refine app

python/paddle_serving_app/models/model_list.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 sys
+import os
+from collections import OrderedDict
+
+
+class ServingModels(object):
+    def __init__(self):
+        self.model_dict = OrderedDict()
+        #senta
+        for key in [
+                "senta_bilstm", "senta_bow", "senta_cnn", "senta_gru",
+                "senta_lstm"
+        ]:
+            self.model_dict[
+                key] = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SentimentAnalysis/" + key + ".tar.gz"
+        #image classification
+        for key in ["alexnet_imagenet"]:
+            self.model_dict[
+                key] = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageClassification/" + key + ".tar.gz"
+
+    def get_model_list(self):
+        return (self.model_dict.keys())
+
+    def download(self, model_name):
+        if model_name in self.model_dict:
+            url = self.model_dict[model_name]
+            r = os.system('wget ' + url + ' --no-check-certificate')
+
+
+if __name__ == "__main__":
+    models = ServingModels()
+    print(models.get_model_list())

python/paddle_serving_app/reader/image_reader.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 cv2
+import numpy as np
+
+
+class ImageReader():
+    def __init__(self,
+                 image_shape=[3, 224, 224],
+                 image_mean=[0.485, 0.456, 0.406],
+                 image_std=[0.229, 0.224, 0.225],
+                 resize_short_size=256,
+                 interpolation=None,
+                 crop_center=True):
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.image_shape = image_shape
+        self.resize_short_size = resize_short_size
+        self.interpolation = interpolation
+        self.crop_center = crop_center
+
+    def resize_short(self, img, target_size, interpolation=None):
+        """resize image
+
+        Args:
+            img: image data
+            target_size: resize short target size
+            interpolation: interpolation mode
+
+        Returns:
+            resized image data
+        """
+        percent = float(target_size) / min(img.shape[0], img.shape[1])
+        resized_width = int(round(img.shape[1] * percent))
+        resized_height = int(round(img.shape[0] * percent))
+        if interpolation:
+            resized = cv2.resize(
+                img, (resized_width, resized_height),
+                interpolation=interpolation)
+        else:
+            resized = cv2.resize(img, (resized_width, resized_height))
+        return resized
+
+    def crop_image(self, img, target_size, center):
+        """crop image
+
+        Args:
+            img: images data
+            target_size: crop target size
+            center: crop mode
+
+        Returns:
+            img: cropped image data
+        """
+        height, width = img.shape[:2]
+        size = target_size
+        if center == True:
+            w_start = (width - size) // 2
+            h_start = (height - size) // 2
+        else:
+            w_start = np.random.randint(0, width - size + 1)
+            h_start = np.random.randint(0, height - size + 1)
+        w_end = w_start + size
+        h_end = h_start + size
+        img = img[h_start:h_end, w_start:w_end, :]
+        return img
+
+    def process_image(self, sample):
+        """ process_image """
+        mean = self.image_mean
+        std = self.image_std
+        crop_size = self.image_shape[1]
+
+        data = np.fromstring(sample, np.uint8)
+        img = cv2.imdecode(data, cv2.IMREAD_COLOR)
+
+        if img is None:
+            print("img is None, pass it.")
+            return None
+
+        if crop_size > 0:
+            target_size = self.resize_short_size
+            img = self.resize_short(
+                img, target_size, interpolation=self.interpolation)
+            img = self.crop_image(
+                img, target_size=crop_size, center=self.crop_center)
+
+        img = img[:, :, ::-1]
+
+        img = img.astype('float32').transpose((2, 0, 1)) / 255
+        img_mean = np.array(mean).reshape((3, 1, 1))
+        img_std = np.array(std).reshape((3, 1, 1))
+        img -= img_mean
+        img /= img_std
+        return img