Function eval

evaluation_DVL.py:355–460

Args: @id: (int) marker image id, for logging @scene_images: (list) test scene images @marker: (array) marker image @source: source image to calculate metrics when blend_type=='lighting' @estimator: twins-onestage @flow_estimator & est

(args, id, scene_images, marker, source=None,    
         estimator=None, 
         coarseModel=None, network=None, 
         flow_estimator=None, estimate_uncertainty=None, 
         matching=None,                  
)

    return blends, masks, blends_colormap

def eval(args, id, scene_images, marker, source=None,    
         estimator=None, 
         coarseModel=None, network=None, 
         flow_estimator=None, estimate_uncertainty=None, 
         matching=None,                  
):   
    '''
    Args:
        @id: (int) marker image id, for logging
        @scene_images: (list) test scene images
        @marker: (array) marker image
        @source: source image to calculate metrics when blend_type=='lighting'            
        @estimator: twins-onestage
        @flow_estimator & estimate_uncertainty: pdc        
        @coarseModel & network: ransac-flow
        @matching: SPSG                
        @args: other args
    '''
    if args.blend_method == 'twins-onestage':
        if estimator is None:
            raise ValueError('estimator not set')        
        out, mask, out_colormap = blend_life(scene_images=scene_images, marker=marker, source=source, estimator=estimator, warp=args.warp, 
                                            blend_type=args.blend_type, use_colormap=args.use_colormap)
    
    elif args.blend_method == 'homography':        
        out, mask, out_colormap = blend_homography(scene_images=scene_images, marker=marker, source=source, detector=args.detector, 
                                            blend_type=args.blend_type, use_colormap=args.use_colormap)
    elif args.blend_method == 'pdc':
        if flow_estimator is None:
            raise ValueError('estimator not set')
        out, mask, out_colormap = blend_pdc(scene_images=scene_images, marker=marker, source=source, flow_estimator=flow_estimator, estimate_uncertainty=estimate_uncertainty, 
                                            blend_type=args.blend_type, use_colormap=args.use_colormap)
    
    elif args.blend_method == 'ransac-flow':
        if coarseModel is None or network is None:
            raise ValueError('coarseModel not set')
        out, mask, out_colormap = blend_RANSAC(scene_images=scene_images, marker=marker, source=source, coarseModel=coarseModel, network=network, 
                                            blend_type=args.blend_type, use_colormap=args.use_colormap)

    elif args.blend_method == 'SPSG':                
        out, mask, out_colormap = blend_SPSG(scene_images=scene_images, marker=marker, source=source, matching=matching, 
                                            blend_type=args.blend_type, use_colormap=args.use_colormap)    

        
    if args.blend_type == 'L':
        result = metrics(args=args, id=id, output_images=out, masks=mask, source=source)
    else:
        result = metrics(args=args, id=id, output_images=out, masks=mask, scene_images=scene_images)
        
    if args.draw or args.save: 
        # save_root = os.path.join(save_root, str(id).zfill(4))
        # if save and not os.path.exists(save_root):
        #     os.makedirs(save_root)
        for i in range(len(scene_images)): 
            if out[i] is None:
                continue                           
            
            if not args.with_mask: