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修复了没有检测到目标时出现error的问题

pull/18/head
ysc 2024-03-29 11:49:54 +08:00
parent 68c003f61f
commit 7cbb67773b
1 changed files with 13 additions and 9 deletions
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22
app.py
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@ -192,10 +192,13 @@ def segment_image(img,prompt_mode, categoryname, custom_category, expressiong, r
boxes = LSJ_box_postprocess(pred_boxes,padding_size,re_size, ori_height,ori_width)
mask_pred = mask_pred[topk_indices]
pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False )
pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]]
pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False )
pred_masks = (pred_masks>0).detach().cpu().numpy()[0]
if len(topk_indices) == 0: # 检查是否有选中的索引
pred_masks = []
else:
pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False )
pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]]
pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False )
pred_masks = (pred_masks>0).detach().cpu().numpy()[0]
if 'mask' in results_select:
@ -324,11 +327,12 @@ def segment_image(img,prompt_mode, categoryname, custom_category, expressiong, r
boxes = LSJ_box_postprocess(pred_boxes,padding_size,re_size, ori_height,ori_width)
mask_pred = mask_pred[topk_indices]
pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False )
pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]]
pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False )
pred_masks = (pred_masks>0).detach().cpu().numpy()[0]
mask_results_list.append(pred_masks)
if len(topk_indices) > 0: # 检查是否有选中的索引
pred_masks = F.interpolate( mask_pred[None,], size=(padding_size[0], padding_size[1]), mode="bilinear", align_corners=False )
pred_masks = pred_masks[:,:,:re_size[0],:re_size[1]]
pred_masks = F.interpolate( pred_masks, size=(ori_height,ori_width), mode="bilinear", align_corners=False )
pred_masks = (pred_masks>0).detach().cpu().numpy()[0]
mask_results_list.append(pred_masks)
zero_mask = np.zeros_like(copyed_img)
for mask,RGB in zip(mask_results_list,visual_prompt_RGB_list):