COCO-pickle(dets)

##---------------------------------------------------------------------------------------------------------------##
dets: ( len(dets)=81, type: <class 'list'>  )

dets[categaroies][image_ids]

type(dets[1][0])=<class 'numpy.ndarray'> or =<class 'list'>

if len(dets[i][j])>0:
   dets[i][j].shape=(n_dets, 5)   for each row, it's  x1, y1, x2, y2, score

##-------------------------------------------------------------------------------------------------------------- ##
det_im_ids: ( len(det_im_ids)=82783 , type: <class 'list'>)

[262118, 283985, 393193, ....... , 131067, 524286, 43645]

##---------------------------------------------------------------------------------------------------------------##

cat_ids: ( len(cat_ids)=81 , type: <class 'list'>)

 [-1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]


##--------------------------------------------------------------------------------------------------------------##
## example for creating <class 'list'>
numbers = [1, 2, 3]
result = isinstance(numbers, list)
print(numbers,'instance of list?', result)


##==========================roidb=====================================##

##--------------------------- im_info ---------------------------------------##

roidb = load_im_info(coco, path_to_images[name])

roidb.append({

'id': im_info['id'],

'width': im_info['width'],

'height': im_info['height'],

'filename': filename,

'flipped': False,

})

gt_roidb = load_annotations(coco, cat_id_to_class_ind)

return {

'id': im_info['id'],

'gt_boxes': boxes,

'gt_classes': classes,

'gt_crowd': crowd,

}

det_roidb = load_detections(coco, name, cfg.train.detector, cat_id_to_class_ind)

roidb.append({

'id': imid,

'dets': imdets,

'det_scores': scores,

'det_classes': cls,

})