update

app.py

@@ -12,18 +12,13 @@ import gradio as gr
 import face_detection
 
 
-def detect_faces(img: np.ndarray, method:str):
+def detect_faces(img: np.ndarray):
     frame = np.array(img) 
     
     kornia_detections = kornia_detect(frame)
     retina_detections = retina_detect(frame)
     retina_mobile_detections = retina_mobilenet_detect(frame)
     dsfd_detections = dsfd_detect(frame)
-
-    # if method == "Kornia YuNet":
-    #     re_im = kornia_detect(frame)
-    # elif method == "RetinaFace":
-    #     re_im = retina_detect(frame)
     
     return kornia_detections, retina_detections, retina_mobile_detections, dsfd_detections
 
@@ -126,14 +121,14 @@ nms_slider = gr.components.Slider(minimum=0.1, maximum=0.9, value=0.5, label="Mi
 # scale_slider = gr.components.Slider(minimum=1.1, maximum=2.0, value=1.3, step=0.1, label="Scale Factor")
 # classifier_radio = gr.components.Radio(s)
 
-methods_dropdown = gr.components.Dropdown(["Kornia YuNet", "RetinaFace", "RetinaMobile", "DSFD"], value="Kornia YuNet", label="Choose a method")
+#methods_dropdown = gr.components.Dropdown(["Kornia YuNet", "RetinaFace", "RetinaMobile", "DSFD"], value="Kornia YuNet", label="Choose a method")
 
 description = """Face Detection"""
 
 
 Iface = gr.Interface(
     fn=detect_faces,
-    inputs=[input_image, methods_dropdown],#, size_slider, neighbour_slider, scale_slider],
+    inputs=[input_image],#, size_slider, neighbour_slider, scale_slider],
     outputs=[image_kornia, image_retina, image_retina_mobile, image_dsfd],
     examples=[["data/9_Press_Conference_Press_Conference_9_86.jpg"], ["data/12_Group_Group_12_Group_Group_12_39.jpg"], ["data/31_Waiter_Waitress_Waiter_Waitress_31_55.jpg"]],
     title="Face Detection",