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@ -25,6 +25,7 @@ batch_size = 1
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parser = argparse.ArgumentParser(description="ssd_mobilenet_v1_fpn inference")
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parser.add_argument("--result_path", type=str, required=True, help="result files path.")
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parser.add_argument("--img_path", type=str, required=True, help="image file path.")
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parser.add_argument("--drop", action="store_true", help="drop iscrowd images or not.")
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args = parser.parse_args()
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def get_imgSize(file_name):
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@ -33,15 +34,43 @@ def get_imgSize(file_name):
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def get_result(result_path, img_id_file_path):
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anno_json = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
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if args.drop:
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from pycocotools.coco import COCO
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train_cls = config.classes
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train_cls_dict = {}
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for i, cls in enumerate(train_cls):
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train_cls_dict[cls] = i
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coco = COCO(anno_json)
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classs_dict = {}
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cat_ids = coco.loadCats(coco.getCatIds())
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for cat in cat_ids:
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classs_dict[cat["id"]] = cat["name"]
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files = os.listdir(img_id_file_path)
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pred_data = []
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for file in files:
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img_ids_name = file.split('.')[0]
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img_id = int(np.squeeze(img_ids_name))
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if args.drop:
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anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
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anno = coco.loadAnns(anno_ids)
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annos = []
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iscrowd = False
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for label in anno:
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bbox = label["bbox"]
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class_name = classs_dict[label["category_id"]]
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iscrowd = iscrowd or label["iscrowd"]
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if class_name in train_cls:
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x_min, x_max = bbox[0], bbox[0] + bbox[2]
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y_min, y_max = bbox[1], bbox[1] + bbox[3]
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annos.append(list(map(round, [y_min, x_min, y_max, x_max])) + [train_cls_dict[class_name]])
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if iscrowd or (not annos):
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continue
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img_size = get_imgSize(os.path.join(img_id_file_path, file))
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image_shape = np.array([img_size[1], img_size[0]])
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result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
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result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
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