masktest.py

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
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)
  detections = faceNet.forward()
  faces = []
  locs = []
  preds = []
  for i in range(0, detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > 0.5:
      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))
      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)
      faces.append(face)
      locs.append((startX, startY, endX, endY))
  if len(faces) > 0:
    faces = np.array(faces, dtype="float32")
    preds = maskNet.predict(faces, batch_size=32)
  return (locs, preds)

print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join(["C:\\Users\\Minwick\\Desktop\\Personal Project\\Covid_Mask\\Face", "deploy.prototxt"])
weightsPath = os.path.sep.join(["C:\\Users\\Minwick\\Desktop\\Personal Project\\Covid_Mask\\Face","res10_300x300_ssd_iter_140000.caffemodel"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
print("[INFO] loading face mask detector model...")
maskNet = load_model("C:\\Users\\Minwick\\Desktop\\Personal Project\\Covid_Mask\\Model")
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
while True:
  frame = vs.read()
  frame = imutils.resize(frame, width=400)
  (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
  for (box, pred) in zip(locs, preds):
    (startX, startY, endX, endY) = box
    (mask, withoutMask) = pred
    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)
    cv2.putText(frame, label, (startX, startY - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
      break
cv2.destroyAllWindows()
vs.stop()