Update README.md

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.  

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()