Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into py_inference_doc

ppocr/postprocess/db_postprocess.py

@@ -138,9 +138,9 @@ class DBPostProcess(object):
 
         boxes_batch = []
         for batch_index in range(pred.shape[0]):
-            height, width = shape_list[batch_index]
+            src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
             boxes, scores = self.boxes_from_bitmap(
-                pred[batch_index], segmentation[batch_index], width, height)
+                pred[batch_index], segmentation[batch_index], src_w, src_h)
 
             boxes_batch.append({'points': boxes})
         return boxes_batch
\ No newline at end of file

ppocr/postprocess/east_postprocess.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from .locality_aware_nms import nms_locality
+import cv2
+
+import os
+import sys
+# __dir__ = os.path.dirname(os.path.abspath(__file__))
+# sys.path.append(__dir__)
+# sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+
+class EASTPostProcess(object):
+    """
+    The post process for EAST.
+    """
+    def __init__(self,
+                 score_thresh=0.8,
+                 cover_thresh=0.1,
+                 nms_thresh=0.2,
+                 **kwargs):
+
+        self.score_thresh = score_thresh
+        self.cover_thresh = cover_thresh
+        self.nms_thresh = nms_thresh
+        
+        # c++ la-nms is faster, but only support python 3.5
+        self.is_python35 = False
+        if sys.version_info.major == 3 and sys.version_info.minor == 5:
+            self.is_python35 = True
+
+    def restore_rectangle_quad(self, origin, geometry):
+        """
+        Restore rectangle from quadrangle.
+        """
+        # quad
+        origin_concat = np.concatenate(
+            (origin, origin, origin, origin), axis=1)  # (n, 8)
+        pred_quads = origin_concat - geometry
+        pred_quads = pred_quads.reshape((-1, 4, 2))  # (n, 4, 2)
+        return pred_quads
+
+    def detect(self,
+               score_map,
+               geo_map,
+               score_thresh=0.8,
+               cover_thresh=0.1,
+               nms_thresh=0.2):
+        """
+        restore text boxes from score map and geo map
+        """
+        score_map = score_map[0]
+        geo_map = np.swapaxes(geo_map, 1, 0)
+        geo_map = np.swapaxes(geo_map, 1, 2)
+        # filter the score map
+        xy_text = np.argwhere(score_map > score_thresh)
+        if len(xy_text) == 0:
+            return []
+        # sort the text boxes via the y axis
+        xy_text = xy_text[np.argsort(xy_text[:, 0])]
+        #restore quad proposals
+        text_box_restored = self.restore_rectangle_quad(
+            xy_text[:, ::-1] * 4, geo_map[xy_text[:, 0], xy_text[:, 1], :])
+        boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
+        boxes[:, :8] = text_box_restored.reshape((-1, 8))
+        boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
+        if self.is_python35:
+            import lanms
+            boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
+        else:
+            boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
+        if boxes.shape[0] == 0:
+            return []
+        # Here we filter some low score boxes by the average score map, 
+        #   this is different from the orginal paper.
+        for i, box in enumerate(boxes):
+            mask = np.zeros_like(score_map, dtype=np.uint8)
+            cv2.fillPoly(mask, box[:8].reshape(
+                (-1, 4, 2)).astype(np.int32) // 4, 1)
+            boxes[i, 8] = cv2.mean(score_map, mask)[0]
+        boxes = boxes[boxes[:, 8] > cover_thresh]
+        return boxes
+
+    def sort_poly(self, p):
+        """
+        Sort polygons.
+        """
+        min_axis = np.argmin(np.sum(p, axis=1))
+        p = p[[min_axis, (min_axis + 1) % 4,\
+            (min_axis + 2) % 4, (min_axis + 3) % 4]]
+        if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]):
+            return p
+        else:
+            return p[[0, 3, 2, 1]]
+
+    def __call__(self, outs_dict, shape_list):
+        score_list = outs_dict['f_score']
+        geo_list = outs_dict['f_geo']
+        img_num = len(shape_list)
+        dt_boxes_list = []
+        for ino in range(img_num):
+            score = score_list[ino].numpy()
+            geo = geo_list[ino].numpy()
+            boxes = self.detect(
+                score_map=score,
+                geo_map=geo,
+                score_thresh=self.score_thresh,
+                cover_thresh=self.cover_thresh,
+                nms_thresh=self.nms_thresh)
+            boxes_norm = []
+            if len(boxes) > 0:
+                h, w = score.shape[1:]
+                src_h, src_w, ratio_h, ratio_w = shape_list[ino]
+                boxes = boxes[:, :8].reshape((-1, 4, 2))
+                boxes[:, :, 0] /= ratio_w
+                boxes[:, :, 1] /= ratio_h
+                for i_box, box in enumerate(boxes):
+                    box = self.sort_poly(box.astype(np.int32))
+                    if np.linalg.norm(box[0] - box[1]) < 5 \
+                        or np.linalg.norm(box[3] - box[0]) < 5:
+                        continue
+                    boxes_norm.append(box)
+            dt_boxes_list.append({'points': np.array(boxes_norm)})
+        return dt_boxes_list
\ No newline at end of file

ppocr/postprocess/locality_aware_nms.py

+"""
+Locality aware nms.
+"""
+
+import numpy as np
+from shapely.geometry import Polygon
+
+
+def intersection(g, p):
+    """
+    Intersection.
+    """
+    g = Polygon(g[:8].reshape((4, 2)))
+    p = Polygon(p[:8].reshape((4, 2)))
+    g = g.buffer(0)
+    p = p.buffer(0)
+    if not g.is_valid or not p.is_valid:
+        return 0
+    inter = Polygon(g).intersection(Polygon(p)).area
+    union = g.area + p.area - inter
+    if union == 0:
+        return 0
+    else:
+        return inter / union
+
+
+def intersection_iog(g, p):
+    """
+    Intersection_iog.
+    """
+    g = Polygon(g[:8].reshape((4, 2)))
+    p = Polygon(p[:8].reshape((4, 2)))
+    if not g.is_valid or not p.is_valid:
+        return 0
+    inter = Polygon(g).intersection(Polygon(p)).area
+    #union = g.area + p.area - inter
+    union = p.area
+    if union == 0:
+        print("p_area is very small")
+        return 0
+    else:
+        return inter / union
+
+
+def weighted_merge(g, p):
+    """
+    Weighted merge.
+    """
+    g[:8] = (g[8] * g[:8] + p[8] * p[:8]) / (g[8] + p[8])
+    g[8] = (g[8] + p[8])
+    return g
+
+
+def standard_nms(S, thres):
+    """
+    Standard nms.
+    """
+    order = np.argsort(S[:, 8])[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
+
+        inds = np.where(ovr <= thres)[0]
+        order = order[inds + 1]
+
+    return S[keep]
+
+
+def standard_nms_inds(S, thres):
+    """
+    Standard nms, retun inds.
+    """
+    order = np.argsort(S[:, 8])[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
+
+        inds = np.where(ovr <= thres)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def nms(S, thres):
+    """
+    nms.
+    """
+    order = np.argsort(S[:, 8])[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
+
+        inds = np.where(ovr <= thres)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def soft_nms(boxes_in, Nt_thres=0.3, threshold=0.8, sigma=0.5, method=2):
+    """
+    soft_nms
+    :para boxes_in, N x 9 (coords + score)
+    :para threshould, eliminate cases min score(0.001)
+    :para Nt_thres, iou_threshi
+    :para sigma, gaussian weght
+    :method, linear or gaussian
+    """
+    boxes = boxes_in.copy()
+    N = boxes.shape[0]
+    if N is None or N < 1:
+        return np.array([])
+    pos, maxpos = 0, 0
+    weight = 0.0
+    inds = np.arange(N)
+    tbox, sbox = boxes[0].copy(), boxes[0].copy()
+    for i in range(N):
+        maxscore = boxes[i, 8]
+        maxpos = i
+        tbox = boxes[i].copy()
+        ti = inds[i]
+        pos = i + 1
+        #get max box
+        while pos < N:
+            if maxscore < boxes[pos, 8]:
+                maxscore = boxes[pos, 8]
+                maxpos = pos
+            pos = pos + 1
+        #add max box as a detection
+        boxes[i, :] = boxes[maxpos, :]
+        inds[i] = inds[maxpos]
+        #swap
+        boxes[maxpos, :] = tbox
+        inds[maxpos] = ti
+        tbox = boxes[i].copy()
+        pos = i + 1
+        #NMS iteration
+        while pos < N:
+            sbox = boxes[pos].copy()
+            ts_iou_val = intersection(tbox, sbox)
+            if ts_iou_val > 0:
+                if method == 1:
+                    if ts_iou_val > Nt_thres:
+                        weight = 1 - ts_iou_val
+                    else:
+                        weight = 1
+                elif method == 2:
+                    weight = np.exp(-1.0 * ts_iou_val**2 / sigma)
+                else:
+                    if ts_iou_val > Nt_thres:
+                        weight = 0
+                    else:
+                        weight = 1
+                boxes[pos, 8] = weight * boxes[pos, 8]
+                #if box score falls below thresold, discard the box by
+                #swaping last box update N
+                if boxes[pos, 8] < threshold:
+                    boxes[pos, :] = boxes[N - 1, :]
+                    inds[pos] = inds[N - 1]
+                    N = N - 1
+                    pos = pos - 1
+            pos = pos + 1
+
+    return boxes[:N]
+
+
+def nms_locality(polys, thres=0.3):
+    """
+    locality aware nms of EAST
+    :param polys: a N*9 numpy array. first 8 coordinates, then prob
+    :return: boxes after nms
+    """
+    S = []
+    p = None
+    for g in polys:
+        if p is not None and intersection(g, p) > thres:
+            p = weighted_merge(g, p)
+        else:
+            if p is not None:
+                S.append(p)
+            p = g
+    if p is not None:
+        S.append(p)
+
+    if len(S) == 0:
+        return np.array([])
+    return standard_nms(np.array(S), thres)
+
+
+if __name__ == '__main__':
+    # 343,350,448,135,474,143,369,359
+    print(
+        Polygon(np.array([[343, 350], [448, 135], [474, 143], [369, 359]]))
+        .area)
\ No newline at end of file

ppocr/postprocess/rec_postprocess.py

@@ -23,14 +23,16 @@ class BaseRecLabelDecode(object):
                  character_dict_path=None,
                  character_type='ch',
                  use_space_char=False):
-        support_character_type = ['ch', 'en', 'en_sensitive']
+        support_character_type = [
+            'ch', 'en', 'en_sensitive', 'french', 'german', 'japan', 'korean'
+        ]
         assert character_type in support_character_type, "Only {} are supported now but get {}".format(
-            support_character_type, self.character_str)
+            support_character_type, character_type)
 
         if character_type == "en":
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
-        elif character_type == "ch":
+        elif character_type in ["ch", "french", "german", "japan", "korean"]:
             self.character_str = ""
             assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch"
             with open(character_dict_path, "rb") as fin:

ppocr/postprocess/sast_postprocess.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+
+import numpy as np
+from .locality_aware_nms import nms_locality
+# import lanms
+import cv2
+import time
+
+
+class SASTPostProcess(object):
+    """
+    The post process for SAST.
+    """
+
+    def __init__(self,
+                 score_thresh=0.5,
+                 nms_thresh=0.2,
+                 sample_pts_num=2,
+                 shrink_ratio_of_width=0.3,
+                 expand_scale=1.0,
+                 tcl_map_thresh=0.5,
+                 **kwargs):
+
+        self.score_thresh = score_thresh
+        self.nms_thresh = nms_thresh
+        self.sample_pts_num = sample_pts_num
+        self.shrink_ratio_of_width = shrink_ratio_of_width
+        self.expand_scale = expand_scale
+        self.tcl_map_thresh = tcl_map_thresh
+        
+        # c++ la-nms is faster, but only support python 3.5
+        self.is_python35 = False
+        if sys.version_info.major == 3 and sys.version_info.minor == 5:
+            self.is_python35 = True
+            
+    def point_pair2poly(self, point_pair_list):
+        """
+        Transfer vertical point_pairs into poly point in clockwise.
+        """
+        # constract poly
+        point_num = len(point_pair_list) * 2
+        point_list = [0] * point_num
+        for idx, point_pair in enumerate(point_pair_list):
+            point_list[idx] = point_pair[0]
+            point_list[point_num - 1 - idx] = point_pair[1]
+        return np.array(point_list).reshape(-1, 2)
+    
+    def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.):
+        """ 
+        Generate shrink_quad_along_width.
+        """
+        ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+        p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
+        p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
+        return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
+    
+    def expand_poly_along_width(self, poly, shrink_ratio_of_width=0.3):
+        """
+        expand poly along width.
+        """
+        point_num = poly.shape[0]
+        left_quad = np.array([poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+        left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
+                    (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
+        left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio, 1.0)
+        right_quad = np.array([poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+                            poly[point_num // 2], poly[point_num // 2 + 1]], dtype=np.float32)
+        right_ratio = 1.0 + \
+                    shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
+                    (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
+        right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0, right_ratio)
+        poly[0] = left_quad_expand[0]
+        poly[-1] = left_quad_expand[-1]
+        poly[point_num // 2 - 1] = right_quad_expand[1]
+        poly[point_num // 2] = right_quad_expand[2]
+        return poly
+
+    def restore_quad(self, tcl_map, tcl_map_thresh, tvo_map):
+        """Restore quad."""
+        xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
+        xy_text = xy_text[:, ::-1] # (n, 2)
+
+        # Sort the text boxes via the y axis
+        xy_text = xy_text[np.argsort(xy_text[:, 1])]
+
+        scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0]
+        scores = scores[:, np.newaxis]
+
+        # Restore
+        point_num = int(tvo_map.shape[-1] / 2)
+        assert point_num == 4
+        tvo_map = tvo_map[xy_text[:, 1], xy_text[:, 0], :]
+        xy_text_tile = np.tile(xy_text, (1, point_num)) # (n, point_num * 2)
+        quads = xy_text_tile - tvo_map
+
+        return scores, quads, xy_text
+
+    def quad_area(self, quad):
+        """
+        compute area of a quad.
+        """
+        edge = [
+            (quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
+            (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
+            (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
+            (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])
+        ]
+        return np.sum(edge) / 2.
+        
+    def nms(self, dets):
+        if self.is_python35:
+            import lanms
+            dets = lanms.merge_quadrangle_n9(dets, self.nms_thresh)
+        else:
+            dets = nms_locality(dets, self.nms_thresh)
+        return dets
+
+    def cluster_by_quads_tco(self, tcl_map, tcl_map_thresh, quads, tco_map):
+        """
+        Cluster pixels in tcl_map based on quads.
+        """
+        instance_count = quads.shape[0] + 1 # contain background
+        instance_label_map = np.zeros(tcl_map.shape[:2], dtype=np.int32)
+        if instance_count == 1:
+            return instance_count, instance_label_map
+
+        # predict text center
+        xy_text = np.argwhere(tcl_map[:, :, 0] > tcl_map_thresh)
+        n = xy_text.shape[0]
+        xy_text = xy_text[:, ::-1] # (n, 2)
+        tco = tco_map[xy_text[:, 1], xy_text[:, 0], :] # (n, 2)
+        pred_tc = xy_text - tco
+        
+        # get gt text center
+        m = quads.shape[0]
+        gt_tc = np.mean(quads, axis=1) # (m, 2)
+
+        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :], (1, m, 1)) # (n, m, 2)
+        gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1)) # (n, m, 2)
+        dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2) # (n, m)
+        xy_text_assign = np.argmin(dist_mat, axis=1) + 1 # (n,)
+
+        instance_label_map[xy_text[:, 1], xy_text[:, 0]] = xy_text_assign
+        return instance_count, instance_label_map
+
+    def estimate_sample_pts_num(self, quad, xy_text):
+        """
+        Estimate sample points number.
+        """
+        eh = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) / 2.0
+        ew = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0
+
+        dense_sample_pts_num = max(2, int(ew))
+        dense_xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, dense_sample_pts_num,
+                                                endpoint=True, dtype=np.float32).astype(np.int32)]
+
+        dense_xy_center_line_diff = dense_xy_center_line[1:] - dense_xy_center_line[:-1]
+        estimate_arc_len = np.sum(np.linalg.norm(dense_xy_center_line_diff, axis=1))
+
+        sample_pts_num = max(2, int(estimate_arc_len / eh))
+        return sample_pts_num
+
+    def detect_sast(self, tcl_map, tvo_map, tbo_map, tco_map, ratio_w, ratio_h, src_w, src_h, 
+                shrink_ratio_of_width=0.3, tcl_map_thresh=0.5, offset_expand=1.0, out_strid=4.0):
+        """
+        first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys
+        """
+        # restore quad
+        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh, tvo_map)
+        dets = np.hstack((quads, scores)).astype(np.float32, copy=False)
+        dets = self.nms(dets)
+        if dets.shape[0] == 0:
+            return []
+        quads = dets[:, :-1].reshape(-1, 4, 2)
+
+        # Compute quad area
+        quad_areas = []
+        for quad in quads:
+            quad_areas.append(-self.quad_area(quad))
+
+        # instance segmentation
+        # instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8)
+        instance_count, instance_label_map = self.cluster_by_quads_tco(tcl_map, tcl_map_thresh, quads, tco_map)
+
+        # restore single poly with tcl instance.
+        poly_list = []
+        for instance_idx in range(1, instance_count):
+            xy_text = np.argwhere(instance_label_map == instance_idx)[:, ::-1]
+            quad = quads[instance_idx - 1]
+            q_area = quad_areas[instance_idx - 1]
+            if q_area < 5:
+                continue
+            
+            #
+            len1 = float(np.linalg.norm(quad[0] -quad[1]))
+            len2 = float(np.linalg.norm(quad[1] -quad[2]))
+            min_len = min(len1, len2)
+            if min_len < 3:
+                continue
+
+            # filter small CC
+            if xy_text.shape[0] <= 0:
+                continue
+
+            # filter low confidence instance
+            xy_text_scores = tcl_map[xy_text[:, 1], xy_text[:, 0], 0] 
+            if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.1:
+            # if np.sum(xy_text_scores) / quad_areas[instance_idx - 1] < 0.05:
+                continue
+
+            # sort xy_text
+            left_center_pt = np.array([[(quad[0, 0] + quad[-1, 0]) / 2.0,
+                                        (quad[0, 1] + quad[-1, 1]) / 2.0]]) # (1, 2)
+            right_center_pt = np.array([[(quad[1, 0] + quad[2, 0]) / 2.0,
+                                        (quad[1, 1] + quad[2, 1]) / 2.0]]) # (1, 2)
+            proj_unit_vec = (right_center_pt - left_center_pt) / \
+                            (np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
+            proj_value = np.sum(xy_text * proj_unit_vec, axis=1)
+            xy_text = xy_text[np.argsort(proj_value)]
+
+            # Sample pts in tcl map
+            if self.sample_pts_num == 0:
+                sample_pts_num = self.estimate_sample_pts_num(quad, xy_text)
+            else:
+                sample_pts_num = self.sample_pts_num
+            xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, sample_pts_num,
+                                                endpoint=True, dtype=np.float32).astype(np.int32)]
+
+            point_pair_list = []
+            for x, y in xy_center_line:
+                # get corresponding offset
+                offset = tbo_map[y, x, :].reshape(2, 2)
+                if offset_expand != 1.0:
+                    offset_length = np.linalg.norm(offset, axis=1, keepdims=True)
+                    expand_length = np.clip(offset_length * (offset_expand - 1), a_min=0.5, a_max=3.0)
+                    offset_detal = offset / offset_length * expand_length
+                    offset = offset + offset_detal                
+                # original point
+                ori_yx = np.array([y, x], dtype=np.float32)
+                point_pair = (ori_yx +  offset)[:, ::-1]* out_strid / np.array([ratio_w, ratio_h]).reshape(-1, 2) 
+                point_pair_list.append(point_pair)
+
+            # ndarry: (x, 2), expand poly along width
+            detected_poly = self.point_pair2poly(point_pair_list)
+            detected_poly = self.expand_poly_along_width(detected_poly, shrink_ratio_of_width)
+            detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
+            detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
+            poly_list.append(detected_poly)
+
+        return poly_list
+
+    def __call__(self, outs_dict, shape_list):                
+        score_list = outs_dict['f_score']
+        border_list = outs_dict['f_border']
+        tvo_list = outs_dict['f_tvo']
+        tco_list = outs_dict['f_tco']
+                    
+        img_num = len(shape_list)
+        poly_lists = []
+        for ino in range(img_num):
+            p_score = score_list[ino].transpose((1,2,0)).numpy()
+            p_border = border_list[ino].transpose((1,2,0)).numpy()
+            p_tvo = tvo_list[ino].transpose((1,2,0)).numpy()
+            p_tco = tco_list[ino].transpose((1,2,0)).numpy()
+            src_h, src_w, ratio_h, ratio_w = shape_list[ino]
+
+            poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h, 
+                                         shrink_ratio_of_width=self.shrink_ratio_of_width, 
+                                         tcl_map_thresh=self.tcl_map_thresh, offset_expand=self.expand_scale)
+            poly_lists.append({'points': np.array(poly_list)})
+
+        return poly_lists
+

ppocr/utils/character.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 numpy as np
-import string
-import re
-from .check import check_config_params
-import sys
-
-
-class CharacterOps(object):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, config):
-        self.character_type = config['character_type']
-        self.loss_type = config['loss_type']
-        self.max_text_len = config['max_text_length']
-        if self.character_type == "en":
-            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
-            dict_character = list(self.character_str)
-        elif self.character_type == "ch":
-            character_dict_path = config['character_dict_path']
-            add_space = False
-            if 'use_space_char' in config:
-                add_space = config['use_space_char']
-            self.character_str = ""
-            with open(character_dict_path, "rb") as fin:
-                lines = fin.readlines()
-                for line in lines:
-                    line = line.decode('utf-8').strip("\n").strip("\r\n")
-                    self.character_str += line
-            if add_space:
-                self.character_str += " "
-            dict_character = list(self.character_str)
-        elif self.character_type == "en_sensitive":
-            # same with ASTER setting (use 94 char).
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        else:
-            self.character_str = None
-        assert self.character_str is not None, \
-            "Nonsupport type of the character: {}".format(self.character_str)
-        self.beg_str = "sos"
-        self.end_str = "eos"
-        if self.loss_type == "attention":
-            dict_character = [self.beg_str, self.end_str] + dict_character
-        elif self.loss_type == "srn":
-            dict_character = dict_character + [self.beg_str, self.end_str]
-        self.dict = {}
-        for i, char in enumerate(dict_character):
-            self.dict[char] = i
-        self.character = dict_character
-
-    def encode(self, text):
-        """convert text-label into text-index.
-        input:
-            text: text labels of each image. [batch_size]
-
-        output:
-            text: concatenated text index for CTCLoss.
-                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
-            length: length of each text. [batch_size]
-        """
-        if self.character_type == "en":
-            text = text.lower()
-
-        text_list = []
-        for char in text:
-            if char not in self.dict:
-                continue
-            text_list.append(self.dict[char])
-        text = np.array(text_list)
-        return text
-
-    def decode(self, text_index, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        char_list = []
-        char_num = self.get_char_num()
-
-        if self.loss_type == "attention":
-            beg_idx = self.get_beg_end_flag_idx("beg")
-            end_idx = self.get_beg_end_flag_idx("end")
-            ignored_tokens = [beg_idx, end_idx]
-        else:
-            ignored_tokens = [char_num]
-
-        for idx in range(len(text_index)):
-            if text_index[idx] in ignored_tokens:
-                continue
-            if is_remove_duplicate:
-                if idx > 0 and text_index[idx - 1] == text_index[idx]:
-                    continue
-            char_list.append(self.character[int(text_index[idx])])
-        text = ''.join(char_list)
-        return text
-
-    def get_char_num(self):
-        return len(self.character)
-
-    def get_beg_end_flag_idx(self, beg_or_end):
-        if self.loss_type == "attention":
-            if beg_or_end == "beg":
-                idx = np.array(self.dict[self.beg_str])
-            elif beg_or_end == "end":
-                idx = np.array(self.dict[self.end_str])
-            else:
-                assert False, "Unsupport type %s in get_beg_end_flag_idx"\
-                    % beg_or_end
-            return idx
-        else:
-            err = "error in get_beg_end_flag_idx when using the loss %s"\
-                % (self.loss_type)
-            assert False, err
-
-
-def cal_predicts_accuracy(char_ops,
-                          preds,
-                          preds_lod,
-                          labels,
-                          labels_lod,
-                          is_remove_duplicate=False):
-    acc_num = 0
-    img_num = 0
-    for ino in range(len(labels_lod) - 1):
-        beg_no = preds_lod[ino]
-        end_no = preds_lod[ino + 1]
-        preds_text = preds[beg_no:end_no].reshape(-1)
-        preds_text = char_ops.decode(preds_text, is_remove_duplicate)
-
-        beg_no = labels_lod[ino]
-        end_no = labels_lod[ino + 1]
-        labels_text = labels[beg_no:end_no].reshape(-1)
-        labels_text = char_ops.decode(labels_text, is_remove_duplicate)
-        img_num += 1
-
-        if preds_text == labels_text:
-            acc_num += 1
-    acc = acc_num * 1.0 / img_num
-    return acc, acc_num, img_num
-
-
-def cal_predicts_accuracy_srn(char_ops,
-                              preds,
-                              labels,
-                              max_text_len,
-                              is_debug=False):
-    acc_num = 0
-    img_num = 0
-
-    char_num = char_ops.get_char_num()
-
-    total_len = preds.shape[0]
-    img_num = int(total_len / max_text_len)
-    for i in range(img_num):
-        cur_label = []
-        cur_pred = []
-        for j in range(max_text_len):
-            if labels[j + i * max_text_len] != int(char_num-1):  #0
-                cur_label.append(labels[j + i * max_text_len][0])
-            else:
-                break
-
-        for j in range(max_text_len + 1):
-            if j < len(cur_label) and preds[j + i * max_text_len][
-                    0] != cur_label[j]:
-                break
-            elif j == len(cur_label) and j == max_text_len:
-                acc_num += 1
-                break
-            elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
-                acc_num += 1
-                break
-    acc = acc_num * 1.0 / img_num
-    return acc, acc_num, img_num
-
-
-def convert_rec_attention_infer_res(preds):
-    img_num = preds.shape[0]
-    target_lod = [0]
-    convert_ids = []
-    for ino in range(img_num):
-        end_pos = np.where(preds[ino, :] == 1)[0]
-        if len(end_pos) <= 1:
-            text_list = preds[ino, 1:]
-        else:
-            text_list = preds[ino, 1:end_pos[1]]
-        target_lod.append(target_lod[ino] + len(text_list))
-        convert_ids = convert_ids + list(text_list)
-    convert_ids = np.array(convert_ids)
-    convert_ids = convert_ids.reshape((-1, 1))
-    return convert_ids, target_lod
-
-
-def convert_rec_label_to_lod(ori_labels):
-    img_num = len(ori_labels)
-    target_lod = [0]
-    convert_ids = []
-    for ino in range(img_num):
-        target_lod.append(target_lod[ino] + len(ori_labels[ino]))
-        convert_ids = convert_ids + list(ori_labels[ino])
-    convert_ids = np.array(convert_ids)
-    convert_ids = convert_ids.reshape((-1, 1))
-    return convert_ids, target_lod

ppocr/utils/check.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.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
-import sys
-
-import logging
-logger = logging.getLogger(__name__)
-
-
-def check_config_params(config, config_name, params):
-    for param in params:
-        if param not in config:
-            err = "param %s didn't find in %s!" % (param, config_name)
-            assert False, err
-    return

ppocr/utils/dict/en_dict.txt

+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+ 

ppocr/utils/dict/french_dict.txt

+f
+e
+n
+c
+h
+_
+i
+m
+g
+/
+r
+v
+a
+l
+t
+w
+o
+d
+6
+1
+.
+p
+B
+u
+2
+à
+3
+R
+y
+4
+U
+E
+A
+5
+P
+O
+S
+T
+D
+7
+Z
+8
+I
+N
+L
+G
+M
+H
+0
+J
+K
+-
+9
+F
+C
+V
+é
+X
+'
+s
+Q
+:
+è
+x
+b
+Y
+Œ
+É
+z
+W
+Ç
+È
+k
+Ô
+ô
+€
+À
+Ê
+q
+ù
+°
+ê
+î
+*
+Â
+j
+"
+,
+â
+%
+û
+ç
+ü
+?
+!
+;
+ö
+(
+)
+ï
+º
+ó
+ø
+å
++
+™
+á
+Ë
+<
+²
+Á
+Î
+&
+@
+œ
+ε
+Ü
+ë
+[
+]
+í
+ò
+Ö
+ä
+ß
+«
+»
+ú
+ñ
+æ
+µ
+³
+Å
+$
+#
+ 

ppocr/utils/dict/german_dict.txt

+!
+"
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+=
+>
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+[
+]
+_
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+¡
+¢
+£
+¤
+¥
+¦
+§
+¨
+©
+ª
+«
+¬
+­
+®
+¯
+°
+±
+²
+³
+´
+µ
+¶
+·
+¸
+¹
+º
+»
+¼
+½
+¿
+Â
+Ã
+Å
+Ê
+Î
+Ð
+á
+â
+å
+æ
+é
+ 

ppocr/utils/dict/japan_dict.txt

+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+<
+=
+>
+?
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+[
+]
+_
+`
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+©
+°
+²
+´
+½
+Á
+Ä
+Å
+Ç
+È
+É
+Í
+Ó
+Ö
+×
+Ü
+ß
+à
+á
+â
+ã
+ä
+å
+æ
+ç
+è
+é
+ê
+ë
+í
+ð
+ñ
+ò
+ó
+ô
+õ
+ö
+ø
+ú
+û
+ü
+ý
+ā
+ă
+ą
+ć
+Č
+č
+đ
+ē
+ė
+ę
+ğ
+ī
+ı
+Ł
+ł
+ń
+ň
+ō
+ř
+Ş
+ş
+Š
+š
+ţ
+ū
+ż
+Ž
+ž
+Ș
+ș
+ț
+Δ
+α
+λ
+μ
+φ
+Г
+О
+а
+в
+л
+о
+р
+с
+т
+я
+ồ
+​
+—
+―
+’
+“
+”
+…
+℃
+→
+∇
+−
+■
+☆
+　
+、
+。
+々
+〆
+〈
+〉
+「
+」
+『
+』
+〔
+〕
+〜
+ぁ
+あ
+ぃ
+い
+う
+ぇ
+え
+ぉ
+お
+か
+が
+き
+ぎ
+く
+ぐ
+け
+げ
+こ
+ご
+さ
+ざ
+し
+じ
+す
+ず
+せ
+ぜ
+そ
+ぞ
+た
+だ
+ち
+ぢ
+っ
+つ
+づ
+て
+で
+と
+ど
+な
+に
+ぬ
+ね
+の
+は
+ば
+ぱ
+ひ
+び
+ぴ
+ふ
+ぶ
+ぷ
+へ
+べ
+ぺ
+ほ
+ぼ
+ぽ
+ま
+み
+む
+め
+も
+ゃ
+や
+ゅ
+ゆ
+ょ
+よ
+ら
+り
+る
+れ
+ろ
+わ
+ゑ
+を
+ん
+ゝ
+ゞ
+ァ
+ア
+ィ
+イ
+ゥ
+ウ
+ェ
+エ
+ォ
+オ
+カ
+ガ
+キ
+ギ
+ク
+グ
+ケ
+ゲ
+コ
+ゴ
+サ
+ザ
+シ
+ジ
+ス
+ズ
+セ
+ゼ
+ソ
+ゾ
+タ
+ダ
+チ
+ヂ
+ッ
+ツ
+ヅ
+テ
+デ
+ト
+ド
+ナ
+ニ
+ヌ
+ネ
+ノ
+ハ
+バ
+パ
+ヒ
+ビ
+ピ
+フ
+ブ
+プ
+ヘ
+ベ
+ペ
+ホ
+ボ
+ポ
+マ
+ミ
+ム
+メ
+モ
+ャ
+ヤ
+ュ
+ユ
+ョ
+ヨ
+ラ
+リ
+ル
+レ
+ロ
+ワ
+ヰ
+ン
+ヴ
+ヵ
+ヶ
+・
+ー
+㈱
+一
+丁
+七
+万
+丈
+三
+上
+下
+不
+与
+丑
+且
+世
+丘
+丙
+丞
+両
+並
+中
+串
+丸
+丹
+主
+丼
+丿
+乃
+久
+之
+乎
+乏
+乗
+乘
+乙
+九
+乞
+也
+乱
+乳
+乾
+亀
+了
+予
+争
+事
+二
+于
+互
+五
+井
+亘
+亙
+些
+亜
+亟
+亡
+交
+亥
+亦
+亨
+享
+京
+亭
+亮
+人
+什
+仁
+仇
+今
+介
+仍
+仏
+仔
+仕
+他
+仗
+付
+仙
+代
+令
+以
+仮
+仰
+仲
+件
+任
+企
+伊
+伍
+伎
+伏
+伐
+休
+会
+伝
+伯
+估
+伴
+伶
+伸
+伺
+似
+伽
+佃
+但
+位
+低
+住
+佐
+佑
+体
+何
+余
+佚
+佛
+作
+佩
+佳
+併
+佶
+使
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+ 

ppocr/utils/dict/korean_dict.txt

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+ 

setup.py

@@ -32,7 +32,7 @@ setup(
     package_dir={'paddleocr': ''},
     include_package_data=True,
     entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
-    version='0.0.3',
+    version='2.0',
     install_requires=requirements,
     license='Apache License 2.0',
     description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',

tools/export_model.py

@@ -39,26 +39,12 @@ def parse_args():
     return parser.parse_args()
 
 
-class Model(paddle.nn.Layer):
-    def __init__(self, model):
-        super(Model, self).__init__()
-        self.pre_model = model
-
-    # Please modify the 'shape' according to actual needs
-    @to_static(input_spec=[
-        paddle.static.InputSpec(
-            shape=[None, 3, 640, 640], dtype='float32')
-    ])
-    def forward(self, inputs):
-        x = self.pre_model(inputs)
-        return x
-
-
 def main():
     FLAGS = parse_args()
     config = load_config(FLAGS.config)
     logger = get_logger()
     # build post process
+
     post_process_class = build_post_process(config['PostProcess'],
                                             config['Global'])
 
@@ -71,9 +57,16 @@ def main():
     init_model(config, model, logger)
     model.eval()
 
-    model = Model(model)
-    save_path = '{}/{}'.format(FLAGS.output_path,
+    save_path = '{}/{}/inference'.format(FLAGS.output_path,
                                          config['Architecture']['model_type'])
+    infer_shape = [3, 32, 100] if config['Architecture'][
+        'model_type'] != "det" else [3, 640, 640]
+    model = to_static(
+        model,
+        input_spec=[
+            paddle.static.InputSpec(
+                shape=[None] + infer_shape, dtype='float32')
+        ])
     paddle.jit.save(model, save_path)
     logger.info('inference model is saved to {}'.format(save_path))
 

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

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 import sys
+
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
@@ -23,17 +24,24 @@ 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
+
+logger = get_logger()
 
 
 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
+        self.drop_score = args.drop_score
+        if self.use_angle_cls:
+            self.text_classifier = predict_cls.TextClassifier(args)
 
     def get_rotate_crop_image(self, img, points):
         '''
@@ -72,12 +80,13 @@ 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 = []
@@ -88,10 +97,23 @@ 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
+        filter_boxes, filter_rec_res = [], []
+        for box, rec_reuslt in zip(dt_boxes, rec_res):
+            text, score = rec_reuslt
+            if score >= self.drop_score:
+                filter_boxes.append(box)
+                filter_rec_res.append(rec_reuslt)
+        return filter_boxes, filter_rec_res
 
 
 def sorted_boxes(dt_boxes):
@@ -107,7 +129,7 @@ def sorted_boxes(dt_boxes):
     _boxes = list(sorted_boxes)
 
     for i in range(num_boxes - 1):
-        if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
+        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
                 (_boxes[i + 1][0][0] < _boxes[i][0][0]):
             tmp = _boxes[i]
             _boxes[i] = _boxes[i + 1]
@@ -119,7 +141,8 @@ 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 +151,12 @@ 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)
+        for text, score in rec_res:
+            logger.info("{}, {:.3f}".format(text, score))
 
         if is_visualize:
             image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@@ -149,18 +164,22 @@ 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))))
 
 
 if __name__ == "__main__":
-    logger = get_logger()
     main(utility.parse_args())
\ No newline at end of file

tools/infer/utility.py

@@ -71,6 +71,7 @@ def parse_args():
     parser.add_argument("--use_space_char", type=str2bool, default=True)
     parser.add_argument(
         "--vis_font_path", type=str, default="./doc/simfang.ttf")
+    parser.add_argument("--drop_score", type=float, default=0.5)
 
     # params for text classifier
     parser.add_argument("--use_angle_cls", type=str2bool, default=False)
@@ -99,8 +100,8 @@ def create_predictor(args, mode, logger):
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
         sys.exit(0)
-    model_file_path = model_dir + "/model"
-    params_file_path = model_dir + "/params"
+    model_file_path = model_dir + ".pdmodel"
+    params_file_path = model_dir + ".pdiparams"
     if not os.path.exists(model_file_path):
         logger.info("not find model file path {}".format(model_file_path))
         sys.exit(0)
@@ -202,7 +203,12 @@ def draw_ocr(image,
     return image
 
 
-def draw_ocr_box_txt(image, boxes, txts):
+def draw_ocr_box_txt(image,
+                     boxes,
+                     txts,
+                     scores=None,
+                     drop_score=0.5,
+                     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))
@@ -212,7 +218,9 @@ def draw_ocr_box_txt(image, boxes, txts):
     random.seed(0)
     draw_left = ImageDraw.Draw(img_left)
     draw_right = ImageDraw.Draw(img_right)
-    for (box, txt) in zip(boxes, txts):
+    for idx, (box, txt) in enumerate(zip(boxes, txts)):
+        if scores is not None and scores[idx] < drop_score:
+            continue
         color = (random.randint(0, 255), random.randint(0, 255),
                  random.randint(0, 255))
         draw_left.polygon(box, fill=color)
@@ -228,8 +236,7 @@ def draw_ocr_box_txt(image, boxes, txts):
             1])**2)
         if box_height > 2 * box_width:
             font_size = max(int(box_width * 0.9), 10)
-            font = ImageFont.truetype(
-                "./doc/simfang.ttf", font_size, encoding="utf-8")
+            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
             cur_y = box[0][1]
             for c in txt:
                 char_size = font.getsize(c)
@@ -238,8 +245,7 @@ def draw_ocr_box_txt(image, boxes, txts):
                 cur_y += char_size[1]
         else:
             font_size = max(int(box_height * 0.8), 10)
-            font = ImageFont.truetype(
-                "./doc/simfang.ttf", font_size, encoding="utf-8")
+            font = ImageFont.truetype(font_path, font_size, 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)
@@ -254,7 +260,6 @@ def str_count(s):
     Count the number of Chinese characters,
     a single English character and a single number
     equal to half the length of Chinese characters.
-
     args:
         s(string): the input of string
     return(int):
@@ -289,7 +294,6 @@ def text_visual(texts,
         img_w(int): the width of blank img
         font_path: the path of font which is used to draw text
     return(array):
-
     """
     if scores is not None:
         assert len(texts) == len(