steps_per_epoch=60, epochs=12, validation_data=validation_generator, validation_steps=30) # 保存模型 save_path = "../output/model" if not os.path.exists(save_path): os.makedirs(save_path) model.save(save_path + "/smiledetect.h5") return history # 展示训练结果 def show_results(history): # 数据增强过后的训练集与验证集的精确度与损失度的图形 acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss'] # 绘制结果 epochs = range(len(acc)) plt.plot(epochs, acc, 'bo', label='training acc') plt.plot(epochs, val_acc, 'b', label='validation acc') plt.title('training and validation accuracy') plt.legend() plt.figure() plt.plot(epochs, loss, 'bo', label='training loss') plt.plot(epochs, val_loss, 'b', label='validation loss') plt.title('training and validation loss') plt.legend() plt.show() if __name__ == "__main__": model = create_model() history = train_model(model) show_results(history)
predict.py
import os from keras import layers from keras import models from tensorflow import optimizers import matplotlib.pyplot as plt from keras.preprocessing.image import imagedatagenerator train_dir = "../resources/data/train" validation_dir = "../resources/data/validation" # 创建网络 # 检测视频或者摄像头中的人脸 import cv2 from keras.preprocessing import image from keras.models import load_model import numpy as np import dlib from pil import image model = load_model('../output/model/smiledetect.h5') detector = dlib.get_frontal_face_detector() video = cv2.videocapture(0) font = cv2.font_hershey_simplex def rec(img): gray = cv2.cvtcolor(img, cv2.color_bgr2gray) dets = detector(gray, 1) if dets is not none: for face in dets: left = face.left() top = face.top() right = face.right() bottom = face.bottom() cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2) img1 = cv2.resize(img[top:bottom, left:right], dsize=(150, 150)) img1 = cv2.cvtcolor(img1, cv2.color_bgr2rgb) img1 = np.array(img1) / 255. img_tensor = img1.reshape(-1, 150, 150, 3) prediction = model.predict(img_tensor) if prediction[0][0] > 0.5: result = 'unsmile' else: result = 'smile' cv2.puttext(img, result, (left, top), font, 2, (0, 255, 0), 2, cv2.line_aa) cv2.imshow('video', img) while video.isopened(): res, img_rd = video.read() if not res: break rec(img_rd) if cv2.waitkey(1) & 0xff == ord('q'): break video.release() cv2.destroyallwindows()
以上就是python基于keras训练实现微笑识别的示例详解的详细内容,更多关于python keras微笑识别的资料请关注其它相关文章!
下一篇: android获取及监听手机网络状态