merge upstream

ppocr/data/simple_dataset.py

@@ -32,12 +32,10 @@ class SimpleDataSet(Dataset):
         self.delimiter = dataset_config.get('delimiter', '\t')
         label_file_list = dataset_config.pop('label_file_list')
         data_source_num = len(label_file_list)
-        if data_source_num == 1:
-            ratio_list = [1.0]
-        else:
-            ratio_list = dataset_config.pop('ratio_list')
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * len(data_source_num)
 
-        assert sum(ratio_list) == 1, "The sum of the ratio_list should be 1."
         assert len(
             ratio_list
         ) == data_source_num, "The length of ratio_list should be the same as the file_list."
@@ -45,62 +43,32 @@ class SimpleDataSet(Dataset):
         self.do_shuffle = loader_config['shuffle']
 
         logger.info("Initialize indexs of datasets:%s" % label_file_list)
-        self.data_lines_list, data_num_list = self.get_image_info_list(
-            label_file_list)
-        self.data_idx_order_list = self.dataset_traversal(
-            data_num_list, ratio_list, batch_size)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
             self.shuffle_data_random()
-
         self.ops = create_operators(dataset_config['transforms'], global_config)
 
-    def get_image_info_list(self, file_list):
+    def get_image_info_list(self, file_list, ratio_list):
         if isinstance(file_list, str):
             file_list = [file_list]
-        data_lines_list = []
-        data_num_list = []
-        for file in file_list:
+        data_lines = []
+        for idx, file in enumerate(file_list):
             with open(file, "rb") as f:
                 lines = f.readlines()
-                data_lines_list.append(lines)
-                data_num_list.append(len(lines))
-        return data_lines_list, data_num_list
-
-    def dataset_traversal(self, data_num_list, ratio_list, batch_size):
-        select_num_list = []
-        dataset_num = len(data_num_list)
-        for dno in range(dataset_num):
-            select_num = round(batch_size * ratio_list[dno])
-            select_num = max(select_num, 1)
-            select_num_list.append(select_num)
-        data_idx_order_list = []
-        cur_index_sets = [0] * dataset_num
-        while True:
-            finish_read_num = 0
-            for dataset_idx in range(dataset_num):
-                cur_index = cur_index_sets[dataset_idx]
-                if cur_index >= data_num_list[dataset_idx]:
-                    finish_read_num += 1
-                else:
-                    select_num = select_num_list[dataset_idx]
-                    for sno in range(select_num):
-                        cur_index = cur_index_sets[dataset_idx]
-                        if cur_index >= data_num_list[dataset_idx]:
-                            break
-                        data_idx_order_list.append((dataset_idx, cur_index))
-                        cur_index_sets[dataset_idx] += 1
-            if finish_read_num == dataset_num:
-                break
-        return data_idx_order_list
+                lines = random.sample(lines,
+                                      round(len(lines) * ratio_list[idx]))
+                data_lines.extend(lines)
+        return data_lines
 
     def shuffle_data_random(self):
         if self.do_shuffle:
-            for dno in range(len(self.data_lines_list)):
-                random.shuffle(self.data_lines_list[dno])
+            random.shuffle(self.data_lines)
         return
 
     def __getitem__(self, idx):
-        dataset_idx, file_idx = self.data_idx_order_list[idx]
-        data_line = self.data_lines_list[dataset_idx][file_idx]
+        file_idx = self.data_idx_order_list[idx]
+        data_line = self.data_lines[file_idx]
         try:
             data_line = data_line.decode('utf-8')
             substr = data_line.strip("\n").split(self.delimiter)

tools/infer/predict_cls.py

@@ -23,7 +23,7 @@ import copy
 import numpy as np
 import math
 import time
-
+import traceback
 import paddle.fluid as fluid
 
 import tools.infer.utility as utility
@@ -106,10 +106,10 @@ class TextClassifier(object):
                 norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
                 self.predictor.run([norm_img_batch])
             prob_out = self.output_tensors[0].copy_to_cpu()
-            cls_res = self.postprocess_op(prob_out)
+            cls_result = self.postprocess_op(prob_out)
             elapse += time.time() - starttime
-            for rno in range(len(cls_res)):
-                label, score = cls_res[rno]
+            for rno in range(len(cls_result)):
+                label, score = cls_result[rno]
                 cls_res[indices[beg_img_no + rno]] = [label, score]
                 if '180' in label and score > self.cls_thresh:
                     img_list[indices[beg_img_no + rno]] = cv2.rotate(
@@ -133,8 +133,8 @@ def main(args):
         img_list.append(img)
     try:
         img_list, cls_res, predict_time = text_classifier(img_list)
-    except Exception as e:
-        print(e)
+    except:
+        logger.info(traceback.format_exc())
         logger.info(
             "ERROR!!!! \n"
             "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@@ -143,10 +143,10 @@ def main(args):
             "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
         exit()
     for ino in range(len(img_list)):
-        print("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
             ino]))
-    print("Total predict time for {} images, cost: {:.3f}".format(
+    logger.info("Total predict time for {} images, cost: {:.3f}".format(
         len(img_list), predict_time))
 
-    if __name__ == "__main__":
+if __name__ == "__main__":
     main(utility.parse_args())

tools/infer/predict_det.py

@@ -178,11 +178,12 @@ if __name__ == "__main__":
         if count > 0:
             total_time += elapse
         count += 1
-        print("Predict time of {}: {}".format(image_file, elapse))
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
         src_im = utility.draw_text_det_res(dt_boxes, image_file)
         img_name_pure = os.path.split(image_file)[-1]
         img_path = os.path.join(draw_img_save,
                                 "det_res_{}".format(img_name_pure))
         cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
     if count > 1:
-        print("Avg Time:", total_time / (count - 1))
+        logger.info("Avg Time:", total_time / (count - 1))

tools/infer/predict_rec.py

@@ -22,7 +22,7 @@ import cv2
 import numpy as np
 import math
 import time
-
+import traceback
 import paddle.fluid as fluid
 
 import tools.infer.utility as utility
@@ -135,8 +135,8 @@ def main(args):
         img_list.append(img)
     try:
         rec_res, predict_time = text_recognizer(img_list)
-    except Exception as e:
-        print(e)
+    except:
+        logger.info(traceback.format_exc())
         logger.info(
             "ERROR!!!! \n"
             "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@@ -145,9 +145,9 @@ def main(args):
             "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
         exit()
     for ino in range(len(img_list)):
-        print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
             ino]))
-    print("Total predict time for {} images, cost: {:.3f}".format(
+    logger.info("Total predict time for {} images, cost: {:.3f}".format(
         len(img_list), predict_time))
 
 

tools/infer/predict_system.py

@@ -23,17 +23,21 @@ import numpy as np
 import time
 from PIL import Image
 import tools.infer.utility as utility
-from tools.infer.utility import draw_ocr
 import tools.infer.predict_rec as predict_rec
 import tools.infer.predict_det as predict_det
+import tools.infer.predict_cls as predict_cls
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
+from tools.infer.utility import draw_ocr_box_txt
 
 
 class TextSystem(object):
     def __init__(self, args):
         self.text_detector = predict_det.TextDetector(args)
         self.text_recognizer = predict_rec.TextRecognizer(args)
+        self.use_angle_cls = args.use_angle_cls
+        if self.use_angle_cls:
+            self.text_classifier = predict_cls.TextClassifier(args)
 
     def get_rotate_crop_image(self, img, points):
         '''
@@ -72,12 +76,13 @@ class TextSystem(object):
         bbox_num = len(img_crop_list)
         for bno in range(bbox_num):
             cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
-            print(bno, rec_res[bno])
+            logger.info(bno, rec_res[bno])
 
     def __call__(self, img):
         ori_im = img.copy()
         dt_boxes, elapse = self.text_detector(img)
-        print("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
+        logger.info("dt_boxes num : {}, elapse : {}".format(
+            len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
         img_crop_list = []
@@ -88,8 +93,15 @@ class TextSystem(object):
             tmp_box = copy.deepcopy(dt_boxes[bno])
             img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
+        if self.use_angle_cls:
+            img_crop_list, angle_list, elapse = self.text_classifier(
+                img_crop_list)
+            logger.info("cls num  : {}, elapse : {}".format(
+                len(img_crop_list), elapse))
+
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        print("rec_res num  : {}, elapse : {}".format(len(rec_res), elapse))
+        logger.info("rec_res num  : {}, elapse : {}".format(
+            len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         return dt_boxes, rec_res
 
@@ -119,7 +131,8 @@ def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_sys = TextSystem(args)
     is_visualize = True
-    tackle_img_num = 0
+    font_path = args.vis_font_path
+    drop_score = args.drop_score
     for image_file in image_file_list:
         img, flag = check_and_read_gif(image_file)
         if not flag:
@@ -128,20 +141,16 @@ def main(args):
             logger.info("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
-        tackle_img_num += 1
-        if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
-            text_sys = TextSystem(args)
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
-        print("Predict time of %s: %.3fs" % (image_file, elapse))
+        logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
 
-        drop_score = 0.5
         dt_num = len(dt_boxes)
         for dno in range(dt_num):
             text, score = rec_res[dno]
             if score >= drop_score:
                 text_str = "%s, %.3f" % (text, score)
-                print(text_str)
+                logger.info(text_str)
 
         if is_visualize:
             image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@@ -149,15 +158,20 @@ def main(args):
             txts = [rec_res[i][0] for i in range(len(rec_res))]
             scores = [rec_res[i][1] for i in range(len(rec_res))]
 
-            draw_img = draw_ocr(
-                image, boxes, txts, scores, drop_score=drop_score)
+            draw_img = draw_ocr_box_txt(
+                image,
+                boxes,
+                txts,
+                scores,
+                drop_score=drop_score,
+                font_path=font_path)
             draw_img_save = "./inference_results/"
             if not os.path.exists(draw_img_save):
                 os.makedirs(draw_img_save)
             cv2.imwrite(
                 os.path.join(draw_img_save, os.path.basename(image_file)),
                 draw_img[:, :, ::-1])
-            print("The visualized image saved in {}".format(
+            logger.info("The visualized image saved in {}".format(
                 os.path.join(draw_img_save, os.path.basename(image_file))))