update pgnet

configs/e2e/e2e_r50_vd_pg.yml

@@ -13,6 +13,7 @@ Global:
   save_inference_dir:
   use_visualdl: False
   infer_img:
+  infer_visual_type: EN # two mode: EN is for english datasets, CN is for chinese datasets
   valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
   save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
   character_dict_path: ppocr/utils/ic15_dict.txt
@@ -32,6 +33,7 @@ Architecture:
     name: PGFPN
   Head:
     name: PGHead
+    tcc_channels: 37 # the length of character dict 
 
 Loss:
   name: PGLoss
@@ -45,16 +47,18 @@ Optimizer:
   beta1: 0.9
   beta2: 0.999
   lr:
+    name: Cosine
     learning_rate: 0.001
+    warmup_epoch: 50
   regularizer:
     name: 'L2'
-    factor: 0
-
+    factor: 0.0001
 
 PostProcess:
   name: PGPostProcess
   score_thresh: 0.5
   mode: fast   # fast or slow two ways
+  tcc_type: v3 # same as PGProcessTrain: tcc_type
 
 Metric:
   name: E2EMetric
@@ -76,9 +80,12 @@ Train:
       - E2ELabelEncodeTrain:
       - PGProcessTrain:
           batch_size: 14  # same as loader: batch_size_per_card
+          use_resize: True
+          use_random_crop: False
           min_crop_size: 24
           min_text_size: 4
           max_text_size: 512
+          tcc_type: v3 # two ways, v2 is original code, v3 is updated code
       - KeepKeys:
           keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
   loader:

ppocr/data/imaug/pg_process.py

@@ -15,6 +15,8 @@
 import math
 import cv2
 import numpy as np
+from skimage.morphology._skeletonize import thin
+from ppocr.utils.e2e_utils.extract_textpoint_fast import sort_and_expand_with_direction_v2
 
 __all__ = ['PGProcessTrain']
 
@@ -26,17 +28,24 @@ class PGProcessTrain(object):
                  max_text_nums,
                  tcl_len,
                  batch_size=14,
+                 use_resize=True,
+                 use_random_crop=False,
                  min_crop_size=24,
                  min_text_size=4,
                  max_text_size=512,
+                 tcc_type='v3',
                  **kwargs):
         self.tcl_len = tcl_len
         self.max_text_length = max_text_length
         self.max_text_nums = max_text_nums
         self.batch_size = batch_size
+        if use_random_crop is True:
             self.min_crop_size = min_crop_size
+            self.use_random_crop = use_random_crop
         self.min_text_size = min_text_size
         self.max_text_size = max_text_size
+        self.use_resize = use_resize
+        self.tcc_type = tcc_type
         self.Lexicon_Table = self.get_dict(character_dict_path)
         self.pad_num = len(self.Lexicon_Table)
         self.img_id = 0
@@ -282,6 +291,95 @@ class PGProcessTrain(object):
         pos_m[:keep] = 1.0
         return pos_l, pos_m
 
+    def fit_and_gather_tcl_points_v3(self,
+                                     min_area_quad,
+                                     poly,
+                                     max_h,
+                                     max_w,
+                                     fixed_point_num=64,
+                                     img_id=0,
+                                     reference_height=3):
+        """
+        Find the center point of poly as key_points, then fit and gather.
+        """
+        det_mask = np.zeros((int(max_h / self.ds_ratio),
+                             int(max_w / self.ds_ratio))).astype(np.float32)
+
+        # score_big_map
+        cv2.fillPoly(det_mask,
+                     np.round(poly / self.ds_ratio).astype(np.int32), 1.0)
+        det_mask = cv2.resize(
+            det_mask, dsize=None, fx=self.ds_ratio, fy=self.ds_ratio)
+        det_mask = np.array(det_mask > 1e-3, dtype='float32')
+
+        f_direction = self.f_direction
+        skeleton_map = thin(det_mask.astype(np.uint8))
+        instance_count, instance_label_map = cv2.connectedComponents(
+            skeleton_map.astype(np.uint8), connectivity=8)
+
+        ys, xs = np.where(instance_label_map == 1)
+        pos_list = list(zip(ys, xs))
+        if len(pos_list) < 3:
+            return None
+        pos_list_sorted = sort_and_expand_with_direction_v2(
+            pos_list, f_direction, det_mask)
+
+        pos_list_sorted = np.array(pos_list_sorted)
+        length = len(pos_list_sorted) - 1
+        insert_num = 0
+        for index in range(length):
+            stride_y = np.abs(pos_list_sorted[index + insert_num][0] -
+                              pos_list_sorted[index + 1 + insert_num][0])
+            stride_x = np.abs(pos_list_sorted[index + insert_num][1] -
+                              pos_list_sorted[index + 1 + insert_num][1])
+            max_points = int(max(stride_x, stride_y))
+
+            stride = (pos_list_sorted[index + insert_num] -
+                      pos_list_sorted[index + 1 + insert_num]) / (max_points)
+            insert_num_temp = max_points - 1
+
+            for i in range(int(insert_num_temp)):
+                insert_value = pos_list_sorted[index + insert_num] - (i + 1
+                                                                      ) * stride
+                insert_index = index + i + 1 + insert_num
+                pos_list_sorted = np.insert(
+                    pos_list_sorted, insert_index, insert_value, axis=0)
+            insert_num += insert_num_temp
+
+        pos_info = np.array(pos_list_sorted).reshape(-1, 2).astype(
+            np.float32)  # xy-> yx
+
+        point_num = len(pos_info)
+        if point_num > fixed_point_num:
+            keep_ids = [
+                int((point_num * 1.0 / fixed_point_num) * x)
+                for x in range(fixed_point_num)
+            ]
+            pos_info = pos_info[keep_ids, :]
+
+        keep = int(min(len(pos_info), fixed_point_num))
+        reference_width = (np.abs(poly[0, 0, 0] - poly[-1, 1, 0]) +
+                           np.abs(poly[0, 3, 0] - poly[-1, 2, 0])) // 2
+        if np.random.rand() < 1:
+            dh = (np.random.rand(keep) - 0.5) * reference_height
+            offset = np.random.rand() - 0.5
+            dw = np.array([[0, offset * reference_width * 0.2]])
+            random_float_h = np.array([1, 0]).reshape([1, 2]) * dh.reshape(
+                [keep, 1])
+            random_float_w = dw.repeat(keep, axis=0)
+            pos_info += random_float_h
+            pos_info += random_float_w
+            pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1)
+            pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1)
+
+        # padding to fixed length
+        pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32)
+        pos_l[:, 0] = np.ones((self.tcl_len, )) * img_id
+        pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32)
+        pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32)
+        pos_m[:keep] = 1.0
+        return pos_l, pos_m
+
     def generate_direction_map(self, poly_quads, n_char, direction_map):
         """
         """
@@ -334,6 +432,7 @@ class PGProcessTrain(object):
         """
         Generate polygon.
         """
+        self.ds_ratio = ds_ratio
         score_map_big = np.zeros(
             (
                 h,
@@ -384,7 +483,6 @@ class PGProcessTrain(object):
                 text_label = text_strs[poly_idx]
                 text_label = self.prepare_text_label(text_label,
                                                      self.Lexicon_Table)
-
                 text_label_index_list = [[self.Lexicon_Table.index(c_)]
                                          for c_ in text_label
                                          if c_ in self.Lexicon_Table]
@@ -432,6 +530,22 @@ class PGProcessTrain(object):
                 # pos info
                 average_shrink_height = self.calculate_average_height(
                     stcl_quads)
+
+                if self.tcc_type == 'v3':
+                    self.f_direction = direction_map[:, :, :-1].copy()
+                    pos_res = self.fit_and_gather_tcl_points_v3(
+                        min_area_quad,
+                        stcl_quads,
+                        max_h=h,
+                        max_w=w,
+                        fixed_point_num=64,
+                        img_id=self.img_id,
+                        reference_height=average_shrink_height)
+                    if pos_res is None:
+                        continue
+                    pos_l, pos_m = pos_res[0], pos_res[1]
+
+                elif self.tcc_type == 'v2':
                     pos_l, pos_m = self.fit_and_gather_tcl_points_v2(
                         min_area_quad,
                         poly,
@@ -770,6 +884,20 @@ class PGProcessTrain(object):
         text_polys[:, :, 0] *= asp_wx
         text_polys[:, :, 1] *= asp_hy
 
+        if self.use_resize is True:
+            ori_h, ori_w, _ = im.shape
+            if max(ori_h, ori_w) < 200:
+                ratio = 200 / max(ori_h, ori_w)
+                im = cv2.resize(im, (int(ori_w * ratio), int(ori_h * ratio)))
+                text_polys[:, :, 0] *= ratio
+                text_polys[:, :, 1] *= ratio
+
+            if max(ori_h, ori_w) > 512:
+                ratio = 512 / max(ori_h, ori_w)
+                im = cv2.resize(im, (int(ori_w * ratio), int(ori_h * ratio)))
+                text_polys[:, :, 0] *= ratio
+                text_polys[:, :, 1] *= ratio
+        elif self.use_random_crop is True:
             h, w, _ = im.shape
             if max(h, w) > 2048:
                 rd_scale = 2048.0 / max(h, w)
@@ -790,7 +918,7 @@ class PGProcessTrain(object):
 
         if text_polys.shape[0] == 0:
             return None
-        # # continue for all ignore case
+        # continue for all ignore case
         if np.sum((text_tags * 1.0)) >= text_tags.size:
             return None
         new_h, new_w, _ = im.shape

ppocr/losses/e2e_pg_loss.py

@@ -89,12 +89,13 @@ class PGLoss(nn.Layer):
         tcl_pos = paddle.reshape(tcl_pos, [-1, 3])
         tcl_pos = paddle.cast(tcl_pos, dtype=int)
         f_tcl_char = paddle.gather_nd(f_char, tcl_pos)
-        f_tcl_char = paddle.reshape(f_tcl_char,
-                                    [-1, 64, 37])  # len(Lexicon_Table)+1
-        f_tcl_char_fg, f_tcl_char_bg = paddle.split(f_tcl_char, [36, 1], axis=2)
+        f_tcl_char = paddle.reshape(
+            f_tcl_char, [-1, 64, self.pad_num + 1])  # len(Lexicon_Table)+1
+        f_tcl_char_fg, f_tcl_char_bg = paddle.split(
+            f_tcl_char, [self.pad_num, 1], axis=2)
         f_tcl_char_bg = f_tcl_char_bg * tcl_mask + (1.0 - tcl_mask) * 20.0
         b, c, l = tcl_mask.shape
-        tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, 36 * l])
+        tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, self.pad_num * l])
         tcl_mask_fg.stop_gradient = True
         f_tcl_char_fg = f_tcl_char_fg * tcl_mask_fg + (1.0 - tcl_mask_fg) * (
             -20.0)

ppocr/modeling/heads/e2e_pg_head.py

@@ -66,7 +66,7 @@ class PGHead(nn.Layer):
     """
     """
 
-    def __init__(self, in_channels, **kwargs):
+    def __init__(self, in_channels, tcc_channels=37, **kwargs):
         super(PGHead, self).__init__()
         self.conv_f_score1 = ConvBNLayer(
             in_channels=in_channels,
@@ -178,7 +178,7 @@ class PGHead(nn.Layer):
             name="conv_f_char{}".format(5))
         self.conv3 = nn.Conv2D(
             in_channels=256,
-            out_channels=37,
+            out_channels=tcc_channels,
             kernel_size=3,
             stride=1,
             padding=1,

ppocr/postprocess/pg_postprocess.py

@@ -31,11 +31,12 @@ class PGPostProcess(object):
     """
 
     def __init__(self, character_dict_path, valid_set, score_thresh, mode,
-                 **kwargs):
+                 tcc_type, **kwargs):
         self.character_dict_path = character_dict_path
         self.valid_set = valid_set
         self.score_thresh = score_thresh
         self.mode = mode
+        self.tcc_type = tcc_type
 
         # c++ la-nms is faster, but only support python 3.5
         self.is_python35 = False
@@ -43,8 +44,13 @@ class PGPostProcess(object):
             self.is_python35 = True
 
     def __call__(self, outs_dict, shape_list):
-        post = PGNet_PostProcess(self.character_dict_path, self.valid_set,
-                                 self.score_thresh, outs_dict, shape_list)
+        post = PGNet_PostProcess(
+            self.character_dict_path,
+            self.valid_set,
+            self.score_thresh,
+            outs_dict,
+            shape_list,
+            tcc_type=self.tcc_type)
         if self.mode == 'fast':
             data = post.pg_postprocess_fast()
         else:

ppocr/utils/e2e_utils/extract_textpoint_fast.py

@@ -88,8 +88,33 @@ def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
     return dst_str, keep_idx_list
 
 
-def instance_ctc_greedy_decoder(gather_info, logits_map, pts_num=4):
+def instance_ctc_greedy_decoder(gather_info,
+                                logits_map,
+                                pts_num=4,
+                                tcc_type='v3'):
     _, _, C = logits_map.shape
+    if tcc_type == 'v3':
+        insert_num = 0
+        gather_info = np.array(gather_info)
+        length = len(gather_info) - 1
+        for index in range(length):
+            stride_y = np.abs(gather_info[index + insert_num][0] - gather_info[
+                index + 1 + insert_num][0])
+            stride_x = np.abs(gather_info[index + insert_num][1] - gather_info[
+                index + 1 + insert_num][1])
+            max_points = int(max(stride_x, stride_y))
+            stride = (gather_info[index + insert_num] -
+                      gather_info[index + 1 + insert_num]) / (max_points)
+            insert_num_temp = max_points - 1
+
+            for i in range(int(insert_num_temp)):
+                insert_value = gather_info[index + insert_num] - (i + 1
+                                                                  ) * stride
+                insert_index = index + i + 1 + insert_num
+                gather_info = np.insert(
+                    gather_info, insert_index, insert_value, axis=0)
+            insert_num += insert_num_temp
+        gather_info = gather_info.tolist()
     ys, xs = zip(*gather_info)
     logits_seq = logits_map[list(ys), list(xs)]
     probs_seq = logits_seq
@@ -104,7 +129,8 @@ def instance_ctc_greedy_decoder(gather_info, logits_map, pts_num=4):
 def ctc_decoder_for_image(gather_info_list,
                           logits_map,
                           Lexicon_Table,
-                          pts_num=6):
+                          pts_num=6,
+                          tcc_type='v3'):
     """
     CTC decoder using multiple processes.
     """
@@ -114,7 +140,7 @@ def ctc_decoder_for_image(gather_info_list,
         if len(gather_info) < pts_num:
             continue
         dst_str, xys_list = instance_ctc_greedy_decoder(
-            gather_info, logits_map, pts_num=pts_num)
+            gather_info, logits_map, pts_num=pts_num, tcc_type='v3')
         dst_str_readable = ''.join([Lexicon_Table[idx] for idx in dst_str])
         if len(dst_str_readable) < 2:
             continue
@@ -356,7 +382,8 @@ def generate_pivot_list_fast(p_score,
                              p_char_maps,
                              f_direction,
                              Lexicon_Table,
-                             score_thresh=0.5):
+                             score_thresh=0.5,
+                             tcc_type='v3'):
     """
     return center point and end point of TCL instance; filter with the char maps;
     """
@@ -384,7 +411,10 @@ def generate_pivot_list_fast(p_score,
 
     p_char_maps = p_char_maps.transpose([1, 2, 0])
     decoded_str, keep_yxs_list = ctc_decoder_for_image(
-        all_pos_yxs, logits_map=p_char_maps, Lexicon_Table=Lexicon_Table)
+        all_pos_yxs,
+        logits_map=p_char_maps,
+        Lexicon_Table=Lexicon_Table,
+        tcc_type='v3')
     return keep_yxs_list, decoded_str
 
 

ppocr/utils/e2e_utils/pgnet_pp_utils.py

@@ -28,13 +28,19 @@ from extract_textpoint_fast import generate_pivot_list_fast, restore_poly
 
 class PGNet_PostProcess(object):
     # two different post-process
-    def __init__(self, character_dict_path, valid_set, score_thresh, outs_dict,
-                 shape_list):
+    def __init__(self,
+                 character_dict_path,
+                 valid_set,
+                 score_thresh,
+                 outs_dict,
+                 shape_list,
+                 tcc_type='v3'):
         self.Lexicon_Table = get_dict(character_dict_path)
         self.valid_set = valid_set
         self.score_thresh = score_thresh
         self.outs_dict = outs_dict
         self.shape_list = shape_list
+        self.tcc_type = tcc_type
 
     def pg_postprocess_fast(self):
         p_score = self.outs_dict['f_score']
@@ -58,7 +64,8 @@ class PGNet_PostProcess(object):
             p_char,
             p_direction,
             self.Lexicon_Table,
-            score_thresh=self.score_thresh)
+            score_thresh=self.score_thresh,
+            tcc_type=self.tcc_type)
         poly_list, keep_str_list = restore_poly(instance_yxs_list, seq_strs,
                                                 p_border, ratio_w, ratio_h,
                                                 src_w, src_h, self.valid_set)

tools/infer_e2e.py

@@ -37,6 +37,46 @@ from ppocr.postprocess import build_post_process
 from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
+from PIL import Image, ImageDraw, ImageFont
+import math
+
+
+def draw_e2e_res_for_chinese(image,
+                             boxes,
+                             txts,
+                             config,
+                             img_name,
+                             font_path="./doc/simfang.ttf"):
+    h, w = image.height, image.width
+    img_left = image.copy()
+    img_right = Image.new('RGB', (w, h), (255, 255, 255))
+
+    import random
+
+    random.seed(0)
+    draw_left = ImageDraw.Draw(img_left)
+    draw_right = ImageDraw.Draw(img_right)
+    for idx, (box, txt) in enumerate(zip(boxes, txts)):
+        box = np.array(box)
+        box = [tuple(x) for x in box]
+        color = (random.randint(0, 255), random.randint(0, 255),
+                 random.randint(0, 255))
+        draw_left.polygon(box, fill=color)
+        draw_right.polygon(box, outline=color)
+        font = ImageFont.truetype(font_path, 15, encoding="utf-8")
+        draw_right.text([box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
+    img_left = Image.blend(image, img_left, 0.5)
+    img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))
+    img_show.paste(img_left, (0, 0, w, h))
+    img_show.paste(img_right, (w, 0, w * 2, h))
+
+    save_e2e_path = os.path.dirname(config['Global'][
+        'save_res_path']) + "/e2e_results/"
+    if not os.path.exists(save_e2e_path):
+        os.makedirs(save_e2e_path)
+    save_path = os.path.join(save_e2e_path, os.path.basename(img_name))
+    cv2.imwrite(save_path, np.array(img_show)[:, :, ::-1])
+    logger.info("The e2e Image saved in {}".format(save_path))
 
 
 def draw_e2e_res(dt_boxes, strs, config, img, img_name):
@@ -113,7 +153,19 @@ def main():
             otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
             fout.write(otstr.encode())
             src_img = cv2.imread(file)
+            if global_config['infer_visual_type'] == 'EN':
                 draw_e2e_res(points, strs, config, src_img, file)
+            elif global_config['infer_visual_type'] == 'CN':
+                src_img = Image.fromarray(
+                    cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB))
+                draw_e2e_res_for_chinese(
+                    src_img,
+                    points,
+                    strs,
+                    config,
+                    file,
+                    font_path="./doc/fonts/simfang.ttf")
+
     logger.info("success!")