Add a hand_pose_localization module

hub_module/modules/image/keypoint_detection/hand_pose_localization/README.md

+## 模型概述
+openpose 手部关键点检测模型
+
+模型详情请参考[openpose开源项目](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
+
+## 模型安装
+
+```shell
+$hub install hand_pose_localization
+```
+
+## API 说明
+
+```python
+def keypoint_detection(
+    self,
+    images=None,
+    paths=None,
+    batch_size=1,
+    output_dir='output',
+    visualization=False
+)
+```
+
+预测API，识别出人体手部关键点。
+
+![手部关键点](https://ai-studio-static-online.cdn.bcebos.com/97e1ae7c1e68477d85b37f53ee997fbc4ef0fc12c7634301bc08749bd003cac0)
+
+**参数**
+
+* images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\], 默认设为 None；
+* paths (list\[str\]): 图片的路径, 默认设为 None；
+* batch\_size (int): batch 的大小，默认设为 1；
+* visualization (bool): 是否将识别结果保存为图片文件，默认设为 False；
+* output\_dir (str): 图片的保存路径，默认设为 output。
+
+**返回**
+
+* res (list[list[list[int]]]): 每张图片识别到的21个手部关键点组成的列表，每个关键点的格式为[x, y]，若有关键点未识别到则为None
+
+
+## 预测代码示例
+
+```python
+import cv2
+import paddlehub as hub
+
+# use_gpu：是否使用GPU进行预测
+model = hub.Module('hand_pose_localization', use_gpu=False)
+
+# 调用关键点检测API
+result = model.keypoint_detection(images=[cv2.imread('/PATH/TO/IMAGE')])
+
+# or
+# result = model.keypoint_detection(paths=['/PATH/TO/IMAGE'])
+
+# 打印预测结果
+print(result)
+```
+
+## 服务部署
+
+PaddleHub Serving可以部署一个在线人体手部关键点检测服务。
+
+## 第一步：启动PaddleHub Serving
+
+运行启动命令：
+```shell
+$ hub serving start -m hand_pose_localization
+```
+
+这样就完成了一个人体手部关键点检测的在线服务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
+
+# 图片Base64编码函数
+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/hand_pose_localization"
+r = requests.post(url=url, headers=headers, data=json.dumps(data))
+
+# 打印预测结果
+print(r.json()["results"])
+```
+
+
+## 模型相关信息
+
+### 模型代码
+
+https://github.com/CMU-Perceptual-Computing-Lab/openpose
+
+### 依赖
+
+paddlepaddle >= 1.8.0
+
+paddlehub >= 1.8.0

hub_module/modules/image/keypoint_detection/hand_pose_localization/model.py

+import os
+import numpy as np
+
+from paddle.fluid.core import AnalysisConfig, create_paddle_predictor
+
+__all__ = ['Model']
+
+class Model():
+    # 初始化函数
+    def __init__(self, modelpath, use_gpu):
+        # 加载模型预测器
+        self.predictor = self.load_model(modelpath, use_gpu)
+
+        # 获取模型的输入输出
+        self.input_names = self.predictor.get_input_names()
+        self.output_names = self.predictor.get_output_names()
+        self.input_tensor = self.predictor.get_input_tensor(self.input_names[0])
+        self.output_tensor = self.predictor.get_output_tensor(self.output_names[0])
+
+    # 模型加载函数
+    def load_model(self, modelpath, use_gpu):
+        # 对运行位置进行配置
+        if use_gpu:
+            try:
+                places = os.environ["CUDA_VISIBLE_DEVICES"]
+                places = int(places[0])
+            except Exception as e:
+                print('Error: %s. Please set the environment variables "CUDA_VISIBLE_DEVICES".' % e)
+                use_gpu = False
+        
+        # 预训练模型路径
+        model = os.path.join(modelpath, "__model__")
+        params = os.path.join(modelpath, "__params__")
+
+        # 加载模型参数
+        config = AnalysisConfig(model, params)
+
+        # 设置参数
+        if use_gpu:   
+            config.enable_use_gpu(100, places)
+        else:
+            config.disable_gpu()
+            config.enable_mkldnn()
+        config.disable_glog_info()
+        config.switch_ir_optim(True)
+        config.switch_use_feed_fetch_ops(False)
+        config.switch_specify_input_names(True)
+        
+        # 通过参数加载模型预测器
+        predictor = create_paddle_predictor(config)
+        
+        # 返回预测器
+        return predictor
+
+    # 模型预测函数
+    def predict(self, input_datas):
+        outputs = []
+
+        # 遍历输入数据进行预测
+        for input_data in input_datas:
+            self.input_tensor.copy_from_cpu(input_data)
+            self.predictor.zero_copy_run()
+            output = self.output_tensor.copy_to_cpu()
+            outputs.append(output)
+        
+        # 预测结果合并
+        outputs = np.concatenate(outputs, 0)
+
+        # 返回预测结果
+        return outputs
\ No newline at end of file

hub_module/modules/image/keypoint_detection/hand_pose_localization/module.py

+# coding=utf-8
+import os
+
+from paddlehub import Module
+from paddlehub.module.module import moduleinfo, serving
+
+from hand_pose_localization.model import Model
+from hand_pose_localization.processor import base64_to_cv2, Processor
+
+@moduleinfo(
+    name="hand_pose_localization", # 模型名称
+    type="CV/keypoint_detection", # 模型类型
+    author="jm12138", # 作者名称
+    author_email="jm12138@qq.com", # 作者邮箱
+    summary="hand_pose_localization", # 模型介绍
+    version="1.0.0" # 版本号
+)
+class Hand_Pose_Localization(Module):
+    # 初始化函数
+    def _initialize(self, use_gpu=False):
+        # 设置模型路径
+        self.model_path = os.path.join(self.directory, "hand_pose_localization")
+
+        # 加载模型
+        self.model = Model(self.model_path, use_gpu)     
+    
+    # 关键点检测函数
+    def keypoint_detection(
+        self,
+        images=None,
+        paths=None,
+        batch_size=1,
+        output_dir='output',
+        visualization=False
+    ):
+        # 加载数据处理器
+        processor = Processor(images, paths, batch_size, output_dir)
+
+        # 模型预测
+        outputs = self.model.predict(processor.input_datas)
+
+        # 结果后处理
+        results = processor.postprocess(outputs, visualization)
+
+        # 返回结果
+        return results
+
+    # Hub Serving
+    @serving
+    def serving_method(self, images, **kwargs):
+        # 获取输入数据
+        images_decode = [base64_to_cv2(image) for image in images]
+        # 关键点检测
+        results = self.keypoint_detection(images_decode, **kwargs)
+        # 返回结果
+        return results
\ No newline at end of file

hub_module/modules/image/keypoint_detection/hand_pose_localization/processor.py

+import os
+import cv2 
+import time
+import base64
+import numpy as np
+
+__all__ = ['base64_to_cv2', 'Processor']
+
+def check_dir(dir_path):
+    # 目录检查函数
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+    elif os.path.isfile(dir_path):
+        os.remove(dir_path)
+        os.makedirs(dir_path)
+
+def base64_to_cv2(b64str):
+    # base64转cv2函数
+    data = base64.b64decode(b64str.encode('utf8'))
+    data = np.fromstring(data, np.uint8)
+    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+    return data
+
+class Processor():
+    # 初始化函数
+    def __init__(self, images=None, paths=None, batch_size=1, output_dir='output'):
+        # 变量设置
+        self.num_points = 21        
+        self.inHeight = 368
+        self.threshold = 0.1
+        self.point_pairs = [[0,1],[1,2],[2,3],[3,4],
+                            [0,5],[5,6],[6,7],[7,8],
+                            [0,9],[9,10],[10,11],[11,12],
+                            [0,13],[13,14],[14,15],[15,16],
+                            [0,17],[17,18],[18,19],[19,20]]
+
+        self.images = images
+        self.paths = paths
+        self.batch_size = batch_size
+        self.output_dir = output_dir
+
+        # 获取原始输入数据
+        self.datas = self.load_datas()
+
+        # 对原始输入数据进行预处理
+        self.input_datas = self.preprocess()
+
+    # 读取数据函数
+    def load_datas(self):
+        datas = []
+
+        # 读取数据列表
+        if self.paths is not None:
+            for im_path in self.paths:
+                assert os.path.isfile(im_path), "The {} isn't a valid file path.".format(im_path)
+                im = cv2.imread(im_path).astype('float32')
+                datas.append(im)
+
+        if self.images is not None:
+            datas = self.images
+        
+        # 返回数据列表
+        return datas
+
+    # 数据预处理函数
+    def preprocess(self):
+        input_datas = []
+
+        # 数据预处理
+        for i, img in enumerate(self.datas):
+            img_height, img_width, _ = img.shape
+            aspect_ratio = img_width / img_height
+            inWidth = int(((aspect_ratio * self.inHeight) * 8) // 8)
+            inpBlob = cv2.dnn.blobFromImage(img, 1.0 / 255, (inWidth, self.inHeight), (0, 0, 0), swapRB=False, crop=False)
+            input_datas.append(inpBlob)
+
+        # 数据按batch_size切分
+        input_datas = np.concatenate(input_datas, 0)
+        split_num = len(self.datas)//self.batch_size+1 if len(self.datas)%self.batch_size!=0 else len(self.datas)//self.batch_size
+        input_datas = np.array_split(input_datas, split_num)   
+
+        # 返回预处理完成的数据
+        return input_datas
+
+    # 结果后处理函数
+    def postprocess(self, outputs, visualization):
+        all_points = []
+
+        # 结果后处理
+        for im_id, img in enumerate(self.datas):
+            points = []
+            for idx in range(self.num_points):
+                probMap = outputs[im_id, idx, :, :]
+                img_height, img_width, _ = img.shape
+                probMap = cv2.resize(probMap, (img_width, img_height))
+                minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
+
+                if prob > self.threshold:
+                    points.append([int(point[0]), int(point[1])])
+                else:
+                    points.append(None)
+
+            all_points.append(points)
+
+            # 结果可视化
+            if visualization:
+                # 检查输出目录
+                check_dir(self.output_dir)
+                # 结果可视化
+                self.vis_pose(img, points, im_id)
+
+        # 返回后处理结果
+        return all_points
+
+    # 结果可视化
+    def vis_pose(self, img, points, im_id):
+        # 根据结果绘制关键点到原图像上
+        for pair in self.point_pairs:
+            partA = pair[0]
+            partB = pair[1]
+
+            if points[partA] and points[partB]:
+                cv2.line(img, tuple(points[partA]), tuple(points[partB]), (0, 255, 255), 3)
+                cv2.circle(img, tuple(points[partA]), 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
+        
+        # 可视化图像保存
+        cv2.imwrite(os.path.join(self.output_dir, '%d_%d.jpg' % (im_id, time.time())), img)