embedding choice

app.py

@@ -25,6 +25,20 @@ dbackends = [
   ['Fast Multi-task Cascade Convolutional Neural Network (PyTorch)','fastmtcnn']
 ]
 
+embedding_backends =  [
+  "VGG-Face", 
+  "Facenet", 
+  "Facenet512", 
+  "OpenFace", 
+  "DeepFace", 
+  "DeepID", 
+  "ArcFace", 
+  "Dlib", 
+  "SFace",
+  "GhostFaceNet",
+]
+
+
 dbackendinfo = 'Detectors with 🌈 require a color image.'
 
 
@@ -91,9 +105,10 @@ with gr.Blocks() as demo:
 
 
 	with gr.Tab("Identify People in One Image"):
+		embedding_backendchoice = gr.Radio(choices=embedding_backends,label='Embedding Backend:',container=True,value='ArcFace')
 		oneimageannotations = []
-		def identify_in_one_image(imgfile):
-			oneimageresults = DeepFace.find(img_path=imgfile, db_path="db")
+		def identify_in_one_image(imgfile, embedding_backendchoice):
+			oneimageresults = DeepFace.find(img_path=imgfile, db_path="db", model_name=embedding_backendchoice)
 			oneimageresults = pd.concat(oneimageresults)
 			for i, found_face in oneimageresults.iterrows():
 				face_coordinates = (found_face["source_x"],found_face["source_y"], (found_face["source_x"] + found_face["source_w"]),(found_face["source_y"] + found_face["source_h"]))
@@ -107,14 +122,14 @@ with gr.Blocks() as demo:
 			)
 			return results, oneimageannotations
 
-		oneimage_input_image = gr.Image()
+		oneimage_input_image = gr.Image(value="TEST_spindler.jpg")
 		
 		found_faces=gr.AnnotatedImage()
 		debug_output = gr.Textbox()
 		gr.Interface(
 			allow_flagging = "never",
 			fn=identify_in_one_image,
-			inputs=oneimage_input_image,
+			inputs=[oneimage_input_image, embedding_backendchoice],
 			outputs=[found_faces, debug_output]
 			)
 		

db/ds_model_arcface_detector_opencv_aligned_normalization_base_expand_0.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c493963c11bc05abf1668a4c009d8a7259009b13c4c81721e8f1a3c77176683
+size 28405