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- # Copyright 2022 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
- #
- # 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.
- # ============================================================================
-
- """visualize for nanodetplus"""
-
- import os
- import cv2
- import matplotlib.pyplot as plt
- from pycocotools.coco import COCO
- from src.model_utils.config import config
-
-
- def visualize_model():
- # load best ckpt to generate instances_val.json and predictions.json
-
- dataset_dir = r'./dataset/val/images/'
- coco_root = config.voc_root
- data_type = config.val_data_type
- annotation_file = os.path.join(coco_root, config.instances_set.format(data_type))
- coco = COCO(annotation_file)
- catids = coco.getCatIds()
- imgids = coco.getImgIds()
- coco_res = coco.loadRes('./predictions.json')
- catids_res = coco_res.getCatIds()
- for i in range(10):
- img = coco.loadImgs(imgids[i])[0]
- image = cv2.imread(dataset_dir + img['file_name'])
- image_res = image
- annids = coco.getAnnIds(imgIds=img['id'], catIds=catids, iscrowd=None)
- annos = coco.loadAnns(annids)
- annids_res = coco_res.getAnnIds(imgIds=img['id'], catIds=catids_res, iscrowd=None)
- annos_res = coco_res.loadAnns(annids_res)
- plt.figure(figsize=(7, 7))
- for anno in annos:
- bbox = anno['bbox']
- x, y, w, h = bbox
- if anno['category_id'] == 1:
- anno_image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (153, 153, 255), 2)
- elif anno['category_id'] == 2:
- anno_image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (153, 255, 153), 2)
- else:
- anno_image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (255, 153, 153), 2)
- plt.subplot(1, 2, 1)
- plt.plot([-2, 3], [1, 5])
- plt.title('true-label')
- plt.imshow(anno_image)
- for anno_res in annos_res:
- bbox_res = anno_res['bbox']
- x, y, w, h = bbox_res
- if anno_res['category_id'] == 1:
- res_image = cv2.rectangle(image_res, (int(x), int(y)), (int(x + w), int(y + h)), (0, 0, 255), 2)
- elif anno_res['category_id'] == 2:
- res_image = cv2.rectangle(image_res, (int(x), int(y)), (int(x + w), int(y + h)), (0, 153, 0), 2)
- else:
- res_image = cv2.rectangle(image_res, (int(x), int(y)), (int(x + w), int(y + h)), (255, 0, 0), 2)
- plt.subplot(1, 2, 2)
- plt.title('pred-label')
- plt.imshow(res_image)
- plt.show()
-
-
- if __name__ == '__main__':
- visualize_model()
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