Update app.py

app.py

@@ -10,7 +10,7 @@ images = []
 personNames = []
 myList = os.listdir(path)
 unkownEncodings=[]
-names=[]
+
 print(myList)
 for cu_img in myList:
     current_Img = cv2.imread(f'{path}/{cu_img}')
@@ -32,36 +32,61 @@ def faceEncodings(images):
 encodeListKnown = faceEncodings(images)
 print('All Encodings Complete!!!')
 
-def Attandance(video):
-  cap = cv2.VideoCapture(video)
-  index=1
-  while True:
-      try:
-        ret, frame = cap.read()
-        #faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
-        faces = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
-        facesCurrentFrame = face_recognition.face_locations(faces)
-        encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame)
-
-        for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
-            matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
-            faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
-            # print(faceDis)
-            matchIndex = np.argmin(faceDis)
-
-            if matches[matchIndex]:
-                name = personNames[matchIndex].upper()    
-                if names.count(name) == 0:
-                    names.append(name)
+def Attandance(text,video,image):
+  names=[]
+  if video is not None:
+      cap = cv2.VideoCapture(video)
+      index=1
+      while True:
+          try:
+            ret, frame = cap.read()
+            #faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
+            faces = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    
+            facesCurrentFrame = face_recognition.face_locations(faces)
+            encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame)
+    
+            for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
+                matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
+                faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
+                # print(faceDis)
+                matchIndex = np.argmin(faceDis)
+    
+                if matches[matchIndex]:
+                    name = personNames[matchIndex].upper()    
+                    if names.count(name) == 0:
+                        names.append(name)
+        
+            cv2.waitKey(1)
+          except:
+              break
+      return ' '.join(names)
+  else:
+        try:
+            #faces = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
+            faces = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+            facesCurrentFrame = face_recognition.face_locations(faces)
+            encodesCurrentFrame = face_recognition.face_encodings(faces, facesCurrentFrame)
+    
+            for encodeFace, faceLoc in zip(encodesCurrentFrame, facesCurrentFrame):
+                matches = face_recognition.compare_faces(encodeListKnown, encodeFace)
+                faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)
+                # print(faceDis)
+                matchIndex = np.argmin(faceDis)
     
-        cv2.waitKey(1)
-      except:
-          break
-  return ' '.join(names)
+                if matches[matchIndex]:
+                    name = personNames[matchIndex].upper()    
+                    if names.count(name) == 0:
+                        names.append(name)
+        
+            cv2.waitKey(1)
+          except:
+              break
+      return ' '.join(names)
 
 demo=gr.Interface(fn=Attandance,
-                  inputs="video",
+                  inputs=["text","video","image"],
                   outputs="text",
                   title="Face Attendance",