Adding face capture,training,recognition script

3 files changed, 100 insertions, 2 deletions

diff --git a/face-detection/01_face_capture_dataset.py b/face-detection/01_face_capture_dataset.py
index 327264c..8836ce6 100644
--- a/face-detection/01_face_capture_dataset.py
+++ b/face-detection/01_face_capture_dataset.py
@@ -6,7 +6,7 @@ cam.set(4, 480) # set video height
 face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
 # For each person, enter one numeric face id
 face_id = input('\n----Enter User-id and press <return>----')
-print("\n Initializing face capture. Look the camera and wait!")
+print("\n [INFO] Initializing face capture. Look the camera and wait!")
 # Initialize individual sampling face count
 count = 0
 while(True):
@@ -25,6 +25,6 @@ while(True):
     elif count >= 30: # Take 30 face sample and stop video capture
          break
 # Do a bit of cleanup
-print("\n Exiting Program and cleaning up stuff")
+print("\n [INFO] Exiting Program and cleaning up stuff")
 cam.release()
 cv2.destroyAllWindows()
diff --git a/face-detection/02_face_training.py b/face-detection/02_face_training.py
new file mode 100644
index 0000000..938761b
--- /dev/null
+++ b/face-detection/02_face_training.py
@@ -0,0 +1,31 @@
+import cv2
+import numpy as np
+from PIL import Image
+import os
+
+path = 'dataset'
+# Using LBPH(Local Binary Patterns Histograms) recognizer
+recognizer = cv2.face.LBPHFaceRecognizer_create()
+detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");
+# function to get the images and label data
+def getImagesAndLabels(path):
+    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]     
+    faceSamples=[]
+    ids = []
+    for imagePath in imagePaths:
+        PIL_img = Image.open(imagePath).convert('L') # grayscale
+        img_numpy = np.array(PIL_img,'uint8')
+        id = int(os.path.split(imagePath)[-1].split(".")[1])
+        faces = detector.detectMultiScale(img_numpy)
+        for (x,y,w,h) in faces:
+            faceSamples.append(img_numpy[y:y+h,x:x+w])
+            ids.append(id)
+    return faceSamples,ids
+print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
+#returns two arrays faces and ids
+faces,ids = getImagesAndLabels(path)
+recognizer.train(faces, np.array(ids))
+#save the model into trainer/trainer.yml
+recognizer.write('trainer/trainer.yml') 
+# Print the numer of faces trained and end program
+print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))
diff --git a/face-detection/03_face_recogition.py b/face-detection/03_face_recogition.py
new file mode 100644
index 0000000..e0da838
--- /dev/null
+++ b/face-detection/03_face_recogition.py
@@ -0,0 +1,67 @@
+import cv2
+import numpy as np
+import os 
+recognizer = cv2.face.LBPHFaceRecognizer_create()
+recognizer.read('trainer/trainer.yml')
+cascadePath = "haarcascade_frontalface_default.xml"
+faceCascade = cv2.CascadeClassifier(cascadePath);
+font = cv2.FONT_HERSHEY_COMPLEX
+#font = cv2.FONT_HERSHEY_TRIPLEX
+id = 0
+# names related to id
+names = ['None', 'Obamna', 'Soda', 'Orange'] 
+# Initialize and start realtime video capture
+cam = cv2.VideoCapture(0)
+cam.set(3, 640) # set video widht
+cam.set(4, 480) # set video height
+# Define min window size to be recognized as a face
+minW = 0.1*cam.get(3)
+minH = 0.1*cam.get(4)
+while True:
+    ret, img =cam.read()
+    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    
+    faces = faceCascade.detectMultiScale( 
+        gray,
+        scaleFactor = 1.2,
+        minNeighbors = 5,
+        minSize = (int(minW), int(minH)),
+       )
+    for(x,y,w,h) in faces:
+        cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
+        id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
+        
+        # If confidence is less them 100 ==> "0" : perfect match 
+        if (confidence < 100):
+            id = names[id]
+            confidence = "  {0}%".format(round(100 - confidence))
+        else:
+            id = "unknown"
+            confidence = "  {0}%".format(round(100 - confidence))
+        
+        cv2.putText(
+                    img, 
+                    str(id), 
+                    (x+5,y-5), 
+                    font, 
+                    1, 
+                    (255,255,255), 
+                    2
+                   )
+        cv2.putText(
+                    img, 
+                    str(confidence), 
+                    (x+5,y+h-5), 
+                    font, 
+                    1, 
+                    (255,255,0), 
+                    1
+                   )  
+    
+    cv2.imshow('camera',img) 
+    k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
+    if k == 27:
+        break
+print("\n [INFO] Exiting Program and cleaning up stuff")
+cam.release()
+cv2.destroyAllWindows()