fix main

launch.py

@@ -2,8 +2,8 @@ import gradio as gr
 import sys
 import cv2
 
-from td_abg import get_foreground
-from convertor import pil2cv
+from scripts.td_abg import get_foreground
+from scripts.convertor import pil2cv
 
 
 

scripts/main.py

@@ -9,22 +9,10 @@ import gradio as gr
 import modules.scripts as scripts
 from modules import script_callbacks
 
-from td_abg import get_foreground
-from convertor import pil2cv
+from scripts.td_abg import get_foreground
+from scripts.convertor import pil2cv
 
 
-"""
-body_estimation = None
-presets_file = os.path.join(scripts.basedir(), "presets.json")
-presets = {}
-
-try: 
-  with open(presets_file) as file:
-    presets = json.load(file)
-except FileNotFoundError:
-  pass
-"""
-
 def processing(self, input_image, td_abg_enabled, h_split, v_split, n_cluster, alpha, th_rate, cascadePSP_enabled, fast, psp_L):
     image = pil2cv(input_image)
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
@@ -49,15 +37,15 @@ def on_ui_tabs():
         with gr.Row():
             with gr.Column():
                 input_image = gr.Image(type="pil")
-                with gr.Accordion("tile division ABG", open=True):
+                with gr.Accordion("tile division BG Remover", open=True):
                     with gr.Box():
-                        td_abg_enabled = gr.Checkbox(label="enabled", show_label=True)
-                        h_split = gr.Slider(1, 2048, value=256, step=4, label="horizontal split num", show_label=True)
-                        v_split = gr.Slider(1, 2048, value=256, step=4, label="vertical split num", show_label=True)
-                        
-                        n_cluster = gr.Slider(1, 1000, value=500, step=10, label="cluster num", show_label=True)
-                        alpha = gr.Slider(1, 255, value=100, step=1, label="alpha threshold", show_label=True)
-                        th_rate = gr.Slider(0, 1, value=0.1, step=0.01, label="mask content ratio", show_label=True)
+                      td_abg_enabled = gr.Checkbox(label="enabled", show_label=True)
+                      h_split = gr.Slider(1, 2048, value=256, step=4, label="horizontal split num", show_label=True)
+                      v_split = gr.Slider(1, 2048, value=256, step=4, label="vertical split num", show_label=True)
+                      
+                      n_cluster = gr.Slider(1, 1000, value=500, step=10, label="cluster num", show_label=True)
+                      alpha = gr.Slider(1, 255, value=50, step=1, label="alpha threshold", show_label=True)
+                      th_rate = gr.Slider(0, 1, value=0.1, step=0.01, label="mask content ratio", show_label=True)
                         
                 with gr.Accordion("cascadePSP", open=True):        
                     with gr.Box():

scripts/td_abg.py

@@ -4,7 +4,7 @@ import numpy as np
 import pandas as pd
 from sklearn.cluster import KMeans, MiniBatchKMeans
 
-from convertor import rgb2df, df2rgba, cv2pil
+from scripts.convertor import rgb2df, df2rgba
 
 import gradio as gr
 import huggingface_hub