PaddleDetection-release-2.6/ppdet/metrics/json_results.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import six
import numpy as np


def get_det_res(bboxes, bbox_nums, image_id, label_to_cat_id_map, bias=0):
    det_res = []
    k = 0
    for i in range(len(bbox_nums)):
        cur_image_id = int(image_id[i][0])
        det_nums = bbox_nums[i]
        for j in range(det_nums):
            dt = bboxes[k]
            k = k + 1
            num_id, score, xmin, ymin, xmax, ymax = dt.tolist()
            if int(num_id) < 0:
                continue
            category_id = label_to_cat_id_map[int(num_id)]
            w = xmax - xmin + bias
            h = ymax - ymin + bias
            bbox = [xmin, ymin, w, h]
            dt_res = {
                'image_id': cur_image_id,
                'category_id': category_id,
                'bbox': bbox,
                'score': score
            }
            det_res.append(dt_res)
    return det_res


def get_det_poly_res(bboxes, bbox_nums, image_id, label_to_cat_id_map, bias=0):
    det_res = []
    k = 0
    for i in range(len(bbox_nums)):
        cur_image_id = int(image_id[i][0])
        det_nums = bbox_nums[i]
        for j in range(det_nums):
            dt = bboxes[k]
            k = k + 1
            num_id, score, x1, y1, x2, y2, x3, y3, x4, y4 = dt.tolist()
            if int(num_id) < 0:
                continue
            category_id = label_to_cat_id_map[int(num_id)]
            rbox = [x1, y1, x2, y2, x3, y3, x4, y4]
            dt_res = {
                'image_id': cur_image_id,
                'category_id': category_id,
                'bbox': rbox,
                'score': score
            }
            det_res.append(dt_res)
    return det_res


def strip_mask(mask):
    row = mask[0, 0, :]
    col = mask[0, :, 0]
    im_h = len(col) - np.count_nonzero(col == -1)
    im_w = len(row) - np.count_nonzero(row == -1)
    return mask[:, :im_h, :im_w]


def get_seg_res(masks, bboxes, mask_nums, image_id, label_to_cat_id_map):
    import pycocotools.mask as mask_util
    seg_res = []
    k = 0
    for i in range(len(mask_nums)):
        cur_image_id = int(image_id[i][0])
        det_nums = mask_nums[i]
        mask_i = masks[k:k + det_nums]
        mask_i = strip_mask(mask_i)
        for j in range(det_nums):
            mask = mask_i[j].astype(np.uint8)
            score = float(bboxes[k][1])
            label = int(bboxes[k][0])
            k = k + 1
            if label == -1:
                continue
            cat_id = label_to_cat_id_map[label]
            rle = mask_util.encode(
                np.array(
                    mask[:, :, None], order="F", dtype="uint8"))[0]
            if six.PY3:
                if 'counts' in rle:
                    rle['counts'] = rle['counts'].decode("utf8")
            sg_res = {
                'image_id': cur_image_id,
                'category_id': cat_id,
                'segmentation': rle,
                'score': score
            }
            seg_res.append(sg_res)
    return seg_res


def get_solov2_segm_res(results, image_id, num_id_to_cat_id_map):
    import pycocotools.mask as mask_util
    segm_res = []
    # for each batch
    segms = results['segm'].astype(np.uint8)
    clsid_labels = results['cate_label']
    clsid_scores = results['cate_score']
    lengths = segms.shape[0]
    im_id = int(image_id[0][0])
    if lengths == 0 or segms is None:
        return None
    # for each sample
    for i in range(lengths - 1):
        clsid = int(clsid_labels[i])
        catid = num_id_to_cat_id_map[clsid]
        score = float(clsid_scores[i])
        mask = segms[i]
        segm = mask_util.encode(np.array(mask[:, :, np.newaxis], order='F'))[0]
        segm['counts'] = segm['counts'].decode('utf8')
        coco_res = {
            'image_id': im_id,
            'category_id': catid,
            'segmentation': segm,
            'score': score
        }
        segm_res.append(coco_res)
    return segm_res


def get_keypoint_res(results, im_id):
    anns = []
    preds = results['keypoint']
    for idx in range(im_id.shape[0]):
        image_id = im_id[idx].item()
        kpts, scores = preds[idx]
        for kpt, score in zip(kpts, scores):
            kpt = kpt.flatten()
            ann = {
                'image_id': image_id,
                'category_id': 1,  # XXX hard code
                'keypoints': kpt.tolist(),
                'score': float(score)
            }
            x = kpt[0::3]
            y = kpt[1::3]
            x0, x1, y0, y1 = np.min(x).item(), np.max(x).item(), np.min(y).item(
            ), np.max(y).item()
            ann['area'] = (x1 - x0) * (y1 - y0)
            ann['bbox'] = [x0, y0, x1 - x0, y1 - y0]
            anns.append(ann)
    return anns


def get_pose3d_res(results, im_id):
    anns = []
    preds = results['pose3d']
    for idx in range(im_id.shape[0]):
        image_id = im_id[idx].item()
        pose3d = preds[idx]
        ann = {
            'image_id': image_id,
            'category_id': 1,  # XXX hard code
            'pose3d': pose3d.tolist(),
            'score': float(1.)
        }
        anns.append(ann)
    return anns