Merge commit '0150e56bddc2cf9c9fe803f3f8eca86a010a2869' of https://github.com/tink2123/PaddleOCR into serving

deploy/pdserving/web_service_det.py

@@ -0,0 +1,77 @@
+# 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 paddle_serving_server.web_service import WebService, Op
+
+import logging
+import numpy as np
+import cv2
+import base64
+# from paddle_serving_app.reader import OCRReader
+from ocr_reader import OCRReader, DetResizeForTest
+from paddle_serving_app.reader import Sequential, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+
+_LOGGER = logging.getLogger()
+
+
+class DetOp(Op):
+    def init_op(self):
+        self.det_preprocess = Sequential([
+            DetResizeForTest(), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.filter_func = FilterBoxes(10, 10)
+        self.post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+
+    def preprocess(self, input_dicts, data_id, log_id):
+        (_, input_dict), = input_dicts.items()
+        data = base64.b64decode(input_dict["image"].encode('utf8'))
+        self.raw_im = data
+        data = np.fromstring(data, np.uint8)
+        # Note: class variables(self.var) can only be used in process op mode
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.ori_h, self.ori_w, _ = im.shape
+        det_img = self.det_preprocess(im)
+        _, self.new_h, self.new_w = det_img.shape
+        return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
+
+    def postprocess(self, input_dicts, fetch_dict, log_id):
+        det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
+        ratio_list = [
+            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
+        ]
+        dt_boxes_list = self.post_func(det_out, [ratio_list])
+        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
+        out_dict = {"dt_boxes": str(dt_boxes)}
+
+        return out_dict, None, ""
+
+
+class OcrService(WebService):
+    def get_pipeline_response(self, read_op):
+        det_op = DetOp(name="det", input_ops=[read_op])
+        return det_op
+
+
+uci_service = OcrService(name="ocr")
+uci_service.prepare_pipeline_config("config.yml")
+uci_service.run_service()

deploy/pdserving/web_service_rec.py

@@ -0,0 +1,86 @@
+# 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 paddle_serving_server.web_service import WebService, Op
+
+import logging
+import numpy as np
+import cv2
+import base64
+# from paddle_serving_app.reader import OCRReader
+from ocr_reader import OCRReader, DetResizeForTest
+from paddle_serving_app.reader import Sequential, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+
+_LOGGER = logging.getLogger()
+
+
+class RecOp(Op):
+    def init_op(self):
+        self.ocr_reader = OCRReader(
+            char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
+
+    def preprocess(self, input_dicts, data_id, log_id):
+        (_, input_dict), = input_dicts.items()
+        raw_im = base64.b64decode(input_dict["image"].encode('utf8'))
+        data = np.fromstring(raw_im, np.uint8)
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        feed_list = []
+        max_wh_ratio = 0
+        ## Many mini-batchs, the type of feed_data is list.
+        max_batch_size = 6  # len(dt_boxes)
+
+        # If max_batch_size is 0, skipping predict stage
+        if max_batch_size == 0:
+            return {}, True, None, ""
+        boxes_size = max_batch_size
+        rem = boxes_size % max_batch_size
+
+        h, w = im.shape[0:2]
+        wh_ratio = w * 1.0 / h
+        max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        _, w, h = self.ocr_reader.resize_norm_img(im, max_wh_ratio).shape
+        norm_img = self.ocr_reader.resize_norm_img(im, max_batch_size)
+        norm_img = norm_img[np.newaxis, :]
+        feed = {"x": norm_img.copy()}
+        feed_list.append(feed)
+        return feed_list, False, None, ""
+
+    def postprocess(self, input_dicts, fetch_data, log_id):
+        res_list = []
+        if isinstance(fetch_data, dict):
+            if len(fetch_data) > 0:
+                rec_batch_res = self.ocr_reader.postprocess(
+                    fetch_data, with_score=True)
+                for res in rec_batch_res:
+                    res_list.append(res[0])
+        elif isinstance(fetch_data, list):
+            for one_batch in fetch_data:
+                one_batch_res = self.ocr_reader.postprocess(
+                    one_batch, with_score=True)
+                for res in one_batch_res:
+                    res_list.append(res[0])
+
+        res = {"res": str(res_list)}
+        return res, None, ""
+
+
+class OcrService(WebService):
+    def get_pipeline_response(self, read_op):
+        rec_op = RecOp(name="rec", input_ops=[read_op])
+        return rec_op
+
+
+uci_service = OcrService(name="ocr")
+uci_service.prepare_pipeline_config("config.yml")
+uci_service.run_service()