Update app.py

app.py

@@ -1,11 +1,7 @@
 import os
-import urllib.request
-
-if not os.path.exists("biden.jpg"):
-    urllib.request.urlretrieve("https://github.com/ageitgey/face_recognition/blob/master/examples/biden.jpg?raw=true", "biden.jpg")
-    urllib.request.urlretrieve("https://github.com/ageitgey/face_recognition/blob/master/examples/obama.jpg?raw=true", "obama.jpg")
-    urllib.request.urlretrieve("https://github.com/ageitgey/face_recognition/blob/master/examples/obama2.jpg?raw=true", "obama2.jpg")
 
+if not os.path.exists("data"):
+    os.mkdir("data")
 
 import face_recognition
 
@@ -20,39 +16,38 @@ import face_recognition
 # smaller distance are more similar to each other than ones with a larger distance.
 
 # Load some images to compare against
-known_obama_image = face_recognition.load_image_file("obama.jpg")
-known_biden_image = face_recognition.load_image_file("biden.jpg")
-
-# Get the face encodings for the known images
-obama_face_encoding = face_recognition.face_encodings(known_obama_image)[0]
-biden_face_encoding = face_recognition.face_encodings(known_biden_image)[0]
-
-known_encodings = [
-    obama_face_encoding,
-    biden_face_encoding
-]
-
-# Load a test image and get encondings for it
-image_to_test = face_recognition.load_image_file("obama2.jpg")
-image_to_test_encoding = face_recognition.face_encodings(image_to_test)[0]
-
-# See how far apart the test image is from the known faces
-face_distances = face_recognition.face_distance(known_encodings, image_to_test_encoding)
+known_encodings = []
+known_persons = []
+valid_images = [".jpg",".jpeg",".png"]
+for f in os.listdir("data"):
+    ext = os.path.splitext(f)[1]
+    if ext.lower() not in valid_images:
+        continue
+
+    # Get the face encodings for the known images
+    known_image = face_recognition.load_image_file(os.path.join(path,f))
+    face_encoding = face_recognition.face_encodings(known_image)[0]
+    known_encodings.append(face_encoding)
+    # known_persons.append(os.path.splitext(os.path.basename(f))[0])
+    known_persons.append(os.path.basename(f))
 
 
 import gradio as gr
 
-def greet(name):
-    ret = ""
-    for i, face_distance in enumerate(face_distances):
-        ret += "The test image has a distance of {:.2} from known image #{}".format(face_distance, i)
-        ret += "\n"
-        ret += "- With a normal cutoff of 0.6, would the test image match the known image? {}".format(face_distance < 0.6)
-        ret += "\n"
-        ret += "- With a very strict cutoff of 0.5, would the test image match the known image? {}".format(face_distance < 0.5)
-        ret += "\n\n"
-
-    return ret
-
-iface = gr.Interface(fn=greet, inputs="image", outputs="text")
+def greet(image_to_test):
+    # # Load a test image and get encondings for it
+    # image_to_test = face_recognition.load_image_file(filepath)
+    image_to_test_encoding = face_recognition.face_encodings(image_to_test)[0]
+    
+    # See how far apart the test image is from the known faces
+    face_distances = face_recognition.face_distance(known_encodings, image_to_test_encoding)
+    idx = face_distances.argmin()
+    filepath = known_persons[idx]
+    face_distance = face_distances[idx]
+    ret = "The most similar person is of {} with score {:.3}".format(os.path.splitext(filenamme)[0],
+                                                                     100 * (1 - face_distance))
+    img = face_recognition.load_image_file(filepath)
+    return img, ret
+
+iface = gr.Interface(fn=greet, inputs=gr.Image(type="numpy"), outputs=["image", "text"])
 iface.launch()