Add files via upload

Face_Rec.py

@@ -0,0 +1,98 @@
+import tensorflow as tf
+import numpy as np
+import pre_trained_facenet
+import cv2
+from mtcnn.mtcnn import MTCNN
+import xlrd
+
+print("Choose")
+print("1.For input from Webcam")
+print("2.For input from the Folder-Input")
+val = input("Enter your value from Keyboard: ")
+print(val)
+if (val=='1'):
+  camera = cv2.VideoCapture(0)
+  return_value, camshot = camera.read()
+  cv2.imwrite('captured/cam.jpg', camshot)
+  img2 = cv2.imread("captured/cam.jpg")
+  del(camera)
+elif (val=='2'):
+  img2 = cv2.imread("Input/cam.jpg")
+else:
+  print("Wrong Choice")
+  exit()  
+
+detector = MTCNN()
+
+  # some constants kept as default from facenet
+minsize = 20
+threshold = [0.6, 0.7, 0.7]
+factor = 0.709
+margin = 44
+input_image_size = 160
+loc = ("data.xls")
+wb = xlrd.open_workbook(loc)
+sheet = wb.sheet_by_index(0)
+sess = tf.Session()
+
+  # read 20170512-110547 model file downloaded from https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk
+pre_trained_facenet.load_model("20170512-110547/20170512-110547.pb")
+
+  # Get input and output tensors
+images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
+embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
+phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
+embedding_size = embeddings.get_shape()[1]
+
+def getFace(img):
+    faces=[]
+    result = detector.detect_faces(img)
+    # Result is an array with all the bounding boxes detected. We know that for 'ivan.jpg' there is only one.
+
+    bounding_box = result[0]['box']
+    keypoints = result[0]['keypoints']
+    cv2.rectangle(img,
+                  (bounding_box[0], bounding_box[1]),
+                  (bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
+                  (0,155,255),
+                  2)
+    cv2.circle(img,(keypoints['left_eye']), 2, (0,155,255), 2)
+    cv2.circle(img,(keypoints['right_eye']), 2, (0,155,255), 2)
+    cv2.circle(img,(keypoints['nose']), 2, (0,155,255), 2)
+    cv2.circle(img,(keypoints['mouth_left']), 2, (0,155,255), 2)
+    cv2.circle(img,(keypoints['mouth_right']), 2, (0,155,255), 2)
+    cv2.imwrite("check/dd_drawn.jpg", img)
+    cropped=img[bounding_box[1]:bounding_box[1] + bounding_box[3], bounding_box[0]:bounding_box[0]+bounding_box[2]]
+    cv2.imwrite("check/dd_cropped.jpg", cropped)
+    rearranged= cv2.resize(cropped, (input_image_size,input_image_size),interpolation=cv2.INTER_CUBIC)
+    cv2.imwrite("check/dd_resized.jpg",rearranged)
+    prewhitened = pre_trained_facenet.prewhiten(rearranged)
+    faces.append({'face':rearranged,'embedding':getEmbedding(prewhitened)})
+    return faces
+def getEmbedding(resized):
+    reshaped = resized.reshape(-1,input_image_size,input_image_size,3)
+    feed_dict = {images_placeholder: reshaped, phase_train_placeholder: False}
+    embedding = sess.run(embeddings, feed_dict=feed_dict)
+    return embedding
+
+def compare2face(img1,img2):
+    face1 = getFace(img1)
+    face2 = getFace(img2)
+    if face1 and face2:
+        # calculate Euclidean distance
+        dist = np.sqrt(np.sum(np.square(np.subtract(face1[0]['embedding'], face2[0]['embedding']))))
+        return dist
+    return -1
+print("Total number of people in database = "+str(sheet.nrows-1))
+
+for j in range (1,sheet.nrows):
+    img1 = cv2.imread("database/Person"+str(j)+".jpg")
+    distance=compare2face(img1,img2)
+    threshold = 0.9    # set yourself to meet your requirement
+    print("distance = "+str(distance))
+    if(distance <= threshold):
+       print("person found is from class  "+str(j))
+       print("The person found is  "+str(sheet.cell_value(j,1)))
+       break
+
+