# import the necessary packages 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 imutils import time import cv2 import os 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, (224, 224), (104.0, 177.0, 123.0)) # pass the blob through the network # and obtain the face detections faceNet.setInput(blob) detections = faceNet.forward() print(detections.shape) # initialize our list of faces, their # corresponding locations, and the list # of predictions from our face mask network faces = [] locs = [] preds = [] # loop over the detections for 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 > 0.5: # 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 it 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) # add the face and bounding boxes # to their respective lists faces.append(face) locs.append((startX, startY, endX, endY)) # only make a predictions if at least one # face was detected if 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` loop faces = np.array(faces, dtype="float32") preds = maskNet.predict(faces, batch_size=32) # return a 2-tuple of the face locations # and their corresponding locations return (locs, preds) # load our serialized face detector model from disk prototxtPath = r"face_detector\deploy.prototxt" weightsPath = r"face_detector\res10_300x300_ssd_iter_140000.caffemodel" faceNet = cv2.dnn.readNet(prototxtPath, weightsPath) # load the face mask detector model from disk maskNet = load_model("mask_detector.model") # initialize the video stream print("[INFO] starting video stream...") vs = VideoStream(src=0).start() # loop over the frames from the video stream while True: # grab the frame from the threaded # video stream and resize it # to have a maximum width of 400 pixels frame = 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 # locations for (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) # 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()