Merge pull request #521 from guru4elephant/refine_serving_app

add pytorch style image preprocessing class and functions

core/sdk-cpp/include/endpoint_config.h

@@ -22,23 +22,23 @@ namespace baidu {
 namespace paddle_serving {
 namespace sdk_cpp {
 
-#define PARSE_CONF_ITEM(conf, item, name, fail)             \
-  do {                                                      \
-    if (conf.has_##name()) {                                \
-      item.set(conf.name());                                \
-    } else {                                                \
-      LOG(ERROR) << "Not found key in configue: " << #name; \
-    }                                                       \
+#define PARSE_CONF_ITEM(conf, item, name, fail)          \
+  do {                                                   \
+    if (conf.has_##name()) {                             \
+      item.set(conf.name());                             \
+    } else {                                             \
+      VLOG(2) << "Not found key in configue: " << #name; \
+    }                                                    \
   } while (0)
 
-#define ASSIGN_CONF_ITEM(dest, src, fail)                          \
-  do {                                                             \
-    if (!src.init) {                                               \
-      LOG(ERROR) << "Cannot assign an unintialized item: " << #src \
-                 << " to dest: " << #dest;                         \
-      return fail;                                                 \
-    }                                                              \
-    dest = src.value;                                              \
+#define ASSIGN_CONF_ITEM(dest, src, fail)                       \
+  do {                                                          \
+    if (!src.init) {                                            \
+      VLOG(2) << "Cannot assign an unintialized item: " << #src \
+              << " to dest: " << #dest;                         \
+      return fail;                                              \
+    }                                                           \
+    dest = src.value;                                           \
   } while (0)
 
 template <typename T>

python/examples/deeplabv3/deeplabv3_client.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize, Transpose, BGR2RGB, SegPostprocess
+import sys
+import cv2
+
+client = Client()
+client.load_client_config("seg_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:9494"])
+
+preprocess = Sequential(
+    [File2Image(), Resize(
+        (512, 512), interpolation=cv2.INTER_LINEAR)])
+
+postprocess = SegPostprocess(2)
+
+filename = "N0060.jpg"
+im = preprocess(filename)
+fetch_map = client.predict(feed={"image": im}, fetch=["output"])
+fetch_map["filename"] = filename
+postprocess(fetch_map)

python/examples/faster_rcnn_model/label_list.txt

+background
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+dining table
+toilet
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

python/examples/faster_rcnn_model/new_test_client.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.
+
+from paddle_serving_client import Client
+import sys
+from paddle_serving_app.reader.pddet import Detection
+from paddle_serving_app.reader import File2Image, Sequential, Normalize, Resize, Transpose, Div, BGR2RGB, RCNNPostprocess
+import numpy as np
+
+preprocess = Sequential([
+    File2Image(), BGR2RGB(), Div(255.0),
+    Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], False),
+    Resize(640, 640), Transpose((2, 0, 1))
+])
+
+postprocess = RCNNPostprocess("label_list.txt", "output")
+
+client = Client()
+client.load_client_config(sys.argv[1])
+client.connect(['127.0.0.1:9393'])
+
+for i in range(100):
+    im = preprocess(sys.argv[2])
+    fetch_map = client.predict(
+        feed={
+            "image": im,
+            "im_info": np.array(list(im.shape[1:]) + [1.0]),
+            "im_shape": np.array(list(im.shape[1:]) + [1.0])
+        },
+        fetch=["multiclass_nms"])
+    fetch_map["image"] = sys.argv[2]
+    postprocess(fetch_map)

python/examples/imagenet/image_rpc_client.py

@@ -13,22 +13,24 @@
 # limitations under the License.
 
 import sys
-from image_reader import ImageReader
 from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize
 import time
 
 client = Client()
 client.load_client_config(sys.argv[1])
 client.connect(["127.0.0.1:9393"])
-reader = ImageReader()
+
+seq = Sequential([
+    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+print(seq)
 
 start = time.time()
+image_file = "daisy.jpg"
 for i in range(1000):
-    with open("./data/n01440764_10026.JPEG", "rb") as f:
-        img = f.read()
-    img = reader.process_image(img)
+    img = seq(image_file)
     fetch_map = client.predict(feed={"image": img}, fetch=["score"])
 end = time.time()
 print(end - start)
-
-#print(fetch_map["score"])

python/examples/mobilenet/mobilenet_tutorial.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize
+from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
+
+client = Client()
+client.load_client_config(
+    "mobilenet_v2_imagenet_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:9393"])
+
+seq = Sequential([
+    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
+])
+
+image_file = "daisy.jpg"
+img = seq(image_file)
+fetch_map = client.predict(feed={"image": img}, fetch=["feature_map"])
+print(fetch_map["feature_map"].reshape(-1))

python/examples/resnet_v2_50/resnet50_v2_tutorial.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop
+from apddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize
+
+client = Client()
+client.load_client_config(
+    "resnet_v2_50_imagenet_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:9393"])
+
+seq = Sequential([
+    File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
+])
+
+image_file = "daisy.jpg"
+img = seq(image_file)
+fetch_map = client.predict(feed={"image": img}, fetch=["feature_map"])
+print(fetch_map["feature_map"].reshape(-1))

python/examples/unet_for_image_seg/seg_client.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import Sequential, File2Image, Resize, Transpose, BGR2RGB, SegPostprocess
+import sys
+import cv2
+
+client = Client()
+client.load_client_config("unet_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:9494"])
+
+preprocess = Sequential(
+    [File2Image(), Resize(
+        (512, 512), interpolation=cv2.INTER_LINEAR)])
+
+postprocess = SegPostprocess(2)
+
+im = preprocess("N0060.jpg")
+fetch_map = client.predict(feed={"image": im}, fetch=["output"])
+fetch_map["filename"] = filename
+postprocess(fetch_map)

python/paddle_serving_app/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from .reader.chinese_bert_reader import ChineseBertReader
-from .reader.image_reader import ImageReader
+from .reader.image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, CenterCrop, Resize
 from .reader.lac_reader import LACReader
 from .reader.senta_reader import SentaReader
 from .models import ServingModels

python/paddle_serving_app/models/model_list.py

@@ -20,78 +20,49 @@ 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",
-                "darknet53-imagenet",
-                "densenet121_imagenet",
-                "densenet161_imagenet",
-                "densenet169_imagenet",
-                "densenet201_imagenet",
-                "densenet264_imagenet"
-                "dpn107_imagenet",
-                "dpn131_imagenet",
-                "dpn68_imagenet",
-                "dpn92_imagenet",
-                "dpn98_imagenet",
-                "efficientnetb0_imagenet",
-                "efficientnetb1_imagenet",
-                "efficientnetb2_imagenet",
-                "efficientnetb3_imagenet",
-                "efficientnetb4_imagenet",
-                "efficientnetb5_imagenet",
-                "efficientnetb6_imagenet",
-                "googlenet_imagenet",
-                "inception_v4_imagenet",
-                "inception_v2_imagenet",
-                "nasnet_imagenet",
-                "pnasnet_imagenet",
-                "resnet_v2_101_imagenet",
-                "resnet_v2_151_imagenet",
-                "resnet_v2_18_imagenet",
-                "resnet_v2_34_imagenet",
-                "resnet_v2_50_imagenet",
-                "resnext101_32x16d_wsl",
-                "resnext101_32x32d_wsl",
-                "resnext101_32x48d_wsl",
-                "resnext101_32x8d_wsl",
-                "resnext101_32x4d_imagenet",
-                "resnext101_64x4d_imagenet",
-                "resnext101_vd_32x4d_imagenet",
-                "resnext101_vd_64x4d_imagenet",
-                "resnext152_64x4d_imagenet",
-                "resnext152_vd_64x4d_imagenet",
-                "resnext50_64x4d_imagenet",
-                "resnext50_vd_32x4d_imagenet",
-                "resnext50_vd_64x4d_imagenet",
-                "se_resnext101_32x4d_imagenet",
-                "se_resnext50_32x4d_imagenet",
-                "shufflenet_v2_imagenet",
-                "vgg11_imagenet",
-                "vgg13_imagenet",
-                "vgg16_imagenet",
-                "vgg19_imagenet",
-                "xception65_imagenet",
-                "xception71_imagenet",
-        ]:
-            self.model_dict[
-                key] = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageClassification/" + key + ".tar.gz"
+        self.model_dict[
+            "SentimentAnalysis"] = ["senta_bilstm", "senta_bow", "senta_cnn"]
+        self.model_dict["SemanticRepresentation"] = ["ernie_base"]
+        self.model_dict["ChineseWordSegmentation"] = ["lac"]
+        self.model_dict["ObjectDetection"] = ["faster_rcnn", "yolov3"]
+        self.model_dict["ImageSegmentation"] = ["unet", "deeplabv3"]
+        self.model_dict["ImageClassification"] = [
+            "resnet_v2_50_imagenet", "mobilenet_v2_imagenet"
+        ]
+
+        image_class_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageClassification/"
+        image_seg_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageSegmentation/"
+        object_detection_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ObjectDetection/"
+        senta_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SentimentAnalysis/"
+        semantic_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticRepresentation/"
+        wordseg_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/ChineseWordSegmentation/"
+
+        self.url_dict = {}
+
+        def pack_url(model_dict, key, url):
+            for i, value in enumerate(model_dict[key]):
+                self.url_dict[model_dict[key][i]] = url + model_dict[key][
+                    i] + ".tar.gz"
+
+        pack_url(self.model_dict, "SentimentAnalysis", senta_url)
+        pack_url(self.model_dict, "SemanticRepresentation", semantic_url)
+        pack_url(self.model_dict, "ChineseWordSegmentation", wordseg_url)
+        pack_url(self.model_dict, "ObjectDetection", object_detection_url)
+        pack_url(self.model_dict, "ImageSegmentation", image_seg_url)
+        pack_url(self.model_dict, "ImageClassification", image_class_url)
 
     def get_model_list(self):
-        return (self.model_dict.keys())
+        return self.model_dict
 
     def download(self, model_name):
-        if model_name in self.model_dict:
-            url = self.model_dict[model_name]
+        if model_name in self.url_dict:
+            url = self.url_dict[model_name]
             r = os.system('wget ' + url + ' --no-check-certificate')
 
+    def get_tutorial(self, model_name):
+        if model_name in self.tutorial_url:
+            return "Tutorial of {} to be added".format(model_name)
+
 
 if __name__ == "__main__":
     models = ServingModels()

python/paddle_serving_app/package.py

@@ -20,6 +20,7 @@ Usage:
 """
 
 import argparse
+import sys
 from .models import ServingModels
 
 
@@ -29,6 +30,8 @@ def parse_args():  # pylint: disable=doc-string-missing
         "--get_model", type=str, default="", help="Download a specific model")
     parser.add_argument(
         '--list_model', nargs='*', default=None, help="List Models")
+    parser.add_argument(
+        '--tutorial', type=str, default="", help="Get running command")
     return parser.parse_args()
 
 
@@ -36,18 +39,33 @@ if __name__ == "__main__":
     args = parse_args()
     if args.list_model != None:
         model_handle = ServingModels()
-        model_names = model_handle.get_model_list()
-        for key in model_names:
-            print(key)
+        model_dict = model_handle.get_model_list()
+        # Task level model list
+        # Text Classification, Semantic Representation
+        # Image Classification, Object Detection, Image Segmentation
+        for key in model_dict:
+            print("-----------------------------------------------")
+            print("{}: {}".format(key, " | ".join(model_dict[key])))
+
     elif args.get_model != "":
         model_handle = ServingModels()
-        model_names = model_handle.get_model_list()
-        if args.get_model not in model_names:
+        model_dict = model_handle.url_dict
+        if args.get_model not in model_dict:
             print(
                 "Your model name does not exist in current model list, stay tuned"
             )
             sys.exit(0)
         model_handle.download(args.get_model)
+    elif args.tutorial != "":
+        model_handle = ServingModels()
+        model_dict = model_handle.url_dict
+        if args.get_model not in model_dict:
+            print(
+                "Your model name does not exist in current model list, stay tuned"
+            )
+            sys.exit(0)
+        tutorial_str = model_handle.get_tutorial()
+        print(tutorial_str)
     else:
         print("Wrong argument")
         print("""

python/paddle_serving_app/reader/__init__.py

@@ -11,3 +11,4 @@
 # 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 .image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, CenterCrop, Resize, Transpose, Div, RGB2BGR, BGR2RGB, RCNNPostprocess, SegPostprocess

python/paddle_serving_app/reader/functional.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
+
+
+def transpose(img, transpose_target):
+    img = img.transpose(transpose_target)
+    return img
+
+
+def normalize(img, mean, std, channel_first):
+    # need to optimize here
+    if channel_first:
+        img_mean = np.array(mean).reshape((3, 1, 1))
+        img_std = np.array(std).reshape((3, 1, 1))
+    else:
+        img_mean = np.array(mean).reshape((1, 1, 3))
+        img_std = np.array(std).reshape((1, 1, 3))
+    img -= img_mean
+    img /= img_std
+    return img
+
+
+def crop(img, target_size, center):
+    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 resize(img, target_size, max_size=2147483647, interpolation=None):
+    if isinstance(target_size, tuple):
+        resized_width = min(target_size[0], max_size)
+        resized_height = min(target_size[1], max_size)
+    else:
+        im_max_size = max(img.shape[0], img.shape[1])
+        percent = float(target_size) / min(img.shape[0], img.shape[1])
+        if np.round(percent * im_max_size) > max_size:
+            percent = float(max_size) / float(im_max_size)
+        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

python/paddle_serving_app/reader/image_reader.py

@@ -11,9 +11,472 @@
 # 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 os
+import urllib
 import numpy as np
+import base64
+import functional as F
+from PIL import Image, ImageDraw
+import json
+
+_cv2_interpolation_to_str = {cv2.INTER_LINEAR: "cv2.INTER_LINEAR", None: "None"}
+
+
+def generate_colormap(num_classes):
+    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
+
+
+class SegPostprocess(object):
+    def __init__(self, class_num):
+        self.class_num = class_num
+
+    def __call__(self, image_with_result):
+        if "filename" not in image_with_result:
+            raise ("filename should be specified in postprocess")
+        img_name = image_with_result["filename"]
+        ori_img = cv2.imread(img_name, -1)
+        ori_shape = ori_img.shape
+        mask = None
+        for key in image_with_result:
+            if ".lod" in key or "filename" in key:
+                continue
+            mask = image_with_result[key]
+        if mask is None:
+            raise ("segment mask should be specified in postprocess")
+        mask = mask.astype("uint8")
+        mask_png = mask.reshape((512, 512, 1))
+        #score_png = mask_png[:, :, np.newaxis]
+        score_png = mask_png
+        score_png = np.concatenate([score_png] * 3, axis=2)
+        color_map = generate_colormap(self.class_num)
+        for i in range(score_png.shape[0]):
+            for j in range(score_png.shape[1]):
+                score_png[i, j] = color_map[score_png[i, j, 0]]
+        ext_pos = img_name.rfind(".")
+        img_name_fix = img_name[:ext_pos] + "_" + img_name[ext_pos + 1:]
+        mask_save_name = img_name_fix + "_mask.png"
+        cv2.imwrite(mask_save_name, mask_png, [cv2.CV_8UC1])
+        vis_result_name = img_name_fix + "_result.png"
+        result_png = score_png
+
+        result_png = cv2.resize(
+            result_png,
+            ori_shape[:2],
+            fx=0,
+            fy=0,
+            interpolation=cv2.INTER_CUBIC)
+        cv2.imwrite(vis_result_name, result_png, [cv2.CV_8UC1])
+
+
+class RCNNPostprocess(object):
+    def __init__(self, label_file, output_dir):
+        self.output_dir = output_dir
+        self.label_file = label_file
+        self.label_list = []
+        with open(label_file) as fin:
+            for line in fin:
+                self.label_list.append(line.strip())
+        self.clsid2catid = {i: i for i in range(len(self.label_list))}
+        self.catid2name = {i: name for i, name in enumerate(self.label_list)}
+
+    def _offset_to_lengths(self, lod):
+        offset = lod[0]
+        lengths = [offset[i + 1] - offset[i] for i in range(len(offset) - 1)]
+        return [lengths]
+
+    def _bbox2out(self, results, clsid2catid, is_bbox_normalized=False):
+        xywh_res = []
+        for t in results:
+            bboxes = t['bbox'][0]
+            lengths = t['bbox'][1][0]
+            if bboxes.shape == (1, 1) or bboxes is None:
+                continue
+
+            k = 0
+            for i in range(len(lengths)):
+                num = lengths[i]
+                for j in range(num):
+                    dt = bboxes[k]
+                    clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
+                    catid = (clsid2catid[int(clsid)])
+
+                    if is_bbox_normalized:
+                        xmin, ymin, xmax, ymax = \
+                            self.clip_bbox([xmin, ymin, xmax, ymax])
+                        w = xmax - xmin
+                        h = ymax - ymin
+                        im_shape = t['im_shape'][0][i].tolist()
+                        im_height, im_width = int(im_shape[0]), int(im_shape[1])
+                        xmin *= im_width
+                        ymin *= im_height
+                        w *= im_width
+                        h *= im_height
+                    else:
+                        w = xmax - xmin + 1
+                        h = ymax - ymin + 1
+
+                    bbox = [xmin, ymin, w, h]
+                    coco_res = {
+                        'category_id': catid,
+                        'bbox': bbox,
+                        'score': score
+                    }
+                    xywh_res.append(coco_res)
+                    k += 1
+        return xywh_res
+
+    def _get_bbox_result(self, fetch_map, fetch_name, clsid2catid):
+        result = {}
+        is_bbox_normalized = False
+        output = fetch_map[fetch_name]
+        lod = [fetch_map[fetch_name + '.lod']]
+        lengths = self._offset_to_lengths(lod)
+        np_data = np.array(output)
+        result['bbox'] = (np_data, lengths)
+        result['im_id'] = np.array([[0]])
+
+        bbox_results = self._bbox2out([result], clsid2catid, is_bbox_normalized)
+        return bbox_results
+
+    def color_map(self, num_classes):
+        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 = np.array(color_map).reshape(-1, 3)
+        return color_map
+
+    def draw_bbox(self, image, catid2name, bboxes, threshold, color_list):
+        """
+        draw bbox on image
+        """
+        draw = ImageDraw.Draw(image)
+
+        for dt in np.array(bboxes):
+            catid, bbox, score = dt['category_id'], dt['bbox'], dt['score']
+            if score < threshold:
+                continue
+
+            xmin, ymin, w, h = bbox
+            xmax = xmin + w
+            ymax = ymin + h
+
+            color = tuple(color_list[catid])
+
+            # draw bbox
+            draw.line(
+                [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
+                 (xmin, ymin)],
+                width=2,
+                fill=color)
+
+            # draw label
+            text = "{} {:.2f}".format(catid2name[catid], 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 image
+
+    def visualize(self, infer_img, bbox_results, catid2name, num_classes):
+        image = Image.open(infer_img).convert('RGB')
+        color_list = self.color_map(num_classes)
+        image = self.draw_bbox(image, self.catid2name, bbox_results, 0.5,
+                               color_list)
+        image_path = os.path.split(infer_img)[-1]
+        if not os.path.exists(self.output_dir):
+            os.makedirs(self.output_dir)
+        out_path = os.path.join(self.output_dir, image_path)
+        image.save(out_path, quality=95)
+
+    def __call__(self, image_with_bbox):
+        fetch_name = ""
+        for key in image_with_bbox:
+            if key == "image":
+                continue
+            if ".lod" in key:
+                continue
+            fetch_name = key
+        bbox_result = self._get_bbox_result(image_with_bbox, fetch_name,
+                                            self.clsid2catid)
+        if os.path.isdir(self.output_dir) is False:
+            os.mkdir(self.output_dir)
+        self.visualize(image_with_bbox["image"], bbox_result, self.catid2name,
+                       len(self.label_list))
+        if os.path.isdir(self.output_dir) is False:
+            os.mkdir(self.output_dir)
+        bbox_file = os.path.join(self.output_dir, 'bbox.json')
+        with open(bbox_file, 'w') as f:
+            json.dump(bbox_result, f, indent=4)
+
+    def __repr__(self):
+        return self.__class__.__name__ + "label_file: {1}, output_dir: {2}".format(
+            self.label_file, self.output_dir)
+
+
+class Sequential(object):
+    """
+    Args:
+        sequence (sequence of ``Transform`` objects): list of transforms to chain.
+
+    This API references some of the design pattern of torchvision
+    Users can simply use this API in training as well
+
+    Example:
+        >>> image_reader.Sequnece([
+        >>>     transforms.CenterCrop(10),
+        >>> ])
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, img):
+        for t in self.transforms:
+            img = t(img)
+        return img
+
+    def __repr__(self):
+        format_string_ = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string_ += '\n'
+            format_string_ += '    {0}'.format(t)
+        format_string_ += '\n)'
+        return format_string_
+
+
+class RGB2BGR(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, img):
+        return img[:, :, ::-1]
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+
+class BGR2RGB(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, img):
+        return img[:, :, ::-1]
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+
+class File2Image(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, img_path):
+        fin = open(img_path)
+        sample = fin.read()
+        data = np.fromstring(sample, np.uint8)
+        img = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        '''
+        img = cv2.imread(img_path, -1)
+        channels = img.shape[2]
+        ori_h = img.shape[0]
+        ori_w = img.shape[1]
+        '''
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+
+class URL2Image(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, img_url):
+        resp = urllib.urlopen(img_url)
+        sample = resp.read()
+        data = np.fromstring(sample, np.uint8)
+        img = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+
+class Base64ToImage(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, img_base64):
+        img = base64.b64decode(img_base64)
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + "()"
+
+
+class Div(object):
+    """ divide by some float number """
+
+    def __init__(self, value):
+        self.value = value
+
+    def __call__(self, img):
+        """
+        Args:
+            img (numpy array): (int8 numpy array)
+
+        Returns:
+            img (numpy array): (float32 numpy array)
+        """
+        img = img.astype('float32') / self.value
+
+        return img
+
+    def __repr__(self):
+        return self.__class__.__name__ + "({})".format(self.value)
+
+
+class Normalize(object):
+    """Normalize a tensor image with mean and standard deviation.
+    Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
+    will normalize each channel of the input ``torch.*Tensor`` i.e.
+    ``output[channel] = (input[channel] - mean[channel]) / std[channel]``
+
+    .. note::
+        This transform acts out of place, i.e., it does not mutate the input tensor.
+
+    Args:
+        mean (sequence): Sequence of means for each channel.
+        std (sequence): Sequence of standard deviations for each channel.
+
+    """
+
+    def __init__(self, mean, std, channel_first=False):
+        self.mean = mean
+        self.std = std
+        self.channel_first = channel_first
+
+    def __call__(self, img):
+        """
+        Args:
+            img (numpy array): (C, H, W) to be normalized.
+
+        Returns:
+            Tensor: Normalized Tensor image.
+        """
+        return F.normalize(img, self.mean, self.std, self.channel_first)
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean,
+                                                                      self.std)
+
+
+class Lambda(object):
+    """Apply a user-defined lambda as a transform.
+       Very shame to just copy from 
+       https://github.com/pytorch/vision/blob/master/torchvision/transforms/transforms.py#L301
+
+    Args:
+        lambd (function): Lambda/function to be used for transform.
+    """
+
+    def __init__(self, lambd):
+        assert callable(lambd), repr(type(lambd)
+                                     .__name__) + " object is not callable"
+        self.lambd = lambd
+
+    def __call__(self, img):
+        return self.lambd(img)
+
+    def __repr__(self):
+        return self.__class__.__name__ + '()'
+
+
+class CenterCrop(object):
+    """Crops the given Image at the center.
+
+    Args:
+        size (sequence or int): Desired output size of the crop. If size is an
+            int instead of sequence like (h, w), a square crop (size, size) is
+            made.
+    """
+
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img):
+        """
+        Args:
+            img (numpy array): Image to be cropped.
+
+        Returns:
+            numpy array Image: Cropped image.
+        """
+        return F.crop(img, self.size, True)
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(size={0})'.format(self.size)
+
+
+class Resize(object):
+    """Resize the input numpy array Image to the given size.
+
+    Args:
+        size (sequence or int): Desired output size. If size is a sequence like
+            (h, w), output size will be matched to this. If size is an int,
+            smaller edge of the image will be matched to this number.
+            i.e, if height > width, then image will be rescaled to
+            (size * height / width, size)
+        interpolation (int, optional): Desired interpolation. Default is
+            ``None``
+    """
+
+    def __init__(self, size, max_size=2147483647, interpolation=None):
+        self.size = size
+        self.max_size = max_size
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        return F.resize(img, self.size, self.max_size, self.interpolation)
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(size={0}, max_size={1}, interpolation={2})'.format(
+            self.size, self.max_size,
+            _cv2_interpolation_to_str[self.interpolation])
+
+
+class Transpose(object):
+    def __init__(self, transpose_target):
+        self.transpose_target = transpose_target
+
+    def __call__(self, img):
+        return F.transpose(img, self.transpose_target)
+        return img
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + \
+                        "({})".format(self.transpose_target)
+        return format_string
 
 
 class ImageReader():

python/paddle_serving_app/reader/test_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.
+
+from image_reader import File2Image
+from image_reader import URL2Image
+from image_reader import Sequential
+from image_reader import Normalize
+from image_reader import CenterCrop
+from image_reader import Resize
+
+seq = Sequential([
+    File2Image(), CenterCrop(30),
+    Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Resize((5, 5))
+])
+
+url = "daisy.jpg"
+for x in range(100):
+    img = seq(url)
+    print(img.shape)