Face recogniton test file

face_recognition_test.py

# Label names for class numbers
person_rep={0:'Lakshmi Narayana',
 1: 'Vladimir Putin',
 2: 'Angela Merkel',
 3: 'Narendra Modi',
 4: 'Donald Trump',
 5: 'Xi Jinping'}

if __name__ == '__main__':
  file_path=input("Path to image with file size < 100 kb ? ")

  img=cv2.imread(file_path)
  if img is None or img.size is 0 :
    print("Please check image path or some error occured")

  else:
    persons_in_img=[]
    gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Detect Faces
    rects=dnnFaceDetector(gray,1)
    left,top,right,bottom=0,0,0,0
    for (i,rect) in enumerate(rects):
      # Extract Each Face
      left=rect.rect.left() #x1
      top=rect.rect.top() #y1
      right=rect.rect.right() #x2
      bottom=rect.rect.bottom() #y2
      width=right-left
      height=bottom-top
      img_crop=img[top:top+height,left:left+width]
      cv2.imwrite(os.getcwd()+'/crop_img.jpg',img_crop)
    
      # Get Embeddings
      crop_img=load_img(os.getcwd()+'/crop_img.jpg',target_size=(224,224))
      crop_img=img_to_array(crop_img)
      crop_img=np.expand_dims(crop_img,axis=0)
      crop_img=preprocess_input(crop_img)
      img_encode=vgg_face(crop_img)

      # Make Predictions
      embed=K.eval(img_encode)
      person=classifier_model.predict(embed)
      name=person_rep[np.argmax(person)]
      os.remove(os.getcwd()+'/crop_img.jpg')
      cv2.rectangle(img,(left,top),(right,bottom),(0,255,0), 2)
      img=cv2.putText(img,name,(left,top-10),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,255),2,cv2.LINE_AA)
      img=cv2.putText(img,str(np.max(person)),(right,bottom+10),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),1,cv2.LINE_AA)
      persons_in_img.append(name)
    # Save images with bounding box,name and accuracy 
    cv2.imwrite(os.getcwd()+'/recognized_img.jpg',img)
    
    #Person in image
    print('Person(s) in image is/are:')
    print(persons_in_img)

    plt.figure(figsize=(8,4))
    plt.imshow(img[:,:,::-1])
    plt.axis('off')
    plt.show()