Merge branch 'dygraph' into fix2013

StyleText/engine/predictors.py

@@ -38,7 +38,15 @@ class StyleTextRecPredictor(object):
         self.std = config["Predictor"]["std"]
         self.expand_result = config["Predictor"]["expand_result"]
 
-    def predict(self, style_input, text_input):
+    def reshape_to_same_height(self, img_list):
+        h = img_list[0].shape[0]
+        for idx in range(1, len(img_list)):
+            new_w = round(1.0 * img_list[idx].shape[1] /
+                          img_list[idx].shape[0] * h)
+            img_list[idx] = cv2.resize(img_list[idx], (new_w, h))
+        return img_list
+
+    def predict_single_image(self, style_input, text_input):
         style_input = self.rep_style_input(style_input, text_input)
         tensor_style_input = self.preprocess(style_input)
         tensor_text_input = self.preprocess(text_input)
@@ -64,6 +72,21 @@ class StyleTextRecPredictor(object):
             "fake_bg": fake_bg,
         }
 
+    def predict(self, style_input, text_input_list):
+        if not isinstance(text_input_list, (tuple, list)):
+            return self.predict_single_image(style_input, text_input_list)
+
+        synth_result_list = []
+        for text_input in text_input_list:
+            synth_result = self.predict_single_image(style_input, text_input)
+            synth_result_list.append(synth_result)
+
+        for key in synth_result:
+            res = [r[key] for r in synth_result_list]
+            res = self.reshape_to_same_height(res)
+            synth_result[key] = np.concatenate(res, axis=1)
+        return synth_result
+
     def preprocess(self, img):
         img = (img.astype('float32') * self.scale - self.mean) / self.std
         img_height, img_width, channel = img.shape

StyleText/engine/synthesisers.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+import numpy as np
+import cv2
 
 from utils.config import ArgsParser, load_config, override_config
 from utils.logging import get_logger
@@ -36,8 +38,9 @@ class ImageSynthesiser(object):
         self.predictor = getattr(predictors, predictor_method)(self.config)
 
     def synth_image(self, corpus, style_input, language="en"):
-        corpus, text_input = self.text_drawer.draw_text(corpus, language)
-        synth_result = self.predictor.predict(style_input, text_input)
+        corpus_list, text_input_list = self.text_drawer.draw_text(
+            corpus, language, style_input_width=style_input.shape[1])
+        synth_result = self.predictor.predict(style_input, text_input_list)
         return synth_result
 
 
@@ -59,12 +62,15 @@ class DatasetSynthesiser(ImageSynthesiser):
         for i in range(self.output_num):
             style_data = self.style_sampler.sample()
             style_input = style_data["image"]
-            corpus_language, text_input_label = self.corpus_generator.generate(
-            )
-            text_input_label, text_input = self.text_drawer.draw_text(
-                text_input_label, corpus_language)
+            corpus_language, text_input_label = self.corpus_generator.generate()
+            text_input_label_list, text_input_list = self.text_drawer.draw_text(
+                text_input_label,
+                corpus_language,
+                style_input_width=style_input.shape[1])
 
-            synth_result = self.predictor.predict(style_input, text_input)
+            text_input_label = "".join(text_input_label_list)
+
+            synth_result = self.predictor.predict(style_input, text_input_list)
             fake_fusion = synth_result["fake_fusion"]
             self.writer.save_image(fake_fusion, text_input_label)
         self.writer.save_label()

StyleText/engine/text_drawers.py

 from PIL import Image, ImageDraw, ImageFont
 import numpy as np
+import cv2
 from utils.logging import get_logger
 
 
@@ -28,7 +29,11 @@ class StdTextDrawer(object):
         else:
             return int((self.height - 4)**2 / font_height)
 
-    def draw_text(self, corpus, language="en", crop=True):
+    def draw_text(self,
+                  corpus,
+                  language="en",
+                  crop=True,
+                  style_input_width=None):
         if language not in self.support_languages:
             self.logger.warning(
                 "language {} not supported, use en instead.".format(language))
@@ -37,21 +42,43 @@ class StdTextDrawer(object):
             width = min(self.max_width, len(corpus) * self.height) + 4
         else:
             width = len(corpus) * self.height + 4
-        bg = Image.new("RGB", (width, self.height), color=(127, 127, 127))
-        draw = ImageDraw.Draw(bg)
-
-        char_x = 2
-        font = self.font_dict[language]
-        for i, char_i in enumerate(corpus):
-            char_size = font.getsize(char_i)[0]
-            draw.text((char_x, 2), char_i, fill=(0, 0, 0), font=font)
-            char_x += char_size
-            if char_x >= width:
-                corpus = corpus[0:i + 1]
-                self.logger.warning("corpus length exceed limit: {}".format(
-                    corpus))
+
+        if style_input_width is not None:
+            width = min(width, style_input_width)
+
+        corpus_list = []
+        text_input_list = []
+
+        while len(corpus) != 0:
+            bg = Image.new("RGB", (width, self.height), color=(127, 127, 127))
+            draw = ImageDraw.Draw(bg)
+            char_x = 2
+            font = self.font_dict[language]
+            i = 0
+            while i < len(corpus):
+                char_i = corpus[i]
+                char_size = font.getsize(char_i)[0]
+                # split when char_x exceeds char size and index is not 0 (at least 1 char should be wroten on the image)
+                if char_x + char_size >= width and i != 0:
+                    text_input = np.array(bg).astype(np.uint8)
+                    text_input = text_input[:, 0:char_x, :]
+
+                    corpus_list.append(corpus[0:i])
+                    text_input_list.append(text_input)
+                    corpus = corpus[i:]
+                    break
+                draw.text((char_x, 2), char_i, fill=(0, 0, 0), font=font)
+                char_x += char_size
+
+                i += 1
+            # the whole text is shorter than style input
+            if i == len(corpus):
+                text_input = np.array(bg).astype(np.uint8)
+                text_input = text_input[:, 0:char_x, :]
+
+                corpus_list.append(corpus[0:i])
+                text_input_list.append(text_input)
+                corpus = corpus[i:]
                 break
 
-        text_input = np.array(bg).astype(np.uint8)
-        text_input = text_input[:, 0:char_x, :]
-        return corpus, text_input
+        return corpus_list, text_input_list

deploy/hubserving/ocr_det/params.py

@@ -20,7 +20,8 @@ def read_params():
     #DB parmas
     cfg.det_db_thresh = 0.3
     cfg.det_db_box_thresh = 0.5
-    cfg.det_db_unclip_ratio = 2.0
+    cfg.det_db_unclip_ratio = 1.6
+    cfg.use_dilation = False
 
     # #EAST parmas
     # cfg.det_east_score_thresh = 0.8

deploy/hubserving/ocr_system/params.py

@@ -20,7 +20,8 @@ def read_params():
     #DB parmas
     cfg.det_db_thresh = 0.3
     cfg.det_db_box_thresh = 0.5
-    cfg.det_db_unclip_ratio = 2.0
+    cfg.det_db_unclip_ratio = 1.6
+    cfg.use_dilation = False
 
     #EAST parmas
     cfg.det_east_score_thresh = 0.8

paddleocr.py

@@ -146,7 +146,8 @@ def parse_args(mMain=True, add_help=True):
         # DB parmas
         parser.add_argument("--det_db_thresh", type=float, default=0.3)
         parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
-        parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
+        parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
+        parser.add_argument("--use_dilation", type=bool, default=False)
 
         # EAST parmas
         parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
@@ -193,7 +194,8 @@ def parse_args(mMain=True, add_help=True):
             det_limit_type='max',
             det_db_thresh=0.3,
             det_db_box_thresh=0.5,
-            det_db_unclip_ratio=2.0,
+            det_db_unclip_ratio=1.6,
+            use_dilation=False,
             det_east_score_thresh=0.8,
             det_east_cover_thresh=0.1,
             det_east_nms_thresh=0.2,

ppocr/losses/det_basic_loss.py

@@ -200,6 +200,6 @@ def ohem_batch(scores, gt_texts, training_masks, ohem_ratio):
                 i, :, :], ohem_ratio))
 
     selected_masks = np.concatenate(selected_masks, 0)
-    selected_masks = paddle.to_variable(selected_masks)
+    selected_masks = paddle.to_tensor(selected_masks)
 
     return selected_masks

ppocr/modeling/heads/rec_att_head.py

@@ -57,6 +57,9 @@ class AttentionHead(nn.Layer):
         else:
             targets = paddle.zeros(shape=[batch_size], dtype="int32")
             probs = None
+            char_onehots = None
+            outputs = None
+            alpha = None
 
             for i in range(num_steps):
                 char_onehots = self._char_to_onehot(
@@ -146,9 +149,6 @@ class AttentionLSTM(nn.Layer):
         else:
             targets = paddle.zeros(shape=[batch_size], dtype="int32")
             probs = None
-            char_onehots = None
-            outputs = None
-            alpha = None
 
             for i in range(num_steps):
                 char_onehots = self._char_to_onehot(

tools/infer/predict_rec.py

@@ -248,9 +248,11 @@ class TextRecognizer(object):
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_recognizer = TextRecognizer(args)
+    total_run_time = 0.0
+    total_images_num = 0
     valid_image_file_list = []
     img_list = []
-    for image_file in image_file_list:
+    for idx, image_file in enumerate(image_file_list):
         img, flag = check_and_read_gif(image_file)
         if not flag:
             img = cv2.imread(image_file)
@@ -259,22 +261,29 @@ def main(args):
             continue
         valid_image_file_list.append(image_file)
         img_list.append(img)
-    try:
-        rec_res, predict_time = text_recognizer(img_list)
-    except:
-        logger.info(traceback.format_exc())
-        logger.info(
-            "ERROR!!!! \n"
-            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
-            "If your model has tps module:  "
-            "TPS does not support variable shape.\n"
-            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
-        exit()
-    for ino in range(len(img_list)):
-        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
-                                               rec_res[ino]))
+        if len(img_list) >= args.rec_batch_num or idx == len(
+                image_file_list) - 1:
+            try:
+                rec_res, predict_time = text_recognizer(img_list)
+                total_run_time += predict_time
+            except:
+                logger.info(traceback.format_exc())
+                logger.info(
+                    "ERROR!!!! \n"
+                    "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
+                    "If your model has tps module:  "
+                    "TPS does not support variable shape.\n"
+                    "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' "
+                )
+                exit()
+            for ino in range(len(img_list)):
+                logger.info("Predicts of {}:{}".format(valid_image_file_list[
+                    ino], rec_res[ino]))
+            total_images_num += len(valid_image_file_list)
+            valid_image_file_list = []
+            img_list = []
     logger.info("Total predict time for {} images, cost: {:.3f}".format(
-        len(img_list), predict_time))
+        total_images_num, total_run_time))
 
 
 if __name__ == "__main__":