fix rec bug and delete infer/predict_eval.py

tools/infer/predict_eval.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 utility
-from ppocr.utils.utility import initial_logger
-logger = initial_logger()
-import cv2
-import predict_system
-import copy
-import numpy as np
-import math
-import time
-import json
-import os
-from PIL import Image, ImageDraw, ImageFont
-from tools.infer.utility import draw_ocr
-from ppocr.utils.utility import get_image_file_list
-
-if __name__ == "__main__":
-    args = utility.parse_args()
-    text_sys = predict_system.TextSystem(args)
-
-    if not os.path.exists(args.image_dir):
-        raise Exception("{} not exists !!".format(args.image_dir))
-    image_file_list = get_image_file_list(args.image_dir)
-
-    total_time_all = 0
-    count = 0
-    save_path = "./inference_output/predict.txt"
-    if not os.path.exists(os.path.dirname(save_path)):
-        os.makedirs(os.path.dirname(save_path))
-    fout = open(save_path, "wb")
-    for image_name in image_file_list:
-        image_file = image_name
-        img = cv2.imread(image_file)
-        if img is None:
-            logger.info("error in loading image:{}".format(image_file))
-            continue
-        count += 1
-        total_time = 0
-        starttime = time.time()
-        dt_boxes, rec_res = text_sys(img)
-        elapse = time.time() - starttime
-        total_time_all += elapse
-        print("Predict time of %s(%d): %.3fs" % (image_file, count, elapse))
-        dt_num = len(dt_boxes)
-        bbox_list = []
-        for dno in range(dt_num):
-            box = dt_boxes[dno]
-            text, score = rec_res[dno]
-            points = []
-            for tno in range(len(box)):
-                points.append([box[tno][0] * 1.0, box[tno][1] * 1.0])
-            bbox_list.append({
-                "transcription": text,
-                "points": points,
-                "scores": score * 1.0
-            })
-        # draw predict box and text in image
-        # and save drawed image in save_path
-        image = Image.open(image_file)
-        boxes, txts, scores = [], [], []
-        for dic in bbox_list:
-            boxes.append(dic['points'])
-            txts.append(dic['transcription'])
-            scores.append(round(dic['scores'], 3))
-        new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
-        draw_img_save = os.path.join(
-            os.path.dirname(save_path), "inference_draw",
-            os.path.basename(image_file))
-        if not os.path.exists(os.path.dirname(draw_img_save)):
-            os.makedirs(os.path.dirname(draw_img_save))
-        cv2.imwrite(draw_img_save, new_img[:, :, ::-1])
-        print("drawed img saved in {}".format(draw_img_save))
-        # save predicted results in txt file
-        otstr = image_name + "\t" + json.dumps(bbox_list) + "\n"
-        fout.write(otstr.encode('utf-8'))
-    avg_time = total_time_all / count
-    logger.info("avg_time: {0}".format(avg_time))
-    logger.info("avg_fps: {0}".format(1.0 / avg_time))
-    fout.close()

tools/infer/predict_eval_new.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 utility
-from ppocr.utils.utility import initial_logger
-logger = initial_logger()
-import cv2
-import predict_system
-import copy
-import numpy as np
-import math
-import time
-import json
-import os
-
-if __name__ == "__main__":
-    args = utility.parse_args()
-    text_sys = predict_system.TextSystem(args)
-
-    image_file_list = []
-    img_set_path = "/paddle/code/dyn/test_imgs/rctw_samples/"
-    image_file_list = os.listdir(img_set_path)
-
-    total_time_all = 0
-    count = 0
-    save_path = "./output/predict.txt"
-    fout = open(save_path, "wb")
-    for image_name in image_file_list:
-        image_file = img_set_path + image_name
-        img = cv2.imread(image_file)
-        if img is None:
-            logger.info("error in loading image:{}".format(image_file))
-            continue
-        count += 1
-        starttime = time.time()
-        dt_boxes, rec_res = text_sys(img)
-        if dt_boxes is None:
-            count -= 1
-            continue
-        elapse = time.time() - starttime
-        total_time_all += elapse
-        print("Predict time of %s(%d): %.3fs" % (image_file, count, elapse))
-        dt_num = len(dt_boxes)
-        bbox_list = []
-        for dno in range(dt_num):
-            box = dt_boxes[dno]
-            text, score = rec_res[dno]
-            points = []
-            for tno in range(len(box)):
-                points.append([box[tno][0] * 1.0, box[tno][1] * 1.0])
-            bbox_list.append({
-                "transcription": text,
-                "points": points,
-                "scores": score * 1.0
-            })
-        otstr = image_name + "\t" + json.dumps(bbox_list) + "\n"
-        fout.write(otstr.encode('utf-8'))
-    avg_time = total_time_all / count
-    logger.info("avg_time: {0}".format(avg_time))
-    logger.info("avg_fps: {0}".format(1.0 / avg_time))
-    fout.close()

tools/infer/predict_rec.py

@@ -36,8 +36,9 @@ class TextRecognizer(object):
         char_ops_params['loss_type'] = 'ctc'
         self.char_ops = CharacterOps(char_ops_params)
 
-    def resize_norm_img(self, img):
+    def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
+        imgW = int(32 * max_wh_ratio)
         h = img.shape[0]
         w = img.shape[1]
         ratio = w / float(h)
@@ -56,14 +57,19 @@ class TextRecognizer(object):
 
     def __call__(self, img_list):
         img_num = len(img_list)
-        batch_num = 15
+        batch_num = 30
         rec_res = []
         predict_time = 0
         for beg_img_no in range(0, img_num, batch_num):
             end_img_no = min(img_num, beg_img_no + batch_num)
             norm_img_batch = []
+            max_wh_ratio = 0
             for ino in range(beg_img_no, end_img_no):
-                norm_img = self.resize_norm_img(img_list[ino])
+                h, w = img_list[ino].shape[0:2]
+                wh_ratio = w * 1.0 / h
+                max_wh_ratio = max(max_wh_ratio, wh_ratio)
+            for ino in range(beg_img_no, end_img_no):
+                norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
                 norm_img = norm_img[np.newaxis, :]
                 norm_img_batch.append(norm_img)
             norm_img_batch = np.concatenate(norm_img_batch)