mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
b565c15bf7
* Add tinypose model * Add PPTinypose python API * Fix picodet preprocess bug && Add Tinypose examples * Update tinypose example code * Update ppseg preprocess if condition * Update ppseg backend support type * Update permute.h * Update README.md * Update code with comments * Move files dir * Delete premute.cc * Add single model pptinypose * Delete pptinypose old code in ppdet * Code format * Add ppdet + pptinypose pipeline model * Fix bug for posedetpipeline * Change Frontend to ModelFormat * Change Frontend to ModelFormat in __init__.py * Add python posedetpipeline/ * Update pptinypose example dir name * Update README.md * Update README.md * Update README.md * Update README.md * Create keypointdetection_result.md * Create README.md * Create README.md * Create README.md * Update README.md * Update README.md * Create README.md * Fix det_keypoint_unite_infer.py bug * Create README.md * Update PP-Tinypose by comment * Update by comment * Add pipeline directory * Add pptinypose dir * Update pptinypose to align accuracy * Addd warpAffine processor * Update GetCpuMat to GetOpenCVMat * Add comment for pptinypose && pipline * Update docs/main_page.md * Add README.md for pptinypose * Add README for det_keypoint_unite * Remove ENABLE_PIPELINE option * Remove ENABLE_PIPELINE option * Change pptinypose default backend * PP-TinyPose Pipeline support multi PP-Detection models * Update pp-tinypose comment * Update by comments * Add single test example Co-authored-by: Jason <jiangjiajun@baidu.com>
101 lines
3.9 KiB
Python
101 lines
3.9 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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from __future__ import absolute_import
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import logging
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from ... import c_lib_wrap as C
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def vis_detection(im_data,
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det_result,
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score_threshold=0.0,
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line_size=1,
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font_size=0.5):
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return C.vision.vis_detection(im_data, det_result, score_threshold,
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line_size, font_size)
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def vis_keypoint_detection(im_data, keypoint_det_result, conf_threshold=0.5):
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return C.vision.Visualize.vis_keypoint_detection(
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im_data, keypoint_det_result, conf_threshold)
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def vis_face_detection(im_data, face_det_result, line_size=1, font_size=0.5):
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return C.vision.vis_face_detection(im_data, face_det_result, line_size,
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font_size)
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def vis_segmentation(im_data, seg_result):
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return C.vision.vis_segmentation(im_data, seg_result)
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def vis_matting_alpha(im_data,
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matting_result,
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remove_small_connected_area=False):
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logging.warning(
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"DEPRECATED: fastdeploy.vision.vis_matting_alpha is deprecated, please use fastdeploy.vision.vis_matting function instead."
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)
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return C.vision.vis_matting(im_data, matting_result,
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remove_small_connected_area)
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def vis_matting(im_data, matting_result, remove_small_connected_area=False):
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return C.vision.vis_matting(im_data, matting_result,
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remove_small_connected_area)
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def swap_background_matting(im_data,
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background,
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result,
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remove_small_connected_area=False):
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logging.warning(
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"DEPRECATED: fastdeploy.vision.swap_background_matting is deprecated, please use fastdeploy.vision.swap_background function instead."
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)
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assert isinstance(
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result, C.vision.MattingResult), "The result must be MattingResult type"
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return C.vision.Visualize.swap_background_matting(
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im_data, background, result, remove_small_connected_area)
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def swap_background_segmentation(im_data, background, background_label, result):
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logging.warning(
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"DEPRECATED: fastdeploy.vision.swap_background_segmentation is deprecated, please use fastdeploy.vision.swap_background function instead."
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)
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assert isinstance(
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result, C.vision.
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SegmentationResult), "The result must be SegmentaitonResult type"
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return C.vision.Visualize.swap_background_segmentation(
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im_data, background, background_label, result)
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def swap_background(im_data,
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background,
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result,
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remove_small_connected_area=False,
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background_label=0):
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if isinstance(result, C.vision.MattingResult):
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return C.vision.swap_background(im_data, background, result,
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remove_small_connected_area)
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elif isinstance(result, C.vision.SegmentationResult):
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return C.vision.swap_background(im_data, background, result,
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background_label)
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else:
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raise Exception(
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"Only support result type of MattingResult or SegmentationResult, but now the data type is {}.".
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format(type(result)))
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def vis_ppocr(im_data, det_result):
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return C.vision.vis_ppocr(im_data, det_result)
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