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mickey9801 · Oct 5, 2019 · 251a75b · 251a75b
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diff --git a/README.md b/README.md
@@ -10,7 +10,7 @@ Face recognition sample with opencv-python.
 
    ```bash
    sudo apt-get update && sudo apt-get upgrade
-   sudo apt-get install python3-opencv
+   sudo apt-get install python3-opencv python3-picamera python3-numpy python3-pil
    ```
 * DB Browser for SQLite
 

diff --git a/detector_picam.py b/detector_picam.py
@@ -1,4 +1,10 @@
+# detector_webcam.py
+# Finding the person in front of the camera is anyone who stored in database
+# Using Pi Camera v2 module (single threading)
+#
+# Project: Face Recognition using OpenCV and Raspberry Pi
 # Ref: https://www.pytorials.com/face-recognition-using-opencv-part-3/
+# By: Mickey Chan @ 2019
 
 # Import required modules
 import cv2
@@ -19,7 +25,7 @@
     print("Please train the data first")
     exit(0)
 
-# Setup GPIO for unlock LED
+# Setup GPIO for door lock
 relayPin = 26
 GPIO.setmode(GPIO.BCM)
 GPIO.setup(relayPin, GPIO.OUT)
@@ -37,24 +43,24 @@
 camera.framerate = 30
 rawCapture = PiRGBArray(camera, size=(640, 480))
 
-# Setup Classifier for detect face
+# Setup Classifier for detecting face
 faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
 # Setup LBPH recognizer for face recognition
 recognizer = cv2.face.createLBPHFaceRecognizer() # or LBPHFaceRecognizer_create()
 # Load training data
-recognizer.load(fname) # read() for LBPHFaceRecognizer_create()
+recognizer.load(fname) # change to read() for LBPHFaceRecognizer_create()
 
 for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
-    # Turn off unlock LED when timeout
+    # Lock the door again when timeout
     if time.time() - lastUnlockedAt > unlockDuration:
         GPIO.output(relayPin, 0)
 
     frame = frame.array
     # Detect face
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.5, minNeighbors = 5)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Convert captured frame to grayscale
+    faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.3, minNeighbors = 5) # Detect face(s) inside the frame
     for (x, y, w, h) in faces:
-        # Try to recognize the face a
+        # Try to recognize the face using recognizer
         roiGray = gray[y:y+h, x:x+w]
         id_, conf = recognizer.predict(roiGray)
         print(id_, conf)
@@ -63,14 +69,15 @@
         # retrieve the user name from database,
         # draw a rectangle around the face,
         # print the name of the user and
-        # light up the unlock LED for 5 secord
+        # unlock the door for 5 secords
         if conf <= 70:
             cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
             # retrieve user name from database
             db.execute("SELECT `name` FROM `users` WHERE `id` = (?);", (id_,))
             result = db.fetchall()
             name = result[0][0]
 
+            # You may do anything below for detected user, e.g. unlock the door
             GPIO.output(relayPin, 1) # Unlock
             lastUnlockedAt = time.time()
             print("[Unlock] " + name + " (" + str(conf) + ")")
@@ -82,7 +89,8 @@
             #cv2.putText(frame, 'No Match', (x+2,y+h-5), font, 1, (0,0,255), 2)
 
     cv2.imshow("Face Recognizer", frame)
-    rawCapture.truncate(0)
+    rawCapture.truncate(0) # Clear frame buffer for next frame
+
     # Press ESC or 'q' to quit the program
     key = cv2.waitKey(1) & 0xff
     if key == 27 or key == ord('q'):

diff --git a/detector_webcam.py b/detector_webcam.py
@@ -1,4 +1,10 @@
+# detector_webcam.py
+# Finding the person in front of the camera is anyone who stored in database
+# Using USB webcam or IP Cam (single threading)
+#
+# Project: Face Recognition using OpenCV and Raspberry Pi
 # Ref: https://www.pytorials.com/face-recognition-using-opencv-part-3/
+# By: Mickey Chan @ 2019
 
 # Import required modules
 import cv2
@@ -17,7 +23,7 @@
     print("Please train the data first")
     exit(0)
 
-# Setup GPIO for unlock LED
+# Setup GPIO for door lock
 relayPin = 26
 GPIO.setmode(GPIO.BCM)
 GPIO.setup(relayPin, GPIO.OUT)
@@ -36,15 +42,15 @@
 vSource = 0 # first USB webcam
 vStream = cv2.VideoCapture(vSource)
 
-# Setup Classifier for detect face
+# Setup Classifier for detecting face
 faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
 # Setup LBPH recognizer for face recognition
 recognizer = cv2.face.createLBPHFaceRecognizer() # or LBPHFaceRecognizer_create()
 # Load training data
-recognizer.load(fname) # read() for LBPHFaceRecognizer_create()
+recognizer.load(fname) # change to read() for LBPHFaceRecognizer_create()
 
 while vStream.isOpened():
-    # Turn off unlock LED when timeout
+    # Lock the door again when timeout
     if time.time() - lastUnlockedAt > unlockDuration:
         GPIO.output(relayPin, 0)
 
@@ -56,11 +62,11 @@
         lastDetectedAt = time.time()
 
         # Detect face
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.1, minNeighbors = 2)
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Convert captured frame to grayscale
+        faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.3, minNeighbors = 5) # Detect face(s) inside the frame
 
         for (x, y, w, h) in faces:
-            # Try to recognize the face a
+            # Try to recognize the face using recognizer
             roiGray = gray[y:y+h, x:x+w]
             id_, conf = recognizer.predict(roiGray)
             print(id_, conf)
@@ -69,14 +75,15 @@
             # retrieve the user name from database,
             # draw a rectangle around the face,
             # print the name of the user and
-            # light up the unlock LED for 5 secord
+            # unlock the door for 5 secords
             if conf <= 70:
                 cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
                 # retrieve user name from database
                 db.execute("SELECT `name` FROM `users` WHERE `id` = (?);", (id_,))
                 result = db.fetchall()
                 name = result[0][0]
 
+                # You may do anything below for detected user, e.g. unlock the door
                 GPIO.output(relayPin, 1) # Unlock
                 lastUnlockedAt = time.time()
                 print("[Unlock] " + str(id_) + ":" + name + " (" + str(conf) + ")")
@@ -89,7 +96,7 @@
 
     cv2.imshow("Face Recognizer", frame)
 
-    # Press ESC to quit the program
+    # Press ESC or 'q' to quit the program
     key = cv2.waitKey(1) & 0xff
     if key == 27 or key == ord('q'):
         break

diff --git a/recordface_picam.py b/recordface_picam.py
@@ -1,4 +1,9 @@
+# recordface_picam.py
+# Capture face image of a person for face recognition, using Pi Camera v2 module
+#
+# Project: Face Recognition using OpenCV and Raspberry Pi
 # Ref: https://www.pytorials.com/face-recognition-using-opencv-part-2/
+# By: Mickey Chan @ 2019
 
 # Import required modules
 import cv2
@@ -18,41 +23,46 @@
     os.makedirs(dirName)
     print("DataSet Directory Created")
 
+# Ask for the user's name
 name = input("What's his/her Name?")
 
+imgCapture = 30 # Number of face image we have to capture
 saveFace = False
-frameColor = (0,0,255)
-userDir = "User_"
+frameColor = (0,0,255) # Frame color for detected face
+userDir = "User_" # Prefix of face image directory name
 beginTime = 0
 
-# Connect to video source
+# Connect to video source: Pi Camera v2
 camera = PiCamera()
 camera.resolution = (640, 480)
 camera.framerate = 30
-rawCapture = PiRGBArray(camera, size=(640, 480))
+rawCapture = PiRGBArray(camera, size=(640, 480)) # A Numpy compatible array for storing catpured frame (frame buffer)
 
-# Setup Classifier for detect face
+# Setup Classifier for detecting face
 faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
 
+# Continuously capture video until collected require amount of face data
 count = 1
-for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
+for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True): # Read a frame
     frame = frame.array;
     cv2.putText(frame, "Press 'f' to start face capture", (10, 480-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,255,255), 2)
 
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.5, minNeighbors = 5)
+    # Find any face in the frame
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Convert captured frame to grayscale
+    faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.3, minNeighbors = 5) # Detect face(s) inside the frame
+    # If found, save captured face image
     for (x, y, w, h) in faces:
-        cv2.rectangle(frame, (x,y), (x+w, y+h), frameColor, 2)
+        cv2.rectangle(frame, (x,y), (x+w, y+h), frameColor, 2) # Draw a frame surrounding the face
+        # Save captured face data
         if saveFace:
             roiGray = gray[y:y+h, x:x+w]
             fileName = userDir + "/" + f'{count:02}' + ".jpg"
             cv2.imwrite(fileName, roiGray)
-            #print(fileName)
             cv2.imshow("face", roiGray)
             count += 1
 
-    cv2.imshow('frame', frame)
-    rawCapture.truncate(0)
+    cv2.imshow('frame', frame) # Show the video frame
+    rawCapture.truncate(0) # Clear frame buffer for next frame
 
     # Press 'f' to begin detect,
     # Press ESC or 'q' to quit
@@ -69,8 +79,8 @@
             os.makedirs(userDir)
         #print("Maked directory: " + userDir)
 
-    # Quit face detection when captured 30 images
-    if count > 30:
+    # Quit face detection when captured required faces
+    if count > imgCapture:
         break
 
 # Clean up
@@ -80,7 +90,7 @@
 db.execute("INSERT INTO `users` (`name`) VALUES(?)", (name,))
 uid = db.lastrowid
 print("User ID:" + str(uid))
-# Rename temperary directory with UID
+# Rename temperary directory with USER ID
 newUserDir = os.path.join(dirName, str(uid))
 os.rename(userDir, newUserDir);
 #print("Renamed user dataset directory name to " + newUserDir)

diff --git a/recordface_webcam.py b/recordface_webcam.py
@@ -1,4 +1,9 @@
+# recordface_webcam.py
+# Capture face image of a person for face recognition, using webcam or IP cam
+#
+# Project: Face Recognition using OpenCV and Raspberry Pi
 # Ref: https://www.pytorials.com/face-recognition-using-opencv-part-2/
+# By: Mickey Chan @ 2019
 
 # Import required modules
 import cv2
@@ -16,45 +21,50 @@
     os.makedirs(dirName)
     print("DataSet Directory Created")
 
+# Ask for the user's name
 name = input("What's his/her Name?")
 
-imgCapture = 30
+imgCapture = 30 # Number of face image we have to capture
 saveFace = False
-frameColor = (0,0,255)
-userDir = "User_"
+frameColor = (0,0,255) # Frame color for detected face
+userDir = "User_" # Prefix of face image directory name
 beginTime = 0
 
 # Connect to video source
 #vSource = "rtsp://192.168.1.100:8554/live.sdp" # RTSP URL of IP Cam
 vSource = 0 # first USB webcam
 vStream = cv2.VideoCapture(vSource)
 
-# Setup Classifier for detect face
+# Setup Classifier for detecting face
 faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
 
+# Continuously capture video until collected require amount of face data
 count = 1
-frameRate = 5
+frameRate = 5 # Frequency for capturing face
 prevTime = 0
 while vStream.isOpened():
     timeElapsed = time.time() - prevTime
-    ok, frame = vStream.read()
+    ok, frame = vStream.read() # Read a frame
     if not ok: break
     cv2.putText(frame, "Press 'f' to start face capture", (10, 480-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,255,255), 2)
 
     if timeElapsed > 1./frameRate:
         prevTime = time.time()
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.1, minNeighbors = 2)
+        # Find any face in the frame
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Convert captured frame to grayscale
+        faces = faceCascade.detectMultiScale(gray, scaleFactor = 1.3, minNeighbors = 5) # Detect face(s) inside the frame
+        # If found, save captured face image
         for (x, y, w, h) in faces:
-            cv2.rectangle(frame, (x,y), (x+w, y+h), frameColor, 2)
+            cv2.rectangle(frame, (x,y), (x+w, y+h), frameColor, 2) # Draw a frame surrounding the face
+            # Save captured face data
             if saveFace:
                 roiGray = gray[y:y+h, x:x+w]
                 fileName = userDir + "/" + f'{count:02}' + ".jpg"
                 cv2.imwrite(fileName, roiGray)
                 cv2.imshow("face", roiGray)
                 count += 1
 
-    cv2.imshow('frame', frame)
+    cv2.imshow('frame', frame) # Show the video frame
     # Press 'f' to begin detect,
     # Press ESC or 'q' to quit
     key = cv2.waitKey(1) & 0xff
@@ -69,7 +79,7 @@
         if not os.path.exists(userDir):
             os.makedirs(userDir)
 
-    # Quit face detection when captured 30 images
+    # Quit face detection when captured required faces
     if count > imgCapture:
         break
 
@@ -80,7 +90,7 @@
 db.execute("INSERT INTO `users` (`name`) VALUES(?)", (name,))
 uid = db.lastrowid
 print("User ID:" + str(uid))
-# Rename temperary directory with UID
+# Rename temperary directory with USER ID
 newUserDir = os.path.join(dirName, str(uid))
 os.rename(userDir, newUserDir);
 #print("Renamed user dataset directory name to " + newUserDir)

diff --git a/setup.py b/setup.py
@@ -1,6 +1,11 @@
+# setup.py
+# Prepare environment for the project
+#
+# Project: Face Recognition using OpenCV and Raspberry Pi
 # Ref: https://www.pytorials.com/face-recognition-using-opencv-part-2/
+# By: Mickey Chan @ 2019
 
-# Setup database
+# Setup user database
 import sqlite3
 
 conn = sqlite3.connect('database.db')

diff --git a/threading/Capturer.py b/threading/Capturer.py
@@ -1,3 +1,10 @@
+# Capture.py
+# Worker object for capturing video frame
+#
+# Project: Face Recognition using OpenCV and Raspberry Pi
+# Ref: https://github.com/nrsyed/computer-vision/tree/master/multithread
+# By: Mickey Chan @ 2019
+
 from threading import Thread
 import cv2
 
@@ -19,7 +26,7 @@ def get(self):
             if not self.grabbed:
                 self.stop()
             else:
-                (self.grabbed, self.frame) = self.stream.read()
+                (self.grabbed, self.frame) = self.stream.read() # Capture a video frame
 
         self.stream.release()
         return