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- from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
- from tensorflow.keras.preprocessing.image import img_to_array
- from tensorflow.keras.models import load_model
- from imutils.video import VideoStream
- import numpy as np
- import argparse
- import imutils
- import time
- import cv2
- import os
- import time
- from pygame import mixer
- mixer.init()
- sound = mixer.Sound('voice/alarm_1.wav')
- IMAGE = "images"
- FACE = "face_detector"
- MODEL = "mask_detector.model"
- CONFIDENCE = 0.5
- def detect_and_predict_mask(frame, faceNet, maskNet):
- (h, w) = frame.shape[:2]
- blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),(104.0, 177.0, 123.0))
- faceNet.setInput(blob)
-
- # 透過模型向前傳遞
- detections = faceNet.forward()
- faces = []
- locs = []
- preds = []
-
- for i in range(0, detections.shape[2]):
- confidence = detections[0, 0, i, 2]
- # 取信賴程度>50%
- if confidence > CONFIDENCE:
- # 取得人臉座標框
- box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
- (startX, startY, endX, endY) = box.astype("int")
- (startX, startY) = (max(0, startX), max(0, startY))
- (endX, endY) = (min(w - 1, endX), min(h - 1, endY))
- # 影像resize 224*224 符合 MobileNetV2
- face = 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)
- # 複數個臉,把座標全部存在locs中
- faces.append(face)
- locs.append((startX, startY, endX, endY))
- # 人臉的數量>0,才進行預測
- if len(faces) > 0:
- faces = np.array(faces, dtype="float32")
- preds = maskNet.predict(faces, batch_size=32)
- # 回傳框框的(座標,是否戴口罩)
- return (locs, preds)
- prototxtPath = os.path.sep.join([FACE, "deploy.prototxt"])
- weightsPath = os.path.sep.join([FACE, "res10_300x300_ssd_iter_140000.caffemodel"])
- faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
- maskNet = load_model(MODEL)
- print("[INFO] Starting stream ......")
- vs = VideoStream(src=0).start()
- time.sleep(2.0)
- # 迴圈視訊串流,反覆執行
- while True:
- # 指定畫面 400 pixels
- frame = vs.read()
- frame = imutils.resize(frame, width=400)
- # 取得座標&是否戴口罩的預測結果
- try:
- (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
-
-
- for (box, pred) in zip(locs, preds):
- # Bounding box 座標
- (startX, startY, endX, endY) = box
-
- # mask & withoutMaks機率
- (mask, withoutMask) = pred
-
- # 口罩大於沒口罩顯示藍色框
- # 沒口罩大於口罩顯示紅色框
- label = "Mask" if mask > withoutMask else "No Mask"
- color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
- label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
-
- if mask < withoutMask:
- sound.play()
- # 將預測用文字顯示在畫面上
- cv2.putText(frame, label, (startX, startY - 10),
- cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
- cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
- except Exception as ex:
- print('Running ......')
- # 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 cleanup
- cv2.destroyAllWindows()
- vs.stop()
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