add pp_tinypose

modules/image/keypoint_detection/pp-tinypose/README.md

+# pp-tinypose
+
+|模型名称|pp-tinypose|
+| :--- | :---: |
+|类别|图像-关键点检测|
+|网络|PicoDet + HRNet|
+|数据集|COCO + AI Challenger|
+|是否支持Fine-tuning|否|
+|模型大小|125M|
+|最新更新日期|2022-05-20|
+|数据指标|-|
+
+
+## 一、模型基本信息
+
+- ### 应用效果展示
+  - 样例结果示例：
+<p align="center">
+    <img src="https://user-images.githubusercontent.com/22424850/169768593-9fcf729a-458e-4bb1-bb3c-b005ff7bcec2.jpg"   hspace='10'/>
+    <br />
+    输入图像
+    <br />
+    <img src="https://user-images.githubusercontent.com/22424850/169768604-d23a1851-c18b-4f9f-a8ab-2c3f3080e393.jpg"   hspace='10'/>
+    <br />
+    输出图像
+
+- ### 模型介绍
+
+  - PP-TinyPose是PaddleDetecion针对移动端设备优化的实时关键点检测模型，可流畅地在移动端设备上执行多人姿态估计任务。借助PaddleDetecion自研的优秀轻量级检测模型PicoDet以及轻量级姿态估计任务骨干网络HRNet, 结合多种策略有效平衡了模型的速度和精度表现。
+
+  - 更多详情参考：[PP-TinyPose](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/keypoint/tiny_pose)。
+
+
+
+## 二、安装
+
+- ### 1、环境依赖  
+
+  - paddlepaddle >= 2.2
+
+  - paddlehub >= 2.2   | [如何安装paddlehub](../../../../docs/docs_ch/get_start/installation.rst)
+
+- ### 2、安装
+
+  - ```shell
+    $ hub install pp-tinypose
+    ```
+  - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
+ | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
+
+
+
+## 三、模型API预测
+
+- ### 1、命令行预测
+
+  - ```shell
+    $ hub run pp-tinypose --input_path "/PATH/TO/IMAGE" --visualization True --use_gpu
+    ```
+  - 通过命令行方式实现关键点检测模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst)
+
+- ### 2、代码示例
+
+  - ```python
+    import paddlehub as hub
+    import cv2
+
+    model = hub.Module(name="pp-tinypose")
+    result = model.predict('/PATH/TO/IMAGE', save_path='pp_tinypose_output', visualization=True, use_gpu=True)
+    ```
+
+- ### 3、API
+
+
+  - ```python
+    def predict(self, img: Union[str, np.ndarray], save_path: str = "pp_tinypose_output", visualization: bool = True, use_gpu = False)
+    ```
+
+    - 预测API，识别输入图片中的所有人肢体关键点。
+
+    - **参数**
+
+      - img (numpy.ndarray|str): 图片数据，使用图片路径或者输入numpy.ndarray，BGR格式；
+      - save_path (str): 图片保存路径， 默认为'pp_tinypose_output'；
+      - visualization (bool): 是否将识别结果保存为图片文件；
+      - use_gpu: 是否使用gpu；
+    - **返回**
+
+      - res (list\[dict\]): 识别结果的列表，列表元素依然为列表，存的内容为[图像名称，检测框，关键点]。
+
+
+## 四、服务部署
+
+- PaddleHub Serving 可以部署一个关键点检测的在线服务。
+
+- ### 第一步：启动PaddleHub Serving
+
+  - 运行启动命令：
+  - ```shell
+    $ hub serving start -m pp-tinypose
+    ```
+
+  - 这样就完成了一个关键点检测的服务化API的部署，默认端口号为8866。
+
+  - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
+
+- ### 第二步：发送预测请求
+
+  - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
+
+  - ```python
+    import requests
+    import json
+    import cv2
+    import base64
+
+    def cv2_to_base64(image):
+        data = cv2.imencode('.jpg', image)[1]
+        return base64.b64encode(data.tostring()).decode('utf8')
+
+    # 发送HTTP请求
+    data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]}
+    headers = {"Content-type": "application/json"}
+    url = "http://127.0.0.1:8866/predict/pp-tinypose"
+    r = requests.post(url=url, headers=headers, data=json.dumps(data))
+    ```
+
+## 五、更新历史
+
+* 1.0.0
+
+  初始发布
+
+  - ```shell
+    $ hub install pp-tinypose==1.0.0
+    ```

modules/image/keypoint_detection/pp-tinypose/__init__.py

+import os
+import sys
+
+CUR_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(CUR_DIR)

modules/image/keypoint_detection/pp-tinypose/benchmark_utils.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import logging
+import os
+from pathlib import Path
+
+import paddle
+import paddle.inference as paddle_infer
+
+CUR_DIR = os.path.dirname(os.path.abspath(__file__))
+LOG_PATH_ROOT = f"{CUR_DIR}/../../output"
+
+
+class PaddleInferBenchmark(object):
+
+    def __init__(self,
+                 config,
+                 model_info: dict = {},
+                 data_info: dict = {},
+                 perf_info: dict = {},
+                 resource_info: dict = {},
+                 **kwargs):
+        """
+        Construct PaddleInferBenchmark Class to format logs.
+        args:
+            config(paddle.inference.Config): paddle inference config
+            model_info(dict): basic model info
+                {'model_name': 'resnet50'
+                 'precision': 'fp32'}
+            data_info(dict): input data info
+                {'batch_size': 1
+                 'shape': '3,224,224'
+                 'data_num': 1000}
+            perf_info(dict): performance result
+                {'preprocess_time_s': 1.0
+                'inference_time_s': 2.0
+                'postprocess_time_s': 1.0
+                'total_time_s': 4.0}
+            resource_info(dict):
+                cpu and gpu resources
+                {'cpu_rss': 100
+                 'gpu_rss': 100
+                 'gpu_util': 60}
+        """
+        # PaddleInferBenchmark Log Version
+        self.log_version = "1.0.3"
+
+        # Paddle Version
+        self.paddle_version = paddle.__version__
+        self.paddle_commit = paddle.__git_commit__
+        paddle_infer_info = paddle_infer.get_version()
+        self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
+
+        # model info
+        self.model_info = model_info
+
+        # data info
+        self.data_info = data_info
+
+        # perf info
+        self.perf_info = perf_info
+
+        try:
+            # required value
+            self.model_name = model_info['model_name']
+            self.precision = model_info['precision']
+
+            self.batch_size = data_info['batch_size']
+            self.shape = data_info['shape']
+            self.data_num = data_info['data_num']
+
+            self.inference_time_s = round(perf_info['inference_time_s'], 4)
+        except:
+            self.print_help()
+            raise ValueError("Set argument wrong, please check input argument and its type")
+
+        self.preprocess_time_s = perf_info.get('preprocess_time_s', 0)
+        self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
+        self.with_tracker = True if 'tracking_time_s' in perf_info else False
+        self.tracking_time_s = perf_info.get('tracking_time_s', 0)
+        self.total_time_s = perf_info.get('total_time_s', 0)
+
+        self.inference_time_s_90 = perf_info.get("inference_time_s_90", "")
+        self.inference_time_s_99 = perf_info.get("inference_time_s_99", "")
+        self.succ_rate = perf_info.get("succ_rate", "")
+        self.qps = perf_info.get("qps", "")
+
+        # conf info
+        self.config_status = self.parse_config(config)
+
+        # mem info
+        if isinstance(resource_info, dict):
+            self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
+            self.cpu_vms_mb = int(resource_info.get('cpu_vms_mb', 0))
+            self.cpu_shared_mb = int(resource_info.get('cpu_shared_mb', 0))
+            self.cpu_dirty_mb = int(resource_info.get('cpu_dirty_mb', 0))
+            self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
+
+            self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
+            self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
+            self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
+        else:
+            self.cpu_rss_mb = 0
+            self.cpu_vms_mb = 0
+            self.cpu_shared_mb = 0
+            self.cpu_dirty_mb = 0
+            self.cpu_util = 0
+
+            self.gpu_rss_mb = 0
+            self.gpu_util = 0
+            self.gpu_mem_util = 0
+
+        # init benchmark logger
+        self.benchmark_logger()
+
+    def benchmark_logger(self):
+        """
+        benchmark logger
+        """
+        # remove other logging handler
+        for handler in logging.root.handlers[:]:
+            logging.root.removeHandler(handler)
+
+        # Init logger
+        FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        log_output = f"{LOG_PATH_ROOT}/{self.model_name}.log"
+        Path(f"{LOG_PATH_ROOT}").mkdir(parents=True, exist_ok=True)
+        logging.basicConfig(level=logging.INFO,
+                            format=FORMAT,
+                            handlers=[
+                                logging.FileHandler(filename=log_output, mode='w'),
+                                logging.StreamHandler(),
+                            ])
+        self.logger = logging.getLogger(__name__)
+        self.logger.info(f"Paddle Inference benchmark log will be saved to {log_output}")
+
+    def parse_config(self, config) -> dict:
+        """
+        parse paddle predictor config
+        args:
+            config(paddle.inference.Config): paddle inference config
+        return:
+            config_status(dict): dict style config info
+        """
+        if isinstance(config, paddle_infer.Config):
+            config_status = {}
+            config_status['runtime_device'] = "gpu" if config.use_gpu() else "cpu"
+            config_status['ir_optim'] = config.ir_optim()
+            config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
+            config_status['precision'] = self.precision
+            config_status['enable_mkldnn'] = config.mkldnn_enabled()
+            config_status['cpu_math_library_num_threads'] = config.cpu_math_library_num_threads()
+        elif isinstance(config, dict):
+            config_status['runtime_device'] = config.get('runtime_device', "")
+            config_status['ir_optim'] = config.get('ir_optim', "")
+            config_status['enable_tensorrt'] = config.get('enable_tensorrt', "")
+            config_status['precision'] = config.get('precision', "")
+            config_status['enable_mkldnn'] = config.get('enable_mkldnn', "")
+            config_status['cpu_math_library_num_threads'] = config.get('cpu_math_library_num_threads', "")
+        else:
+            self.print_help()
+            raise ValueError("Set argument config wrong, please check input argument and its type")
+        return config_status
+
+    def report(self, identifier=None):
+        """
+        print log report
+        args:
+            identifier(string): identify log
+        """
+        if identifier:
+            identifier = f"[{identifier}]"
+        else:
+            identifier = ""
+
+        self.logger.info("\n")
+        self.logger.info("---------------------- Paddle info ----------------------")
+        self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
+        self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
+        self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
+        self.logger.info(f"{identifier} log_api_version: {self.log_version}")
+        self.logger.info("----------------------- Conf info -----------------------")
+        self.logger.info(f"{identifier} runtime_device: {self.config_status['runtime_device']}")
+        self.logger.info(f"{identifier} ir_optim: {self.config_status['ir_optim']}")
+        self.logger.info(f"{identifier} enable_memory_optim: {True}")
+        self.logger.info(f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}")
+        self.logger.info(f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}")
+        self.logger.info(
+            f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}")
+        self.logger.info("----------------------- Model info ----------------------")
+        self.logger.info(f"{identifier} model_name: {self.model_name}")
+        self.logger.info(f"{identifier} precision: {self.precision}")
+        self.logger.info("----------------------- Data info -----------------------")
+        self.logger.info(f"{identifier} batch_size: {self.batch_size}")
+        self.logger.info(f"{identifier} input_shape: {self.shape}")
+        self.logger.info(f"{identifier} data_num: {self.data_num}")
+        self.logger.info("----------------------- Perf info -----------------------")
+        self.logger.info(
+            f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, cpu_vms: {self.cpu_vms_mb}, cpu_shared_mb: {self.cpu_shared_mb}, cpu_dirty_mb: {self.cpu_dirty_mb}, cpu_util: {self.cpu_util}%"
+        )
+        self.logger.info(
+            f"{identifier} gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%, gpu_mem_util: {self.gpu_mem_util}%"
+        )
+        self.logger.info(f"{identifier} total time spent(s): {self.total_time_s}")
+
+        if self.with_tracker:
+            self.logger.info(f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
+                             f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
+                             f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}, "
+                             f"tracking_time(ms): {round(self.tracking_time_s*1000, 1)}")
+        else:
+            self.logger.info(f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
+                             f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
+                             f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}")
+        if self.inference_time_s_90:
+            self.looger.info(
+                f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
+            )
+        if self.qps:
+            self.logger.info(f"{identifier} QPS: {self.qps}")
+
+    def print_help(self):
+        """
+        print function help
+        """
+        print("""Usage:
+            ==== Print inference benchmark logs. ====
+            config = paddle.inference.Config()
+            model_info = {'model_name': 'resnet50'
+                          'precision': 'fp32'}
+            data_info = {'batch_size': 1
+                         'shape': '3,224,224'
+                         'data_num': 1000}
+            perf_info = {'preprocess_time_s': 1.0
+                         'inference_time_s': 2.0
+                         'postprocess_time_s': 1.0
+                         'total_time_s': 4.0}
+            resource_info = {'cpu_rss_mb': 100
+                             'gpu_rss_mb': 100
+                             'gpu_util': 60}
+            log = PaddleInferBenchmark(config, model_info, data_info, perf_info, resource_info)
+            log('Test')
+            """)
+
+    def __call__(self, identifier=None):
+        """
+        __call__
+        args:
+            identifier(string): identify log
+        """
+        self.report(identifier)

modules/image/keypoint_detection/pp-tinypose/det_keypoint_unite_infer.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import json
+import math
+import os
+
+import cv2
+import numpy as np
+import paddle
+import yaml
+from benchmark_utils import PaddleInferBenchmark
+from det_keypoint_unite_utils import argsparser
+from infer import bench_log
+from infer import Detector
+from infer import get_test_images
+from infer import PredictConfig
+from infer import print_arguments
+from keypoint_infer import KeyPointDetector
+from keypoint_infer import PredictConfig_KeyPoint
+from keypoint_postprocess import translate_to_ori_images
+from preprocess import decode_image
+from utils import get_current_memory_mb
+from visualize import visualize_pose
+
+KEYPOINT_SUPPORT_MODELS = {'HigherHRNet': 'keypoint_bottomup', 'HRNet': 'keypoint_topdown'}
+
+
+def predict_with_given_det(image, det_res, keypoint_detector, keypoint_batch_size, run_benchmark):
+    rec_images, records, det_rects = keypoint_detector.get_person_from_rect(image, det_res)
+    keypoint_vector = []
+    score_vector = []
+
+    rect_vector = det_rects
+    keypoint_results = keypoint_detector.predict_image(rec_images, run_benchmark, repeats=10, visual=False)
+    keypoint_vector, score_vector = translate_to_ori_images(keypoint_results, np.array(records))
+    keypoint_res = {}
+    keypoint_res['keypoint'] = [keypoint_vector.tolist(), score_vector.tolist()] if len(keypoint_vector) > 0 else [[],
+                                                                                                                   []]
+    keypoint_res['bbox'] = rect_vector
+    return keypoint_res
+
+
+def topdown_unite_predict(detector, topdown_keypoint_detector, image_list, keypoint_batch_size=1, save_res=False):
+    det_timer = detector.get_timer()
+    store_res = []
+    for i, img_file in enumerate(image_list):
+        # Decode image in advance in det + pose prediction
+        det_timer.preprocess_time_s.start()
+        image, _ = decode_image(img_file, {})
+        det_timer.preprocess_time_s.end()
+
+        if FLAGS.run_benchmark:
+            results = detector.predict_image([image], run_benchmark=True, repeats=10)
+
+            cm, gm, gu = get_current_memory_mb()
+            detector.cpu_mem += cm
+            detector.gpu_mem += gm
+            detector.gpu_util += gu
+        else:
+            results = detector.predict_image([image], visual=False)
+        results = detector.filter_box(results, FLAGS.det_threshold)
+        if results['boxes_num'] > 0:
+            keypoint_res = predict_with_given_det(image, results, topdown_keypoint_detector, keypoint_batch_size,
+                                                  FLAGS.run_benchmark)
+
+            if save_res:
+                save_name = img_file if isinstance(img_file, str) else i
+                store_res.append(
+                    [save_name, keypoint_res['bbox'], [keypoint_res['keypoint'][0], keypoint_res['keypoint'][1]]])
+        else:
+            results["keypoint"] = [[], []]
+            keypoint_res = results
+        if FLAGS.run_benchmark:
+            cm, gm, gu = get_current_memory_mb()
+            topdown_keypoint_detector.cpu_mem += cm
+            topdown_keypoint_detector.gpu_mem += gm
+            topdown_keypoint_detector.gpu_util += gu
+        else:
+            if not os.path.exists(FLAGS.output_dir):
+                os.makedirs(FLAGS.output_dir)
+            visualize_pose(img_file, keypoint_res, visual_thresh=FLAGS.keypoint_threshold, save_dir=FLAGS.output_dir)
+    if save_res:
+        """
+        1) store_res: a list of image_data
+        2) image_data: [imageid, rects, [keypoints, scores]]
+        3) rects: list of rect [xmin, ymin, xmax, ymax]
+        4) keypoints: 17(joint numbers)*[x, y, conf], total 51 data in list
+        5) scores: mean of all joint conf
+        """
+        with open("det_keypoint_unite_image_results.json", 'w') as wf:
+            json.dump(store_res, wf, indent=4)
+
+
+def topdown_unite_predict_video(detector, topdown_keypoint_detector, camera_id, keypoint_batch_size=1, save_res=False):
+    video_name = 'output.mp4'
+    if camera_id != -1:
+        capture = cv2.VideoCapture(camera_id)
+    else:
+        capture = cv2.VideoCapture(FLAGS.video_file)
+        video_name = os.path.split(FLAGS.video_file)[-1]
+    # Get Video info : resolution, fps, frame count
+    width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = int(capture.get(cv2.CAP_PROP_FPS))
+    frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    print("fps: %d, frame_count: %d" % (fps, frame_count))
+
+    if not os.path.exists(FLAGS.output_dir):
+        os.makedirs(FLAGS.output_dir)
+    out_path = os.path.join(FLAGS.output_dir, video_name)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
+    index = 0
+    store_res = []
+    while (1):
+        ret, frame = capture.read()
+        if not ret:
+            break
+        index += 1
+        print('detect frame: %d' % (index))
+
+        frame2 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        results = detector.predict_image([frame2], visual=False)
+        results = detector.filter_box(results, FLAGS.det_threshold)
+        if results['boxes_num'] == 0:
+            writer.write(frame)
+            continue
+
+        keypoint_res = predict_with_given_det(frame2, results, topdown_keypoint_detector, keypoint_batch_size,
+                                              FLAGS.run_benchmark)
+
+        im = visualize_pose(frame, keypoint_res, visual_thresh=FLAGS.keypoint_threshold, returnimg=True)
+        if save_res:
+            store_res.append([index, keypoint_res['bbox'], [keypoint_res['keypoint'][0], keypoint_res['keypoint'][1]]])
+
+        writer.write(im)
+        if camera_id != -1:
+            cv2.imshow('Mask Detection', im)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+    writer.release()
+    print('output_video saved to: {}'.format(out_path))
+    if save_res:
+        """
+        1) store_res: a list of frame_data
+        2) frame_data: [frameid, rects, [keypoints, scores]]
+        3) rects: list of rect [xmin, ymin, xmax, ymax]
+        4) keypoints: 17(joint numbers)*[x, y, conf], total 51 data in list
+        5) scores: mean of all joint conf
+        """
+        with open("det_keypoint_unite_video_results.json", 'w') as wf:
+            json.dump(store_res, wf, indent=4)
+
+
+def main():
+    deploy_file = os.path.join(FLAGS.det_model_dir, 'infer_cfg.yml')
+    with open(deploy_file) as f:
+        yml_conf = yaml.safe_load(f)
+    arch = yml_conf['arch']
+    detector = Detector(FLAGS.det_model_dir,
+                        device=FLAGS.device,
+                        run_mode=FLAGS.run_mode,
+                        trt_min_shape=FLAGS.trt_min_shape,
+                        trt_max_shape=FLAGS.trt_max_shape,
+                        trt_opt_shape=FLAGS.trt_opt_shape,
+                        trt_calib_mode=FLAGS.trt_calib_mode,
+                        cpu_threads=FLAGS.cpu_threads,
+                        enable_mkldnn=FLAGS.enable_mkldnn,
+                        threshold=FLAGS.det_threshold)
+
+    topdown_keypoint_detector = KeyPointDetector(FLAGS.keypoint_model_dir,
+                                                 device=FLAGS.device,
+                                                 run_mode=FLAGS.run_mode,
+                                                 batch_size=FLAGS.keypoint_batch_size,
+                                                 trt_min_shape=FLAGS.trt_min_shape,
+                                                 trt_max_shape=FLAGS.trt_max_shape,
+                                                 trt_opt_shape=FLAGS.trt_opt_shape,
+                                                 trt_calib_mode=FLAGS.trt_calib_mode,
+                                                 cpu_threads=FLAGS.cpu_threads,
+                                                 enable_mkldnn=FLAGS.enable_mkldnn,
+                                                 use_dark=FLAGS.use_dark)
+    keypoint_arch = topdown_keypoint_detector.pred_config.arch
+    assert KEYPOINT_SUPPORT_MODELS[
+        keypoint_arch] == 'keypoint_topdown', 'Detection-Keypoint unite inference only supports topdown models.'
+
+    # predict from video file or camera video stream
+    if FLAGS.video_file is not None or FLAGS.camera_id != -1:
+        topdown_unite_predict_video(detector, topdown_keypoint_detector, FLAGS.camera_id, FLAGS.keypoint_batch_size,
+                                    FLAGS.save_res)
+    else:
+        # predict from image
+        img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
+        topdown_unite_predict(detector, topdown_keypoint_detector, img_list, FLAGS.keypoint_batch_size, FLAGS.save_res)
+        if not FLAGS.run_benchmark:
+            detector.det_times.info(average=True)
+            topdown_keypoint_detector.det_times.info(average=True)
+        else:
+            mode = FLAGS.run_mode
+            det_model_dir = FLAGS.det_model_dir
+            det_model_info = {'model_name': det_model_dir.strip('/').split('/')[-1], 'precision': mode.split('_')[-1]}
+            bench_log(detector, img_list, det_model_info, name='Det')
+            keypoint_model_dir = FLAGS.keypoint_model_dir
+            keypoint_model_info = {
+                'model_name': keypoint_model_dir.strip('/').split('/')[-1],
+                'precision': mode.split('_')[-1]
+            }
+            bench_log(topdown_keypoint_detector, img_list, keypoint_model_info, FLAGS.keypoint_batch_size, 'KeyPoint')
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+    FLAGS.device = FLAGS.device.upper()
+    assert FLAGS.device in ['CPU', 'GPU', 'XPU'], "device should be CPU, GPU or XPU"
+
+    main()

modules/image/keypoint_detection/pp-tinypose/det_keypoint_unite_utils.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import argparse
+import ast
+
+
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--det_model_dir",
+                        type=str,
+                        default=None,
+                        help=("Directory include:'model.pdiparams', 'model.pdmodel', "
+                              "'infer_cfg.yml', created by tools/export_model.py."),
+                        required=True)
+    parser.add_argument("--keypoint_model_dir",
+                        type=str,
+                        default=None,
+                        help=("Directory include:'model.pdiparams', 'model.pdmodel', "
+                              "'infer_cfg.yml', created by tools/export_model.py."),
+                        required=True)
+    parser.add_argument("--image_file", type=str, default=None, help="Path of image file.")
+    parser.add_argument("--image_dir",
+                        type=str,
+                        default=None,
+                        help="Dir of image file, `image_file` has a higher priority.")
+    parser.add_argument("--keypoint_batch_size",
+                        type=int,
+                        default=8,
+                        help=("batch_size for keypoint inference. In detection-keypoint unit"
+                              "inference, the batch size in detection is 1. Then collate det "
+                              "result in batch for keypoint inference."))
+    parser.add_argument("--video_file",
+                        type=str,
+                        default=None,
+                        help="Path of video file, `video_file` or `camera_id` has a highest priority.")
+    parser.add_argument("--camera_id", type=int, default=-1, help="device id of camera to predict.")
+    parser.add_argument("--det_threshold", type=float, default=0.5, help="Threshold of score.")
+    parser.add_argument("--keypoint_threshold", type=float, default=0.5, help="Threshold of score.")
+    parser.add_argument("--output_dir", type=str, default="output", help="Directory of output visualization files.")
+    parser.add_argument("--run_mode",
+                        type=str,
+                        default='paddle',
+                        help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)")
+    parser.add_argument("--device",
+                        type=str,
+                        default='cpu',
+                        help="Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU.")
+    parser.add_argument("--run_benchmark",
+                        type=ast.literal_eval,
+                        default=False,
+                        help="Whether to predict a image_file repeatedly for benchmark")
+    parser.add_argument("--enable_mkldnn", type=ast.literal_eval, default=False, help="Whether use mkldnn with CPU.")
+    parser.add_argument("--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
+    parser.add_argument("--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
+    parser.add_argument("--trt_max_shape", type=int, default=1280, help="max_shape for TensorRT.")
+    parser.add_argument("--trt_opt_shape", type=int, default=640, help="opt_shape for TensorRT.")
+    parser.add_argument("--trt_calib_mode",
+                        type=bool,
+                        default=False,
+                        help="If the model is produced by TRT offline quantitative "
+                        "calibration, trt_calib_mode need to set True.")
+    parser.add_argument('--use_dark',
+                        type=ast.literal_eval,
+                        default=True,
+                        help='whether to use darkpose to get better keypoint position predict ')
+    parser.add_argument('--save_res',
+                        type=bool,
+                        default=False,
+                        help=("whether to save predict results to json file"
+                              "1) store_res: a list of image_data"
+                              "2) image_data: [imageid, rects, [keypoints, scores]]"
+                              "3) rects: list of rect [xmin, ymin, xmax, ymax]"
+                              "4) keypoints: 17(joint numbers)*[x, y, conf], total 51 data in list"
+                              "5) scores: mean of all joint conf"))
+    return parser

modules/image/keypoint_detection/pp-tinypose/infer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import glob
+import json
+import math
+import os
+import sys
+from functools import reduce
+from pathlib import Path
+
+import cv2
+import numpy as np
+import paddle
+import yaml
+from benchmark_utils import PaddleInferBenchmark
+from keypoint_preprocess import EvalAffine
+from keypoint_preprocess import expand_crop
+from keypoint_preprocess import TopDownEvalAffine
+from paddle.inference import Config
+from paddle.inference import create_predictor
+from preprocess import decode_image
+from preprocess import LetterBoxResize
+from preprocess import NormalizeImage
+from preprocess import Pad
+from preprocess import PadStride
+from preprocess import Permute
+from preprocess import preprocess
+from preprocess import Resize
+from preprocess import WarpAffine
+from utils import argsparser
+from utils import get_current_memory_mb
+from utils import Timer
+from visualize import visualize_box
+
+# Global dictionary
+SUPPORT_MODELS = {
+    'YOLO',
+    'RCNN',
+    'SSD',
+    'Face',
+    'FCOS',
+    'SOLOv2',
+    'TTFNet',
+    'S2ANet',
+    'JDE',
+    'FairMOT',
+    'DeepSORT',
+    'GFL',
+    'PicoDet',
+    'CenterNet',
+    'TOOD',
+    'RetinaNet',
+    'StrongBaseline',
+    'STGCN',
+    'YOLOX',
+}
+
+
+def bench_log(detector, img_list, model_info, batch_size=1, name=None):
+    mems = {
+        'cpu_rss_mb': detector.cpu_mem / len(img_list),
+        'gpu_rss_mb': detector.gpu_mem / len(img_list),
+        'gpu_util': detector.gpu_util * 100 / len(img_list)
+    }
+    perf_info = detector.det_times.report(average=True)
+    data_info = {'batch_size': batch_size, 'shape': "dynamic_shape", 'data_num': perf_info['img_num']}
+    log = PaddleInferBenchmark(detector.config, model_info, data_info, perf_info, mems)
+    log(name)
+
+
+class Detector(object):
+    """
+    Args:
+        pred_config (object): config of model, defined by `Config(model_dir)`
+        model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
+        device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
+        run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
+        batch_size (int): size of pre batch in inference
+        trt_min_shape (int): min shape for dynamic shape in trt
+        trt_max_shape (int): max shape for dynamic shape in trt
+        trt_opt_shape (int): opt shape for dynamic shape in trt
+        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
+            calibration, trt_calib_mode need to set True
+        cpu_threads (int): cpu threads
+        enable_mkldnn (bool): whether to open MKLDNN
+        enable_mkldnn_bfloat16 (bool): whether to turn on mkldnn bfloat16
+        output_dir (str): The path of output
+        threshold (float): The threshold of score for visualization
+        delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
+                                    Used by action model.
+    """
+
+    def __init__(self,
+                 model_dir,
+                 device='CPU',
+                 run_mode='paddle',
+                 batch_size=1,
+                 trt_min_shape=1,
+                 trt_max_shape=1280,
+                 trt_opt_shape=640,
+                 trt_calib_mode=False,
+                 cpu_threads=1,
+                 enable_mkldnn=False,
+                 enable_mkldnn_bfloat16=False,
+                 output_dir='output',
+                 threshold=0.5,
+                 delete_shuffle_pass=False):
+        self.pred_config = self.set_config(model_dir)
+        self.device = device
+        self.predictor, self.config = load_predictor(model_dir,
+                                                     run_mode=run_mode,
+                                                     batch_size=batch_size,
+                                                     min_subgraph_size=self.pred_config.min_subgraph_size,
+                                                     device=device,
+                                                     use_dynamic_shape=self.pred_config.use_dynamic_shape,
+                                                     trt_min_shape=trt_min_shape,
+                                                     trt_max_shape=trt_max_shape,
+                                                     trt_opt_shape=trt_opt_shape,
+                                                     trt_calib_mode=trt_calib_mode,
+                                                     cpu_threads=cpu_threads,
+                                                     enable_mkldnn=enable_mkldnn,
+                                                     enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
+                                                     delete_shuffle_pass=delete_shuffle_pass)
+        self.det_times = Timer()
+        self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
+        self.batch_size = batch_size
+        self.output_dir = output_dir
+        self.threshold = threshold
+
+    def set_config(self, model_dir):
+        return PredictConfig(model_dir)
+
+    def preprocess(self, image_list):
+        preprocess_ops = []
+        for op_info in self.pred_config.preprocess_infos:
+            new_op_info = op_info.copy()
+            op_type = new_op_info.pop('type')
+            preprocess_ops.append(eval(op_type)(**new_op_info))
+
+        input_im_lst = []
+        input_im_info_lst = []
+        for im_path in image_list:
+            im, im_info = preprocess(im_path, preprocess_ops)
+            input_im_lst.append(im)
+            input_im_info_lst.append(im_info)
+        inputs = create_inputs(input_im_lst, input_im_info_lst)
+        input_names = self.predictor.get_input_names()
+        for i in range(len(input_names)):
+            input_tensor = self.predictor.get_input_handle(input_names[i])
+            input_tensor.copy_from_cpu(inputs[input_names[i]])
+
+        return inputs
+
+    def postprocess(self, inputs, result):
+        # postprocess output of predictor
+        np_boxes_num = result['boxes_num']
+        if np_boxes_num[0] <= 0:
+            print('[WARNNING] No object detected.')
+            result = {'boxes': np.zeros([0, 6]), 'boxes_num': [0]}
+        result = {k: v for k, v in result.items() if v is not None}
+        return result
+
+    def filter_box(self, result, threshold):
+        np_boxes_num = result['boxes_num']
+        boxes = result['boxes']
+        start_idx = 0
+        filter_boxes = []
+        filter_num = []
+        for i in range(len(np_boxes_num)):
+            boxes_num = np_boxes_num[i]
+            boxes_i = boxes[start_idx:start_idx + boxes_num, :]
+            idx = boxes_i[:, 1] > threshold
+            filter_boxes_i = boxes_i[idx, :]
+            filter_boxes.append(filter_boxes_i)
+            filter_num.append(filter_boxes_i.shape[0])
+            start_idx += boxes_num
+        boxes = np.concatenate(filter_boxes)
+        filter_num = np.array(filter_num)
+        filter_res = {'boxes': boxes, 'boxes_num': filter_num}
+        return filter_res
+
+    def predict(self, repeats=1):
+        '''
+        Args:
+            repeats (int): repeats number for prediction
+        Returns:
+            result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
+                            matix element:[class, score, x_min, y_min, x_max, y_max]
+                            MaskRCNN's result include 'masks': np.ndarray:
+                            shape: [N, im_h, im_w]
+        '''
+        # model prediction
+        np_boxes, np_masks = None, None
+        for i in range(repeats):
+            self.predictor.run()
+            output_names = self.predictor.get_output_names()
+            boxes_tensor = self.predictor.get_output_handle(output_names[0])
+            np_boxes = boxes_tensor.copy_to_cpu()
+            boxes_num = self.predictor.get_output_handle(output_names[1])
+            np_boxes_num = boxes_num.copy_to_cpu()
+            if self.pred_config.mask:
+                masks_tensor = self.predictor.get_output_handle(output_names[2])
+                np_masks = masks_tensor.copy_to_cpu()
+        result = dict(boxes=np_boxes, masks=np_masks, boxes_num=np_boxes_num)
+        return result
+
+    def merge_batch_result(self, batch_result):
+        if len(batch_result) == 1:
+            return batch_result[0]
+        res_key = batch_result[0].keys()
+        results = {k: [] for k in res_key}
+        for res in batch_result:
+            for k, v in res.items():
+                results[k].append(v)
+        for k, v in results.items():
+            if k != 'masks':
+                results[k] = np.concatenate(v)
+        return results
+
+    def get_timer(self):
+        return self.det_times
+
+    def predict_image(self, image_list, run_benchmark=False, repeats=1, visual=True, save_file=None):
+        batch_loop_cnt = math.ceil(float(len(image_list)) / self.batch_size)
+        results = []
+        for i in range(batch_loop_cnt):
+            start_index = i * self.batch_size
+            end_index = min((i + 1) * self.batch_size, len(image_list))
+            batch_image_list = image_list[start_index:end_index]
+            if run_benchmark:
+                # preprocess
+                inputs = self.preprocess(batch_image_list)  # warmup
+                self.det_times.preprocess_time_s.start()
+                inputs = self.preprocess(batch_image_list)
+                self.det_times.preprocess_time_s.end()
+
+                # model prediction
+                result = self.predict(repeats=50)  # warmup
+                self.det_times.inference_time_s.start()
+                result = self.predict(repeats=repeats)
+                self.det_times.inference_time_s.end(repeats=repeats)
+
+                # postprocess
+                result_warmup = self.postprocess(inputs, result)  # warmup
+                self.det_times.postprocess_time_s.start()
+                result = self.postprocess(inputs, result)
+                self.det_times.postprocess_time_s.end()
+                self.det_times.img_num += len(batch_image_list)
+
+                cm, gm, gu = get_current_memory_mb()
+                self.cpu_mem += cm
+                self.gpu_mem += gm
+                self.gpu_util += gu
+            else:
+                # preprocess
+                self.det_times.preprocess_time_s.start()
+                inputs = self.preprocess(batch_image_list)
+                self.det_times.preprocess_time_s.end()
+
+                # model prediction
+                self.det_times.inference_time_s.start()
+                result = self.predict()
+                self.det_times.inference_time_s.end()
+
+                # postprocess
+                self.det_times.postprocess_time_s.start()
+                result = self.postprocess(inputs, result)
+                self.det_times.postprocess_time_s.end()
+                self.det_times.img_num += len(batch_image_list)
+
+                if visual:
+                    visualize(batch_image_list,
+                              result,
+                              self.pred_config.labels,
+                              output_dir=self.output_dir,
+                              threshold=self.threshold)
+
+            results.append(result)
+            if visual:
+                print('Test iter {}'.format(i))
+
+        if save_file is not None:
+            Path(self.output_dir).mkdir(exist_ok=True)
+            self.format_coco_results(image_list, results, save_file=save_file)
+
+        results = self.merge_batch_result(results)
+        return results
+
+    def predict_video(self, video_file, camera_id):
+        video_out_name = 'output.mp4'
+        if camera_id != -1:
+            capture = cv2.VideoCapture(camera_id)
+        else:
+            capture = cv2.VideoCapture(video_file)
+            video_out_name = os.path.split(video_file)[-1]
+        # Get Video info : resolution, fps, frame count
+        width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = int(capture.get(cv2.CAP_PROP_FPS))
+        frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+        print("fps: %d, frame_count: %d" % (fps, frame_count))
+
+        if not os.path.exists(self.output_dir):
+            os.makedirs(self.output_dir)
+        out_path = os.path.join(self.output_dir, video_out_name)
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
+        index = 1
+        while (1):
+            ret, frame = capture.read()
+            if not ret:
+                break
+            print('detect frame: %d' % (index))
+            index += 1
+            results = self.predict_image([frame[:, :, ::-1]], visual=False)
+
+            im = visualize_box(frame, results, self.pred_config.labels, threshold=self.threshold)
+            im = np.array(im)
+            writer.write(im)
+            if camera_id != -1:
+                cv2.imshow('Mask Detection', im)
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+        writer.release()
+
+    @staticmethod
+    def format_coco_results(image_list, results, save_file=None):
+        coco_results = []
+        image_id = 0
+
+        for result in results:
+            start_idx = 0
+            for box_num in result['boxes_num']:
+                idx_slice = slice(start_idx, start_idx + box_num)
+                start_idx += box_num
+
+                image_file = image_list[image_id]
+                image_id += 1
+
+                if 'boxes' in result:
+                    boxes = result['boxes'][idx_slice, :]
+                    per_result = [
+                        {
+                            'image_file': image_file,
+                            'bbox': [box[2], box[3], box[4] - box[2], box[5] - box[3]],  # xyxy -> xywh
+                            'score': box[1],
+                            'category_id': int(box[0]),
+                        } for k, box in enumerate(boxes.tolist())
+                    ]
+
+                elif 'segm' in result:
+                    import pycocotools.mask as mask_util
+
+                    scores = result['score'][idx_slice].tolist()
+                    category_ids = result['label'][idx_slice].tolist()
+                    segms = result['segm'][idx_slice, :]
+                    rles = [
+                        mask_util.encode(np.array(mask[:, :, np.newaxis], dtype=np.uint8, order='F'))[0]
+                        for mask in segms
+                    ]
+                    for rle in rles:
+                        rle['counts'] = rle['counts'].decode('utf-8')
+
+                    per_result = [{
+                        'image_file': image_file,
+                        'segmentation': rle,
+                        'score': scores[k],
+                        'category_id': category_ids[k],
+                    } for k, rle in enumerate(rles)]
+
+                else:
+                    raise RuntimeError('')
+
+                # per_result = [item for item in per_result if item['score'] > threshold]
+                coco_results.extend(per_result)
+
+        if save_file:
+            with open(os.path.join(save_file), 'w') as f:
+                json.dump(coco_results, f)
+
+        return coco_results
+
+
+def create_inputs(imgs, im_info):
+    """generate input for different model type
+    Args:
+        imgs (list(numpy)): list of images (np.ndarray)
+        im_info (list(dict)): list of image info
+    Returns:
+        inputs (dict): input of model
+    """
+    inputs = {}
+
+    im_shape = []
+    scale_factor = []
+    if len(imgs) == 1:
+        inputs['image'] = np.array((imgs[0], )).astype('float32')
+        inputs['im_shape'] = np.array((im_info[0]['im_shape'], )).astype('float32')
+        inputs['scale_factor'] = np.array((im_info[0]['scale_factor'], )).astype('float32')
+        return inputs
+
+    for e in im_info:
+        im_shape.append(np.array((e['im_shape'], )).astype('float32'))
+        scale_factor.append(np.array((e['scale_factor'], )).astype('float32'))
+
+    inputs['im_shape'] = np.concatenate(im_shape, axis=0)
+    inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)
+
+    imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
+    max_shape_h = max([e[0] for e in imgs_shape])
+    max_shape_w = max([e[1] for e in imgs_shape])
+    padding_imgs = []
+    for img in imgs:
+        im_c, im_h, im_w = img.shape[:]
+        padding_im = np.zeros((im_c, max_shape_h, max_shape_w), dtype=np.float32)
+        padding_im[:, :im_h, :im_w] = img
+        padding_imgs.append(padding_im)
+    inputs['image'] = np.stack(padding_imgs, axis=0)
+    return inputs
+
+
+class PredictConfig():
+    """set config of preprocess, postprocess and visualize
+    Args:
+        model_dir (str): root path of model.yml
+    """
+
+    def __init__(self, model_dir):
+        # parsing Yaml config for Preprocess
+        deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
+        with open(deploy_file) as f:
+            yml_conf = yaml.safe_load(f)
+        self.check_model(yml_conf)
+        self.arch = yml_conf['arch']
+        self.preprocess_infos = yml_conf['Preprocess']
+        self.min_subgraph_size = yml_conf['min_subgraph_size']
+        self.labels = yml_conf['label_list']
+        self.mask = False
+        self.use_dynamic_shape = yml_conf['use_dynamic_shape']
+        if 'mask' in yml_conf:
+            self.mask = yml_conf['mask']
+        self.tracker = None
+        if 'tracker' in yml_conf:
+            self.tracker = yml_conf['tracker']
+        if 'NMS' in yml_conf:
+            self.nms = yml_conf['NMS']
+        if 'fpn_stride' in yml_conf:
+            self.fpn_stride = yml_conf['fpn_stride']
+        if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
+            print('The RCNN export model is used for ONNX and it only supports batch_size = 1')
+        self.print_config()
+
+    def check_model(self, yml_conf):
+        """
+        Raises:
+            ValueError: loaded model not in supported model type
+        """
+        for support_model in SUPPORT_MODELS:
+            if support_model in yml_conf['arch']:
+                return True
+        raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf['arch'], SUPPORT_MODELS))
+
+    def print_config(self):
+        print('-----------  Model Configuration -----------')
+        print('%s: %s' % ('Model Arch', self.arch))
+        print('%s: ' % ('Transform Order'))
+        for op_info in self.preprocess_infos:
+            print('--%s: %s' % ('transform op', op_info['type']))
+        print('--------------------------------------------')
+
+
+def load_predictor(model_dir,
+                   run_mode='paddle',
+                   batch_size=1,
+                   device='CPU',
+                   min_subgraph_size=3,
+                   use_dynamic_shape=False,
+                   trt_min_shape=1,
+                   trt_max_shape=1280,
+                   trt_opt_shape=640,
+                   trt_calib_mode=False,
+                   cpu_threads=1,
+                   enable_mkldnn=False,
+                   enable_mkldnn_bfloat16=False,
+                   delete_shuffle_pass=False):
+    """set AnalysisConfig, generate AnalysisPredictor
+    Args:
+        model_dir (str): root path of __model__ and __params__
+        device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
+        run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
+        use_dynamic_shape (bool): use dynamic shape or not
+        trt_min_shape (int): min shape for dynamic shape in trt
+        trt_max_shape (int): max shape for dynamic shape in trt
+        trt_opt_shape (int): opt shape for dynamic shape in trt
+        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
+            calibration, trt_calib_mode need to set True
+        delete_shuffle_pass (bool): whether to remove shuffle_channel_detect_pass in TensorRT.
+                                    Used by action model.
+    Returns:
+        predictor (PaddlePredictor): AnalysisPredictor
+    Raises:
+        ValueError: predict by TensorRT need device == 'GPU'.
+    """
+    if device != 'GPU' and run_mode != 'paddle':
+        raise ValueError("Predict by TensorRT mode: {}, expect device=='GPU', but device == {}".format(
+            run_mode, device))
+    config = Config(os.path.join(model_dir, 'model.pdmodel'), os.path.join(model_dir, 'model.pdiparams'))
+    if device == 'GPU':
+        # initial GPU memory(M), device ID
+        config.enable_use_gpu(200, 0)
+        # optimize graph and fuse op
+        config.switch_ir_optim(True)
+    elif device == 'XPU':
+        config.enable_lite_engine()
+        config.enable_xpu(10 * 1024 * 1024)
+    else:
+        config.disable_gpu()
+        config.set_cpu_math_library_num_threads(cpu_threads)
+        if enable_mkldnn:
+            try:
+                # cache 10 different shapes for mkldnn to avoid memory leak
+                config.set_mkldnn_cache_capacity(10)
+                config.enable_mkldnn()
+                if enable_mkldnn_bfloat16:
+                    config.enable_mkldnn_bfloat16()
+            except Exception as e:
+                print("The current environment does not support `mkldnn`, so disable mkldnn.")
+                pass
+
+    precision_map = {
+        'trt_int8': Config.Precision.Int8,
+        'trt_fp32': Config.Precision.Float32,
+        'trt_fp16': Config.Precision.Half
+    }
+    if run_mode in precision_map.keys():
+        config.enable_tensorrt_engine(workspace_size=(1 << 25) * batch_size,
+                                      max_batch_size=batch_size,
+                                      min_subgraph_size=min_subgraph_size,
+                                      precision_mode=precision_map[run_mode],
+                                      use_static=False,
+                                      use_calib_mode=trt_calib_mode)
+
+        if use_dynamic_shape:
+            min_input_shape = {'image': [batch_size, 3, trt_min_shape, trt_min_shape]}
+            max_input_shape = {'image': [batch_size, 3, trt_max_shape, trt_max_shape]}
+            opt_input_shape = {'image': [batch_size, 3, trt_opt_shape, trt_opt_shape]}
+            config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape, opt_input_shape)
+            print('trt set dynamic shape done!')
+
+    # disable print log when predict
+    config.disable_glog_info()
+    # enable shared memory
+    config.enable_memory_optim()
+    # disable feed, fetch OP, needed by zero_copy_run
+    config.switch_use_feed_fetch_ops(False)
+    if delete_shuffle_pass:
+        config.delete_pass("shuffle_channel_detect_pass")
+    predictor = create_predictor(config)
+    return predictor, config
+
+
+def get_test_images(infer_dir, infer_img):
+    """
+    Get image path list in TEST mode
+    """
+    assert infer_img is not None or infer_dir is not None, \
+        "--image_file or --image_dir should be set"
+    assert infer_img is None or os.path.isfile(infer_img), \
+            "{} is not a file".format(infer_img)
+    assert infer_dir is None or os.path.isdir(infer_dir), \
+            "{} is not a directory".format(infer_dir)
+
+    # infer_img has a higher priority
+    if infer_img and os.path.isfile(infer_img):
+        return [infer_img]
+
+    images = set()
+    infer_dir = os.path.abspath(infer_dir)
+    assert os.path.isdir(infer_dir), \
+        "infer_dir {} is not a directory".format(infer_dir)
+    exts = ['jpg', 'jpeg', 'png', 'bmp']
+    exts += [ext.upper() for ext in exts]
+    for ext in exts:
+        images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
+    images = list(images)
+
+    assert len(images) > 0, "no image found in {}".format(infer_dir)
+    print("Found {} inference images in total.".format(len(images)))
+
+    return images
+
+
+def visualize(image_list, result, labels, output_dir='output/', threshold=0.5):
+    # visualize the predict result
+    start_idx = 0
+    for idx, image_file in enumerate(image_list):
+        im_bboxes_num = result['boxes_num'][idx]
+        im_results = {}
+        if 'boxes' in result:
+            im_results['boxes'] = result['boxes'][start_idx:start_idx + im_bboxes_num, :]
+        if 'masks' in result:
+            im_results['masks'] = result['masks'][start_idx:start_idx + im_bboxes_num, :]
+        if 'segm' in result:
+            im_results['segm'] = result['segm'][start_idx:start_idx + im_bboxes_num, :]
+        if 'label' in result:
+            im_results['label'] = result['label'][start_idx:start_idx + im_bboxes_num]
+        if 'score' in result:
+            im_results['score'] = result['score'][start_idx:start_idx + im_bboxes_num]
+
+        start_idx += im_bboxes_num
+        im = visualize_box(image_file, im_results, labels, threshold=threshold)
+        img_name = os.path.split(image_file)[-1]
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+        out_path = os.path.join(output_dir, img_name)
+        im.save(out_path, quality=95)
+        print("save result to: " + out_path)
+
+
+def print_arguments(args):
+    print('-----------  Running Arguments -----------')
+    for arg, value in sorted(vars(args).items()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------')
+
+
+def main():
+    deploy_file = os.path.join(FLAGS.model_dir, 'infer_cfg.yml')
+    with open(deploy_file) as f:
+        yml_conf = yaml.safe_load(f)
+    arch = yml_conf['arch']
+    detector_func = 'Detector'
+    if arch == 'SOLOv2':
+        detector_func = 'DetectorSOLOv2'
+    elif arch == 'PicoDet':
+        detector_func = 'DetectorPicoDet'
+
+    detector = eval(detector_func)(FLAGS.model_dir,
+                                   device=FLAGS.device,
+                                   run_mode=FLAGS.run_mode,
+                                   batch_size=FLAGS.batch_size,
+                                   trt_min_shape=FLAGS.trt_min_shape,
+                                   trt_max_shape=FLAGS.trt_max_shape,
+                                   trt_opt_shape=FLAGS.trt_opt_shape,
+                                   trt_calib_mode=FLAGS.trt_calib_mode,
+                                   cpu_threads=FLAGS.cpu_threads,
+                                   enable_mkldnn=FLAGS.enable_mkldnn,
+                                   enable_mkldnn_bfloat16=FLAGS.enable_mkldnn_bfloat16,
+                                   threshold=FLAGS.threshold,
+                                   output_dir=FLAGS.output_dir)
+
+    # predict from video file or camera video stream
+    if FLAGS.video_file is not None or FLAGS.camera_id != -1:
+        detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
+    else:
+        # predict from image
+        if FLAGS.image_dir is None and FLAGS.image_file is not None:
+            assert FLAGS.batch_size == 1, "batch_size should be 1, when image_file is not None"
+        img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
+        save_file = os.path.join(FLAGS.output_dir, 'results.json') if FLAGS.save_results else None
+        detector.predict_image(img_list, FLAGS.run_benchmark, repeats=100, save_file=save_file)
+        if not FLAGS.run_benchmark:
+            detector.det_times.info(average=True)
+        else:
+            mode = FLAGS.run_mode
+            model_dir = FLAGS.model_dir
+            model_info = {'model_name': model_dir.strip('/').split('/')[-1], 'precision': mode.split('_')[-1]}
+            bench_log(detector, img_list, model_info, name='DET')
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+    FLAGS.device = FLAGS.device.upper()
+    assert FLAGS.device in ['CPU', 'GPU', 'XPU'], "device should be CPU, GPU or XPU"
+    assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device"
+
+    assert not (FLAGS.enable_mkldnn == False and FLAGS.enable_mkldnn_bfloat16
+                == True), 'To enable mkldnn bfloat, please turn on both enable_mkldnn and enable_mkldnn_bfloat16'
+
+    main()

modules/image/keypoint_detection/pp-tinypose/keypoint_infer.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import glob
+import math
+import os
+import sys
+import time
+from functools import reduce
+
+import cv2
+import numpy as np
+import paddle
+import yaml
+from PIL import Image
+# add deploy path of PadleDetection to sys.path
+parent_path = os.path.abspath(os.path.join(__file__, *(['..'])))
+sys.path.insert(0, parent_path)
+
+from preprocess import preprocess, NormalizeImage, Permute
+from keypoint_preprocess import EvalAffine, TopDownEvalAffine, expand_crop
+from keypoint_postprocess import HRNetPostProcess
+from visualize import visualize_pose
+from paddle.inference import Config
+from paddle.inference import create_predictor
+from utils import argsparser, Timer, get_current_memory_mb
+from benchmark_utils import PaddleInferBenchmark
+from infer import Detector, get_test_images, print_arguments
+
+# Global dictionary
+KEYPOINT_SUPPORT_MODELS = {'HigherHRNet': 'keypoint_bottomup', 'HRNet': 'keypoint_topdown'}
+
+
+class KeyPointDetector(Detector):
+    """
+    Args:
+        model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
+        device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
+        run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
+        batch_size (int): size of pre batch in inference
+        trt_min_shape (int): min shape for dynamic shape in trt
+        trt_max_shape (int): max shape for dynamic shape in trt
+        trt_opt_shape (int): opt shape for dynamic shape in trt
+        trt_calib_mode (bool): If the model is produced by TRT offline quantitative
+            calibration, trt_calib_mode need to set True
+        cpu_threads (int): cpu threads
+        enable_mkldnn (bool): whether to open MKLDNN
+        use_dark(bool): whether to use postprocess in DarkPose
+    """
+
+    def __init__(self,
+                 model_dir,
+                 device='CPU',
+                 run_mode='paddle',
+                 batch_size=1,
+                 trt_min_shape=1,
+                 trt_max_shape=1280,
+                 trt_opt_shape=640,
+                 trt_calib_mode=False,
+                 cpu_threads=1,
+                 enable_mkldnn=False,
+                 output_dir='output',
+                 threshold=0.5,
+                 use_dark=True):
+        super(KeyPointDetector, self).__init__(
+            model_dir=model_dir,
+            device=device,
+            run_mode=run_mode,
+            batch_size=batch_size,
+            trt_min_shape=trt_min_shape,
+            trt_max_shape=trt_max_shape,
+            trt_opt_shape=trt_opt_shape,
+            trt_calib_mode=trt_calib_mode,
+            cpu_threads=cpu_threads,
+            enable_mkldnn=enable_mkldnn,
+            output_dir=output_dir,
+            threshold=threshold,
+        )
+        self.use_dark = use_dark
+
+    def set_config(self, model_dir):
+        return PredictConfig_KeyPoint(model_dir)
+
+    def get_person_from_rect(self, image, results):
+        # crop the person result from image
+        self.det_times.preprocess_time_s.start()
+        valid_rects = results['boxes']
+        rect_images = []
+        new_rects = []
+        org_rects = []
+        for rect in valid_rects:
+            rect_image, new_rect, org_rect = expand_crop(image, rect)
+            if rect_image is None or rect_image.size == 0:
+                continue
+            rect_images.append(rect_image)
+            new_rects.append(new_rect)
+            org_rects.append(org_rect)
+        self.det_times.preprocess_time_s.end()
+        return rect_images, new_rects, org_rects
+
+    def postprocess(self, inputs, result):
+        np_heatmap = result['heatmap']
+        np_masks = result['masks']
+        # postprocess output of predictor
+        if KEYPOINT_SUPPORT_MODELS[self.pred_config.arch] == 'keypoint_bottomup':
+            results = {}
+            h, w = inputs['im_shape'][0]
+            preds = [np_heatmap]
+            if np_masks is not None:
+                preds += np_masks
+            preds += [h, w]
+            keypoint_postprocess = HRNetPostProcess()
+            kpts, scores = keypoint_postprocess(*preds)
+            results['keypoint'] = kpts
+            results['score'] = scores
+            return results
+        elif KEYPOINT_SUPPORT_MODELS[self.pred_config.arch] == 'keypoint_topdown':
+            results = {}
+            imshape = inputs['im_shape'][:, ::-1]
+            center = np.round(imshape / 2.)
+            scale = imshape / 200.
+            keypoint_postprocess = HRNetPostProcess(use_dark=self.use_dark)
+            kpts, scores = keypoint_postprocess(np_heatmap, center, scale)
+            results['keypoint'] = kpts
+            results['score'] = scores
+            return results
+        else:
+            raise ValueError("Unsupported arch: {}, expect {}".format(self.pred_config.arch, KEYPOINT_SUPPORT_MODELS))
+
+    def predict(self, repeats=1):
+        '''
+        Args:
+            repeats (int): repeat number for prediction
+        Returns:
+            results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
+                            matix element:[class, score, x_min, y_min, x_max, y_max]
+                            MaskRCNN's results include 'masks': np.ndarray:
+                            shape: [N, im_h, im_w]
+        '''
+        # model prediction
+        np_heatmap, np_masks = None, None
+        for i in range(repeats):
+            self.predictor.run()
+            output_names = self.predictor.get_output_names()
+            heatmap_tensor = self.predictor.get_output_handle(output_names[0])
+            np_heatmap = heatmap_tensor.copy_to_cpu()
+            if self.pred_config.tagmap:
+                masks_tensor = self.predictor.get_output_handle(output_names[1])
+                heat_k = self.predictor.get_output_handle(output_names[2])
+                inds_k = self.predictor.get_output_handle(output_names[3])
+                np_masks = [masks_tensor.copy_to_cpu(), heat_k.copy_to_cpu(), inds_k.copy_to_cpu()]
+        result = dict(heatmap=np_heatmap, masks=np_masks)
+        return result
+
+    def predict_image(self, image_list, run_benchmark=False, repeats=1, visual=True):
+        results = []
+        batch_loop_cnt = math.ceil(float(len(image_list)) / self.batch_size)
+        for i in range(batch_loop_cnt):
+            start_index = i * self.batch_size
+            end_index = min((i + 1) * self.batch_size, len(image_list))
+            batch_image_list = image_list[start_index:end_index]
+            if run_benchmark:
+                # preprocess
+                inputs = self.preprocess(batch_image_list)  # warmup
+                self.det_times.preprocess_time_s.start()
+                inputs = self.preprocess(batch_image_list)
+                self.det_times.preprocess_time_s.end()
+
+                # model prediction
+                result_warmup = self.predict(repeats=repeats)  # warmup
+                self.det_times.inference_time_s.start()
+                result = self.predict(repeats=repeats)
+                self.det_times.inference_time_s.end(repeats=repeats)
+
+                # postprocess
+                result_warmup = self.postprocess(inputs, result)  # warmup
+                self.det_times.postprocess_time_s.start()
+                result = self.postprocess(inputs, result)
+                self.det_times.postprocess_time_s.end()
+                self.det_times.img_num += len(batch_image_list)
+
+                cm, gm, gu = get_current_memory_mb()
+                self.cpu_mem += cm
+                self.gpu_mem += gm
+                self.gpu_util += gu
+
+            else:
+                # preprocess
+                self.det_times.preprocess_time_s.start()
+                inputs = self.preprocess(batch_image_list)
+                self.det_times.preprocess_time_s.end()
+
+                # model prediction
+                self.det_times.inference_time_s.start()
+                result = self.predict()
+                self.det_times.inference_time_s.end()
+
+                # postprocess
+                self.det_times.postprocess_time_s.start()
+                result = self.postprocess(inputs, result)
+                self.det_times.postprocess_time_s.end()
+                self.det_times.img_num += len(batch_image_list)
+
+                if visual:
+                    if not os.path.exists(self.output_dir):
+                        os.makedirs(self.output_dir)
+                    visualize(batch_image_list, result, visual_thresh=self.threshold, save_dir=self.output_dir)
+
+            results.append(result)
+            if visual:
+                print('Test iter {}'.format(i))
+        results = self.merge_batch_result(results)
+        return results
+
+    def predict_video(self, video_file, camera_id):
+        video_name = 'output.mp4'
+        if camera_id != -1:
+            capture = cv2.VideoCapture(camera_id)
+        else:
+            capture = cv2.VideoCapture(video_file)
+            video_name = os.path.split(video_file)[-1]
+        # Get Video info : resolution, fps, frame count
+        width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = int(capture.get(cv2.CAP_PROP_FPS))
+        frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+        print("fps: %d, frame_count: %d" % (fps, frame_count))
+
+        if not os.path.exists(self.output_dir):
+            os.makedirs(self.output_dir)
+        out_path = os.path.join(self.output_dir, video_name)
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
+        index = 1
+        while (1):
+            ret, frame = capture.read()
+            if not ret:
+                break
+            print('detect frame: %d' % (index))
+            index += 1
+            results = self.predict_image([frame[:, :, ::-1]], visual=False)
+            im_results = {}
+            im_results['keypoint'] = [results['keypoint'], results['score']]
+            im = visualize_pose(frame, im_results, visual_thresh=self.threshold, returnimg=True)
+            writer.write(im)
+            if camera_id != -1:
+                cv2.imshow('Mask Detection', im)
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+        writer.release()
+
+
+def create_inputs(imgs, im_info):
+    """generate input for different model type
+    Args:
+        imgs (list(numpy)): list of image (np.ndarray)
+        im_info (list(dict)): list of image info
+    Returns:
+        inputs (dict): input of model
+    """
+    inputs = {}
+    inputs['image'] = np.stack(imgs, axis=0).astype('float32')
+    im_shape = []
+    for e in im_info:
+        im_shape.append(np.array((e['im_shape'])).astype('float32'))
+    inputs['im_shape'] = np.stack(im_shape, axis=0)
+    return inputs
+
+
+class PredictConfig_KeyPoint():
+    """set config of preprocess, postprocess and visualize
+    Args:
+        model_dir (str): root path of model.yml
+    """
+
+    def __init__(self, model_dir):
+        # parsing Yaml config for Preprocess
+        deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
+        with open(deploy_file) as f:
+            yml_conf = yaml.safe_load(f)
+        self.check_model(yml_conf)
+        self.arch = yml_conf['arch']
+        self.archcls = KEYPOINT_SUPPORT_MODELS[yml_conf['arch']]
+        self.preprocess_infos = yml_conf['Preprocess']
+        self.min_subgraph_size = yml_conf['min_subgraph_size']
+        self.labels = yml_conf['label_list']
+        self.tagmap = False
+        self.use_dynamic_shape = yml_conf['use_dynamic_shape']
+        if 'keypoint_bottomup' == self.archcls:
+            self.tagmap = True
+        self.print_config()
+
+    def check_model(self, yml_conf):
+        """
+        Raises:
+            ValueError: loaded model not in supported model type
+        """
+        for support_model in KEYPOINT_SUPPORT_MODELS:
+            if support_model in yml_conf['arch']:
+                return True
+        raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf['arch'], KEYPOINT_SUPPORT_MODELS))
+
+    def print_config(self):
+        print('-----------  Model Configuration -----------')
+        print('%s: %s' % ('Model Arch', self.arch))
+        print('%s: ' % ('Transform Order'))
+        for op_info in self.preprocess_infos:
+            print('--%s: %s' % ('transform op', op_info['type']))
+        print('--------------------------------------------')
+
+
+def visualize(image_list, results, visual_thresh=0.6, save_dir='output'):
+    im_results = {}
+    for i, image_file in enumerate(image_list):
+        skeletons = results['keypoint']
+        scores = results['score']
+        skeleton = skeletons[i:i + 1]
+        score = scores[i:i + 1]
+        im_results['keypoint'] = [skeleton, score]
+        visualize_pose(image_file, im_results, visual_thresh=visual_thresh, save_dir=save_dir)
+
+
+def main():
+    detector = KeyPointDetector(FLAGS.model_dir,
+                                device=FLAGS.device,
+                                run_mode=FLAGS.run_mode,
+                                batch_size=FLAGS.batch_size,
+                                trt_min_shape=FLAGS.trt_min_shape,
+                                trt_max_shape=FLAGS.trt_max_shape,
+                                trt_opt_shape=FLAGS.trt_opt_shape,
+                                trt_calib_mode=FLAGS.trt_calib_mode,
+                                cpu_threads=FLAGS.cpu_threads,
+                                enable_mkldnn=FLAGS.enable_mkldnn,
+                                threshold=FLAGS.threshold,
+                                output_dir=FLAGS.output_dir,
+                                use_dark=FLAGS.use_dark)
+
+    # predict from video file or camera video stream
+    if FLAGS.video_file is not None or FLAGS.camera_id != -1:
+        detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
+    else:
+        # predict from image
+        img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
+        detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10)
+        if not FLAGS.run_benchmark:
+            detector.det_times.info(average=True)
+        else:
+            mems = {
+                'cpu_rss_mb': detector.cpu_mem / len(img_list),
+                'gpu_rss_mb': detector.gpu_mem / len(img_list),
+                'gpu_util': detector.gpu_util * 100 / len(img_list)
+            }
+            perf_info = detector.det_times.report(average=True)
+            model_dir = FLAGS.model_dir
+            mode = FLAGS.run_mode
+            model_info = {'model_name': model_dir.strip('/').split('/')[-1], 'precision': mode.split('_')[-1]}
+            data_info = {'batch_size': 1, 'shape': "dynamic_shape", 'data_num': perf_info['img_num']}
+            det_log = PaddleInferBenchmark(detector.config, model_info, data_info, perf_info, mems)
+            det_log('KeyPoint')
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+    FLAGS.device = FLAGS.device.upper()
+    assert FLAGS.device in ['CPU', 'GPU', 'XPU'], "device should be CPU, GPU or XPU"
+    assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device"
+
+    main()

modules/image/keypoint_detection/pp-tinypose/keypoint_postprocess.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import math
+from collections import abc
+from collections import defaultdict
+
+import cv2
+import numpy as np
+import paddle
+import paddle.nn as nn
+from keypoint_preprocess import get_affine_mat_kernel
+from keypoint_preprocess import get_affine_transform
+from scipy.optimize import linear_sum_assignment
+
+
+class HRNetPostProcess(object):
+
+    def __init__(self, use_dark=True):
+        self.use_dark = use_dark
+
+    def flip_back(self, output_flipped, matched_parts):
+        assert output_flipped.ndim == 4,\
+                'output_flipped should be [batch_size, num_joints, height, width]'
+
+        output_flipped = output_flipped[:, :, :, ::-1]
+
+        for pair in matched_parts:
+            tmp = output_flipped[:, pair[0], :, :].copy()
+            output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
+            output_flipped[:, pair[1], :, :] = tmp
+
+        return output_flipped
+
+    def get_max_preds(self, heatmaps):
+        """get predictions from score maps
+
+        Args:
+            heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
+
+        Returns:
+            preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
+            maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
+        """
+        assert isinstance(heatmaps, np.ndarray), 'heatmaps should be numpy.ndarray'
+        assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
+
+        batch_size = heatmaps.shape[0]
+        num_joints = heatmaps.shape[1]
+        width = heatmaps.shape[3]
+        heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
+        idx = np.argmax(heatmaps_reshaped, 2)
+        maxvals = np.amax(heatmaps_reshaped, 2)
+
+        maxvals = maxvals.reshape((batch_size, num_joints, 1))
+        idx = idx.reshape((batch_size, num_joints, 1))
+
+        preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
+
+        preds[:, :, 0] = (preds[:, :, 0]) % width
+        preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
+
+        pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
+        pred_mask = pred_mask.astype(np.float32)
+
+        preds *= pred_mask
+
+        return preds, maxvals
+
+    def gaussian_blur(self, heatmap, kernel):
+        border = (kernel - 1) // 2
+        batch_size = heatmap.shape[0]
+        num_joints = heatmap.shape[1]
+        height = heatmap.shape[2]
+        width = heatmap.shape[3]
+        for i in range(batch_size):
+            for j in range(num_joints):
+                origin_max = np.max(heatmap[i, j])
+                dr = np.zeros((height + 2 * border, width + 2 * border))
+                dr[border:-border, border:-border] = heatmap[i, j].copy()
+                dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
+                heatmap[i, j] = dr[border:-border, border:-border].copy()
+                heatmap[i, j] *= origin_max / np.max(heatmap[i, j])
+        return heatmap
+
+    def dark_parse(self, hm, coord):
+        heatmap_height = hm.shape[0]
+        heatmap_width = hm.shape[1]
+        px = int(coord[0])
+        py = int(coord[1])
+        if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2:
+            dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1])
+            dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px])
+            dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2])
+            dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \
+                + hm[py-1][px-1])
+            dyy = 0.25 * (hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px])
+            derivative = np.matrix([[dx], [dy]])
+            hessian = np.matrix([[dxx, dxy], [dxy, dyy]])
+            if dxx * dyy - dxy**2 != 0:
+                hessianinv = hessian.I
+                offset = -hessianinv * derivative
+                offset = np.squeeze(np.array(offset.T), axis=0)
+                coord += offset
+        return coord
+
+    def dark_postprocess(self, hm, coords, kernelsize):
+        """
+        refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py
+
+        """
+        hm = self.gaussian_blur(hm, kernelsize)
+        hm = np.maximum(hm, 1e-10)
+        hm = np.log(hm)
+        for n in range(coords.shape[0]):
+            for p in range(coords.shape[1]):
+                coords[n, p] = self.dark_parse(hm[n][p], coords[n][p])
+        return coords
+
+    def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
+        """the highest heatvalue location with a quarter offset in the
+        direction from the highest response to the second highest response.
+
+        Args:
+            heatmaps (numpy.ndarray): The predicted heatmaps
+            center (numpy.ndarray): The boxes center
+            scale (numpy.ndarray): The scale factor
+
+        Returns:
+            preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
+            maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
+        """
+
+        coords, maxvals = self.get_max_preds(heatmaps)
+
+        heatmap_height = heatmaps.shape[2]
+        heatmap_width = heatmaps.shape[3]
+
+        if self.use_dark:
+            coords = self.dark_postprocess(heatmaps, coords, kernelsize)
+        else:
+            for n in range(coords.shape[0]):
+                for p in range(coords.shape[1]):
+                    hm = heatmaps[n][p]
+                    px = int(math.floor(coords[n][p][0] + 0.5))
+                    py = int(math.floor(coords[n][p][1] + 0.5))
+                    if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
+                        diff = np.array([hm[py][px + 1] - hm[py][px - 1], hm[py + 1][px] - hm[py - 1][px]])
+                        coords[n][p] += np.sign(diff) * .25
+        preds = coords.copy()
+
+        # Transform back
+        for i in range(coords.shape[0]):
+            preds[i] = transform_preds(coords[i], center[i], scale[i], [heatmap_width, heatmap_height])
+
+        return preds, maxvals
+
+    def __call__(self, output, center, scale):
+        preds, maxvals = self.get_final_preds(output, center, scale)
+        return np.concatenate((preds, maxvals), axis=-1), np.mean(maxvals, axis=1)
+
+
+def transform_preds(coords, center, scale, output_size):
+    target_coords = np.zeros(coords.shape)
+    trans = get_affine_transform(center, scale * 200, 0, output_size, inv=1)
+    for p in range(coords.shape[0]):
+        target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
+    return target_coords
+
+
+def affine_transform(pt, t):
+    new_pt = np.array([pt[0], pt[1], 1.]).T
+    new_pt = np.dot(t, new_pt)
+    return new_pt[:2]
+
+
+def translate_to_ori_images(keypoint_result, batch_records):
+    kpts = keypoint_result['keypoint']
+    scores = keypoint_result['score']
+    kpts[..., 0] += batch_records[:, 0:1]
+    kpts[..., 1] += batch_records[:, 1:2]
+    return kpts, scores

modules/image/keypoint_detection/pp-tinypose/keypoint_preprocess.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+"""
+this code is based on https://github.com/open-mmlab/mmpose/mmpose/core/post_processing/post_transforms.py
+"""
+import cv2
+import numpy as np
+
+
+class EvalAffine(object):
+
+    def __init__(self, size, stride=64):
+        super(EvalAffine, self).__init__()
+        self.size = size
+        self.stride = stride
+
+    def __call__(self, image, im_info):
+        s = self.size
+        h, w, _ = image.shape
+        trans, size_resized = get_affine_mat_kernel(h, w, s, inv=False)
+        image_resized = cv2.warpAffine(image, trans, size_resized)
+        return image_resized, im_info
+
+
+def get_affine_mat_kernel(h, w, s, inv=False):
+    if w < h:
+        w_ = s
+        h_ = int(np.ceil((s / w * h) / 64.) * 64)
+        scale_w = w
+        scale_h = h_ / w_ * w
+
+    else:
+        h_ = s
+        w_ = int(np.ceil((s / h * w) / 64.) * 64)
+        scale_h = h
+        scale_w = w_ / h_ * h
+
+    center = np.array([np.round(w / 2.), np.round(h / 2.)])
+
+    size_resized = (w_, h_)
+    trans = get_affine_transform(center, np.array([scale_w, scale_h]), 0, size_resized, inv=inv)
+
+    return trans, size_resized
+
+
+def get_affine_transform(center, input_size, rot, output_size, shift=(0., 0.), inv=False):
+    """Get the affine transform matrix, given the center/scale/rot/output_size.
+
+    Args:
+        center (np.ndarray[2, ]): Center of the bounding box (x, y).
+        scale (np.ndarray[2, ]): Scale of the bounding box
+            wrt [width, height].
+        rot (float): Rotation angle (degree).
+        output_size (np.ndarray[2, ]): Size of the destination heatmaps.
+        shift (0-100%): Shift translation ratio wrt the width/height.
+            Default (0., 0.).
+        inv (bool): Option to inverse the affine transform direction.
+            (inv=False: src->dst or inv=True: dst->src)
+
+    Returns:
+        np.ndarray: The transform matrix.
+    """
+    assert len(center) == 2
+    assert len(output_size) == 2
+    assert len(shift) == 2
+    if not isinstance(input_size, (np.ndarray, list)):
+        input_size = np.array([input_size, input_size], dtype=np.float32)
+    scale_tmp = input_size
+
+    shift = np.array(shift)
+    src_w = scale_tmp[0]
+    dst_w = output_size[0]
+    dst_h = output_size[1]
+
+    rot_rad = np.pi * rot / 180
+    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
+    dst_dir = np.array([0., dst_w * -0.5])
+
+    src = np.zeros((3, 2), dtype=np.float32)
+    src[0, :] = center + scale_tmp * shift
+    src[1, :] = center + src_dir + scale_tmp * shift
+    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
+
+    dst = np.zeros((3, 2), dtype=np.float32)
+    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
+    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
+    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
+
+    if inv:
+        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
+    else:
+        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    return trans
+
+
+def get_warp_matrix(theta, size_input, size_dst, size_target):
+    """This code is based on
+        https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
+
+        Calculate the transformation matrix under the constraint of unbiased.
+    Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
+    Data Processing for Human Pose Estimation (CVPR 2020).
+
+    Args:
+        theta (float): Rotation angle in degrees.
+        size_input (np.ndarray): Size of input image [w, h].
+        size_dst (np.ndarray): Size of output image [w, h].
+        size_target (np.ndarray): Size of ROI in input plane [w, h].
+
+    Returns:
+        matrix (np.ndarray): A matrix for transformation.
+    """
+    theta = np.deg2rad(theta)
+    matrix = np.zeros((2, 3), dtype=np.float32)
+    scale_x = size_dst[0] / size_target[0]
+    scale_y = size_dst[1] / size_target[1]
+    matrix[0, 0] = np.cos(theta) * scale_x
+    matrix[0, 1] = -np.sin(theta) * scale_x
+    matrix[0, 2] = scale_x * (-0.5 * size_input[0] * np.cos(theta) + 0.5 * size_input[1] * np.sin(theta) +
+                              0.5 * size_target[0])
+    matrix[1, 0] = np.sin(theta) * scale_y
+    matrix[1, 1] = np.cos(theta) * scale_y
+    matrix[1, 2] = scale_y * (-0.5 * size_input[0] * np.sin(theta) - 0.5 * size_input[1] * np.cos(theta) +
+                              0.5 * size_target[1])
+    return matrix
+
+
+def rotate_point(pt, angle_rad):
+    """Rotate a point by an angle.
+
+    Args:
+        pt (list[float]): 2 dimensional point to be rotated
+        angle_rad (float): rotation angle by radian
+
+    Returns:
+        list[float]: Rotated point.
+    """
+    assert len(pt) == 2
+    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
+    new_x = pt[0] * cs - pt[1] * sn
+    new_y = pt[0] * sn + pt[1] * cs
+    rotated_pt = [new_x, new_y]
+
+    return rotated_pt
+
+
+def _get_3rd_point(a, b):
+    """To calculate the affine matrix, three pairs of points are required. This
+    function is used to get the 3rd point, given 2D points a & b.
+
+    The 3rd point is defined by rotating vector `a - b` by 90 degrees
+    anticlockwise, using b as the rotation center.
+
+    Args:
+        a (np.ndarray): point(x,y)
+        b (np.ndarray): point(x,y)
+
+    Returns:
+        np.ndarray: The 3rd point.
+    """
+    assert len(a) == 2
+    assert len(b) == 2
+    direction = a - b
+    third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)
+
+    return third_pt
+
+
+class TopDownEvalAffine(object):
+    """apply affine transform to image and coords
+
+    Args:
+        trainsize (list): [w, h], the standard size used to train
+        use_udp (bool): whether to use Unbiased Data Processing.
+        records(dict): the dict contained the image and coords
+
+    Returns:
+        records (dict): contain the image and coords after tranformed
+
+    """
+
+    def __init__(self, trainsize, use_udp=False):
+        self.trainsize = trainsize
+        self.use_udp = use_udp
+
+    def __call__(self, image, im_info):
+        rot = 0
+        imshape = im_info['im_shape'][::-1]
+        center = im_info['center'] if 'center' in im_info else imshape / 2.
+        scale = im_info['scale'] if 'scale' in im_info else imshape
+        if self.use_udp:
+            trans = get_warp_matrix(rot, center * 2.0, [self.trainsize[0] - 1.0, self.trainsize[1] - 1.0], scale)
+            image = cv2.warpAffine(image,
+                                   trans, (int(self.trainsize[0]), int(self.trainsize[1])),
+                                   flags=cv2.INTER_LINEAR)
+        else:
+            trans = get_affine_transform(center, scale, rot, self.trainsize)
+            image = cv2.warpAffine(image,
+                                   trans, (int(self.trainsize[0]), int(self.trainsize[1])),
+                                   flags=cv2.INTER_LINEAR)
+
+        return image, im_info
+
+
+def expand_crop(images, rect, expand_ratio=0.3):
+    imgh, imgw, c = images.shape
+    label, conf, xmin, ymin, xmax, ymax = [int(x) for x in rect.tolist()]
+    if label != 0:
+        return None, None, None
+    org_rect = [xmin, ymin, xmax, ymax]
+    h_half = (ymax - ymin) * (1 + expand_ratio) / 2.
+    w_half = (xmax - xmin) * (1 + expand_ratio) / 2.
+    if h_half > w_half * 4 / 3:
+        w_half = h_half * 0.75
+    center = [(ymin + ymax) / 2., (xmin + xmax) / 2.]
+    ymin = max(0, int(center[0] - h_half))
+    ymax = min(imgh - 1, int(center[0] + h_half))
+    xmin = max(0, int(center[1] - w_half))
+    xmax = min(imgw - 1, int(center[1] + w_half))
+    return images[ymin:ymax, xmin:xmax, :], [xmin, ymin, xmax, ymax], org_rect

modules/image/keypoint_detection/pp-tinypose/logger.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import functools
+import logging
+import os
+import sys
+
+import paddle.distributed as dist
+
+__all__ = ['setup_logger']
+
+logger_initialized = []
+
+
+def setup_logger(name="ppdet", output=None):
+    """
+    Initialize logger and set its verbosity level to INFO.
+    Args:
+        output (str): a file name or a directory to save log. If None, will not save log file.
+            If ends with ".txt" or ".log", assumed to be a file name.
+            Otherwise, logs will be saved to `output/log.txt`.
+        name (str): the root module name of this logger
+
+    Returns:
+        logging.Logger: a logger
+    """
+    logger = logging.getLogger(name)
+    if name in logger_initialized:
+        return logger
+
+    logger.setLevel(logging.INFO)
+    logger.propagate = False
+
+    formatter = logging.Formatter("[%(asctime)s] %(name)s %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S")
+    # stdout logging: master only
+    local_rank = dist.get_rank()
+    if local_rank == 0:
+        ch = logging.StreamHandler(stream=sys.stdout)
+        ch.setLevel(logging.DEBUG)
+        ch.setFormatter(formatter)
+        logger.addHandler(ch)
+
+    # file logging: all workers
+    if output is not None:
+        if output.endswith(".txt") or output.endswith(".log"):
+            filename = output
+        else:
+            filename = os.path.join(output, "log.txt")
+        if local_rank > 0:
+            filename = filename + ".rank{}".format(local_rank)
+        os.makedirs(os.path.dirname(filename))
+        fh = logging.FileHandler(filename, mode='a')
+        fh.setLevel(logging.DEBUG)
+        fh.setFormatter(logging.Formatter())
+        logger.addHandler(fh)
+    logger_initialized.append(name)
+    return logger

modules/image/keypoint_detection/pp-tinypose/module.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+import argparse
+import json
+import math
+import os
+import time
+from typing import Union
+
+import cv2
+import numpy as np
+import paddle
+import yaml
+from det_keypoint_unite_infer import predict_with_given_det
+from infer import bench_log
+from infer import Detector
+from infer import get_test_images
+from infer import PredictConfig
+from infer import print_arguments
+from keypoint_infer import KeyPointDetector
+from keypoint_infer import PredictConfig_KeyPoint
+from keypoint_postprocess import translate_to_ori_images
+from preprocess import base64_to_cv2
+from preprocess import decode_image
+from visualize import visualize_pose
+
+import paddlehub.vision.transforms as T
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.module import runnable
+from paddlehub.module.module import serving
+
+
+@moduleinfo(name="pp-tinypose",
+            type="CV/image_editing",
+            author="paddlepaddle",
+            author_email="",
+            summary="Openpose_body_estimation is a body pose estimation model based on Realtime Multi-Person 2D Pose \
+            Estimation using Part Affinity Fields.",
+            version="1.0.0")
+class PP_TinyPose:
+    """
+    PP-TinyPose Model.
+
+    Args:
+        load_checkpoint(str): Checkpoint save path, default is None.
+    """
+
+    def __init__(self):
+        self.det_model_dir = os.path.join(self.directory, 'model/picodet_s_320_coco_lcnet/')
+        self.keypoint_model_dir = os.path.join(self.directory, 'model/dark_hrnet_w32_256x192/')
+        self.detector = Detector(self.det_model_dir)
+        self.topdown_keypoint_detector = KeyPointDetector(self.keypoint_model_dir)
+
+    def predict(self,
+                img: Union[str, np.ndarray],
+                save_path: str = "pp_tinypose_output",
+                visualization: bool = False,
+                use_gpu=False):
+        if use_gpu:
+            device = 'GPU'
+        else:
+            device = 'CPU'
+        if self.detector.device != device:
+            self.detector = Detector(self.det_model_dir, device=device)
+            self.topdown_keypoint_detector = KeyPointDetector(self.keypoint_model_dir, device=device)
+
+        self.visualization = visualization
+        store_res = []
+
+        # Decode image in advance in det + pose prediction
+        image, _ = decode_image(img, {})
+        results = self.detector.predict_image([image], visual=False)
+        results = self.detector.filter_box(results, 0.5)
+        if results['boxes_num'] > 0:
+            keypoint_res = predict_with_given_det(image, results, self.topdown_keypoint_detector, 1, False)
+            save_name = img if isinstance(img, str) else (str(time.time()) + '.png')
+            store_res.append(
+                [save_name, keypoint_res['bbox'], [keypoint_res['keypoint'][0], keypoint_res['keypoint'][1]]])
+            if not os.path.exists(save_path):
+                os.makedirs(save_path)
+            if self.visualization:
+                visualize_pose(save_name, keypoint_res, visual_thresh=0.5, save_dir=save_path)
+        return store_res
+
+    @serving
+    def serving_method(self, images: list, **kwargs):
+        """
+        Run as a service.
+        """
+        images_decode = [base64_to_cv2(image) for image in images]
+        results = self.predict(img=images_decode[0], **kwargs)
+        results = json.dumps(results)
+        return results
+
+    @runnable
+    def run_cmd(self, argvs: list):
+        """
+        Run as a command.
+        """
+        self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name),
+                                              prog='hub run {}'.format(self.name),
+                                              usage='%(prog)s',
+                                              add_help=True)
+        self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
+        self.arg_config_group = self.parser.add_argument_group(
+            title="Config options", description="Run configuration for controlling module behavior, not required.")
+        self.add_module_config_arg()
+        self.add_module_input_arg()
+        args = self.parser.parse_args(argvs)
+        results = self.predict(img=args.input_path,
+                               save_path=args.output_dir,
+                               visualization=args.visualization,
+                               use_gpu=args.use_gpu)
+
+        return results
+
+    def add_module_config_arg(self):
+        """
+        Add the command config options.
+        """
+
+        self.arg_config_group.add_argument('--output_dir',
+                                           type=str,
+                                           default='pp_tinypose_output',
+                                           help="The directory to save output images.")
+        self.arg_config_group.add_argument('--visualization',
+                                           type=bool,
+                                           default=True,
+                                           help="whether to save output as images.")
+
+        self.arg_config_group.add_argument('--use_gpu', action='store_true', help="use GPU or not")
+
+    def add_module_input_arg(self):
+        """
+        Add the command input options.
+        """
+        self.arg_input_group.add_argument('--input_path', type=str, help="path to image.")

modules/image/keypoint_detection/pp-tinypose/preprocess.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import base64
+
+import cv2
+import numpy as np
+from keypoint_preprocess import get_affine_transform
+
+
+def decode_image(im_file, im_info):
+    """read rgb image
+    Args:
+        im_file (str|np.ndarray): input can be image path or np.ndarray
+        im_info (dict): info of image
+    Returns:
+        im (np.ndarray):  processed image (np.ndarray)
+        im_info (dict): info of processed image
+    """
+    if isinstance(im_file, str):
+        with open(im_file, 'rb') as f:
+            im_read = f.read()
+        data = np.frombuffer(im_read, dtype='uint8')
+        im = cv2.imdecode(data, 1)  # BGR mode, but need RGB mode
+        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+    else:
+        im = cv2.cvtColor(im_file, cv2.COLOR_BGR2RGB)
+    im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
+    im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
+    return im, im_info
+
+
+class Resize(object):
+    """resize image by target_size and max_size
+    Args:
+        target_size (int): the target size of image
+        keep_ratio (bool): whether keep_ratio or not, default true
+        interp (int): method of resize
+    """
+
+    def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
+        if isinstance(target_size, int):
+            target_size = [target_size, target_size]
+        self.target_size = target_size
+        self.keep_ratio = keep_ratio
+        self.interp = interp
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        assert len(self.target_size) == 2
+        assert self.target_size[0] > 0 and self.target_size[1] > 0
+        im_channel = im.shape[2]
+        im_scale_y, im_scale_x = self.generate_scale(im)
+        im = cv2.resize(im, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=self.interp)
+        im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
+        im_info['scale_factor'] = np.array([im_scale_y, im_scale_x]).astype('float32')
+        return im, im_info
+
+    def generate_scale(self, im):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+        Returns:
+            im_scale_x: the resize ratio of X
+            im_scale_y: the resize ratio of Y
+        """
+        origin_shape = im.shape[:2]
+        im_c = im.shape[2]
+        if self.keep_ratio:
+            im_size_min = np.min(origin_shape)
+            im_size_max = np.max(origin_shape)
+            target_size_min = np.min(self.target_size)
+            target_size_max = np.max(self.target_size)
+            im_scale = float(target_size_min) / float(im_size_min)
+            if np.round(im_scale * im_size_max) > target_size_max:
+                im_scale = float(target_size_max) / float(im_size_max)
+            im_scale_x = im_scale
+            im_scale_y = im_scale
+        else:
+            resize_h, resize_w = self.target_size
+            im_scale_y = resize_h / float(origin_shape[0])
+            im_scale_x = resize_w / float(origin_shape[1])
+        return im_scale_y, im_scale_x
+
+
+class NormalizeImage(object):
+    """normalize image
+    Args:
+        mean (list): im - mean
+        std (list): im / std
+        is_scale (bool): whether need im / 255
+        is_channel_first (bool): if True: image shape is CHW, else: HWC
+    """
+
+    def __init__(self, mean, std, is_scale=True):
+        self.mean = mean
+        self.std = std
+        self.is_scale = is_scale
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        im = im.astype(np.float32, copy=False)
+        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+        std = np.array(self.std)[np.newaxis, np.newaxis, :]
+
+        if self.is_scale:
+            im = im / 255.0
+        im -= mean
+        im /= std
+        return im, im_info
+
+
+class Permute(object):
+    """permute image
+    Args:
+        to_bgr (bool): whether convert RGB to BGR
+        channel_first (bool): whether convert HWC to CHW
+    """
+
+    def __init__(self, ):
+        super(Permute, self).__init__()
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        im = im.transpose((2, 0, 1)).copy()
+        return im, im_info
+
+
+class PadStride(object):
+    """ padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
+    Args:
+        stride (bool): model with FPN need image shape % stride == 0
+    """
+
+    def __init__(self, stride=0):
+        self.coarsest_stride = stride
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        coarsest_stride = self.coarsest_stride
+        if coarsest_stride <= 0:
+            return im, im_info
+        im_c, im_h, im_w = im.shape
+        pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
+        pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
+        padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
+        padding_im[:, :im_h, :im_w] = im
+        return padding_im, im_info
+
+
+class LetterBoxResize(object):
+
+    def __init__(self, target_size):
+        """
+        Resize image to target size, convert normalized xywh to pixel xyxy
+        format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
+        Args:
+            target_size (int|list): image target size.
+        """
+        super(LetterBoxResize, self).__init__()
+        if isinstance(target_size, int):
+            target_size = [target_size, target_size]
+        self.target_size = target_size
+
+    def letterbox(self, img, height, width, color=(127.5, 127.5, 127.5)):
+        # letterbox: resize a rectangular image to a padded rectangular
+        shape = img.shape[:2]  # [height, width]
+        ratio_h = float(height) / shape[0]
+        ratio_w = float(width) / shape[1]
+        ratio = min(ratio_h, ratio_w)
+        new_shape = (round(shape[1] * ratio), round(shape[0] * ratio))  # [width, height]
+        padw = (width - new_shape[0]) / 2
+        padh = (height - new_shape[1]) / 2
+        top, bottom = round(padh - 0.1), round(padh + 0.1)
+        left, right = round(padw - 0.1), round(padw + 0.1)
+
+        img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA)  # resized, no border
+        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # padded rectangular
+        return img, ratio, padw, padh
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        assert len(self.target_size) == 2
+        assert self.target_size[0] > 0 and self.target_size[1] > 0
+        height, width = self.target_size
+        h, w = im.shape[:2]
+        im, ratio, padw, padh = self.letterbox(im, height=height, width=width)
+
+        new_shape = [round(h * ratio), round(w * ratio)]
+        im_info['im_shape'] = np.array(new_shape, dtype=np.float32)
+        im_info['scale_factor'] = np.array([ratio, ratio], dtype=np.float32)
+        return im, im_info
+
+
+class Pad(object):
+
+    def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
+        """
+        Pad image to a specified size.
+        Args:
+            size (list[int]): image target size
+            fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
+        """
+        super(Pad, self).__init__()
+        if isinstance(size, int):
+            size = [size, size]
+        self.size = size
+        self.fill_value = fill_value
+
+    def __call__(self, im, im_info):
+        im_h, im_w = im.shape[:2]
+        h, w = self.size
+        if h == im_h and w == im_w:
+            im = im.astype(np.float32)
+            return im, im_info
+
+        canvas = np.ones((h, w, 3), dtype=np.float32)
+        canvas *= np.array(self.fill_value, dtype=np.float32)
+        canvas[0:im_h, 0:im_w, :] = im.astype(np.float32)
+        im = canvas
+        return im, im_info
+
+
+class WarpAffine(object):
+    """Warp affine the image
+    """
+
+    def __init__(self, keep_res=False, pad=31, input_h=512, input_w=512, scale=0.4, shift=0.1):
+        self.keep_res = keep_res
+        self.pad = pad
+        self.input_h = input_h
+        self.input_w = input_w
+        self.scale = scale
+        self.shift = shift
+
+    def __call__(self, im, im_info):
+        """
+        Args:
+            im (np.ndarray): image (np.ndarray)
+            im_info (dict): info of image
+        Returns:
+            im (np.ndarray):  processed image (np.ndarray)
+            im_info (dict): info of processed image
+        """
+        img = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
+
+        h, w = img.shape[:2]
+
+        if self.keep_res:
+            input_h = (h | self.pad) + 1
+            input_w = (w | self.pad) + 1
+            s = np.array([input_w, input_h], dtype=np.float32)
+            c = np.array([w // 2, h // 2], dtype=np.float32)
+
+        else:
+            s = max(h, w) * 1.0
+            input_h, input_w = self.input_h, self.input_w
+            c = np.array([w / 2., h / 2.], dtype=np.float32)
+
+        trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
+        img = cv2.resize(img, (w, h))
+        inp = cv2.warpAffine(img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR)
+        return inp, im_info
+
+
+def preprocess(im, preprocess_ops):
+    # process image by preprocess_ops
+    im_info = {
+        'scale_factor': np.array([1., 1.], dtype=np.float32),
+        'im_shape': None,
+    }
+    im, im_info = decode_image(im, im_info)
+    for operator in preprocess_ops:
+        im, im_info = operator(im, im_info)
+    return im, im_info
+
+
+def cv2_to_base64(image: np.ndarray):
+    data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(data.tostring()).decode('utf8')
+
+
+def base64_to_cv2(b64str: str):
+    data = base64.b64decode(b64str.encode('utf8'))
+    data = np.fromstring(data, np.uint8)
+    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+    return data

modules/image/keypoint_detection/pp-tinypose/utils.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+import argparse
+import ast
+import os
+import time
+
+
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model_dir",
+                        type=str,
+                        default=None,
+                        help=("Directory include:'model.pdiparams', 'model.pdmodel', "
+                              "'infer_cfg.yml', created by tools/export_model.py."),
+                        required=True)
+    parser.add_argument("--image_file", type=str, default=None, help="Path of image file.")
+    parser.add_argument("--image_dir",
+                        type=str,
+                        default=None,
+                        help="Dir of image file, `image_file` has a higher priority.")
+    parser.add_argument("--batch_size", type=int, default=1, help="batch_size for inference.")
+    parser.add_argument("--video_file",
+                        type=str,
+                        default=None,
+                        help="Path of video file, `video_file` or `camera_id` has a highest priority.")
+    parser.add_argument("--camera_id", type=int, default=-1, help="device id of camera to predict.")
+    parser.add_argument("--threshold", type=float, default=0.5, help="Threshold of score.")
+    parser.add_argument("--output_dir", type=str, default="output", help="Directory of output visualization files.")
+    parser.add_argument("--run_mode",
+                        type=str,
+                        default='paddle',
+                        help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)")
+    parser.add_argument("--device",
+                        type=str,
+                        default='cpu',
+                        help="Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU.")
+    parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Deprecated, please use `--device`.")
+    parser.add_argument("--run_benchmark",
+                        type=ast.literal_eval,
+                        default=False,
+                        help="Whether to predict a image_file repeatedly for benchmark")
+    parser.add_argument("--enable_mkldnn", type=ast.literal_eval, default=False, help="Whether use mkldnn with CPU.")
+    parser.add_argument("--enable_mkldnn_bfloat16",
+                        type=ast.literal_eval,
+                        default=False,
+                        help="Whether use mkldnn bfloat16 inference with CPU.")
+    parser.add_argument("--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
+    parser.add_argument("--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
+    parser.add_argument("--trt_max_shape", type=int, default=1280, help="max_shape for TensorRT.")
+    parser.add_argument("--trt_opt_shape", type=int, default=640, help="opt_shape for TensorRT.")
+    parser.add_argument("--trt_calib_mode",
+                        type=bool,
+                        default=False,
+                        help="If the model is produced by TRT offline quantitative "
+                        "calibration, trt_calib_mode need to set True.")
+    parser.add_argument('--save_images', action='store_true', help='Save visualization image results.')
+    parser.add_argument('--save_mot_txts', action='store_true', help='Save tracking results (txt).')
+    parser.add_argument('--save_mot_txt_per_img',
+                        action='store_true',
+                        help='Save tracking results (txt) for each image.')
+    parser.add_argument('--scaled',
+                        type=bool,
+                        default=False,
+                        help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 "
+                        "True in general detector.")
+    parser.add_argument("--tracker_config", type=str, default=None, help=("tracker donfig"))
+    parser.add_argument("--reid_model_dir",
+                        type=str,
+                        default=None,
+                        help=("Directory include:'model.pdiparams', 'model.pdmodel', "
+                              "'infer_cfg.yml', created by tools/export_model.py."))
+    parser.add_argument("--reid_batch_size", type=int, default=50, help="max batch_size for reid model inference.")
+    parser.add_argument('--use_dark',
+                        type=ast.literal_eval,
+                        default=True,
+                        help='whether to use darkpose to get better keypoint position predict ')
+    parser.add_argument("--action_file", type=str, default=None, help="Path of input file for action recognition.")
+    parser.add_argument("--window_size",
+                        type=int,
+                        default=50,
+                        help="Temporal size of skeleton feature for action recognition.")
+    parser.add_argument("--random_pad",
+                        type=ast.literal_eval,
+                        default=False,
+                        help="Whether do random padding for action recognition.")
+    parser.add_argument("--save_results",
+                        type=bool,
+                        default=False,
+                        help="Whether save detection result to file using coco format")
+
+    return parser
+
+
+class Times(object):
+
+    def __init__(self):
+        self.time = 0.
+        # start time
+        self.st = 0.
+        # end time
+        self.et = 0.
+
+    def start(self):
+        self.st = time.time()
+
+    def end(self, repeats=1, accumulative=True):
+        self.et = time.time()
+        if accumulative:
+            self.time += (self.et - self.st) / repeats
+        else:
+            self.time = (self.et - self.st) / repeats
+
+    def reset(self):
+        self.time = 0.
+        self.st = 0.
+        self.et = 0.
+
+    def value(self):
+        return round(self.time, 4)
+
+
+class Timer(Times):
+
+    def __init__(self, with_tracker=False):
+        super(Timer, self).__init__()
+        self.with_tracker = with_tracker
+        self.preprocess_time_s = Times()
+        self.inference_time_s = Times()
+        self.postprocess_time_s = Times()
+        self.tracking_time_s = Times()
+        self.img_num = 0
+
+    def info(self, average=False):
+        pre_time = self.preprocess_time_s.value()
+        infer_time = self.inference_time_s.value()
+        post_time = self.postprocess_time_s.value()
+        track_time = self.tracking_time_s.value()
+
+        total_time = pre_time + infer_time + post_time
+        if self.with_tracker:
+            total_time = total_time + track_time
+        total_time = round(total_time, 4)
+        print("------------------ Inference Time Info ----------------------")
+        print("total_time(ms): {}, img_num: {}".format(total_time * 1000, self.img_num))
+        preprocess_time = round(pre_time / max(1, self.img_num), 4) if average else pre_time
+        postprocess_time = round(post_time / max(1, self.img_num), 4) if average else post_time
+        inference_time = round(infer_time / max(1, self.img_num), 4) if average else infer_time
+        tracking_time = round(track_time / max(1, self.img_num), 4) if average else track_time
+
+        average_latency = total_time / max(1, self.img_num)
+        qps = 0
+        if total_time > 0:
+            qps = 1 / average_latency
+        print("average latency time(ms): {:.2f}, QPS: {:2f}".format(average_latency * 1000, qps))
+        if self.with_tracker:
+            print(
+                "preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}, tracking_time(ms): {:.2f}"
+                .format(preprocess_time * 1000, inference_time * 1000, postprocess_time * 1000, tracking_time * 1000))
+        else:
+            print("preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}".format(
+                preprocess_time * 1000, inference_time * 1000, postprocess_time * 1000))
+
+    def report(self, average=False):
+        dic = {}
+        pre_time = self.preprocess_time_s.value()
+        infer_time = self.inference_time_s.value()
+        post_time = self.postprocess_time_s.value()
+        track_time = self.tracking_time_s.value()
+
+        dic['preprocess_time_s'] = round(pre_time / max(1, self.img_num), 4) if average else pre_time
+        dic['inference_time_s'] = round(infer_time / max(1, self.img_num), 4) if average else infer_time
+        dic['postprocess_time_s'] = round(post_time / max(1, self.img_num), 4) if average else post_time
+        dic['img_num'] = self.img_num
+        total_time = pre_time + infer_time + post_time
+        if self.with_tracker:
+            dic['tracking_time_s'] = round(track_time / max(1, self.img_num), 4) if average else track_time
+            total_time = total_time + track_time
+        dic['total_time_s'] = round(total_time, 4)
+        return dic
+
+
+def get_current_memory_mb():
+    """
+    It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
+    And this function Current program is time-consuming.
+    """
+    import pynvml
+    import psutil
+    import GPUtil
+    gpu_id = int(os.environ.get('CUDA_VISIBLE_DEVICES', 0))
+
+    pid = os.getpid()
+    p = psutil.Process(pid)
+    info = p.memory_full_info()
+    cpu_mem = info.uss / 1024. / 1024.
+    gpu_mem = 0
+    gpu_percent = 0
+    gpus = GPUtil.getGPUs()
+    if gpu_id is not None and len(gpus) > 0:
+        gpu_percent = gpus[gpu_id].load
+        pynvml.nvmlInit()
+        handle = pynvml.nvmlDeviceGetHandleByIndex(0)
+        meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
+        gpu_mem = meminfo.used / 1024. / 1024.
+    return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4)

modules/image/keypoint_detection/pp-tinypose/visualize.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+from __future__ import division
+
+import os
+
+import cv2
+import numpy as np
+from PIL import Image
+from PIL import ImageDraw
+from PIL import ImageFile
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+import math
+
+
+def visualize_box(im, results, labels, threshold=0.5):
+    """
+    Args:
+        im (str/np.ndarray): path of image/np.ndarray read by cv2
+        results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
+                        matix element:[class, score, x_min, y_min, x_max, y_max]
+                        MaskRCNN's results include 'masks': np.ndarray:
+                        shape:[N, im_h, im_w]
+        labels (list): labels:['class1', ..., 'classn']
+        threshold (float): Threshold of score.
+    Returns:
+        im (PIL.Image.Image): visualized image
+    """
+    if isinstance(im, str):
+        im = Image.open(im).convert('RGB')
+    elif isinstance(im, np.ndarray):
+        im = Image.fromarray(im)
+    if 'boxes' in results and len(results['boxes']) > 0:
+        im = draw_box(im, results['boxes'], labels, threshold=threshold)
+    return im
+
+
+def get_color_map_list(num_classes):
+    """
+    Args:
+        num_classes (int): number of class
+    Returns:
+        color_map (list): RGB color list
+    """
+    color_map = num_classes * [0, 0, 0]
+    for i in range(0, num_classes):
+        j = 0
+        lab = i
+        while lab:
+            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+            j += 1
+            lab >>= 3
+    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
+    return color_map
+
+
+def draw_box(im, np_boxes, labels, threshold=0.5):
+    """
+    Args:
+        im (PIL.Image.Image): PIL image
+        np_boxes (np.ndarray): shape:[N,6], N: number of box,
+                               matix element:[class, score, x_min, y_min, x_max, y_max]
+        labels (list): labels:['class1', ..., 'classn']
+        threshold (float): threshold of box
+    Returns:
+        im (PIL.Image.Image): visualized image
+    """
+    draw_thickness = min(im.size) // 320
+    draw = ImageDraw.Draw(im)
+    clsid2color = {}
+    color_list = get_color_map_list(len(labels))
+    expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
+    np_boxes = np_boxes[expect_boxes, :]
+
+    for dt in np_boxes:
+        clsid, bbox, score = int(dt[0]), dt[2:], dt[1]
+        if clsid not in clsid2color:
+            clsid2color[clsid] = color_list[clsid]
+        color = tuple(clsid2color[clsid])
+
+        if len(bbox) == 4:
+            xmin, ymin, xmax, ymax = bbox
+            print('class_id:{:d}, confidence:{:.4f}, left_top:[{:.2f},{:.2f}],'
+                  'right_bottom:[{:.2f},{:.2f}]'.format(int(clsid), score, xmin, ymin, xmax, ymax))
+            # draw bbox
+            draw.line([(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)],
+                      width=draw_thickness,
+                      fill=color)
+        elif len(bbox) == 8:
+            x1, y1, x2, y2, x3, y3, x4, y4 = bbox
+            draw.line([(x1, y1), (x2, y2), (x3, y3), (x4, y4), (x1, y1)], width=2, fill=color)
+            xmin = min(x1, x2, x3, x4)
+            ymin = min(y1, y2, y3, y4)
+
+        # draw label
+        text = "{} {:.4f}".format(labels[clsid], score)
+        tw, th = draw.textsize(text)
+        draw.rectangle([(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
+        draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
+    return im
+
+
+def get_color(idx):
+    idx = idx * 3
+    color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
+    return color
+
+
+def visualize_pose(imgfile,
+                   results,
+                   visual_thresh=0.6,
+                   save_name='pose.jpg',
+                   save_dir='output',
+                   returnimg=False,
+                   ids=None):
+    try:
+        import matplotlib.pyplot as plt
+        import matplotlib
+        plt.switch_backend('agg')
+    except Exception as e:
+        raise e
+    skeletons, scores = results['keypoint']
+    skeletons = np.array(skeletons)
+    kpt_nums = 17
+    if len(skeletons) > 0:
+        kpt_nums = skeletons.shape[1]
+    if kpt_nums == 17:  #plot coco keypoint
+        EDGES = [(0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6), (5, 7), (6, 8), (7, 9), (8, 10), (5, 11), (6, 12),
+                 (11, 13), (12, 14), (13, 15), (14, 16), (11, 12)]
+    else:  #plot mpii keypoint
+        EDGES = [(0, 1), (1, 2), (3, 4), (4, 5), (2, 6), (3, 6), (6, 7), (7, 8), (8, 9), (10, 11), (11, 12), (13, 14),
+                 (14, 15), (8, 12), (8, 13)]
+    NUM_EDGES = len(EDGES)
+
+    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+            [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+            [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
+    cmap = matplotlib.cm.get_cmap('hsv')
+    plt.figure()
+
+    img = cv2.imread(imgfile) if type(imgfile) == str else imgfile
+
+    color_set = results['colors'] if 'colors' in results else None
+
+    if 'bbox' in results and ids is None:
+        bboxs = results['bbox']
+        for j, rect in enumerate(bboxs):
+            xmin, ymin, xmax, ymax = rect
+            color = colors[0] if color_set is None else colors[color_set[j] % len(colors)]
+            cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 1)
+
+    canvas = img.copy()
+    for i in range(kpt_nums):
+        for j in range(len(skeletons)):
+            if skeletons[j][i, 2] < visual_thresh:
+                continue
+            if ids is None:
+                color = colors[i] if color_set is None else colors[color_set[j] % len(colors)]
+            else:
+                color = get_color(ids[j])
+
+            cv2.circle(canvas, tuple(skeletons[j][i, 0:2].astype('int32')), 2, color, thickness=-1)
+
+    to_plot = cv2.addWeighted(img, 0.3, canvas, 0.7, 0)
+    fig = matplotlib.pyplot.gcf()
+
+    stickwidth = 2
+
+    for i in range(NUM_EDGES):
+        for j in range(len(skeletons)):
+            edge = EDGES[i]
+            if skeletons[j][edge[0], 2] < visual_thresh or skeletons[j][edge[1], 2] < visual_thresh:
+                continue
+
+            cur_canvas = canvas.copy()
+            X = [skeletons[j][edge[0], 1], skeletons[j][edge[1], 1]]
+            Y = [skeletons[j][edge[0], 0], skeletons[j][edge[1], 0]]
+            mX = np.mean(X)
+            mY = np.mean(Y)
+            length = ((X[0] - X[1])**2 + (Y[0] - Y[1])**2)**0.5
+            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+            if ids is None:
+                color = colors[i] if color_set is None else colors[color_set[j] % len(colors)]
+            else:
+                color = get_color(ids[j])
+            cv2.fillConvexPoly(cur_canvas, polygon, color)
+            canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
+    if returnimg:
+        return canvas
+    save_name = os.path.join(save_dir, os.path.splitext(os.path.basename(imgfile))[0] + '_vis.jpg')
+    plt.imsave(save_name, canvas[:, :, ::-1])
+    print("keypoint visualize image saved to: " + save_name)
+    plt.close()