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https://github.com/PaddlePaddle/FastDeploy.git
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ed3d6f2187
* Fix bug in ppyoloe * fix ppyoloe output format * remove some requirements * fix conflicts
74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import copy
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import collections
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def eval_detection(model,
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data_dir,
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ann_file,
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conf_threshold=None,
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nms_iou_threshold=None,
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plot=False):
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from .utils import CocoDetection
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from .utils import COCOMetric
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import cv2
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from tqdm import trange
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if conf_threshold is not None or nms_iou_threshold is not None:
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assert conf_threshold is not None and nms_iou_threshold is not None, "The conf_threshold and nms_iou_threshold should be setted at the same time"
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assert isinstance(conf_threshold, (
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float,
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int)), "The conf_threshold:{} need to be int or float".format(
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conf_threshold)
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assert isinstance(nms_iou_threshold, (
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float,
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int)), "The nms_iou_threshold:{} need to be int or float".format(
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nms_iou_threshold)
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eval_dataset = CocoDetection(
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data_dir=data_dir, ann_file=ann_file, shuffle=False)
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all_image_info = eval_dataset.file_list
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image_num = eval_dataset.num_samples
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eval_dataset.data_fields = {
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'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class', 'is_crowd'
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}
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eval_metric = COCOMetric(
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coco_gt=copy.deepcopy(eval_dataset.coco_gt), classwise=False)
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scores = collections.OrderedDict()
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for image_info, i in zip(all_image_info,
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trange(
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image_num, desc="Inference Progress")):
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im = cv2.imread(image_info["image"])
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im_id = image_info["im_id"]
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if conf_threshold is None and nms_iou_threshold is None:
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result = model.predict(im)
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else:
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result = model.predict(im, conf_threshold, nms_iou_threshold)
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pred = {
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'bbox':
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[[c] + [s] + b
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for b, s, c in zip(result.boxes, result.scores, result.label_ids)
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],
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'bbox_num': len(result.boxes),
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'im_id': im_id
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}
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eval_metric.update(im_id, pred)
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eval_metric.accumulate()
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eval_details = eval_metric.details
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scores.update(eval_metric.get())
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eval_metric.reset()
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return scores
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