diff --git a/README.md b/README.md index d674c9e..44e32a1 100644 --- a/README.md +++ b/README.md @@ -4,3 +4,6 @@ Use Deep Learning and Opencv for face mask Detection. Used the Dataset provided by Prajna Bhandary(https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A6655711815361761280/) Used tensorflow,Keras for building the model + +APPLICATION : +It can be used in hospitals,offices,schools and any of the building. diff --git a/final_detect.py b/final_detect.py new file mode 100644 index 0000000..0f25a74 --- /dev/null +++ b/final_detect.py @@ -0,0 +1,198 @@ +# 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 argparse +import imutils +import time +import cv2 +import os +from smbus2 import SMBus +from mlx90614 import MLX90614 +import time +import pyttsx3 +from gpiozero import LED +engine = pyttsx3.init() +red = LED(23) +white = LED(24) +def speak(audio): + engine.setProperty('rate', 125) + engine.say(audio) + engine.runAndWait() +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 = [] + + # 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 > 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 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) + +# construct the argument parser and parse the arguments +ap = 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()) + +# load our serialized face detector model from disk +print("[INFO] loading face detector model...") +speak('i am starting please wait') +white.on() +red.on() +time.sleep(2) +white.off() +red.off() +prototxtPath = r"/home/pi/Desktop/tf_pi/deploy.prototxt" +weightsPath = r"/home/pi/Desktop/tf_pi/res10_300x300_ssd_iter_140000.caffemodel" +faceNet = cv2.dnn.readNet(prototxtPath, weightsPath) + +# load the face mask detector model from disk +print("[INFO] loading face mask detector model...") +maskNet = load_model(args["model"]) + +# initialize the video stream and allow the camera sensor to warm up +print("[INFO] starting video stream...") +#vs = VideoStream(src=0).start() +vs = VideoStream(usePiCamera=True).start() +time.sleep(2.0) + +# 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=500) + + # 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 + if mask > withoutMask: + label = "Thank You. Mask On." + + color = (0, 255, 0) + speak('thank you for wearing mask please stand near temperature sensor') + time.sleep(5) + bus = SMBus(1) + sensor = MLX90614(bus, address=0x5A) + + + + print ("Ambient Temperature :", sensor.get_ambient()) + print ("Object Temperature :", sensor.get_object_1()) + temp = sensor.get_object_1() + bus.close() + if temp<25.0: + print("you are normal") + white.on() + speak('you can go') + time.sleep(1) + white.off() + + else: + print("you are ill") + speak("you cant go") + red.on() + time.sleep(1) + red.off() + + else: + label = "No Face Mask Detected" + speak("please wear mask then i allow you to go") + color = (0, 0, 255) + red.on() + time.sleep(1) + red.off() + + #label = "Thank you" if mask > withoutMask else "Please wear your face mask" + #color = (0, 255, 0) if label == "Thank you" 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-50, startY - 10), + cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2) + cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2) + + # show the output frame + cv2.imshow("Face Mask Detector", 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()