COVID-19：具有OpenCV，Keras / TensorFlow和深度學習的面罩檢測器

在本教程中，你將學習如何使用OpenCV，Keras / TensorFlow和Deep Learning訓練COVID-19面罩檢測器。

以下內容由公衆號：AIRX社區（國內領先的AI、AR、VR技術學習與交流平臺） 整理

在使用Keras和TensorFlow的數據集上訓練一個面罩檢測器，然後使用這個Python腳訓練口罩檢測器並檢查結果。

• 在圖像中檢測COVID-19口罩

• 檢測實時視頻流中的口罩

具體的實現過程主要分成兩個階段：

爲了訓練一個自定義口罩檢測器，我們需要將我們的項目分爲兩個不同的階段，每個階段都有其各自的子步驟(如上圖所示):

• 培訓:這裏我們主要從磁盤加載口罩檢測數據集，在該數據集上培訓一個模型(使用Keras/TensorFlow)，然後將口罩檢測器序列化到磁盤

• 部署:一旦口罩檢測器接受了訓練，我們就可以加載口罩檢測器，執行人臉檢測，然後將每個臉分類爲with_mask或without_mask

我們的COVID-19面罩檢測數據集，此數據集包含1,376張圖像，類別：

• with_mask：690張圖片

• without_mask：686張圖片

 

數據集下載鏈接：

面部標誌可以讓我們自動推斷出面部結構的位置，包括：眼睛、眉毛、鼻子、口、顎線。要使用面部標誌構建戴着口罩的面部數據集，我們首先需要從不戴着口罩的人的圖像開始：

我們應用面部檢測（opencv）來計算圖像中面部的邊界框位置：

使用OpenCV和NumPy切片提取面部ROI：

然後，我們使用dlib 檢測面部標誌（能夠定位眼睛，鼻子，嘴巴等）以便我們知道將遮罩放置在臉上哪裏：

接下來，我們需要一張帶有透明背景的蒙版圖像，例如以下圖像，COVID-19 冠狀病毒面罩/護罩的示例。由於我們知道面部標誌位置，因此該面罩將自動覆蓋在原始面部ROI上：

 

然後調整面罩的大小並旋轉，將其放在臉上：

然後，我們可以對所有輸入圖像重複此過程，從而創建人造面罩數據集：

涵蓋如何使用ROI將蒙版應用於面部的方法不在本教程的討論範圍之內，但是，如果你想了解更多信息，可以查看：

https://github.com/prajnasb/observations/tree/master/mask_classifier/Data_Generator

項目結構

下載文件之後的目錄結構如下：

$ tree --dirsfirst --filelimit 10.├── dataset│   ├── with_mask [690 entries]│   └── without_mask [686 entries]├── examples│   ├── example_01.png│   ├── example_02.png│   └── example_03.png├── face_detector│   ├── deploy.prototxt│   └── res10_300x300_ssd_iter_140000.caffemodel├── detect_mask_image.py├── detect_mask_video.py├── mask_detector.model├── plot.png└── train_mask_detector.py5 directories, 10 files

數據集/目錄包含“COVID-19口罩檢測數據集”的數據。提供了三個圖像示例，以便你可以測試靜態圖像口罩檢測器。在本教程中，我們將使用三個Python腳本:

• train_mask_detector.py：接受我們的輸入數據集並對MobileNetV2進行微調，以創建我們的mask_detector.model。同時製作了包含精度/損失曲線的訓練歷史圖。

• detect_mask_image.py：使用靜態圖像中執行口罩檢測

• detect_mask_video.py：使用camera流

訓練腳本

接下來使用Keras和TensorFlow訓練分類器，以自動檢測一個人是否戴着口罩。打開 train_mask_detector.py 文件並插入以下代碼：​​​​​​​

# import the necessary packagesfrom tensorflow.keras.preprocessing.image import ImageDataGeneratorfrom tensorflow.keras.applications import MobileNetV2from tensorflow.keras.layers import AveragePooling2Dfrom tensorflow.keras.layers import Dropoutfrom tensorflow.keras.layers import Flattenfrom tensorflow.keras.layers import Densefrom tensorflow.keras.layers import Inputfrom tensorflow.keras.models import Modelfrom tensorflow.keras.optimizers import Adamfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_inputfrom tensorflow.keras.preprocessing.image import img_to_arrayfrom tensorflow.keras.preprocessing.image import load_imgfrom tensorflow.keras.utils import to_categoricalfrom sklearn.preprocessing import LabelBinarizerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classification_reportfrom imutils import pathsimport matplotlib.pyplot as pltimport numpy as npimport argparseimport os

上述import的目的主要是：

• 數據增加

• 加載MobilNetV2分類器(我們將使用預先訓練的ImageNet權重對該模型進行微調)

• 預處理

• 加載圖像數據

我們使用scikit-learn (sklearn)對類標籤進行二值化，對數據集進行分段，並打印分類報告。imutils路徑實現將幫助我們在數據集中查找和列出圖像。我們將使用matplotlib來繪製訓練曲線。

接下來來分析一些命令行參數，從終端啓動腳本:​​​​​​​

# construct the argument parser and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-d", "--dataset", required=True,  help="path to input dataset")ap.add_argument("-p", "--plot", type=str, default="plot.png",  help="path to output loss/accuracy plot")ap.add_argument("-m", "--model", type=str,  default="mask_detector.model",  help="path to output face mask detector model")args = vars(ap.parse_args())

--dataset:面部和帶有口罩的面部的輸入數據集的路徑

--plot:輸出訓練歷史plot的路徑，它將使用matplotlib生成

--model:生成的序列化口罩分類模型的路徑​​​​​​​

INIT_LR = 1e-4EPOCHS = 20BS = 32

在這裏，我指定了超參數常量，包括初始學習率、訓練時間和批處理大小。稍後，我們將應用一個學習速率衰減時間表，這就是爲什麼我們將學習速率變量命名爲INIT_LR。

至此，我們已經準備好加載和預處理的訓練數據：​​​​​​​

# grab the list of images in our dataset directory, then initialize# the list of data (i.e., images) and class imagesprint("[INFO] loading images...")imagePaths = list(paths.list_images(args["dataset"]))data = []labels = []# loop over the image pathsfor imagePath in imagePaths:  # extract the class label from the filename  label = imagePath.split(os.path.sep)[-2]  # load the input image (224x224) and preprocess it  image = load_img(imagePath, target_size=(224, 224))  image = img_to_array(image)  image = preprocess_input(image)  # update the data and labels lists, respectively  data.append(image)  labels.append(label)# convert the data and labels to NumPy arraysdata = np.array(data, dtype="float32")labels = np.array(labels)

上面的代碼行假設你的整個數據集足夠小，可以裝入內存。如果數據集大於可用內存，我建議使用HDF5。​​​​​​​

# perform one-hot encoding on the labelslb = LabelBinarizer()labels = lb.fit_transform(labels)labels = to_categorical(labels)# partition the data into training and testing splits using 80% of# the data for training and the remaining 20% for testing(trainX, testX, trainY, testY) = train_test_split(data, labels,  test_size=0.20, stratify=labels, random_state=42)# construct the training image generator for data augmentationaug = ImageDataGenerator(  rotation_range=20,  zoom_range=0.15,  width_shift_range=0.2,  height_shift_range=0.2,  shear_range=0.15,  horizontal_flip=True,  fill_mode="nearest")

數據將採用以下格式：​​​​​​​

$ python  train_mask_detector.py --dataset  dataset [INFO] loading images...-> (trainX, testX, trainY, testY) = train_test_split(data, labels,(Pdb) labels[500:]array([[1., 0.],       [1., 0.],       [1., 0.],       ...,       [0., 1.],       [0., 1.],       [0., 1.]], dtype=float32)(Pdb)我們標籤數組的每個元素都由一個數組組成，在訓練過程中，我們將對圖像進行動態突變，以提高泛化能力。隨機旋轉、縮放、剪切、偏移和翻轉參數。我們將在訓練時使用aug對象。我們需要準備MobileNetV2微調:​​​​​​​

# load the MobileNetV2 network, ensuring the head FC layer sets are# left offbaseModel = MobileNetV2(weights="imagenet", include_top=False,  input_tensor=Input(shape=(224, 224, 3)))# construct the head of the model that will be placed on top of the# the base modelheadModel = baseModel.outputheadModel = AveragePooling2D(pool_size=(7, 7))(headModel)headModel = Flatten(name="flatten")(headModel)headModel = Dense(128, activation="relu")(headModel)headModel = Dropout(0.5)(headModel)headModel = Dense(2, activation="softmax")(headModel)# place the head FC model on top of the base model (this will become# the actual model we will train)model = Model(inputs=baseModel.input, outputs=headModel)# loop over all layers in the base model and freeze them so they will# *not* be updated during the first training processfor layer in baseModel.layers:  layer.trainable = False

通過準備好數據並進行模型調整以進行微調，我們現在可以編譯和訓練面罩檢測器網絡了：

# compile our modelprint("[INFO] compiling model...")opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)model.compile(loss="binary_crossentropy", optimizer=opt,  metrics=["accuracy"])# train the head of the networkprint("[INFO] training head...")H = model.fit(  aug.flow(trainX, trainY, batch_size=BS),  steps_per_epoch=len(trainX) // BS,  validation_data=(testX, testY),  validation_steps=len(testX) // BS,  epochs=EPOCHS)

訓練完成後，我們將在測試集中評估結果模型：​​​​​​​

# make predictions on the testing setprint("[INFO] evaluating network...")predIdxs = model.predict(testX, batch_size=BS)# for each image in the testing set we need to find the index of the# label with corresponding largest predicted probabilitypredIdxs = np.argmax(predIdxs, axis=1)# show a nicely formatted classification reportprint(classification_report(testY.argmax(axis=1), predIdxs,  target_names=lb.classes_))# serialize the model to diskprint("[INFO] saving mask detector model...")model.save(args["model"], save_format="h5")

我們的最後一步是繪製精度和損耗曲線：​​​​​​​

# plot the training loss and accuracyN = EPOCHSplt.style.use("ggplot")plt.figure()plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")plt.plot(np.arange(0, N), H.history["accuracy"], label="train_acc")plt.plot(np.arange(0, N), H.history["val_accuracy"], label="val_acc")plt.title("Training Loss and Accuracy")plt.xlabel("Epoch #")plt.ylabel("Loss/Accuracy")plt.legend(loc="lower left")plt.savefig(args["plot"])

訓練COVID-19面罩檢測器

現在，我們準備使用Keras，TensorFlow和Deep Learning訓練我們的面罩檢測器。確保在文章開頭下載源代碼和麪罩數據集。從那裏打開一個終端，然後執行以下命令：​​​​​​​

$ python train_mask_detector.py --dataset dataset[INFO] loading images...[INFO] compiling model...[INFO] training head...Train for 34 steps, validate on 276 samplesEpoch 1/2034/34 [==============================] - 30s 885ms/step - loss: 0.6431 - accuracy: 0.6676 - val_loss: 0.3696 - val_accuracy: 0.8242Epoch 2/2034/34 [==============================] - 29s 853ms/step - loss: 0.3507 - accuracy: 0.8567 - val_loss: 0.1964 - val_accuracy: 0.9375Epoch 3/2034/34 [==============================] - 27s 800ms/step - loss: 0.2792 - accuracy: 0.8820 - val_loss: 0.1383 - val_accuracy: 0.9531Epoch 4/2034/34 [==============================] - 28s 814ms/step - loss: 0.2196 - accuracy: 0.9148 - val_loss: 0.1306 - val_accuracy: 0.9492Epoch 5/2034/34 [==============================] - 27s 792ms/step - loss: 0.2006 - accuracy: 0.9213 - val_loss: 0.0863 - val_accuracy: 0.9688...Epoch 16/2034/34 [==============================] - 27s 801ms/step - loss: 0.0767 - accuracy: 0.9766 - val_loss: 0.0291 - val_accuracy: 0.9922Epoch 17/2034/34 [==============================] - 27s 795ms/step - loss: 0.1042 - accuracy: 0.9616 - val_loss: 0.0243 - val_accuracy: 1.0000Epoch 18/2034/34 [==============================] - 27s 796ms/step - loss: 0.0804 - accuracy: 0.9672 - val_loss: 0.0244 - val_accuracy: 0.9961Epoch 19/2034/34 [==============================] - 27s 793ms/step - loss: 0.0836 - accuracy: 0.9710 - val_loss: 0.0440 - val_accuracy: 0.9883Epoch 20/2034/34 [==============================] - 28s 838ms/step - loss: 0.0717 - accuracy: 0.9710 - val_loss: 0.0270 - val_accuracy: 0.9922[INFO] evaluating network...              precision    recall  f1-score   support   with_mask       0.99      1.00      0.99       138without_mask       1.00      0.99      0.99       138    accuracy                           0.99       276   macro avg       0.99      0.99      0.99       276weighted avg       0.99      0.99      0.99       276

實施COVID-19面罩檢測器

現在我們的口罩檢測器已經訓練好了，讓我們學習如何:

• 從磁盤加載輸入圖像

• 檢測圖像中的人臉

• 應用我們的口罩檢測器來將臉部分類爲with_mask或without_mask

打開目錄結構中的detect_mask_image.py文件：​​​​​​​

# import the necessary packagesfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_inputfrom tensorflow.keras.preprocessing.image import img_to_arrayfrom tensorflow.keras.models import load_modelimport numpy as npimport argparseimport cv2import os

我們的驅動腳本需要三個TensorFlow/Keras導入(1)加載MaskNet模型，(2)預處理輸入圖像。OpenCV用於顯示和圖像操作。下一步是解析命令行參數:​​​​​​​

# construct the argument parser and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-i", "--image", required=True,  help="path to input image")ap.add_argument("-f", "--face", type=str,  default="face_detector",  help="path to face detector model directory")ap.add_argument("-m", "--model", type=str,  default="mask_detector.model",  help="path to trained face mask detector model")ap.add_argument("-c", "--confidence", type=float, default=0.5,  help="minimum probability to filter weak detections")args = vars(ap.parse_args())

四個命令行參數包括:

--image:包含用於推理的人臉的輸入圖像的路徑

--face: face detector model目錄的路徑(在對人臉進行分類之前，我們需要對人臉進行本地化)

--model:我們在本教程前面培訓過的口罩檢測器模型的路徑

--confidence:可選的概率閾值可設置爲覆蓋50%，以過濾微弱的人臉檢測

接下來，我們將加載我們的人臉檢測器和口罩分類器模型:​​​​​​​

# load our serialized face detector model from diskprint("[INFO] loading face detector model...")prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])weightsPath = os.path.sep.join([args["face"],  "res10_300x300_ssd_iter_140000.caffemodel"])net = cv2.dnn.readNet(prototxtPath, weightsPath)# load the face mask detector model from diskprint("[INFO] loading face mask detector model...")model = load_model(args["model"])

下一步是加載和預處理一個輸入圖像:​​​​​​

# load the input image from disk, clone it, and grab the image spatial# dimensionsimage = cv2.imread(args["image"])orig = image.copy()(h, w) = image.shape[:2]# construct a blob from the imageblob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),  (104.0, 177.0, 123.0))# pass the blob through the network and obtain the face detectionsprint("[INFO] computing face detections...")net.setInput(blob)detections = net.forward()

知道每張面孔的預測位置後，我們將確保它們滿足 --confidence提取faceROIs之前的閾值：​​​​​​​

# loop over the detectionsfor i in range(0, detections.shape[2]):  # extract the confidence (i.e., probability) associated with  # the detection  confidence = detections[0, 0, i, 2]  # filter out weak detections by ensuring the confidence is  # greater than the minimum confidence  if confidence > args["confidence"]:    # compute the (x, y)-coordinates of the bounding box for    # the object    box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])    (startX, startY, endX, endY) = box.astype("int")    # ensure the bounding boxes fall within the dimensions of    # the frame    (startX, startY) = (max(0, startX), max(0, startY))    (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

接下來，我們將通過我們的MaskNet模型運行面部ROI:​​​​​​​

# extract the face ROI, convert it from BGR to RGB channel    # ordering, resize it to 224x224, and preprocess it    face = image[startY:endY, startX:endX]    face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)    face = cv2.resize(face, (224, 224))    face = img_to_array(face)    face = preprocess_input(face)    face = np.expand_dims(face, axis=0)    # pass the face through the model to determine if the face    # has a mask or not    (mask, withoutMask) = model.predict(face)[0]​​​​​​

# determine the class label and color we'll use to draw    # the bounding box and text    label = "Mask" if mask > withoutMask else "No Mask"    color = (0, 255, 0) if label == "Mask" else (0, 0, 255)    # include the probability in the label    label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)    # display the label and bounding box rectangle on the output    # frame    cv2.putText(image, label, (startX, startY - 10),      cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)    cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)# show the output imagecv2.imshow("Output", image)cv2.waitKey(0)

首先，我們根據掩碼檢測器模型返回的概率確定類標籤，並分配一個關聯的顏色。with_mask的顏色爲“綠色”，without_mask的顏色爲“紅色”。然後，我們使用OpenCV繪圖函數繪製標籤文本(包括類和概率)，以及用於face的邊框矩形。

使用OpenCV在圖像中進行COVID-19面罩檢測

打開一個終端，執行以下命令:​​​​​​​

$ python detect_mask_image.py --image examples/example_01.png [INFO] loading face detector model...[INFO] loading face mask detector model...[INFO] computing face detections...

我們的面罩檢測器正確地將此圖像標記爲戴口罩。讓我們再看另一張圖片，這張是一個沒有戴口罩的人:​​​​​​​

$ python detect_mask_image.py --image examples/example_02.png [INFO] loading face detector model...[INFO] loading face mask detector model...[INFO] computing face detections...

 

$ python detect_mask_image.py --image examples/example_03.png [INFO] loading face detector model...[INFO] loading face mask detector model...[INFO] computing face detections...

這裏發生了什麼?爲什麼我們能夠檢測到背景中的兩位先生的臉，並正確地爲他們分類蒙版/無蒙版，但我們無法檢測到前景中的女性?請記住，爲了區分一個人是否戴着口罩，我們首先需要進行人臉檢測——如果沒有找到一張臉，那麼就不能使用口罩檢測器!我們無法在前景中檢測到人臉的原因是:它被面具遮住了，用於訓練人臉檢測器的數據集不包含戴口罩的人的示例圖像。因此，如果面部很大一部分被遮擋，我們的面部檢測器很可能無法檢測到面部。

使用OpenCV在實時視頻流中實現COVID-19面罩檢測器

打開 detect_mask_video.py文件放在目錄結構中，並插入以下代碼：​​​​​​​

# import the necessary packagesfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_inputfrom tensorflow.keras.preprocessing.image import img_to_arrayfrom tensorflow.keras.models import load_modelfrom imutils.video import VideoStreamimport numpy as npimport argparseimport imutilsimport timeimport cv2import os

我們針對此腳本的面部檢測/遮罩預測邏輯位於 detect_and_predict_mas：​​​​​​​

def detect_and_predict_mask(frame, faceNet, maskNet):  # grab the dimensions of the frame and then construct a blob  # from it  (h, w) = frame.shape[:2]  blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),    (104.0, 177.0, 123.0))  # pass the blob through the network and obtain the face detections  faceNet.setInput(blob)  detections = faceNet.forward()  # initialize our list of faces, their corresponding locations,  # and the list of predictions from our face mask network  faces = []  locs = []  preds = []

這個函數檢測人臉，然後將我們的人臉面具分類器應用到每個人臉ROI。這樣一個函數合併了我們的代碼，它可以被移動到一個單獨的Python文件中。

我們的detect_and_predict_mask函數接受三個參數:

• frame幀:我們的流的幀

• faceNet:用於檢測圖像中人臉位置的模型

• maskNet:我們的COVID-19口罩分類器模

# loop over the detectionsfor i in range(0, detections.shape[2]):# extract the confidence (i.e., probability) associated with# the detection    confidence = detections[0, 0, i, 2]# filter out weak detections by ensuring the confidence is# greater than the minimum confidenceif confidence > args["confidence"]:# compute the (x, y)-coordinates of the bounding box for# the object      box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])      (startX, startY, endX, endY) = box.astype("int")# ensure the bounding boxes fall within the dimensions of# the frame      (startX, startY) = (max(0, startX), max(0, startY))      (endX, endY) = (min(w - 1, endX), min(h - 1, endY))​​​​​​
# extract the face ROI, convert it from BGR to RGB channel      # ordering, resize it to 224x224, and preprocess itface = frame[startY:endY, startX:endX]face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)face = cv2.resize(face, (224, 224))face = img_to_array(face)face = preprocess_input(face)face = np.expand_dims(face, axis=0)      # add the face and bounding boxes to their respective      # listsfaces.append(face)locs.append((startX, startY, endX, endY))

我們現在準備好讓我們的臉通過面具預測器:​​​​​​​

# only make a predictions if at least one face was detectedif len(faces) > 0:    # for faster inference we'll make batch predictions on *all*    # faces at the same time rather than one-by-one predictions    # in the above `for` looppreds = maskNet.predict(faces)  # return a 2-tuple of the face locations and their corresponding  # locationsreturn (locs, preds)

接下來，我們將定義命令行參數：​​​​​​​

# construct the argument parser and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-f", "--face", type=str,  default="face_detector",help="path to face detector model directory")ap.add_argument("-m", "--model", type=str,  default="mask_detector.model",help="path to trained face mask detector model")ap.add_argument("-c", "--confidence", type=float, default=0.5,help="minimum probability to filter weak detections")args = vars(ap.parse_args())

我們的命令行參數包括：

• --face：面部檢測器目錄的路徑

• --model：我們訓練好的口罩分類器的路徑

• --confidence：過濾弱臉檢測的最小概率閾值

通過我們的導入，便捷功能和命令行 args 準備好了，在循環遍歷幀之前，我們只需要處理一些初始化工作：​​​​​​​

# load our serialized face detector model from diskprint("[INFO] loading face detector model...")prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])weightsPath = os.path.sep.join([args["face"],"res10_300x300_ssd_iter_140000.caffemodel"])faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)# load the face mask detector model from diskprint("[INFO] loading face mask detector model...")maskNet = load_model(args["model"])# initialize the video stream and allow the camera sensor to warm upprint("[INFO] starting video stream...")vs = VideoStream(src=0).start()time.sleep(2.0)

在這裏，我們已經初始化了：

• 人臉檢測器

• COVID-19面罩檢測儀

• 網絡攝像頭視頻

讓我們繼續遍歷流中的幀：​​​​​​

# loop over the frames from the video streamwhile True:  # grab the frame from the threaded video stream and resize it  # to have a maximum width of 400 pixelsframe = vs.read()frame = imutils.resize(frame, width=400)  # detect faces in the frame and determine if they are wearing a  # face mask or not(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)​​​​​​

# loop over the detected face locations and their corresponding# locationsfor (box, pred) in zip(locs, preds):# unpack the bounding box and predictions    (startX, startY, endX, endY) = box    (mask, withoutMask) = pred# determine the class label and color we'll use to draw# the bounding box and text    label = "Mask" if mask > withoutMask else "No Mask"    color = (0, 255, 0) if label == "Mask" else (0, 0, 255)# include the probability in the label    label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)# display the label and bounding box rectangle on the output# frame    cv2.putText(frame, label, (startX, startY - 10),      cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

在我們對預測結果的循環中，我們:

• 打開臉部包圍框和蒙版/非蒙版預測

• 確定標籤和顏色

• 標註標籤和麪包圍框

• 最後，我們顯示結果並執行清理:​​​​​​​

# show the output frame  cv2.imshow("Frame", frame)  key = cv2.waitKey(1) & 0xFF  # if the `q` key was pressed, break from the loop  if key == ord("q"):    break# do a bit of cleanupcv2.destroyAllWindows()vs.stop()

使用OpenCV實時檢測COVID-19口罩

你可以使用以下命令在實時視頻流中啓動掩碼檢測器:​​​​​​​

$ python detect_mask_video.py[INFO] loading face detector model...[INFO] loading face mask detector model...[INFO] starting video stream...

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