Delete clipseg/datasets/utils.py

clipseg/datasets/utils.py

@@ -1,68 +0,0 @@
-
-import numpy as np
-import torch
-
-
-def blend_image_segmentation(img, seg, mode, image_size=224):
-
-
-    if mode in {'blur_highlight', 'blur3_highlight', 'blur3_highlight01', 'blur_highlight_random', 'crop'}:
-        if isinstance(img, np.ndarray):
-            img = torch.from_numpy(img)
-
-        if isinstance(seg, np.ndarray):
-            seg = torch.from_numpy(seg)            
-
-    if mode == 'overlay':
-        out = img * seg
-        out = [out.astype('float32')]
-    elif mode == 'highlight':
-        out = img * seg[None, :, :] * 0.85 + 0.15 * img
-        out = [out.astype('float32')]
-    elif mode == 'highlight2':
-        img = img / 2
-        out = (img+0.1) * seg[None, :, :] + 0.3 * img
-        out = [out.astype('float32')]
-    elif mode == 'blur_highlight':
-        from evaluation_utils import img_preprocess
-        out  = [img_preprocess((None, [img], [seg]), blur=1, bg_fac=0.5).numpy()[0] - 0.01]
-    elif mode == 'blur3_highlight':
-        from evaluation_utils import img_preprocess
-        out  = [img_preprocess((None, [img], [seg]), blur=3, bg_fac=0.5).numpy()[0] - 0.01]
-    elif mode == 'blur3_highlight01':
-        from evaluation_utils import img_preprocess
-        out  = [img_preprocess((None, [img], [seg]), blur=3, bg_fac=0.1).numpy()[0] - 0.01]                
-    elif mode == 'blur_highlight_random':
-        from evaluation_utils import img_preprocess
-        out  = [img_preprocess((None, [img], [seg]), blur=0 + torch.randint(0, 3, (1,)).item(), bg_fac=0.1 + 0.8*torch.rand(1).item()).numpy()[0] - 0.01]               
-    elif mode == 'crop':
-        from evaluation_utils import img_preprocess
-        out  = [img_preprocess((None, [img], [seg]), blur=1, center_context=0.1, image_size=image_size)[0].numpy()]  
-    elif mode == 'crop_blur_highlight':
-        from evaluation_utils import img_preprocess
-        out  = [img_preprocess((None, [img], [seg]), blur=3, center_context=0.1, bg_fac=0.1, image_size=image_size)[0].numpy()]  
-    elif mode == 'crop_blur_highlight352':
-        from evaluation_utils import img_preprocess
-        out  = [img_preprocess((None, [img], [seg]), blur=3, center_context=0.1, bg_fac=0.1, image_size=352)[0].numpy()]          
-    elif mode == 'shape':
-        out = [np.stack([seg[:, :]]*3).astype('float32')]
-    elif mode == 'concat':
-        out = [np.concatenate([img, seg[None, :, :]]).astype('float32')]
-    elif mode == 'image_only':
-        out = [img.astype('float32')]
-    elif mode == 'image_black':
-        out = [img.astype('float32')*0]        
-    elif mode is None:
-        out = [img.astype('float32')]
-    elif mode == 'separate':
-        out = [img.astype('float32'), seg.astype('int64')]
-    elif mode == 'separate_img_black':
-        out = [img.astype('float32')*0, seg.astype('int64')]        
-    elif mode == 'separate_seg_ones':
-        out = [img.astype('float32'), np.ones_like(seg).astype('int64')]                
-    elif mode == 'separate_both_black':
-        out = [img.astype('float32')*0, seg.astype('int64')*0]        
-    else:
-        raise ValueError(f'invalid mode: {mode}')
-
-    return out