Intial Commit

.gitignore

@@ -0,0 +1 @@
+__pycache__

bin/face_mask_detection.py

@@ -0,0 +1,115 @@
+import cv2
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+import mysql.connector
+
+
+gpus = tf.config.list_physical_devices('GPU')
+if gpus:
+  # Restrict TensorFlow to only use the first GPU
+  try:
+    tf.config.set_visible_devices(gpus[0], 'GPU')
+    logical_gpus = tf.config.list_logical_devices('GPU')
+    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
+  except RuntimeError as e:
+    # Visible devices must be set before GPUs have been initialized
+    print(e)
+
+    gpus = tf.config.list_physical_devices('GPU')
+
+if gpus:
+  try:
+    # Currently, memory growth needs to be the same across GPUs
+    for gpu in gpus:
+      tf.config.experimental.set_memory_growth(gpu, True)
+    logical_gpus = tf.config.list_logical_devices('GPU')
+    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+  except RuntimeError as e:
+    # Memory growth must be set before GPUs have been initialized
+    print(e)
+
+cap = cv2.VideoCapture(1) # Video source capturing
+cap.set(3, 640) # Width of the video window
+cap.set(4, 480) # Height of the video window
+
+faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # Face detector
+maskClassifier = load_model('maskclassifier.model') # Mask classifier
+
+while True:
+
+    _, frame = cap.read() # Reading frame from video source
+
+    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) # Converting RGB to Grayscale
+
+    faces = faceCascade.detectMultiScale( # Detecting faces
+        gray,
+        scaleFactor = 1.2,
+        minNeighbors = 5,
+        )
+
+    for (x, y, h, w) in faces: 
+      faceROI = frame[y : y + h, x : x + w, :] # Cropping face region of interest
+
+      faceROI = cv2.resize(faceROI, (160, 160)) # Resizing faceROI to 160x160
+                                                # Because, Our VGG16 model accepts 160x160 as input 
+      faceROI = img_to_array(faceROI)
+      faceROI = faceROI.reshape(1, 160, 160, 3) # Changing dimensions to 1x160x160x3, Because our VGG16 
+                                                # take input as 4D matrix(BATCH_SIZE, 160, 160, #Channels)
+
+
+      conn = mysql.connector.connect(
+          host="localhost", user="root", password="", database="spycrop")
+      my_cursor = conn.cursor()
+
+      my_cursor.execute(
+          "SELECT fname, lname FROM `user_table` WHERE id="+str(id))
+      n = my_cursor.fetchone()
+      n = " ".join(n)
+
+      my_cursor.execute(
+          "SELECT rollNo FROM `user_table` WHERE id="+str(id))
+      r = my_cursor.fetchone()
+      r = "+".join(r)
+
+      my_cursor.execute(
+          "SELECT dept FROM `user_table` WHERE id="+str(id))
+      d = my_cursor.fetchone()
+      d = "+".join(d)
+
+      my_cursor.execute(
+          "SELECT email FROM `user_table` WHERE id="+str(id))
+      e = my_cursor.fetchone()
+      e = "+".join(e)
+
+
+
+      prediction = maskClassifier(faceROI) # Making predictions
+      (withoutmask, withmask) = prediction[0].numpy()
+
+      # Drawing bounding boxes using OpenCV
+      if withmask > withoutmask:
+          (label, color, prob) = ('Mask', (0, 255, 0), withmask*100.0)
+      else:
+          (label, color, prob) = ('No mask', (0, 0, 255), withoutmask*100.0)
+
+
+    # (label, color, prob) = ('Mask', (0, 255, 0), withmask*100.0) if withmask > withoutmask else ('No mask', (0, 0, 255), withoutmask*100.0)
+
+      cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
+
+      cv2.rectangle(frame, (x + 15, y + 2), (x + w - 15, y + 20), (0, 0, 0), -1) #lower
+      cv2.rectangle(frame, (x + 15, y - 2), (x + w - 15, y - 20), (0, 0, 0), -1) #upper
+
+      cv2.putText(frame, str(prob)+' %', (x + 20, y - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+      cv2.putText(frame, label, (x + 20, y + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+
+
+    cv2.imshow('Video', frame) # Displaying the video
+
+
+    if cv2.waitKey(1) & 0xff == ord('q'):
+        break
+
+cap.release() # Releasing the capture
+cv2.destroyAllWindows()

bin/test.py

@@ -0,0 +1,137 @@
+import os
+from tkinter import *
+import tkinter
+from PIL import Image, ImageTk
+from tkinter.ttk import *
+import mysql.connector
+import cv2
+from datetime import datetime
+import csv
+import time
+import schedule
+import smtplib
+import ssl
+import schedule
+import threading
+from mysql.connector.locales.eng import client_error
+import cv2
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+
+
+
+# gpus = tf.config.list_physical_devices('GPU')
+# if gpus:
+#   # Restrict TensorFlow to only use the first GPU
+#   try:
+#     tf.config.set_visible_devices(gpus[0], 'GPU')
+#     logical_gpus = tf.config.list_logical_devices('GPU')
+#     print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
+#   except RuntimeError as e:
+#     # Visible devices must be set before GPUs have been initialized
+#     print(e)
+
+#     gpus = tf.config.list_physical_devices('GPU')
+
+# if gpus:
+#   try:
+#     # Currently, memory growth needs to be the same across GPUs
+#     for gpu in gpus:
+#       tf.config.experimental.set_memory_growth(gpu, True)
+#     logical_gpus = tf.config.list_logical_devices('GPU')
+#     print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
+#   except RuntimeError as e:
+#     # Memory growth must be set before GPUs have been initialized
+#     print(e)
+clf = cv2.face.LBPHFaceRecognizer_create()
+clf.read("studentclassifier.xml")
+
+cap = cv2.VideoCapture(1) # Video source capturing
+cap.set(3, 640) # Width of the video window
+cap.set(4, 480) # Height of the video window
+faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # Face detector
+maskClassifier = load_model('maskclassifier.model') # Mask classifier
+
+while True:
+
+    _, frame = cap.read() # Reading frame from video source
+
+    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) # Converting RGB to Grayscale
+
+    faces = faceCascade.detectMultiScale( # Detecting faces
+        gray,
+        scaleFactor = 1.2,
+        minNeighbors = 5,
+        )
+
+    for (x, y, h, w) in faces: 
+
+        faceROI = frame[y : y + h, x : x + w, :] # Cropping face region of interest
+
+        faceROI = cv2.resize(faceROI, (160, 160)) # Resizing faceROI to 160x160
+                                                # Because, Our VGG16 model accepts 160x160 as input 
+        faceROI = img_to_array(faceROI)
+        faceROI = faceROI.reshape(1, 160, 160, 3) # Changing dimensions to 1x160x160x3, Because our VGG16 
+                                                # take input as 4D matrix(BATCH_SIZE, 160, 160, #Channels)
+
+        prediction = maskClassifier(faceROI) # Making predictions
+        (withoutmask, withmask) = prediction[0].numpy()
+
+        id, predict = clf.predict(gray[y:y+h, x:x+w])
+        confidence = int((100*(1-predict/300)))
+
+        conn = mysql.connector.connect(
+            host="localhost", user="root", password="", database="spycrop")
+        my_cursor = conn.cursor()
+
+        id = 27
+
+        my_cursor.execute(
+            "SELECT fname, lname FROM `user_table` WHERE id="+str(id))
+        n = my_cursor.fetchone()
+        n = " ".join(n)
+
+        my_cursor.execute(
+            "SELECT rollNo FROM `user_table` WHERE id="+str(id))
+        r = my_cursor.fetchone()
+        r = "+".join(r)
+
+        my_cursor.execute(
+            "SELECT dept FROM `user_table` WHERE id="+str(id))
+        d = my_cursor.fetchone()
+        d = "+".join(d)
+
+        my_cursor.execute(
+            "SELECT email FROM `user_table` WHERE id="+str(id))
+        e = my_cursor.fetchone()
+        e = "+".join(e)
+
+        # Drawing bounding boxes using OpenCV
+        if withmask > withoutmask:
+            (label, color, prob) = ('Mask', (0, 255, 0), withmask*100.0)
+        else:
+            (label, color, prob) = ('No mask', (0, 0, 255), withoutmask*100.0)
+            self.mark_attendance(id, r, n, e, d)
+
+
+    # (label, color, prob) = ('Mask', (0, 255, 0), withmask*100.0) if withmask > withoutmask else ('No mask', (0, 0, 255), withoutmask*100.0)
+
+        cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
+
+        cv2.rectangle(frame, (x + 15, y + 2), (x + w - 15, y + 20), (0, 0, 0), -1) #lower
+        cv2.rectangle(frame, (x + 15, y - 2), (x + w - 15, y - 20), (0, 0, 0), -1) #upper
+
+        cv2.putText(frame, str(prob)+' %', (x + 20, y - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+        cv2.putText(frame, label, (x + 20, y + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+
+
+    cv2.imshow('Video', frame) # Displaying the video
+
+
+    if cv2.waitKey(1) & 0xff == ord('q'):
+        break
+
+cap.release() # Releasing the capture
+cv2.destroyAllWindows()
+