mindspore/model_zoo/official/cv/maskrcnn/postprocess.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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
#
# less 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.
# ============================================================================
"""post process for 310 inference"""
import argparse
import numpy as np
from PIL import Image
from pycocotools.coco import COCO

from src.config import config
from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks

dst_width = 1280
dst_height = 768

parser = argparse.ArgumentParser(description="maskrcnn inference")
parser.add_argument("--ann_file", type=str, required=True, help="ann file.")
parser.add_argument("--img_path", type=str, required=True, help="image file path.")
args = parser.parse_args()

def get_imgSize(file_name):
    img = Image.open(file_name)
    return img.size

def get_resizeRatio(img_size):
    org_width, org_height = img_size
    resize_ratio = dst_width / org_width
    if resize_ratio > dst_height / org_height:
        resize_ratio = dst_height / org_height

    return resize_ratio

def get_eval_result(ann_file, img_path):
    max_num = 128
    result_path = "./result_Files/"
    outputs = []

    dataset_coco = COCO(ann_file)
    img_ids = dataset_coco.getImgIds()

    for img_id in img_ids:
        file_id = str(img_id).zfill(12)
        file = img_path + "/" + file_id + ".jpg"
        img_size = get_imgSize(file)
        resize_ratio = get_resizeRatio(img_size)

        img_metas = np.array([img_size[1], img_size[0]] + [resize_ratio, resize_ratio])

        bbox_result_file = result_path + file_id + "_0.bin"
        label_result_file = result_path + file_id + "_1.bin"
        mask_result_file = result_path + file_id + "_2.bin"
        mask_fb_result_file = result_path + file_id + "_3.bin"

        all_bbox = np.fromfile(bbox_result_file, dtype=np.float16).reshape(80000, 5)
        all_label = np.fromfile(label_result_file, dtype=np.int32).reshape(80000, 1)
        all_mask = np.fromfile(mask_result_file, dtype=np.bool_).reshape(80000, 1)
        all_mask_fb = np.fromfile(mask_fb_result_file, dtype=np.float16).reshape(80000, 28, 28)

        all_bbox_squee = np.squeeze(all_bbox)
        all_label_squee = np.squeeze(all_label)
        all_mask_squee = np.squeeze(all_mask)
        all_mask_fb_squee = np.squeeze(all_mask_fb)

        all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
        all_labels_tmp_mask = all_label_squee[all_mask_squee]
        all_mask_fb_tmp_mask = all_mask_fb_squee[all_mask_squee, :, :]

        if all_bboxes_tmp_mask.shape[0] > max_num:
            inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
            inds = inds[:max_num]
            all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
            all_labels_tmp_mask = all_labels_tmp_mask[inds]
            all_mask_fb_tmp_mask = all_mask_fb_tmp_mask[inds]

        bbox_results = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, config.num_classes)
        segm_results = get_seg_masks(all_mask_fb_tmp_mask, all_bboxes_tmp_mask, all_labels_tmp_mask, img_metas,
                                     True, config.num_classes)
        outputs.append((bbox_results, segm_results))

    eval_types = ["bbox", "segm"]
    result_files = results2json(dataset_coco, outputs, "./results.pkl")
    coco_eval(result_files, eval_types, dataset_coco, single_result=False)

if __name__ == '__main__':
    get_eval_result(args.ann_file, args.img_path)