bugsfix

app.py

@@ -22,10 +22,7 @@ from deepface import DeepFace
 nb_images=50
 
 
-# If faces path is empty then make it
-faces_path = Path(__file__).parent/"faces"
-if not faces_path.exists():
-    faces_path.mkdir(parents=True, exist_ok=True)
+
 
 
 # Build face analyzer while specifying that we want to extract just a single face
@@ -46,6 +43,10 @@ import gradio as gr
 import numpy as np
 class UI():
     def __init__(self) -> None:
+        # If faces path is empty then make it
+        self.faces_path = Path(__file__).parent/"faces"
+        if not self.faces_path.exists():
+            self.faces_path.mkdir(parents=True, exist_ok=True)
         self.i=0
         self.embeddings_cloud = []
         self.is_recording=False
@@ -113,8 +114,8 @@ class UI():
                                 self.status = gr.Label(label="Status")
 
                                 self.gallery = gr.Gallery(
-                                    label="Uploaded Images", show_label=True, height=300, elem_id="gallery"
-                                ).style(grid=[2], height="auto")
+                                    label="Uploaded Images", show_label=True, height=300, elem_id="gallery", visible=False  
+                                ).style(grid=[8], height="auto")
                                 self.btn_clear = gr.Button("Clear Gallery")
                                 self.btn_start.click(self.record_from_files, inputs=[self.gallery, self.txtFace_name2], outputs=self.status, show_progress=True)
                                 self.btn_clear.click(self.clear_galery,[],[self.gallery, self.add_file])
@@ -123,19 +124,15 @@ class UI():
                     with gr.Blocks():
                         with gr.Row():
                             with gr.Column():
-                                if len(self.known_faces_names)>0:
-                                    self.faces_list = gr.Dataframe(
-                                        headers=["Face Name"],
-                                        datatype=["str"],
-                                        label="Faces",
-                                        value=[[n] for n in self.known_faces_names]
-                                    )
-                                else:
-                                    self.faces_list = gr.Dataframe(
-                                        headers=["Face Name"],
-                                        datatype=["str"],
-                                        label="Faces"
-                                    )
+                                self.faces_list = gr.Dataframe(
+                                    headers=["Face Name"],
+                                    datatype=["str"],
+                                    label="Faces",
+                                    value=[[n] for n in self.known_faces_names]
+                                )
+                                self.btn_reset_faces = gr.Button("clear faces list")
+                                self.faces_list_status = gr.Label(label="Status")
+                                self.btn_reset_faces.click(self.clear_faces,[],[self.faces_list_status])
             with gr.Row():
                 with gr.Accordion(label="Options", open=False):
                     self.sld_threshold = gr.Slider(1e-2,10,4e-1,step=1e-2,label="Recognition threshold")
@@ -154,8 +151,34 @@ class UI():
 
         demo.queue().launch()
 
+    def clear_directory(self, directory_path):
+        """
+        Recursively removes all files and subdirectories within the specified directory.
+
+        Args:
+            directory_path (str): The path to the directory to clear.
+
+        Returns:
+            None
+        """
+        directory = Path(directory_path)
+        for item in directory.iterdir():
+            if item.is_file():
+                item.unlink()
+            elif item.is_dir():
+                self.clear_directory(item)
+                item.rmdir()
+
+    def clear_faces(self):
+        """
+            clears faces
+        """
+        self.clear_directory(self.faces_path)
+        self.upgrade_faces()
+        return "Faces removed"
+
     def change_distance(self, type):
-        self.distance_type=type
+        return self.distance_type.update(value=type)
 
     def clear_galery(self):
         return self.gallery.update(value=[]), self.add_file.update(value=[])
@@ -209,7 +232,7 @@ class UI():
         print("Reloading faces")
         self.known_faces=[]
         self.known_faces_names=[]
-        face_files = [f for f in faces_path.iterdir() if f.name.endswith("pkl")]
+        face_files = [f for f in self.faces_path.iterdir() if f.name.endswith("pkl")]
         for file in face_files:
             with open(str(file),"rb") as f:
                 finger_print = pickle.load(f)
@@ -254,7 +277,7 @@ class UI():
         # Now we save it.
         # create a dialog box to ask for the subject name
         name = self.face_name
-        with open(str(faces_path/f"{name}.pkl"),"wb") as f:
+        with open(str(self.faces_path/f"{name}.pkl"),"wb") as f:
             pickle.dump({"mean":embeddings_cloud_mean, "inv_cov":embeddings_cloud_inv_cov},f)
         print(f"Saved {name}")
 
@@ -290,7 +313,7 @@ class UI():
                 # Now we save it.
                 # create a dialog box to ask for the subject name
                 name = self.face_name
-                with open(str(faces_path/f"{name}.pkl"),"wb") as f:
+                with open(str(self.faces_path/f"{name}.pkl"),"wb") as f:
                     pickle.dump({"mean":embeddings_cloud_mean, "inv_cov":embeddings_cloud_inv_cov},f)
                 print(f"Saved {name} embeddings")
                 self.i=0
@@ -338,7 +361,7 @@ class UI():
             embeddings_cloud_inv_cov = embeddings_cloud.std(axis=0)
             # Now we save it.
             # create a dialog box to ask for the subject name
-            with open(str(faces_path/f"{face_name}.pkl"),"wb") as f:
+            with open(str(self.faces_path/f"{face_name}.pkl"),"wb") as f:
                 pickle.dump({"mean":embeddings_cloud_mean, "inv_cov":embeddings_cloud_inv_cov},f)
             print(f"Saved {face_name} embeddings")
             self.i=0
@@ -387,7 +410,8 @@ class UI():
                     bboxes_and_names.append([face.bounding_box, f"Unknown:{nearest_distance:.2e}" if nearest_distance>self.threshold else f"{self.known_faces_names[nearest]}:{nearest_distance:.2e}"])
                 except Exception as ex:
                     pass
-            image = fa.draw_names_on_bboxes(image,bboxes_and_names,upscale=2)
+            if len(bboxes_and_names)>0:
+                image = fa.draw_names_on_bboxes(image,bboxes_and_names,upscale=2)
         # Return the resulting frame
         return image      
         
@@ -430,7 +454,8 @@ class UI():
                     bboxes_and_names.append([face.bounding_box, f"Unknown:{nearest_distance:.2e}" if nearest_distance>self.threshold else f"{self.known_faces_names[nearest]}:{nearest_distance:.2e}"])
                 except Exception as ex:
                     image=face_image
-            image = fa.draw_names_on_bboxes(image,bboxes_and_names,upscale=2)
+            if len(bboxes_and_names)>0:
+                image = fa.draw_names_on_bboxes(image,bboxes_and_names,upscale=2)
 
         # Return the resulting frame
         return image