Upload New Modules about Food (#1330)

* add food classification module

modules/thirdparty/image/classification/food_classification/README.md

+# food_classification
+
+类别 图像 - 图像分类
+
+网络 ResNet50_vd_ssld
+
+
+> 模型概述
+
+美食分类（food_classification），该模型可识别苹果派，小排骨，烤面包，牛肉馅饼，牛肉鞑靼。该PaddleHub Module支持API预测及命令行预测。
+
+> 选择模型版本进行安装
+
+```shell
+$ hub install food_classification==1.0.0
+```
+> Module API说明
+
+```python
+def predict(self,
+                images=None,
+                paths=None,
+                batch_size=1,
+                use_gpu=False,
+                **kwargs):
+```
+美食分类预测接口，输入一张图像，输出该图像上食物的类别
+
+参数
+
+* images (list[numpy.ndarray]): 图片数据，ndarray.shape 为 [H, W, C]，BGR格式；
+* paths (list[str]): 图片的路径；
+* batch_size (int): batch 的大小；
+* use_gpu (bool): 是否使用 GPU；
+
+返回
+
+* res (list[dict]): 识别结果的列表，列表中每一个元素为 dict，各字段为：
+    * category_id (int): 类别的id；
+    * category（str）: 类别;
+    * score（float）: 准确率;
+
+## 代码示例
+
+### API调用
+
+```python
+import cv2
+import paddlehub as hub
+
+module = hub.Module(name="food_classification")
+
+images = [cv2.imread('PATH/TO/IMAGE')]
+
+# execute predict and print the result
+results = module.predict(images=images)
+for result in results:
+    print(result)
+```
+
+### 命令行调用
+```shell
+$ hub run food_classification --input_path /PATH/TO/IMAGE --use_gpu True
+```
+
+## 效果展示
+
+### 原图
+<img src="/docs/imgs/Readme_Related/Image_Classification_apple_pie.png">
+
+### 输出结果
+```python
+[{'category_id': 0, 'category': 'apple_pie', 'score': 0.9985085}]
+```
+
+## 贡献者
+彭兆帅、郑博培
+
+## 依赖
+paddlepaddle >= 2.0.0
+
+paddlehub >= 2.0.0
+
+paddlex >= 1.3.7

modules/thirdparty/image/classification/food_classification/__init__.py

+

modules/thirdparty/image/classification/food_classification/module.py

+from __future__ import absolute_import
+from __future__ import division
+
+import os
+import cv2
+import argparse
+import base64
+import paddlex as pdx
+
+import numpy as np
+import paddlehub as hub
+from paddlehub.module.module import moduleinfo, runnable, serving
+
+
+def base64_to_cv2(b64str):
+    data = base64.b64decode(b64str.encode('utf8'))
+    data = np.fromstring(data, np.uint8)
+    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+    return data
+
+
+def cv2_to_base64(image):
+    # return base64.b64encode(image)
+    data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(data.tostring()).decode('utf8')
+
+
+def read_images(paths):
+    images = []
+    for path in paths:
+        images.append(cv2.imread(path))
+    return images
+
+
+@moduleinfo(
+    name='food_classification',
+    type='cv/classification',
+    author='郑博培、彭兆帅',
+    author_email='2733821739@qq.com, 1084667371@qq.com',
+    summary='Food classification',
+    version='1.0.0')
+class MODULE(hub.Module):
+    def _initialize(self, **kwargs):
+        self.default_pretrained_model_path = os.path.join(
+            self.directory, 'assets')
+        self.model = pdx.deploy.Predictor(self.default_pretrained_model_path,
+                                          **kwargs)
+
+    def predict(self,
+                images=None,
+                paths=None,
+                data=None,
+                batch_size=1,
+                use_gpu=False,
+                **kwargs):
+
+        all_data = images if images is not None else read_images(paths)
+        total_num = len(all_data)
+        loop_num = int(np.ceil(total_num / batch_size))
+
+        res = []
+        for iter_id in range(loop_num):
+            batch_data = list()
+            handle_id = iter_id * batch_size
+            for image_id in range(batch_size):
+                try:
+                    batch_data.append(all_data[handle_id + image_id])
+                except IndexError:
+                    break
+            out = self.model.batch_predict(batch_data, **kwargs)
+            res.extend(out)
+        return res
+
+    @serving
+    def serving_method(self, images, **kwargs):
+        """
+        Run as a service.
+        """
+        images_decode = [base64_to_cv2(image) for image in images]
+        results = self.predict(images_decode, **kwargs)
+        res = []
+        for result in results:
+            if isinstance(result, dict):
+                # result_new = dict()
+                for key, value in result.items():
+                    if isinstance(value, np.ndarray):
+                        result[key] = cv2_to_base64(value)
+                    elif isinstance(value, np.generic):
+                        result[key] = np.asscalar(value)
+
+            elif isinstance(result, list):
+                for index in range(len(result)):
+                    for key, value in result[index].items():
+                        if isinstance(value, np.ndarray):
+                            result[index][key] = cv2_to_base64(value)
+                        elif isinstance(value, np.generic):
+                            result[index][key] = np.asscalar(value)
+            else:
+                raise RuntimeError('The result cannot be used in serving.')
+            res.append(result)
+        return res
+
+    @runnable
+    def run_cmd(self, argvs):
+        """
+        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(
+            paths=[args.input_path],
+            use_gpu=args.use_gpu)
+        return results
+
+    def add_module_config_arg(self):
+        """
+        Add the command config options.
+        """
+        self.arg_config_group.add_argument(
+            '--use_gpu',
+            type=bool,
+            default=False,
+            help="whether 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.")
+
+if __name__ == '__main__':
+    module = MODULE(directory='./new_model')
+    images = [cv2.imread('./cat.jpg'), cv2.imread('./cat.jpg'), cv2.imread('./cat.jpg')]
+    res = module.predict(images=images)

modules/thirdparty/image/classification/food_classification/requirements.txt

+paddlepaddle >= 2.0.0
+paddlehub >= 2.0.0
+paddlex >= 1.3.7