Update app.py

app.py

@@ -27,13 +27,9 @@ from datetime import (
 import cv2
 import numpy as np
 
-last_glb_path = None
-
 def prepare_to_run_demo():
     config = load_config('configs/infer.json')
     kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=config['accelerator']['timeout']))
-    output_dir = f'output/infer'
-    if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
     accu_steps = config['accelerator']['accumulation_nsteps']
     accelerator = Accelerator(
         gradient_accumulation_steps=accu_steps,
@@ -70,7 +66,7 @@ def ply2glb(ply_path, glb_path):
     os.remove(ply_path)
 
 def fn(image_path, mask_path):
-    global last_glb_path
+    name_base, _ = os.path.splitext(os.path.basename(image_path))
     config, accelerator = prepare_to_run_demo()
     model = load_model(config, accelerator)
     image, cv2_image, mask = load_infer_data_demo(image_path, mask_path, 
@@ -81,11 +77,8 @@ def fn(image_path, mask_path):
         autocast_ctx = torch.autocast(accelerator.device.type)
     with autocast_ctx, torch.no_grad():
         distance = model(image).cpu().numpy()[0]
-        if last_glb_path is not None:
-            os.remove(last_glb_path)
         distance_vis = colorize_distance(distance, mask)
-        save_path = f'cache/tmp_{datetime.now().strftime("%Y%m%d_%H%M%S")}.glb'
-        last_glb_path = save_path
+        save_path = f'files/cache/{name_base}.glb'
         normal_image = distance2pointcloud(distance, cv2_image, mask, save_path=save_path.replace('.glb', '.ply'), return_normal=True, save_distance=False)
         ply2glb(save_path.replace('.glb', '.ply'), save_path)
         return save_path, [distance_vis, normal_image]