Update app.py

app.py

@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
-#import face_recognition
 import os
 import pickle
 from deepface import DeepFace
@@ -10,28 +9,9 @@ import fast_colorthief
 import webcolors
 from PIL import Image
 
-# data1 = []
-# try:
-#   data = pickle.loads(open("encodings.pickle", "rb").read())
-#   data1 = pickle.loads(open("encodings.pickle", "rb").read())
-#   data = np.array(data)
-#   encodings = [d["encoding"] for d in data]
-#   personNames=[d["name"] for d in data]
-# except:
-#   data=[]
-#   data1=[]
-#   encodings=[]
-#   personNames=[]
-
-# cluster the embeddings
-print("[INFO] clustering...")
-
-print('All Encodings Complete!!!')
 
 
 thres = 0.45
-path = 'images'
-images = []
 classNames= []
 classFile = 'coco.names'
 with open(classFile,'rt') as f:
@@ -52,21 +32,7 @@ net.setInputSwapRB(True)
 
 
 
-def faceEncodings(images):
-    
-    encodeList = []
-    for img in images:
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        encode = face_recognition.face_encodings(img)[0]
-        encodeList.append(encode)
-    return encodeList
-
-
-#encodeListKnown = faceEncodings(images)
-
 def main(image): 
-  personsinimage=[]
-  index=len(encodings)
   gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
   rgb=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
   names=[]
@@ -113,51 +79,14 @@ def main(image):
       names.append(diff[min(diff.keys())])
 
 
-  # 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):
-      
-  #     if len(data1)>0:
-  #       matches = face_recognition.compare_faces(encodings, encodeFace)
-  #       faceDis = face_recognition.face_distance(encodings, encodeFace)
-  #       # print(faceDis)
-  #       matchIndex = np.argmin(faceDis)
-
-  #       if matches[matchIndex]:
-  #           name = personNames[matchIndex].upper()
-  #           personsinimage.append(name)
-  #           names.append(name)
-  #       else:
-  #         d = [{"name": "UNKNOWN"+str(index), "loc": faceLoc, "encoding": encodeFace}]
-  #         encodings.append(encodeFace)
-  #         names.append("UNKNOWN"+str(index))
-  #         personNames.append("UNKNOWN"+str(index))
-  #         index+=1
-  #         data1.extend(d)
-  #         personsinimage.append("UNKNOWN"+str(index))
-  #     else:
-  #         d = [{"name": "UNKNOWN"+str(index), "loc": faceLoc, "encoding": encodeFace}]
-  #         encodings.append(encodeFace)
-  #         names.append("UNKNOWN"+str(index))
-  #         personNames.append("UNKNOWN"+str(index))
-  #         index+=1
-  #         data1.extend(d)
-
-  # # dump the facial encodings data to disk
-  # print("[INFO] serializing encodings...")
-  # f = open("encodings.pickle", "wb")
-  # f.write(pickle.dumps(data1))
-  # f.close()
+  
 
   return ' '.join(names)
 interface = gr.Interface(fn=main, 
                         inputs=["image"],
                         outputs=[gr.inputs.Textbox(label='Names of person in image')], 
                         title='Face Recognition ',
-                        description='This Space:\n1) Recognize face \n2) Detect Emotion \n3) Detect Colors.\n4) Object Detection \n<br>This space is for personal use. For the time being, it only recognizes few persons. To use it, simply duplicate the space and replace the images in the image folder.')
+                        description='This Space:\n \n2) Detect Emotion \n3) Detect Colors.\n4) Object Detection \n<br>This space is for personal use. For the time being, it only recognizes few persons. To use it, simply duplicate the space and replace the images in the image folder.')
                         
 
 interface.launch(inline=False)