first commit

ppocr/utils/e2e_metric/polygon_fast.py

+# Copyright (c) 2021 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
+from shapely.geometry import Polygon
+"""
+:param det_x: [1, N] Xs of detection's vertices 
+:param det_y: [1, N] Ys of detection's vertices
+:param gt_x: [1, N] Xs of groundtruth's vertices
+:param gt_y: [1, N] Ys of groundtruth's vertices
+
+##############
+All the calculation of 'AREA' in this script is handled by:
+1) First generating a binary mask with the polygon area filled up with 1's
+2) Summing up all the 1's
+"""
+
+
+def area(x, y):
+    polygon = Polygon(np.stack([x, y], axis=1))
+    return float(polygon.area)
+
+
+def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine if both polygons are intersecting with each others with an approximation method.
+    Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
+    """
+    det_ymax = np.max(det_y)
+    det_xmax = np.max(det_x)
+    det_ymin = np.min(det_y)
+    det_xmin = np.min(det_x)
+
+    gt_ymax = np.max(gt_y)
+    gt_xmax = np.max(gt_x)
+    gt_ymin = np.min(gt_y)
+    gt_xmin = np.min(gt_x)
+
+    all_min_ymax = np.minimum(det_ymax, gt_ymax)
+    all_max_ymin = np.maximum(det_ymin, gt_ymin)
+
+    intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
+
+    all_min_xmax = np.minimum(det_xmax, gt_xmax)
+    all_max_xmin = np.maximum(det_xmin, gt_xmin)
+    intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
+
+    return intersect_heights * intersect_widths
+
+
+def area_of_intersection(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.intersection(p2).area)
+
+
+def area_of_union(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.union(p2).area)
+
+
+def iou(det_x, det_y, gt_x, gt_y):
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area_of_union(det_x, det_y, gt_x, gt_y) + 1.0)
+
+
+def iod(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine the fraction of intersection area over detection area
+    """
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area(det_x, det_y) + 1.0)

ppocr/utils/e2e_utils/extract_batchsize.py

+import paddle
+import numpy as np
+import copy
+
+
+def org_tcl_rois(batch_size, pos_lists, pos_masks, label_lists, tcl_bs):
+    """
+    """
+    pos_lists_, pos_masks_, label_lists_ = [], [], []
+    img_bs = batch_size
+    ngpu = int(batch_size / img_bs)
+    img_ids = np.array(pos_lists, dtype=np.int32)[:, 0, 0].copy()
+    pos_lists_split, pos_masks_split, label_lists_split = [], [], []
+    for i in range(ngpu):
+        pos_lists_split.append([])
+        pos_masks_split.append([])
+        label_lists_split.append([])
+
+    for i in range(img_ids.shape[0]):
+        img_id = img_ids[i]
+        gpu_id = int(img_id / img_bs)
+        img_id = img_id % img_bs
+        pos_list = pos_lists[i].copy()
+        pos_list[:, 0] = img_id
+        pos_lists_split[gpu_id].append(pos_list)
+        pos_masks_split[gpu_id].append(pos_masks[i].copy())
+        label_lists_split[gpu_id].append(copy.deepcopy(label_lists[i]))
+    # repeat or delete
+    for i in range(ngpu):
+        vp_len = len(pos_lists_split[i])
+        if vp_len <= tcl_bs:
+            for j in range(0, tcl_bs - vp_len):
+                pos_list = pos_lists_split[i][j].copy()
+                pos_lists_split[i].append(pos_list)
+                pos_mask = pos_masks_split[i][j].copy()
+                pos_masks_split[i].append(pos_mask)
+                label_list = copy.deepcopy(label_lists_split[i][j])
+                label_lists_split[i].append(label_list)
+        else:
+            for j in range(0, vp_len - tcl_bs):
+                c_len = len(pos_lists_split[i])
+                pop_id = np.random.permutation(c_len)[0]
+                pos_lists_split[i].pop(pop_id)
+                pos_masks_split[i].pop(pop_id)
+                label_lists_split[i].pop(pop_id)
+    # merge
+    for i in range(ngpu):
+        pos_lists_.extend(pos_lists_split[i])
+        pos_masks_.extend(pos_masks_split[i])
+        label_lists_.extend(label_lists_split[i])
+    return pos_lists_, pos_masks_, label_lists_
+
+
+def pre_process(label_list, pos_list, pos_mask, max_text_length, max_text_nums,
+                pad_num, tcl_bs):
+    label_list = label_list.numpy()
+    batch, _, _, _ = label_list.shape
+    pos_list = pos_list.numpy()
+    pos_mask = pos_mask.numpy()
+    pos_list_t = []
+    pos_mask_t = []
+    label_list_t = []
+    for i in range(batch):
+        for j in range(max_text_nums):
+            if pos_mask[i, j].any():
+                pos_list_t.append(pos_list[i][j])
+                pos_mask_t.append(pos_mask[i][j])
+                label_list_t.append(label_list[i][j])
+    pos_list, pos_mask, label_list = org_tcl_rois(batch, pos_list_t, pos_mask_t,
+                                                  label_list_t, tcl_bs)
+    label = []
+    tt = [l.tolist() for l in label_list]
+    for i in range(tcl_bs):
+        k = 0
+        for j in range(max_text_length):
+            if tt[i][j][0] != pad_num:
+                k += 1
+            else:
+                break
+        label.append(k)
+    label = paddle.to_tensor(label)
+    label = paddle.cast(label, dtype='int64')
+    pos_list = paddle.to_tensor(pos_list)
+    pos_mask = paddle.to_tensor(pos_mask)
+    label_list = paddle.squeeze(paddle.to_tensor(label_list), axis=2)
+    label_list = paddle.cast(label_list, dtype='int32')
+    return pos_list, pos_mask, label_list, label

ppocr/utils/e2e_utils/extract_textpoint_fast.py

+# Copyright (c) 2021 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.
+"""Contains various CTC decoders."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import cv2
+import math
+
+import numpy as np
+from itertools import groupby
+from skimage.morphology._skeletonize import thin
+
+
+def get_dict(character_dict_path):
+    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")
+            character_str += line
+        dict_character = list(character_str)
+    return dict_character
+
+
+def softmax(logits):
+    """
+    logits: N x d
+    """
+    max_value = np.max(logits, axis=1, keepdims=True)
+    exp = np.exp(logits - max_value)
+    exp_sum = np.sum(exp, axis=1, keepdims=True)
+    dist = exp / exp_sum
+    return dist
+
+
+def get_keep_pos_idxs(labels, remove_blank=None):
+    """
+    Remove duplicate and get pos idxs of keep items.
+    The value of keep_blank should be [None, 95].
+    """
+    duplicate_len_list = []
+    keep_pos_idx_list = []
+    keep_char_idx_list = []
+    for k, v_ in groupby(labels):
+        current_len = len(list(v_))
+        if k != remove_blank:
+            current_idx = int(sum(duplicate_len_list) + current_len // 2)
+            keep_pos_idx_list.append(current_idx)
+            keep_char_idx_list.append(k)
+        duplicate_len_list.append(current_len)
+    return keep_char_idx_list, keep_pos_idx_list
+
+
+def remove_blank(labels, blank=0):
+    new_labels = [x for x in labels if x != blank]
+    return new_labels
+
+
+def insert_blank(labels, blank=0):
+    new_labels = [blank]
+    for l in labels:
+        new_labels += [l, blank]
+    return new_labels
+
+
+def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
+    """
+    CTC greedy (best path) decoder.
+    """
+    raw_str = np.argmax(np.array(probs_seq), axis=1)
+    remove_blank_in_pos = None if keep_blank_in_idxs else blank
+    dedup_str, keep_idx_list = get_keep_pos_idxs(
+        raw_str, remove_blank=remove_blank_in_pos)
+    dst_str = remove_blank(dedup_str, blank=blank)
+    return dst_str, keep_idx_list
+
+
+def instance_ctc_greedy_decoder(gather_info, logits_map, pts_num=4):
+    _, _, C = logits_map.shape
+    ys, xs = zip(*gather_info)
+    logits_seq = logits_map[list(ys), list(xs)]
+    probs_seq = logits_seq
+    labels = np.argmax(probs_seq, axis=1)
+    dst_str = [k for k, v_ in groupby(labels) if k != C - 1]
+    detal = len(gather_info) // (pts_num - 1)
+    keep_idx_list = [0] + [detal * (i + 1) for i in range(pts_num - 2)] + [-1]
+    keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
+    return dst_str, keep_gather_list
+
+
+def ctc_decoder_for_image(gather_info_list,
+                          logits_map,
+                          Lexicon_Table,
+                          pts_num=6):
+    """
+    CTC decoder using multiple processes.
+    """
+    decoder_str = []
+    decoder_xys = []
+    for gather_info in 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)
+        dst_str_readable = ''.join([Lexicon_Table[idx] for idx in dst_str])
+        if len(dst_str_readable) < 2:
+            continue
+        decoder_str.append(dst_str_readable)
+        decoder_xys.append(xys_list)
+    return decoder_str, decoder_xys
+
+
+def sort_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list, point_direction):
+        pos_list = np.array(pos_list).reshape(-1, 2)
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 2)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point, np.array(sorted_direction)
+
+
+def add_id(pos_list, image_id=0):
+    """
+    Add id for gather feature, for inference.
+    """
+    new_list = []
+    for item in pos_list:
+        new_list.append((image_id, item[0], item[1]))
+    return new_list
+
+
+def sort_and_expand_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    left_list = []
+    right_list = []
+    for i in range(append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            left_list.append((ly, lx))
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            right_list.append((ry, rx))
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    binary_tcl_map: h x w
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    max_append_num = 2 * append_num
+
+    left_list = []
+    right_list = []
+    for i in range(max_append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            if binary_tcl_map[ly, lx] > 0.5:
+                left_list.append((ly, lx))
+            else:
+                break
+
+    for i in range(max_append_num):
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            if binary_tcl_map[ry, rx] > 0.5:
+                right_list.append((ry, rx))
+            else:
+                break
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    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(quad, begin_width_ratio=0., end_width_ratio=1.):
+    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(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 = 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 = 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_poly(instance_yxs_list, seq_strs, p_border, ratio_w, ratio_h, src_w,
+                 src_h, valid_set):
+    poly_list = []
+    keep_str_list = []
+    for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
+        if len(keep_str) < 2:
+            print('--> too short, {}'.format(keep_str))
+            continue
+
+        offset_expand = 1.0
+        if valid_set == 'totaltext':
+            offset_expand = 1.2
+
+        point_pair_list = []
+        for y, x in yx_center_line:
+            offset = p_border[:, y, x].reshape(2, 2) * offset_expand
+            ori_yx = np.array([y, x], dtype=np.float32)
+            point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
+                [ratio_w, ratio_h]).reshape(-1, 2)
+            point_pair_list.append(point_pair)
+
+        detected_poly = point_pair2poly(point_pair_list)
+        detected_poly = expand_poly_along_width(
+            detected_poly, shrink_ratio_of_width=0.2)
+        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)
+
+        keep_str_list.append(keep_str)
+        if valid_set == 'partvgg':
+            middle_point = len(detected_poly) // 2
+            detected_poly = detected_poly[
+                [0, middle_point - 1, middle_point, -1], :]
+            poly_list.append(detected_poly)
+        elif valid_set == 'totaltext':
+            poly_list.append(detected_poly)
+        else:
+            print('--> Not supported format.')
+            exit(-1)
+    return poly_list, keep_str_list
+
+
+def generate_pivot_list_fast(p_score,
+                             p_char_maps,
+                             f_direction,
+                             Lexicon_Table,
+                             score_thresh=0.5):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map.astype(np.uint8))
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            if len(pos_list) < 3:
+                continue
+
+            pos_list_sorted = sort_and_expand_with_direction_v2(
+                pos_list, f_direction, p_tcl_map)
+            all_pos_yxs.append(pos_list_sorted)
+
+    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)
+    return keep_yxs_list, decoded_str
+
+
+def extract_main_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    pos_list = np.array(pos_list)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    average_direction = average_direction / (
+        np.linalg.norm(average_direction) + 1e-6)
+    return average_direction
+
+
+def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
+    """
+    pos_list_full = np.array(pos_list).reshape(-1, 3)
+    pos_list = pos_list_full[:, 1:]
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+    sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+    return sorted_list
+
+
+def sort_by_direction_with_image_id(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list_full, point_direction):
+        pos_list_full = np.array(pos_list_full).reshape(-1, 3)
+        pos_list = pos_list_full[:, 1:]
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 3)
+    point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point

ppocr/utils/e2e_utils/extract_textpoint_slow.py

+# Copyright (c) 2021 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.
+"""Contains various CTC decoders."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import cv2
+import math
+
+import numpy as np
+from itertools import groupby
+from skimage.morphology._skeletonize import thin
+
+
+def get_dict(character_dict_path):
+    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")
+            character_str += line
+        dict_character = list(character_str)
+    return dict_character
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    pair_length_list = []
+    for point_pair in point_pair_list:
+        pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
+        pair_length_list.append(pair_length)
+    pair_length_list = np.array(pair_length_list)
+    pair_info = (pair_length_list.max(), pair_length_list.min(),
+                 pair_length_list.mean())
+
+    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), pair_info
+
+
+def shrink_quad_along_width(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(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 = 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 = 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 softmax(logits):
+    """
+    logits: N x d
+    """
+    max_value = np.max(logits, axis=1, keepdims=True)
+    exp = np.exp(logits - max_value)
+    exp_sum = np.sum(exp, axis=1, keepdims=True)
+    dist = exp / exp_sum
+    return dist
+
+
+def get_keep_pos_idxs(labels, remove_blank=None):
+    """
+    Remove duplicate and get pos idxs of keep items.
+    The value of keep_blank should be [None, 95].
+    """
+    duplicate_len_list = []
+    keep_pos_idx_list = []
+    keep_char_idx_list = []
+    for k, v_ in groupby(labels):
+        current_len = len(list(v_))
+        if k != remove_blank:
+            current_idx = int(sum(duplicate_len_list) + current_len // 2)
+            keep_pos_idx_list.append(current_idx)
+            keep_char_idx_list.append(k)
+        duplicate_len_list.append(current_len)
+    return keep_char_idx_list, keep_pos_idx_list
+
+
+def remove_blank(labels, blank=0):
+    new_labels = [x for x in labels if x != blank]
+    return new_labels
+
+
+def insert_blank(labels, blank=0):
+    new_labels = [blank]
+    for l in labels:
+        new_labels += [l, blank]
+    return new_labels
+
+
+def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
+    """
+    CTC greedy (best path) decoder.
+    """
+    raw_str = np.argmax(np.array(probs_seq), axis=1)
+    remove_blank_in_pos = None if keep_blank_in_idxs else blank
+    dedup_str, keep_idx_list = get_keep_pos_idxs(
+        raw_str, remove_blank=remove_blank_in_pos)
+    dst_str = remove_blank(dedup_str, blank=blank)
+    return dst_str, keep_idx_list
+
+
+def instance_ctc_greedy_decoder(gather_info,
+                                logits_map,
+                                keep_blank_in_idxs=True):
+    """
+    gather_info: [[x, y], [x, y] ...]
+    logits_map: H x W X (n_chars + 1)
+    """
+    _, _, C = logits_map.shape
+    ys, xs = zip(*gather_info)
+    logits_seq = logits_map[list(ys), list(xs)]  # n x 96
+    probs_seq = softmax(logits_seq)
+    dst_str, keep_idx_list = ctc_greedy_decoder(
+        probs_seq, blank=C - 1, keep_blank_in_idxs=keep_blank_in_idxs)
+    keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
+    return dst_str, keep_gather_list
+
+
+def ctc_decoder_for_image(gather_info_list, logits_map,
+                          keep_blank_in_idxs=True):
+    """
+    CTC decoder using multiple processes.
+    """
+    decoder_results = []
+    for gather_info in gather_info_list:
+        res = instance_ctc_greedy_decoder(
+            gather_info, logits_map, keep_blank_in_idxs=keep_blank_in_idxs)
+        decoder_results.append(res)
+    return decoder_results
+
+
+def sort_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list, point_direction):
+        pos_list = np.array(pos_list).reshape(-1, 2)
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 2)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point, np.array(sorted_direction)
+
+
+def add_id(pos_list, image_id=0):
+    """
+    Add id for gather feature, for inference.
+    """
+    new_list = []
+    for item in pos_list:
+        new_list.append((image_id, item[0], item[1]))
+    return new_list
+
+
+def sort_and_expand_with_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    # expand along
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    left_list = []
+    right_list = []
+    for i in range(append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            left_list.append((ly, lx))
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            right_list.append((ry, rx))
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    binary_tcl_map: h x w
+    """
+    h, w, _ = f_direction.shape
+    sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
+
+    # expand along
+    point_num = len(sorted_list)
+    sub_direction_len = max(point_num // 3, 2)
+    left_direction = point_direction[:sub_direction_len, :]
+    right_dirction = point_direction[point_num - sub_direction_len:, :]
+
+    left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
+    left_average_len = np.linalg.norm(left_average_direction)
+    left_start = np.array(sorted_list[0])
+    left_step = left_average_direction / (left_average_len + 1e-6)
+
+    right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
+    right_average_len = np.linalg.norm(right_average_direction)
+    right_step = right_average_direction / (right_average_len + 1e-6)
+    right_start = np.array(sorted_list[-1])
+
+    append_num = max(
+        int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
+    max_append_num = 2 * append_num
+
+    left_list = []
+    right_list = []
+    for i in range(max_append_num):
+        ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ly < h and lx < w and (ly, lx) not in left_list:
+            if binary_tcl_map[ly, lx] > 0.5:
+                left_list.append((ly, lx))
+            else:
+                break
+
+    for i in range(max_append_num):
+        ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
+            'int32').tolist()
+        if ry < h and rx < w and (ry, rx) not in right_list:
+            if binary_tcl_map[ry, rx] > 0.5:
+                right_list.append((ry, rx))
+            else:
+                break
+
+    all_list = left_list[::-1] + sorted_list + right_list
+    return all_list
+
+
+def generate_pivot_list_curved(p_score,
+                               p_char_maps,
+                               f_direction,
+                               score_thresh=0.5,
+                               is_expand=True,
+                               is_backbone=False,
+                               image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map)
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    center_pos_yxs = []
+    end_points_yxs = []
+    instance_center_pos_yxs = []
+    pred_strs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 3:
+                continue
+
+            if is_expand:
+                pos_list_sorted = sort_and_expand_with_direction_v2(
+                    pos_list, f_direction, p_tcl_map)
+            else:
+                pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
+            all_pos_yxs.append(pos_list_sorted)
+
+    # use decoder to filter backgroud points.
+    p_char_maps = p_char_maps.transpose([1, 2, 0])
+    decode_res = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
+    for decoded_str, keep_yxs_list in decode_res:
+        if is_backbone:
+            keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
+            instance_center_pos_yxs.append(keep_yxs_list_with_id)
+            pred_strs.append(decoded_str)
+        else:
+            end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
+            center_pos_yxs.extend(keep_yxs_list)
+
+    if is_backbone:
+        return pred_strs, instance_center_pos_yxs
+    else:
+        return center_pos_yxs, end_points_yxs
+
+
+def generate_pivot_list_horizontal(p_score,
+                                   p_char_maps,
+                                   f_direction,
+                                   score_thresh=0.5,
+                                   is_backbone=False,
+                                   image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map_bi = (p_score > score_thresh) * 1.0
+    instance_count, instance_label_map = cv2.connectedComponents(
+        p_tcl_map_bi.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    center_pos_yxs = []
+    end_points_yxs = []
+    instance_center_pos_yxs = []
+
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 5:
+                continue
+
+            # add rule here
+            main_direction = extract_main_direction(pos_list,
+                                                    f_direction)  # y x
+            reference_directin = np.array([0, 1]).reshape([-1, 2])  # y x
+            is_h_angle = abs(np.sum(
+                main_direction * reference_directin)) < math.cos(math.pi / 180 *
+                                                                 70)
+
+            point_yxs = np.array(pos_list)
+            max_y, max_x = np.max(point_yxs, axis=0)
+            min_y, min_x = np.min(point_yxs, axis=0)
+            is_h_len = (max_y - min_y) < 1.5 * (max_x - min_x)
+
+            pos_list_final = []
+            if is_h_len:
+                xs = np.unique(xs)
+                for x in xs:
+                    ys = instance_label_map[:, x].copy().reshape((-1, ))
+                    y = int(np.where(ys == instance_id)[0].mean())
+                    pos_list_final.append((y, x))
+            else:
+                ys = np.unique(ys)
+                for y in ys:
+                    xs = instance_label_map[y, :].copy().reshape((-1, ))
+                    x = int(np.where(xs == instance_id)[0].mean())
+                    pos_list_final.append((y, x))
+
+            pos_list_sorted, _ = sort_with_direction(pos_list_final,
+                                                     f_direction)
+            all_pos_yxs.append(pos_list_sorted)
+
+    # use decoder to filter backgroud points.
+    p_char_maps = p_char_maps.transpose([1, 2, 0])
+    decode_res = ctc_decoder_for_image(
+        all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
+    for decoded_str, keep_yxs_list in decode_res:
+        if is_backbone:
+            keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
+            instance_center_pos_yxs.append(keep_yxs_list_with_id)
+        else:
+            end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
+            center_pos_yxs.extend(keep_yxs_list)
+
+    if is_backbone:
+        return instance_center_pos_yxs
+    else:
+        return center_pos_yxs, end_points_yxs
+
+
+def generate_pivot_list_slow(p_score,
+                             p_char_maps,
+                             f_direction,
+                             score_thresh=0.5,
+                             is_backbone=False,
+                             is_curved=True,
+                             image_id=0):
+    """
+    Warp all the function together.
+    """
+    if is_curved:
+        return generate_pivot_list_curved(
+            p_score,
+            p_char_maps,
+            f_direction,
+            score_thresh=score_thresh,
+            is_expand=True,
+            is_backbone=is_backbone,
+            image_id=image_id)
+    else:
+        return generate_pivot_list_horizontal(
+            p_score,
+            p_char_maps,
+            f_direction,
+            score_thresh=score_thresh,
+            is_backbone=is_backbone,
+            image_id=image_id)
+
+
+# for refine module
+def extract_main_direction(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+    pos_list = np.array(pos_list)
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    average_direction = average_direction / (
+        np.linalg.norm(average_direction) + 1e-6)
+    return average_direction
+
+
+def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
+    """
+    pos_list_full = np.array(pos_list).reshape(-1, 3)
+    pos_list = pos_list_full[:, 1:]
+    point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    average_direction = np.mean(point_direction, axis=0, keepdims=True)
+    pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+    sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+    return sorted_list
+
+
+def sort_by_direction_with_image_id(pos_list, f_direction):
+    """
+    f_direction: h x w x 2
+    pos_list: [[y, x], [y, x], [y, x] ...]
+    """
+
+    def sort_part_with_direction(pos_list_full, point_direction):
+        pos_list_full = np.array(pos_list_full).reshape(-1, 3)
+        pos_list = pos_list_full[:, 1:]
+        point_direction = np.array(point_direction).reshape(-1, 2)
+        average_direction = np.mean(point_direction, axis=0, keepdims=True)
+        pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
+        sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
+        sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
+        return sorted_list, sorted_direction
+
+    pos_list = np.array(pos_list).reshape(-1, 3)
+    point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]]  # x, y
+    point_direction = point_direction[:, ::-1]  # x, y -> y, x
+    sorted_point, sorted_direction = sort_part_with_direction(pos_list,
+                                                              point_direction)
+
+    point_num = len(sorted_point)
+    if point_num >= 16:
+        middle_num = point_num // 2
+        first_part_point = sorted_point[:middle_num]
+        first_point_direction = sorted_direction[:middle_num]
+        sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
+            first_part_point, first_point_direction)
+
+        last_part_point = sorted_point[middle_num:]
+        last_point_direction = sorted_direction[middle_num:]
+        sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
+            last_part_point, last_point_direction)
+        sorted_point = sorted_fist_part_point + sorted_last_part_point
+        sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
+
+    return sorted_point
+
+
+def generate_pivot_list_tt_inference(p_score,
+                                     p_char_maps,
+                                     f_direction,
+                                     score_thresh=0.5,
+                                     is_backbone=False,
+                                     is_curved=True,
+                                     image_id=0):
+    """
+    return center point and end point of TCL instance; filter with the char maps;
+    """
+    p_score = p_score[0]
+    f_direction = f_direction.transpose(1, 2, 0)
+    p_tcl_map = (p_score > score_thresh) * 1.0
+    skeleton_map = thin(p_tcl_map)
+    instance_count, instance_label_map = cv2.connectedComponents(
+        skeleton_map.astype(np.uint8), connectivity=8)
+
+    # get TCL Instance
+    all_pos_yxs = []
+    if instance_count > 0:
+        for instance_id in range(1, instance_count):
+            pos_list = []
+            ys, xs = np.where(instance_label_map == instance_id)
+            pos_list = list(zip(ys, xs))
+            ### FIX-ME, eliminate outlier
+            if len(pos_list) < 3:
+                continue
+            pos_list_sorted = sort_and_expand_with_direction_v2(
+                pos_list, f_direction, p_tcl_map)
+            pos_list_sorted_with_id = add_id(pos_list_sorted, image_id=image_id)
+            all_pos_yxs.append(pos_list_sorted_with_id)
+    return all_pos_yxs

ppocr/utils/e2e_utils/pgnet_pp_utils.py

+# Copyright (c) 2021 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 paddle
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+from extract_textpoint_slow import *
+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):
+        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
+
+    def pg_postprocess_fast(self):
+        p_score = self.outs_dict['f_score']
+        p_border = self.outs_dict['f_border']
+        p_char = self.outs_dict['f_char']
+        p_direction = self.outs_dict['f_direction']
+        if isinstance(p_score, paddle.Tensor):
+            p_score = p_score[0].numpy()
+            p_border = p_border[0].numpy()
+            p_direction = p_direction[0].numpy()
+            p_char = p_char[0].numpy()
+        else:
+            p_score = p_score[0]
+            p_border = p_border[0]
+            p_direction = p_direction[0]
+            p_char = p_char[0]
+
+        src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
+        instance_yxs_list, seq_strs = generate_pivot_list_fast(
+            p_score,
+            p_char,
+            p_direction,
+            self.Lexicon_Table,
+            score_thresh=self.score_thresh)
+        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)
+        data = {
+            'points': poly_list,
+            'texts': keep_str_list,
+        }
+        return data
+
+    def pg_postprocess_slow(self):
+        p_score = self.outs_dict['f_score']
+        p_border = self.outs_dict['f_border']
+        p_char = self.outs_dict['f_char']
+        p_direction = self.outs_dict['f_direction']
+        if isinstance(p_score, paddle.Tensor):
+            p_score = p_score[0].numpy()
+            p_border = p_border[0].numpy()
+            p_direction = p_direction[0].numpy()
+            p_char = p_char[0].numpy()
+        else:
+            p_score = p_score[0]
+            p_border = p_border[0]
+            p_direction = p_direction[0]
+            p_char = p_char[0]
+        src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
+        is_curved = self.valid_set == "totaltext"
+        char_seq_idx_set, instance_yxs_list = generate_pivot_list_slow(
+            p_score,
+            p_char,
+            p_direction,
+            score_thresh=self.score_thresh,
+            is_backbone=True,
+            is_curved=is_curved)
+        seq_strs = []
+        for char_idx_set in char_seq_idx_set:
+            pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
+            seq_strs.append(pr_str)
+        poly_list = []
+        keep_str_list = []
+        all_point_list = []
+        all_point_pair_list = []
+        for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
+            if len(yx_center_line) == 1:
+                yx_center_line.append(yx_center_line[-1])
+
+            offset_expand = 1.0
+            if self.valid_set == 'totaltext':
+                offset_expand = 1.2
+
+            point_pair_list = []
+            for batch_id, y, x in yx_center_line:
+                offset = p_border[:, 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
+                ori_yx = np.array([y, x], dtype=np.float32)
+                point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
+                point_pair_list.append(point_pair)
+
+                all_point_list.append([
+                    int(round(x * 4.0 / ratio_w)),
+                    int(round(y * 4.0 / ratio_h))
+                ])
+                all_point_pair_list.append(point_pair.round().astype(np.int32)
+                                           .tolist())
+
+            detected_poly, pair_length_info = point_pair2poly(point_pair_list)
+            detected_poly = expand_poly_along_width(
+                detected_poly, shrink_ratio_of_width=0.2)
+            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)
+
+            if len(keep_str) < 2:
+                continue
+
+            keep_str_list.append(keep_str)
+            detected_poly = np.round(detected_poly).astype('int32')
+            if self.valid_set == 'partvgg':
+                middle_point = len(detected_poly) // 2
+                detected_poly = detected_poly[
+                    [0, middle_point - 1, middle_point, -1], :]
+                poly_list.append(detected_poly)
+            elif self.valid_set == 'totaltext':
+                poly_list.append(detected_poly)
+            else:
+                print('--> Not supported format.')
+                exit(-1)
+        data = {
+            'points': poly_list,
+            'texts': keep_str_list,
+        }
+        return data

ppocr/utils/e2e_utils/visual.py

+# Copyright (c) 2021 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 cv2
+import time
+
+
+def resize_image(im, max_side_len=512):
+    """
+    resize image to a size multiple of max_stride which is required by the network
+    :param im: the resized image
+    :param max_side_len: limit of max image size to avoid out of memory in gpu
+    :return: the resized image and the resize ratio
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    if resize_h > resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_min(im, max_side_len=512):
+    """
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    if resize_h < resize_w:
+        ratio = float(max_side_len) / resize_h
+    else:
+        ratio = float(max_side_len) / resize_w
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def resize_image_for_totaltext(im, max_side_len=512):
+    """
+    """
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+    ratio = 1.25
+    if h * ratio > max_side_len:
+        ratio = float(max_side_len) / resize_h
+
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    max_stride = 128
+    resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+    resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    return im, (ratio_h, ratio_w)
+
+
+def point_pair2poly(point_pair_list):
+    """
+    Transfer vertical point_pairs into poly point in clockwise.
+    """
+    pair_length_list = []
+    for point_pair in point_pair_list:
+        pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
+        pair_length_list.append(pair_length)
+    pair_length_list = np.array(pair_length_list)
+    pair_info = (pair_length_list.max(), pair_length_list.min(),
+                 pair_length_list.mean())
+
+    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), pair_info
+
+
+def shrink_quad_along_width(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(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 = 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 = 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 norm2(x, axis=None):
+    if axis:
+        return np.sqrt(np.sum(x**2, axis=axis))
+    return np.sqrt(np.sum(x**2))
+
+
+def cos(p1, p2):
+    return (p1 * p2).sum() / (norm2(p1) * norm2(p2))

ppocr/utils/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/ic15_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
\ No newline at end of file

ppocr/utils/iou.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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.
+"""
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/loss/iou.py
+"""
+
+import paddle
+
+EPS = 1e-6
+
+
+def iou_single(a, b, mask, n_class):
+    valid = mask == 1
+    a = a.masked_select(valid)
+    b = b.masked_select(valid)
+    miou = []
+    for i in range(n_class):
+        if a.shape == [0] and a.shape == b.shape:
+            inter = paddle.to_tensor(0.0)
+            union = paddle.to_tensor(0.0)
+        else:
+            inter = ((a == i).logical_and(b == i)).astype('float32')
+            union = ((a == i).logical_or(b == i)).astype('float32')
+        miou.append(paddle.sum(inter) / (paddle.sum(union) + EPS))
+    miou = sum(miou) / len(miou)
+    return miou
+
+
+def iou(a, b, mask, n_class=2, reduce=True):
+    batch_size = a.shape[0]
+
+    a = a.reshape([batch_size, -1])
+    b = b.reshape([batch_size, -1])
+    mask = mask.reshape([batch_size, -1])
+
+    iou = paddle.zeros((batch_size, ), dtype='float32')
+    for i in range(batch_size):
+        iou[i] = iou_single(a[i], b[i], mask[i], n_class)
+
+    if reduce:
+        iou = paddle.mean(iou)
+    return iou

ppocr/utils/loggers/__init__.py

+from .vdl_logger import VDLLogger
+from .wandb_logger import WandbLogger
+from .loggers import Loggers