misc.py

import mmcv
import numpy as np
import torch

__all__ = ['tensor2imgs', 'unique', 'unmap', 'results2json']


def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenorm(img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs


def unique(tensor):
    if tensor.is_cuda:
        u_tensor = np.unique(tensor.cpu().numpy())
        return tensor.new_tensor(u_tensor)
    else:
        return torch.unique(tensor)


def unmap(data, count, inds, fill=0):
    """ Unmap a subset of item (data) back to the original set of items (of
    size count) """
    if data.dim() == 1:
        ret = data.new_full((count, ), fill)
        ret[inds] = data
    else:
        new_size = (count, ) + data.size()[1:]
        ret = data.new_full(new_size, fill)
        ret[inds, :] = data
    return ret


def xyxy2xywh(bbox):
    _bbox = bbox.tolist()
    return [
        _bbox[0],
        _bbox[1],
        _bbox[2] - _bbox[0] + 1,
        _bbox[3] - _bbox[1] + 1,
    ]


def det2json(dataset, results):
    json_results = []
    for idx in range(len(dataset)):
        img_id = dataset.img_ids[idx]
        result = results[idx]
        for label in range(len(result)):
            bboxes = result[label]
            for i in range(bboxes.shape[0]):
                data = dict()
                data['image_id'] = img_id
                data['bbox'] = xyxy2xywh(bboxes[i])
                data['score'] = float(bboxes[i][4])
                data['category_id'] = dataset.cat_ids[label]
                json_results.append(data)
    return json_results


def segm2json(dataset, results):
    json_results = []
    for idx in range(len(dataset)):
        img_id = dataset.img_ids[idx]
        det, seg = results[idx]
        for label in range(len(det)):
            bboxes = det[label]
            segms = seg[label]
            for i in range(bboxes.shape[0]):
                data = dict()
                data['image_id'] = img_id
                data['bbox'] = xyxy2xywh(bboxes[i])
                data['score'] = float(bboxes[i][4])
                data['category_id'] = dataset.cat_ids[label]
                segms[i]['counts'] = segms[i]['counts'].decode()
                data['segmentation'] = segms[i]
                json_results.append(data)
    return json_results


def proposal2json(dataset, results):
    json_results = []
    for idx in range(len(dataset)):
        img_id = dataset.img_ids[idx]
        bboxes = results[idx]
        for i in range(bboxes.shape[0]):
            data = dict()
            data['image_id'] = img_id
            data['bbox'] = xyxy2xywh(bboxes[i])
            data['score'] = float(bboxes[i][4])
            data['category_id'] = 1
            json_results.append(data)
    return json_results


def results2json(dataset, results, out_file):
    if isinstance(results[0], list):
        json_results = det2json(dataset, results)
    elif isinstance(results[0], tuple):
        json_results = segm2json(dataset, results)
    elif isinstance(results[0], np.ndarray):
        json_results = proposal2json(dataset, results)
    else:
        raise TypeError('invalid type of results')
    mmcv.dump(json_results, out_file)