win10 x64搭建tensorflow

win10 x64搭建tensorflow

我的显卡是两块丽台 RTX 4000， 

准备软件资源如下：

pycharm-professional-2020.2.2
Anaconda3-5.3.1-Windows-x86_64
CUDA 10.1.243
cudnn 10.1
tensorflow-gpu 2.3.0

步骤：

1，安装Anaconda，建库，配Windows环境变量；

2，安装Pycharm 安装社区版或企业版，删除探测文件可无限使用
文件地址：C:UsersjerryAppDataRoamingJetBrainsPyCharm2020.2eval

3，配置pycharm的所需的tensorflow的环境
pycharm->file->settings->Project XXX ->Python Interpreter->Python Interpreter Add
选择anaconda创建的，如：C:UsersjerryAppDataLocalcondacondaenvs

4，安装cmake（有些python模块是编译安装的，需要cmake）

5，安装VS 2019 社区版，仅勾选C++基础库即可（有些python模块是编译安装的，需要C++编译器）

4，安装CUDA 10.1,在线安装（已有VS的话去掉VS选项）

5，下载cudnn 8.0.3.33，CUDA安装完成后解压到CUDA根目录

6，pip或conda安装tensorflow-gpu 2.3.0

7，pip或conda安装dlib（人脸识别库，可以不装）

8，pip或conda安装face_recognition（人脸识别库，可以不装）

试验：

案例1，猫狗大战（准备数据、先训练，再载入模型预测）

# -*- coding:utf-8 -*-
# @Time : 2020/10/11 15:41
# @Author: jerry
# @File : image_classification.py
import tensorflow as tf
import numpy as np
import pandas as pd
from keras.preprocessing.image import ImageDataGenerator, load_img
del_selection import train_test_split
dels import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from matplotlib import pyplot as plt
from tensorflow.python.keras.callbacks import EarlyStopping, ReduceLROnPlateau
import osdef train_model_ex(train_input_dir,IMAGE_WIDTH,IMAGE_HEIGHT,IMAGE_CHANNELS,batch_size,saved_model_path,FAST_RUN = False):df,train_df, validate_df,total_train,total_validate = prepare_data_ex(train_input_dir,IMAGE_WIDTH, IMAGE_HEIGHT,batch_size)train_datagen, train_generator = traning_generator_ex(train_df, train_input_dir, IMAGE_WIDTH, IMAGE_HEIGHT,batch_size)validation_datagen, validation_generator = validation_generator_ex(validate_df,train_input_dir,IMAGE_WIDTH,IMAGE_HEIGHT,batch_size)model = build_model(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)earlystop = EarlyStopping(patience=10)learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',patience=2,verbose=1,factor=0.5,min_lr=0.00001)callbacks = [earlystop, learning_rate_reduction]# See how our generator workexample_df = train_df.sample(n=1).reset_index(drop=True)example_generator = train_datagen.flow_from_dataframe(example_df,train_input_dir,x_col='filename',y_col='category',target_size=(IMAGE_WIDTH, IMAGE_HEIGHT),class_mode='categorical')plt.figure(figsize=(12, 12))for i in range(0, 15):plt.subplot(5, 3, i + 1)for X_batch, Y_batch in example_generator:image = X_batch[0]plt.imshow(image)breakplt.tight_layout()plt.show()fit_ex(model,FAST_RUN,train_generator,total_train,validation_generator,total_validate,batch_size,callbacks,saved_model_path)def prepare_data_ex(train_input_dir,IMAGE_WIDTH, IMAGE_HEIGHT,batch_size):# Prepare Traning Datafilenames = os.listdir(train_input_dir)categories = []for filename in filenames:category = filename.split('.')[0]if category == 'dog':categories.append(1)else:categories.append(0)df = pd.DataFrame({'filename': filenames,'category': categories})df.head()df.tail()# Prepare datadf["category"] = df["category"].replace({0: 'cat', 1: 'dog'})train_df, validate_df = train_test_split(df, test_size=0.20, random_state=42)train_df = set_index(drop=True)validate_df = set_index(drop=True)train_df['category'].value_counts().plot.bar()validate_df['category'].value_counts().plot.bar()total_train = train_df.shape[0]total_validate = validate_df.shape[0]return df,train_df, validate_df,total_train,total_validatedef traning_generator_ex(train_df,train_input_dir,IMAGE_WIDTH,IMAGE_HEIGHT,batch_size):# Traning Generatortrain_datagen = ImageDataGenerator(rotation_range=15,rescale=1. / 255,shear_range=0.1,zoom_range=0.2,horizontal_flip=True,width_shift_range=0.1,height_shift_range=0.1)train_generator = train_datagen.flow_from_dataframe(train_df,train_input_dir,x_col='filename',y_col='category',target_size=(IMAGE_WIDTH, IMAGE_HEIGHT),class_mode='categorical',batch_size=batch_size)return train_datagen,train_generatordef validation_generator_ex(validate_df,validate_input_dir,IMAGE_WIDTH,IMAGE_HEIGHT,batch_size):# Validation Generatorvalidation_datagen = ImageDataGenerator(rescale=1. / 255)validation_generator = validation_datagen.flow_from_dataframe(validate_df,validate_input_dir,x_col='filename',y_col='category',target_size=(IMAGE_WIDTH, IMAGE_HEIGHT),class_mode='categorical',batch_size=batch_size)return validation_datagen,validation_generatordef build_model(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS):# Build Modelmodel = Sequential()model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)))model.add(BatchNormalization())model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(BatchNormalization())model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(128, (3, 3), activation='relu'))model.add(BatchNormalization())model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(512, activation='relu'))model.add(BatchNormalization())model.add(Dropout(0.5))model.add(Dense(2, activation='softmax'))  # 2 because we have cat and dog classesmodelpile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])model.summary()return modeldef fit_ex(model,FAST_RUN,train_generator,total_train,validation_generator,total_validate,batch_size,callbacks,saved_model_path):# Fit Modelepochs = 1 if FAST_RUN else 100history = model.fit_generator(train_generator,epochs=epochs,validation_data=validation_generator,validation_steps=total_validate // batch_size,steps_per_epoch=total_train // batch_size,callbacks=callbacks)#model.save_weights(saved_model_path)model.save(saved_model_path)virtualize_training(history,epochs)def virtualize_training(history,epochs):# Virtualize Trainingfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))ax1.plot(history.history['loss'], color='b', label="Training loss")ax1.plot(history.history['val_loss'], color='r', label="validation loss")ax1.set_xticks(np.arange(1, epochs, 1))ax1.set_yticks(np.arange(0, 1, 0.1))ax2.plot(history.history['accuracy'], color='b', label="Training accuracy")ax2.plot(history.history['val_accuracy'], color='r', label="Validation accuracy")ax2.set_xticks(np.arange(1, epochs, 1))legend = plt.legend(loc='best', shadow=True)plt.tight_layout()plt.show()def testing_ex(test_input_dir,IMAGE_WIDTH,IMAGE_HEIGHT,batch_size):# Prepare Testing Datatest_filenames = os.listdir(test_input_dir)test_df = pd.DataFrame({'filename': test_filenames})nb_samples = test_df.shape[0]# Create Testing Generatortest_gen = ImageDataGenerator(rescale=1. / 255)test_generator = test_gen.flow_from_dataframe(test_df,test_input_dir,x_col='filename',y_col=None,class_mode=None,target_size=(IMAGE_WIDTH, IMAGE_HEIGHT),batch_size=batch_size,shuffle=False)return test_df,nb_samples,test_generatordef predict_ex(model_path, predict_input_dir, output_dir, IMAGE_WIDTH, IMAGE_HEIGHT, batch_size):test_df, nb_samples, test_generator = testing_ex(predict_input_dir, IMAGE_WIDTH, IMAGE_HEIGHT, batch_size)model = dels.load_model(model_path)# 检查其架构model.summary()# Predictpredict = model.predict_generator(test_generator, steps&#il(nb_samples / batch_size))test_df['category'] = np.argmax(predict, axis=-1)# Submissionsubmission_df = py()submission_df['id'] = submission_df['filename'].str.split('.').str[0]submission_df['label'] = submission_df['category']submission_df.drop(['filename', 'category'], axis=1, inplace=True)_csv(output_dir + 'submission.csv', index=False)def test(model_path,predict_image_path, IMAGE_WIDTH, IMAGE_HEIGHT):model = dels.load_model(model_path)# Predictimg = tf.keras.preprocessing.image.load_img(predict_image_path, target_size=(IMAGE_HEIGHT, IMAGE_WIDTH))img_array = tf.keras.preprocessing.image.img_to_array(img)img_array = tf.expand_dims(img_array, 0)  # Create a batchpredictions = model.predict(img_array)score = tf.nn.softmax(predictions[0])print("This image most likely belongs to {} with a {:.2f} percent confidence.".format(np.argmax(score), 100 * np.max(score)))def evaluate_ex(model_path, input_dir, IMAGE_HEIGHT, IMAGE_WIDTH, batch_size):model = dels.load_model(model_path)model.summary()df, train_df, validate_df, total_train, total_validate = prepare_data_ex(input_dir, IMAGE_WIDTH, IMAGE_HEIGHT,batch_size)train_datagen, train_generator = traning_generator_ex(train_df, input_dir, IMAGE_WIDTH, IMAGE_HEIGHT,batch_size)validation_datagen, validation_generator = validation_generator_ex(validate_df, input_dir, IMAGE_WIDTH,IMAGE_HEIGHT, batch_size)test_loss, test_acc = model.evaluate(validate_df, verbose=2)print('nTest accuracy:', test_acc)def main_ex():FAST_RUN = Falsetrain_input_dir = "./dataset/cat_and_dog/input/train/"model_path = "./dataset/cat_and_dog/models/saved_model/model.h5"test_input_dir = "./dataset/cat_and_dog/input/test1/"output_dir = "./dataset/cat_and_dog/output/"predict_image_path = "./dataset/cat_and_dog/input/test1/10.jpg"IMAGE_WIDTH = 128IMAGE_HEIGHT = 128IMAGE_CHANNELS = 3batch_size = 16#train_model_ex(train_input_dir,IMAGE_WIDTH,IMAGE_HEIGHT,IMAGE_CHANNELS,batch_size,model_path,FAST_RUN)#evaluate_ex(model_path, train_input_dir, IMAGE_HEIGHT, IMAGE_WIDTH, batch_size)predict_ex(model_path, test_input_dir, output_dir, IMAGE_WIDTH, IMAGE_HEIGHT, batch_size)#test(model_path,predict_image_path,IMAGE_WIDTH, IMAGE_HEIGHT)main_ex()

案例2，鲜花分类（准备数据、先训练，再载入模型预测）

import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf
import pathlib
from tensorflow import keras
from tensorflow.keras import layers
from dels import Sequential
import matplotlib.pyplot as plt
from tensorflow.python.keras.callbacks import EarlyStopping, ReduceLROnPlateaudef train_model_ex(model_path, input_dir, img_height, img_width, batch_size,FAST_RUN):train_ds,val_ds,data_augmentation = prepare_data_ex(input_dir, img_height, img_width, batch_size)model = bulid_model(data_augmentation,img_height, img_width)history,epochs = fit_ex(model,model_path,train_ds,val_ds,FAST_RUN)virtualize_training(history, epochs)def prepare_data_ex(input_dir, img_height, img_width, batch_size):input_dir = pathlib.Path(input_dir)image_count = len(list(input_dir.glob('*/*.jpg')))print(image_count)train_ds = tf.keras.preprocessing.image_dataset_from_directory(input_dir,validation_split=0.2,subset="training",seed=123,image_size=(img_height, img_width),batch_size=batch_size)val_ds = tf.keras.preprocessing.image_dataset_from_directory(input_dir,validation_split=0.2,subset="validation",seed=123,image_size=(img_height, img_width),batch_size=batch_size)class_names = train_ds.class_namesprint(class_names)# 可视化数据# plt.figure(figsize=(10, 10))# for images, labels in train_ds.take(1):#     for i in range(9):#         ax = plt.subplot(3, 3, i + 1)#         plt.imshow(images[i].numpy().astype("uint8"))#         plt.title(class_names[labels[i]])#         plt.axis("off")# plt.show()for image_batch, labels_batch in train_ds:print(image_batch.shape)print(labels_batch.shape)break# 配置数据集以提高性能AUTOTUNE = perimental.AUTOTUNEtrain_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)# 标准化数据normalization_layer = perimental.preprocessing.Rescaling(1. / 255)normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))image_batch, labels_batch = next(iter(normalized_ds))first_image = image_batch[0]# Notice the pixels values are now in `[0,1]`.print(np.min(first_image), np.max(first_image))data_augmentation = keras.Sequential([perimental.preprocessing.RandomFlip("horizontal",input_shape=(img_height,img_width,3)),perimental.preprocessing.RandomRotation(0.1),perimental.preprocessing.RandomZoom(0.1),])plt.figure(figsize=(10, 10))for images, _ in train_ds.take(1):for i in range(9):augmented_images = data_augmentation(images)ax = plt.subplot(3, 3, i + 1)plt.imshow(augmented_images[0].numpy().astype("uint8"))plt.axis("off")plt.show()return train_ds,val_ds,data_augmentationdef bulid_model(data_augmentation,img_height, img_width):# 创建模型num_classes = 5model = Sequential([data_perimental.preprocessing.Rescaling(1. / 255),layers.Conv2D(16, 3, padding='same', activation='relu'),layers.MaxPooling2D(),layers.Conv2D(32, 3, padding='same', activation='relu'),layers.MaxPooling2D(),layers.Conv2D(64, 3, padding='same', activation='relu'),layers.MaxPooling2D(),layers.Dropout(0.2),layers.Flatten(),layers.Dense(128, activation='relu'),layers.Dense(num_classes)])# 编译模型modelpile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])# 查看模型信息model.summary()return modeldef fit_ex(model,model_path,train_ds,val_ds,FAST_RUN):earlystop = EarlyStopping(patience=10)learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',patience=2,verbose=2,factor=0.5,min_lr=0.00001)callbacks = [earlystop, learning_rate_reduction]# 训练模型# Fit Modelepochs = 1 if FAST_RUN else 100history = model.fit_generator(train_ds,validation_data=val_ds,epochs=epochs,callbacks=callbacks)model.save(model_path)return history,epochsdef virtualize_training(history,epochs):# 可视化培训结果fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))ax1.plot(history.history['loss'], color='b', label="Training loss")ax1.plot(history.history['val_loss'], color='r', label="validation loss")ax1.set_xticks(np.arange(1, epochs, 1))ax1.set_yticks(np.arange(0, 1, 0.1))ax2.plot(history.history['accuracy'], color='b', label="Training accuracy")ax2.plot(history.history['val_accuracy'], color='r', label="Validation accuracy")ax2.set_xticks(np.arange(1, epochs, 1))legend = plt.legend(loc='best', shadow=True)plt.tight_layout()plt.show()print(legend)def predict_ex(model_path, predict_image_path, img_width, img_height):model = dels.load_model(model_path)model.summary()img = keras.preprocessing.image.load_img(predict_image_path, target_size=(img_height, img_width))img_array = keras.preprocessing.image.img_to_array(img)img_array = tf.expand_dims(img_array, 0)  # Create a batchpredictions = model.predict(img_array)score = tf.nn.softmax(predictions[0])print("This image most likely belongs to {} with a {:.2f} percent confidence.".format(np.argmax(score), 100 * np.max(score)))def evaluate_ex(model_path, input_dir, img_height, img_width, batch_size):model = dels.load_model(model_path)model.summary()train_ds, val_ds, data_augmentation = prepare_data_ex(input_dir, img_height, img_width, batch_size)test_loss, test_acc = model.evaluate(train_ds, verbose=2)print('nTest accuracy:', test_acc)def main_ex():FAST_RUN = Falsebatch_size = 15img_height = 180img_width = 180model_path = "./dataset/flower_photos/models/saved_model/model.h5"input_dir = "./dataset/flower_photos/input/"predict_image_path = "./dataset/flower_photos/input/roses/319298955_0c72bd36bf.jpg"# train_model_ex(model_path, input_dir, img_height, img_width, batch_size, FAST_RUN)evaluate_ex(model_path, input_dir, img_height, img_width, batch_size)# 0 daisy 雏菊、1 dandelion 蒲公英、2 roses 玫瑰、3 sunflowers 向日葵、4 tulips 郁金香# predict_ex(model_path, predict_image_path, img_width, img_height)main_ex()

案例3，dlib人脸识别

#coding=utf-8
#图片检测 - Dlib版本
import cv2
import dlibpath = "img/tly0.jpg"
img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#人脸分类器
detector = _frontal_face_detector()
# 获取人脸检测器
predictor = dlib.shape_predictor("data/shape_predictor_68_face_landmarks.dat"
)dets = detector(gray, 1)
for face in dets:# 在图片中标注人脸，并显示# left = face.left()# top = p()# right = face.right()# bottom = face.bottom()# angle(img, (left, top), (right, bottom), (0, 255, 0), 2)# cv2.imshow("image", img)shape = predictor(img, face)  # 寻找人脸的68个标定点# 遍历所有点，打印出其坐标，并圈出来for pt in shape.parts():pt_pos = (pt.x, pt.y)cv2.circle(img, pt_pos, 1, (0, 255, 0), 2)cv2.imshow("image", img)cv2.waitKey(0)
cv2.destroyAllWindows()

案例4，opencv人脸识别

#coding=utf-8
#图片检测 - OpenCV版本
import cv2
import datetime
import timefilepath = "img/duoren.jpg"
# OpenCV人脸识别分类器
classifier = cv2.CascadeClassifier("datahaarcascadeshaarcascade_l"
)# 程序开始时间
startTime = w()img = cv2.imread(filepath)  # 读取图片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 转换灰色
color = (0, 255, 0)  # 定义绘制颜色
# 调用识别人脸
faceRects = classifier.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
if len(faceRects):  # 大于0则检测到人脸for faceRect in faceRects:  # 单独框出每一张人脸x, y, w, h = faceRect# 框出人脸angle(img, (x, y), (x + h, y + w), color, 1)# 左眼 (不对# cv2.circle(img, (x + w // 4, y + h // 4 + 30), min(w // 8, h // 8),color)#右眼 (不对)# cv2.circle(img, (x + 3 * w // 4, y + h // 4 + 30), min(w // 8, h // 8), color)#嘴巴 (不对# angle(img, (x + 3 * w // 8, y + 3 * h // 4),(x + 5 * w // 8, y + 7 * h // 8), color)# 程序结束时间
endTime = w()
print((endTime - startTime))
cv2.imshow("image", img)  # 显示图像
cv2.waitKey(0)
cv2.destroyAllWindows()

案例5，opecv面部表情识别

#coding=utf-8
#表情识别import cv2
dels import load_model
import numpy as np
import chineseText
import datetimestartTime = w()
emotion_classifier = load_model('classifier/emotion_models/simple_CNN.530-0.65.hdf5')
endTime = w()
print(endTime - startTime)emotion_labels = {0: '生气',1: '厌恶',2: '恐惧',3: '开心',4: '难过',5: '惊喜',6: '平静'
}img = cv2.imread("img/duoren.jpg")
face_classifier = cv2.CascadeClassifier("datahaarcascadeshaarcascade_l"
)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3, minSize=(40, 40))
color = (255, 0, 0)for (x, y, w, h) in faces:gray_face = gray[(y):(y + h), (x):(x + w)]gray_face = size(gray_face, (48, 48))gray_face = gray_face / 255.0gray_face = np.expand_dims(gray_face, 0)gray_face = np.expand_dims(gray_face, -1)emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))emotion = emotion_labels[emotion_label_angle(img, (x + 10, y + 10), (x + h - 10, y + w - 10),(255, 255, 255), 2)img = chineseText.cv2ImgAddText(img, emotion, x + h * 0.3, y, color, 20)cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

案例6，face_recognition 自动化妆

#coding=utf-8
#数字化妆类
import face_recognition
from PIL import Image, ImageDraw#加载图片到numpy array
image = face_recognition.load_image_file("img/tly0.jpg")#标识脸部特征
face_landmarks_list = face_recognition.face_landmarks(image)for face_landmarks in face_landmarks_list:pil_image = Image.fromarray(image)d = ImageDraw.Draw(pil_image, 'RGBA')# 绘制眉毛d.polygon(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 128))d.polygon(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 128))d.line(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 150), width=2)d.line(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 150), width=2)# 绘制嘴唇d.polygon(face_landmarks['top_lip'], fill=(120, 0, 0, 128))d.polygon(face_landmarks['bottom_lip'], fill=(120, 0, 0, 128))d.line(face_landmarks['top_lip'], fill=(120, 0, 0, 64), width=2)d.line(face_landmarks['bottom_lip'], fill=(120, 0, 0, 64), width=2)# 绘制眼睛d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))# 绘制眼线d.line(face_landmarks['left_eye'] + [face_landmarks['left_eye'][0]],fill=(0, 0, 0, 110),width=6)d.line(face_landmarks['right_eye'] + [face_landmarks['right_eye'][0]],fill=(0, 0, 0, 110),width=6)pil_image.show()

案例7，face_recognition人脸识别（用opencv拿摄像头数据，要有摄像头）

#coding=utf-8
#人脸识别类 - 使用face_recognition模块
import cv2
import face_recognition
import ospath = "img/face_recognition"  # 模型数据图片目录
cap = cv2.VideoCapture(0)
total_image_name = []
total_face_encoding = []
for fn in os.listdir(path):  #fn 表示的是文件名qprint(path + "/" + fn)total_face_encoding.append(face_recognition.face_encodings(face_recognition.load_image_file(path + "/" + fn))[0])fn = fn[:(len(fn) - 4)]  #截取图片名（这里应该把images文件中的图片名命名为为人物名）total_image_name.append(fn)  #图片名字列表
while (1):ret, frame = ad()# 发现在视频帧所有的脸和face_enqcodingsface_locations = face_recognition.face_locations(frame)face_encodings = face_recognition.face_encodings(frame, face_locations)# 在这个视频帧中循环遍历每个人脸for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):# 看看面部是否与已知人脸相匹配。for i, v in enumerate(total_face_encoding):match = face_recognitionpare_faces([v], face_encoding, tolerance=0.5)name = "Unknown"if match[0]:name = total_image_name[i]break# 画出一个框，框住脸angle(frame, (left, top), (right, bottom), (0, 0, 255), 2)# 画出一个带名字的标签，放在框下angle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255),cv2.FILLED)font = cv2.FONT_HERSHEY_DUPLEXcv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0,(255, 255, 255), 1)# 显示结果图像cv2.imshow('Video', frame)if cv2.waitKey(1) & 0xFF == ord('q'):lease()
cv2.destroyAllWindows()

案例8，face_recognition勾画面部轮廓

#coding=utf-8
#绘制面部轮廓
import face_recognition
from PIL import Image, ImageDraw# 将图片文件加载到numpy 数组中
image = face_recognition.load_image_file("img/tly0.jpg")#查找图像中所有面部的所有面部特征
face_landmarks_list = face_recognition.face_landmarks(image)for face_landmarks in face_landmarks_list:facial_features = ['chin',  # 下巴'left_eyebrow',  # 左眉毛'right_eyebrow',  # 右眉毛'nose_bridge',  # 鼻樑'nose_tip',  # 鼻尖'left_eye',  # 左眼'right_eye',  # 右眼'top_lip',  # 上嘴唇'bottom_lip'  # 下嘴唇]pil_image = Image.fromarray(image)d = ImageDraw.Draw(pil_image)for facial_feature in facial_features:d.line(face_landmarks[facial_feature], fill=(0, 255, 0), width=2)pil_image.show()

案例9，OCR 文本识别（装个Tesseract-OCR，记得勾选中文支持，修改python pytesseract模块里的环境）

#coding=utf-8
#文字识别类
from PIL import Image
import pytesseract
import cv2path = "img/text-img.png"text = pytesseract.image_to_string(Image.open(path),lang='chi_sim')
print(text)img = cv2.imread(path)
cv2.imshow("Image", img)cv2.waitKey(0)
cv2.destroyAllWindows()