Update app.py

app.py

@@ -170,7 +170,6 @@ def save_image(img):
 
 @spaces.GPU
 def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed, categorie = 'upper_body'):
-    
     device = "cuda"
     openpose_model.preprocessor.body_estimation.model.to(device)
     pipe.to(device)
@@ -183,10 +182,10 @@ def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denois
         width, height = human_img_orig.size
         target_width = int(min(width, height * (3 / 4)))
         target_height = int(min(height, width * (4 / 3)))
-        left = (width - target_width) // 2
-        top = (height - target_height) // 2
-        right = (width + target_width) // 2
-        bottom = (height + target_height) // 2
+        left = (width - target_width) / 2
+        top = (height - target_height) / 2
+        right = (width + target_width) / 2
+        bottom = (height + target_height) / 2
         cropped_img = human_img_orig.crop((left, top, right, bottom))
         crop_size = cropped_img.size
         human_img = cropped_img.resize((768, 1024))
@@ -196,19 +195,17 @@ def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denois
     if is_checked:
         keypoints = openpose_model(human_img.resize((384, 512)))
         model_parse, _ = parsing_model(human_img.resize((384, 512)))
-        mask, mask_gray = get_mask_location('hd', categorie, model_parse, keypoints)
+        mask, mask_gray = get_mask_location('hd', categorie , model_parse, keypoints)
         mask = mask.resize((768, 1024))
     else:
         mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
-
     mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
     mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)
 
-    # Potentially modify args.func to accept PIL images directly
     human_img_arg = _apply_exif_orientation(human_img.resize((384, 512)))
     human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
 
-    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts',
+    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
     pose_img = args.func(args, human_img_arg)
     pose_img = pose_img[:, :, ::-1]
     pose_img = Image.fromarray(pose_img).resize((768, 1024))