3 files

ImageEncoder.py

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+import face_recognition as fr
+import pickle
+import os
+def img_enc(face):
+    encoded={}
+    
+    faces = fr.load_image_file(face)
+    face_enc = fr.face_encodings(faces)[0]
+    encoded[face.split(".")[0]] = face_enc
+    return list(encoded.keys()),list(encoded.values())
+
+
+# face_known,face_enco_done= img_enc()
+# with open("data.pickle","wb")as f:
+#     pickle.dump((face_known,face_enco_done),f)
+    
+

face_checker.py

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+import face_recognition as fr
+import cv2
+import numpy as np
+import ImageEncoder as ie
+
+#load encoded images 
+# with open("Resource\\data.pickle",'rb')as f:
+#     face_known,face_enco_done = pickle.load(f)
+
+
+def check(face,test_face):
+    face_known,face_enco_done=ie.img_enc(face)        
+    face_loca = []
+    face_enco = []
+    face_name =[]
+
+    #to read  video and capture attendance
+
+    img = cv2.imread(test_face)
+    #small_frame = cv2.resize(frame,(0,0),fx = 0.25,fy = 0.25)
+    rgb_frame = img#small_frame#[:,:,::-1]
+    if True:
+        face_loca = fr.face_locations(rgb_frame)
+        face_enco = fr.face_encodings(rgb_frame,face_loca)
+        face_name = []
+        for face_enc in face_enco:
+            matches = fr.compare_faces(face_enco_done,face_enc)
+            name = "Unknown_Unknown"
+            face_distance = fr.face_distance(face_enco_done,face_enc)
+            best_match = np.argmin(face_distance)
+            
+            if matches[best_match]:
+                return True
+            else:
+                return False
+     
+# to add a box on the detected face        
+##        for (top, right, bottom, left), name in zip(face_loca, face_name):
+##            # Draw a box around the face
+##            cv2.rectangle(frame, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
+##
+##            # Draw a label with a name below the face
+##            cv2.rectangle(frame, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
+##            font = cv2.FONT_HERSHEY_DUPLEX
+##            cv2.putText(frame, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
+    
+
+                

face_extract.py

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+import cv2
+import sys
+
+
+
+
+def extract(face):
+    imagePath = face
+
+    image = cv2.imread(imagePath)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+    faces = faceCascade.detectMultiScale(
+        gray,
+        scaleFactor=1.3,
+        minNeighbors=3,
+        minSize=(30, 30)
+    )
+
+    # print("[INFO] Found {0} Faces.".format(len(faces)))
+
+    for (x, y, w, h) in faces:
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        roi_color = image[y:y + h, x:x + w]
+      
+        name = str(w)+str(h)+'_faces.jpg'
+        cv2.imwrite(str(w) + str(h) + '_faces.jpg', roi_color)
+    return name
+# status = cv2.imwrite('faces_detected.jpg', image)
+# print("[INFO] Image faces_detected.jpg written to filesystem: ", status)