Updated to Python 3

.gitignore

@@ -0,0 +1,2 @@
+
+/__pycache__

face_detect.py

@@ -2,31 +2,31 @@
 import sys
 
 def face_detect(imagePath):
-	# HaarCascade file, to detect the face.
-	faceCascade = cv2.CascadeClassifier("opencv-files/haarcascade_frontalface_alt.xml")
-	image = cv2.imread(imagePath)
-	#Convert image to grayscale
-	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-	grays = []
-	faces = faceCascade.detectMultiScale(
-		gray,
-		scaleFactor=1.1,
-		minNeighbors=5,
-		minSize=(30, 30)
-	)
-	
-	# For drawing rectangles over multiple faces in the image
-	for (x, y, w, h) in faces:
-		cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
-	
-	# Show Detected faces.
-	cv2.imshow("Faces found", image)
-	cv2.waitKey(1)
-	
-	# Append the detected faces into grays list.
-	for i in range(0, len(faces)):
-		(x, y, w, h) = faces[i]
-		grays.append(gray[y:y+w, x:x+h])
-	print "------------------------------------------------------------"
-	print "Detecting Face -\-"
-	return grays, faces, len(faces)
+    # HaarCascade file, to detect the face.
+    faceCascade = cv2.CascadeClassifier("opencv-files/haarcascade_frontalface_alt.xml")
+    image = cv2.imread(imagePath)
+    # Convert image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    grays = []
+    faces = faceCascade.detectMultiScale(
+        gray,
+        scaleFactor=1.1,
+        minNeighbors=5,
+        minSize=(30, 30)
+    )
+    
+    # For drawing rectangles over multiple faces in the image
+    for (x, y, w, h) in faces:
+        cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+    
+    # Show Detected faces.
+    cv2.imshow("Faces found", image)
+    cv2.waitKey(1)
+    
+    # Append the detected faces into grays list.
+    for i in range(0, len(faces)):
+        (x, y, w, h) = faces[i]
+        grays.append(gray[y:y+w, x:x+h])
+    print("------------------------------------------------------------")
+    print("Detecting Face -\\-")
+    return grays, faces, len(faces)

facemaster.py

@@ -5,25 +5,24 @@
 
 label = []
 def predict(test_img):
-	img = cv2.imread(test_img).copy()
-	print "\n\n\n"
-	print "Face Prediction Running -\-"
-	face, rect, length = face_detect.face_detect(test_img)
-	print len(face), "faces detected."
-	for i in range(0, len(face)):
-		labeltemp, confidence = face_recognizer.predict(face[i])
-		label.append(labeltemp)
-	return img, label
+    img = cv2.imread(test_img).copy()
+    print("\n\n\n")
+    print("Face Prediction Running -\\-")
+    face, rect, length = face_detect.face_detect(test_img)
+    print(f"{len(face)} faces detected.")
+    for i in range(0, len(face)):
+        labeltemp, confidence = face_recognizer.predict(face[i])
+        label.append(labeltemp)
+    return img, label
 
 faces, labels = training_data.training_data("training-data")
 face_recognizer = cv2.face.LBPHFaceRecognizer_create()
 face_recognizer.train(faces, np.array(labels))
 
-
 # Read the test image.
 test_img = "test-data/test.jpg"
-predicted_img , label= predict(test_img)
+predicted_img, label = predict(test_img)
 cv2.destroyAllWindows()
 cv2.waitKey(1)
 cv2.destroyAllWindows()
-print "Recognized faces = ", label
+print("Recognized faces = ", label)

requirement.txt

@@ -0,0 +1,3 @@
+numpy==2.1.2
+opencv-contrib-python==4.10.0.84
+opencv-python==4.10.0.84

training_data.py

@@ -1,4 +1,6 @@
-import cv2, os, sys
+import cv2
+import os
+import sys
 import numpy as np
 import face_detect as face_detect
 
@@ -8,17 +10,17 @@ def training_data(data_folder):
     labels = []
     for dir_name in dirs:
         if not dir_name.startswith("s"):
-            continue;
+            continue
         label = int(dir_name.replace("s", ""))
-        subject_dir = data_folder + "/" + dir_name
+        subject_dir = os.path.join(data_folder, dir_name)
         subject_images_names = os.listdir(subject_dir)
         for image_name in subject_images_names:
             if image_name.startswith("."):
-                continue;
-            image_path = subject_dir + "/" + image_name
+                continue
+            image_path = os.path.join(subject_dir, image_name)
             face, rect, length = face_detect.face_detect(image_path)
             if face is not None:
                 faces.append(face[0])
                 labels.append(label)
 
-    return faces, labels
+    return faces, labels