Update app.py

app.py

@@ -3,7 +3,41 @@ from sklearn.metrics.pairwise import cosine_similarity
 from sentence_transformers import SentenceTransformer
 from PIL import Image
 import cv2
+import os
 
+def extract_face(im):
+  
+    prototxt_path = 'deploy.prototxt'
+    caffemodel_path = 'weights.caffemodel'
+
+    # Read the model
+    cv2_model = cv2.dnn.readNetFromCaffe(prototxt_path, caffemodel_path) 
+
+    #pil_image = PIL.Image.open('image.jpg')
+    image = cv2.cvtColor(np.array(im), cv2.COLOR_RGB2BGR)
+    #image = cv2.imread(im)
+
+    (h, w) = image.shape[:2]
+    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
+
+    cv2_model.setInput(blob)
+    detections = cv2_model.forward()
+
+    # Identify each face
+    for i in range(0, detections.shape[2]):
+      box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+      (startX, startY, endX, endY) = box.astype("int")
+
+      confidence = detections[0, 0, i, 2]
+
+      # If confidence > 0.5, save it as a separate file
+      if (confidence > 0.5):
+        frame = image[startY:endY, startX:endX]
+        #PIL_image = Image.fromarray(frame)
+        file_name = 'faces/' + str(np.random.randint(1,10)) + '_' + 'face.png'
+        cv2.imwrite(file_name, frame)
+
+    return file_name
 def predict(im1, im2,thresh,model_name):
   im1_face = Image.open(im1)
   im2_face = Image.open(im2)