From e2bf22dac83f72ab2e2be0f096345a1645e77f39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=89=E6=B4=8B=E4=B8=89=E6=B4=8B?= <1258009915@qq.com> Date: Mon, 27 May 2024 16:48:42 +0000 Subject: [PATCH] Added code for PaddleOCR inference --- .../paddleocr/infer/CTCLabelDecode.py | 215 + .../paddleocr/infer/DBPostProcess.py | 229 + .../thrid_party/paddleocr/infer/operators.py | 186 + .../paddleocr/infer/ppocr_keys_v1.txt | 6623 +++++++++++++++++ .../paddleocr/infer/predict_det.py | 298 + .../paddleocr/infer/predict_rec.py | 383 + .../thrid_party/paddleocr/infer/utility.py | 713 ++ 7 files changed, 8647 insertions(+) create mode 100644 src/models/thrid_party/paddleocr/infer/CTCLabelDecode.py create mode 100644 src/models/thrid_party/paddleocr/infer/DBPostProcess.py create mode 100644 src/models/thrid_party/paddleocr/infer/operators.py create mode 100644 src/models/thrid_party/paddleocr/infer/ppocr_keys_v1.txt create mode 100755 src/models/thrid_party/paddleocr/infer/predict_det.py create mode 100755 src/models/thrid_party/paddleocr/infer/predict_rec.py create mode 100644 src/models/thrid_party/paddleocr/infer/utility.py diff --git a/src/models/thrid_party/paddleocr/infer/CTCLabelDecode.py b/src/models/thrid_party/paddleocr/infer/CTCLabelDecode.py new file mode 100644 index 0000000..9ee9d34 --- /dev/null +++ b/src/models/thrid_party/paddleocr/infer/CTCLabelDecode.py @@ -0,0 +1,215 @@ +import re +import numpy as np +import os +from pathlib import Path + + +class BaseRecLabelDecode(object): + """Convert between text-label and text-index""" + + def __init__(self, character_dict_path=None, use_space_char=False): + cur_path = os.getcwd() + scriptDir = Path(__file__).resolve().parent + os.chdir(scriptDir) + character_dict_path = str(Path(scriptDir / "ppocr_keys_v1.txt")) + + self.beg_str = "sos" + self.end_str = "eos" + self.reverse = False + self.character_str = [] + + if character_dict_path is None: + self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" + dict_character = list(self.character_str) + else: + 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.append(line) + if use_space_char: + self.character_str.append(" ") + dict_character = list(self.character_str) + if "arabic" in character_dict_path: + self.reverse = True + + dict_character = self.add_special_char(dict_character) + self.dict = {} + for i, char in enumerate(dict_character): + self.dict[char] = i + self.character = dict_character + os.chdir(cur_path) + + def pred_reverse(self, pred): + pred_re = [] + c_current = "" + for c in pred: + if not bool(re.search("[a-zA-Z0-9 :*./%+-]", c)): + if c_current != "": + pred_re.append(c_current) + pred_re.append(c) + c_current = "" + else: + c_current += c + if c_current != "": + pred_re.append(c_current) + + return "".join(pred_re[::-1]) + + def add_special_char(self, dict_character): + return dict_character + + def get_word_info(self, text, selection): + """ + Group the decoded characters and record the corresponding decoded positions. + + Args: + text: the decoded text + selection: the bool array that identifies which columns of features are decoded as non-separated characters + Returns: + word_list: list of the grouped words + word_col_list: list of decoding positions corresponding to each character in the grouped word + state_list: list of marker to identify the type of grouping words, including two types of grouping words: + - 'cn': continous chinese characters (e.g., 你好啊) + - 'en&num': continous english characters (e.g., hello), number (e.g., 123, 1.123), or mixed of them connected by '-' (e.g., VGG-16) + The remaining characters in text are treated as separators between groups (e.g., space, '(', ')', etc.). + """ + state = None + word_content = [] + word_col_content = [] + word_list = [] + word_col_list = [] + state_list = [] + valid_col = np.where(selection == True)[0] + + for c_i, char in enumerate(text): + if "\u4e00" <= char <= "\u9fff": + c_state = "cn" + elif bool(re.search("[a-zA-Z0-9]", char)): + c_state = "en&num" + else: + c_state = "splitter" + + if ( + char == "." + and state == "en&num" + and c_i + 1 < len(text) + and bool(re.search("[0-9]", text[c_i + 1])) + ): # grouping floting number + c_state = "en&num" + if ( + char == "-" and state == "en&num" + ): # grouping word with '-', such as 'state-of-the-art' + c_state = "en&num" + + if state == None: + state = c_state + + if state != c_state: + if len(word_content) != 0: + word_list.append(word_content) + word_col_list.append(word_col_content) + state_list.append(state) + word_content = [] + word_col_content = [] + state = c_state + + if state != "splitter": + word_content.append(char) + word_col_content.append(valid_col[c_i]) + + if len(word_content) != 0: + word_list.append(word_content) + word_col_list.append(word_col_content) + state_list.append(state) + + return word_list, word_col_list, state_list + + def decode( + self, + text_index, + text_prob=None, + is_remove_duplicate=False, + return_word_box=False, + ): + """convert text-index into text-label.""" + result_list = [] + ignored_tokens = self.get_ignored_tokens() + batch_size = len(text_index) + for batch_idx in range(batch_size): + selection = np.ones(len(text_index[batch_idx]), dtype=bool) + if is_remove_duplicate: + selection[1:] = text_index[batch_idx][1:] != text_index[batch_idx][:-1] + for ignored_token in ignored_tokens: + selection &= text_index[batch_idx] != ignored_token + + char_list = [ + self.character[text_id] for text_id in text_index[batch_idx][selection] + ] + if text_prob is not None: + conf_list = text_prob[batch_idx][selection] + else: + conf_list = [1] * len(selection) + if len(conf_list) == 0: + conf_list = [0] + + text = "".join(char_list) + + if self.reverse: # for arabic rec + text = self.pred_reverse(text) + + if return_word_box: + word_list, word_col_list, state_list = self.get_word_info( + text, selection + ) + result_list.append( + ( + text, + np.mean(conf_list).tolist(), + [ + len(text_index[batch_idx]), + word_list, + word_col_list, + state_list, + ], + ) + ) + else: + result_list.append((text, np.mean(conf_list).tolist())) + return result_list + + def get_ignored_tokens(self): + return [0] # for ctc blank + + +class CTCLabelDecode(BaseRecLabelDecode): + """Convert between text-label and text-index""" + + def __init__(self, character_dict_path=None, use_space_char=False, **kwargs): + super(CTCLabelDecode, self).__init__(character_dict_path, use_space_char) + + def __call__(self, preds, label=None, return_word_box=False, *args, **kwargs): + if isinstance(preds, tuple) or isinstance(preds, list): + preds = preds[-1] + assert isinstance(preds, np.ndarray) + preds_idx = preds.argmax(axis=2) + preds_prob = preds.max(axis=2) + text = self.decode( + preds_idx, + preds_prob, + is_remove_duplicate=True, + return_word_box=return_word_box, + ) + if return_word_box: + for rec_idx, rec in enumerate(text): + wh_ratio = kwargs["wh_ratio_list"][rec_idx] + max_wh_ratio = kwargs["max_wh_ratio"] + rec[2][0] = rec[2][0] * (wh_ratio / max_wh_ratio) + if label is None: + return text + label = self.decode(label) + return text, label + + def add_special_char(self, dict_character): + dict_character = ["blank"] + dict_character + return dict_character \ No newline at end of file diff --git a/src/models/thrid_party/paddleocr/infer/DBPostProcess.py b/src/models/thrid_party/paddleocr/infer/DBPostProcess.py new file mode 100644 index 0000000..84919e4 --- /dev/null +++ b/src/models/thrid_party/paddleocr/infer/DBPostProcess.py @@ -0,0 +1,229 @@ +import numpy as np +import cv2 + +from shapely.geometry import Polygon +import pyclipper + + +class DBPostProcess(object): + """ + The post process for Differentiable Binarization (DB). + """ + + def __init__( + self, + thresh=0.3, + box_thresh=0.7, + max_candidates=1000, + unclip_ratio=2.0, + use_dilation=False, + score_mode="fast", + box_type="quad", + **kwargs + ): + self.thresh = thresh + self.box_thresh = box_thresh + self.max_candidates = max_candidates + self.unclip_ratio = unclip_ratio + self.min_size = 3 + self.score_mode = score_mode + self.box_type = box_type + assert score_mode in [ + "slow", + "fast", + ], "Score mode must be in [slow, fast] but got: {}".format(score_mode) + + self.dilation_kernel = None if not use_dilation else np.array([[1, 1], [1, 1]]) + + def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height): + """ + _bitmap: single map with shape (1, H, W), + whose values are binarized as {0, 1} + """ + + bitmap = _bitmap + height, width = bitmap.shape + + boxes = [] + scores = [] + + contours, _ = cv2.findContours( + (bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE + ) + + for contour in contours[: self.max_candidates]: + epsilon = 0.002 * cv2.arcLength(contour, True) + approx = cv2.approxPolyDP(contour, epsilon, True) + points = approx.reshape((-1, 2)) + if points.shape[0] < 4: + continue + + score = self.box_score_fast(pred, points.reshape(-1, 2)) + if self.box_thresh > score: + continue + + if points.shape[0] > 2: + box = self.unclip(points, self.unclip_ratio) + if len(box) > 1: + continue + else: + continue + box = box.reshape(-1, 2) + + _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2))) + if sside < self.min_size + 2: + continue + + box = np.array(box) + box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip( + np.round(box[:, 1] / height * dest_height), 0, dest_height + ) + boxes.append(box.tolist()) + scores.append(score) + return boxes, scores + + def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): + """ + _bitmap: single map with shape (1, H, W), + whose values are binarized as {0, 1} + """ + + bitmap = _bitmap + height, width = bitmap.shape + + outs = cv2.findContours( + (bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE + ) + if len(outs) == 3: + img, contours, _ = outs[0], outs[1], outs[2] + elif len(outs) == 2: + contours, _ = outs[0], outs[1] + + num_contours = min(len(contours), self.max_candidates) + + boxes = [] + scores = [] + for index in range(num_contours): + contour = contours[index] + points, sside = self.get_mini_boxes(contour) + if sside < self.min_size: + continue + points = np.array(points) + if self.score_mode == "fast": + score = self.box_score_fast(pred, points.reshape(-1, 2)) + else: + score = self.box_score_slow(pred, contour) + if self.box_thresh > score: + continue + + box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2) + box, sside = self.get_mini_boxes(box) + if sside < self.min_size + 2: + continue + box = np.array(box) + + box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip( + np.round(box[:, 1] / height * dest_height), 0, dest_height + ) + boxes.append(box.astype("int32")) + scores.append(score) + return np.array(boxes, dtype="int32"), scores + + def unclip(self, box, unclip_ratio): + poly = Polygon(box) + distance = poly.area * unclip_ratio / poly.length + offset = pyclipper.PyclipperOffset() + offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) + expanded = np.array(offset.Execute(distance)) + return expanded + + def get_mini_boxes(self, contour): + bounding_box = cv2.minAreaRect(contour) + points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) + + index_1, index_2, index_3, index_4 = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_1 = 0 + index_4 = 1 + else: + index_1 = 1 + index_4 = 0 + if points[3][1] > points[2][1]: + index_2 = 2 + index_3 = 3 + else: + index_2 = 3 + index_3 = 2 + + box = [points[index_1], points[index_2], points[index_3], points[index_4]] + return box, min(bounding_box[1]) + + def box_score_fast(self, bitmap, _box): + """ + box_score_fast: use bbox mean score as the mean score + """ + h, w = bitmap.shape[:2] + box = _box.copy() + xmin = np.clip(np.floor(box[:, 0].min()).astype("int32"), 0, w - 1) + xmax = np.clip(np.ceil(box[:, 0].max()).astype("int32"), 0, w - 1) + ymin = np.clip(np.floor(box[:, 1].min()).astype("int32"), 0, h - 1) + ymax = np.clip(np.ceil(box[:, 1].max()).astype("int32"), 0, h - 1) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + box[:, 0] = box[:, 0] - xmin + box[:, 1] = box[:, 1] - ymin + cv2.fillPoly(mask, box.reshape(1, -1, 2).astype("int32"), 1) + return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0] + + def box_score_slow(self, bitmap, contour): + """ + box_score_slow: use polyon mean score as the mean score + """ + h, w = bitmap.shape[:2] + contour = contour.copy() + contour = np.reshape(contour, (-1, 2)) + + xmin = np.clip(np.min(contour[:, 0]), 0, w - 1) + xmax = np.clip(np.max(contour[:, 0]), 0, w - 1) + ymin = np.clip(np.min(contour[:, 1]), 0, h - 1) + ymax = np.clip(np.max(contour[:, 1]), 0, h - 1) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + + contour[:, 0] = contour[:, 0] - xmin + contour[:, 1] = contour[:, 1] - ymin + + cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype("int32"), 1) + return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0] + + def __call__(self, outs_dict, shape_list): + pred = outs_dict["maps"] + assert isinstance(pred, np.ndarray) + pred = pred[:, 0, :, :] + segmentation = pred > self.thresh + + boxes_batch = [] + for batch_index in range(pred.shape[0]): + src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] + if self.dilation_kernel is not None: + mask = cv2.dilate( + np.array(segmentation[batch_index]).astype(np.uint8), + self.dilation_kernel, + ) + else: + mask = segmentation[batch_index] + if self.box_type == "poly": + boxes, scores = self.polygons_from_bitmap( + pred[batch_index], mask, src_w, src_h + ) + elif self.box_type == "quad": + boxes, scores = self.boxes_from_bitmap( + pred[batch_index], mask, src_w, src_h + ) + else: + raise ValueError("box_type can only be one of ['quad', 'poly']") + + boxes_batch.append({"points": boxes}) + return boxes_batch \ No newline at end of file diff --git a/src/models/thrid_party/paddleocr/infer/operators.py b/src/models/thrid_party/paddleocr/infer/operators.py new file mode 100644 index 0000000..5b1e284 --- /dev/null +++ b/src/models/thrid_party/paddleocr/infer/operators.py @@ -0,0 +1,186 @@ +import numpy as np +import cv2 +import math +import sys + + +class DetResizeForTest(object): + def __init__(self, **kwargs): + super(DetResizeForTest, self).__init__() + self.resize_type = 0 + self.keep_ratio = False + if "image_shape" in kwargs: + self.image_shape = kwargs["image_shape"] + self.resize_type = 1 + if "keep_ratio" in kwargs: + self.keep_ratio = kwargs["keep_ratio"] + elif "limit_side_len" in kwargs: + self.limit_side_len = kwargs["limit_side_len"] + self.limit_type = kwargs.get("limit_type", "min") + elif "resize_long" in kwargs: + self.resize_type = 2 + self.resize_long = kwargs.get("resize_long", 960) + else: + self.limit_side_len = 736 + self.limit_type = "min" + + def __call__(self, data): + img = data["image"] + src_h, src_w, _ = img.shape + if sum([src_h, src_w]) < 64: + img = self.image_padding(img) + + if self.resize_type == 0: + # img, shape = self.resize_image_type0(img) + img, [ratio_h, ratio_w] = self.resize_image_type0(img) + elif self.resize_type == 2: + img, [ratio_h, ratio_w] = self.resize_image_type2(img) + else: + # img, shape = self.resize_image_type1(img) + img, [ratio_h, ratio_w] = self.resize_image_type1(img) + data["image"] = img + data["shape"] = np.array([src_h, src_w, ratio_h, ratio_w]) + return data + + def image_padding(self, im, value=0): + h, w, c = im.shape + im_pad = np.zeros((max(32, h), max(32, w), c), np.uint8) + value + im_pad[:h, :w, :] = im + return im_pad + + def resize_image_type1(self, img): + resize_h, resize_w = self.image_shape + ori_h, ori_w = img.shape[:2] # (h, w, c) + if self.keep_ratio is True: + resize_w = ori_w * resize_h / ori_h + N = math.ceil(resize_w / 32) + resize_w = N * 32 + ratio_h = float(resize_h) / ori_h + ratio_w = float(resize_w) / ori_w + img = cv2.resize(img, (int(resize_w), int(resize_h))) + # return img, np.array([ori_h, ori_w]) + return img, [ratio_h, ratio_w] + + def resize_image_type0(self, img): + """ + resize image to a size multiple of 32 which is required by the network + args: + img(array): array with shape [h, w, c] + return(tuple): + img, (ratio_h, ratio_w) + """ + limit_side_len = self.limit_side_len + h, w, c = img.shape + + # limit the max side + if self.limit_type == "max": + if max(h, w) > limit_side_len: + if h > w: + ratio = float(limit_side_len) / h + else: + ratio = float(limit_side_len) / w + else: + ratio = 1.0 + elif self.limit_type == "min": + if min(h, w) < limit_side_len: + if h < w: + ratio = float(limit_side_len) / h + else: + ratio = float(limit_side_len) / w + else: + ratio = 1.0 + elif self.limit_type == "resize_long": + ratio = float(limit_side_len) / max(h, w) + else: + raise Exception("not support limit type, image ") + resize_h = int(h * ratio) + resize_w = int(w * ratio) + + resize_h = max(int(round(resize_h / 32) * 32), 32) + resize_w = max(int(round(resize_w / 32) * 32), 32) + + try: + if int(resize_w) <= 0 or int(resize_h) <= 0: + return None, (None, None) + img = cv2.resize(img, (int(resize_w), int(resize_h))) + except: + print(img.shape, resize_w, resize_h) + sys.exit(0) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + return img, [ratio_h, ratio_w] + + def resize_image_type2(self, img): + h, w, _ = img.shape + + resize_w = w + resize_h = h + + if resize_h > resize_w: + ratio = float(self.resize_long) / resize_h + else: + ratio = float(self.resize_long) / 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 + img = cv2.resize(img, (int(resize_w), int(resize_h))) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + + return img, [ratio_h, ratio_w] + + +class NormalizeImage(object): + """normalize image such as substract mean, divide std""" + + def __init__(self, scale=None, mean=None, std=None, order="chw", **kwargs): + if isinstance(scale, str): + scale = eval(scale) + self.scale = np.float32(scale if scale is not None else 1.0 / 255.0) + mean = mean if mean is not None else [0.485, 0.456, 0.406] + std = std if std is not None else [0.229, 0.224, 0.225] + + shape = (3, 1, 1) if order == "chw" else (1, 1, 3) + self.mean = np.array(mean).reshape(shape).astype("float32") + self.std = np.array(std).reshape(shape).astype("float32") + + def __call__(self, data): + img = data["image"] + from PIL import Image + + if isinstance(img, Image.Image): + img = np.array(img) + assert isinstance(img, np.ndarray), "invalid input 'img' in NormalizeImage" + data["image"] = (img.astype("float32") * self.scale - self.mean) / self.std + return data + + +class ToCHWImage(object): + """convert hwc image to chw image""" + + def __init__(self, **kwargs): + pass + + def __call__(self, data): + img = data["image"] + from PIL import Image + + if isinstance(img, Image.Image): + img = np.array(img) + data["image"] = img.transpose((2, 0, 1)) + return data + + +class KeepKeys(object): + def __init__(self, keep_keys, **kwargs): + self.keep_keys = keep_keys + + def __call__(self, data): + data_list = [] + for key in self.keep_keys: + data_list.append(data[key]) + return data_list \ No newline at end of file diff --git a/src/models/thrid_party/paddleocr/infer/ppocr_keys_v1.txt b/src/models/thrid_party/paddleocr/infer/ppocr_keys_v1.txt new file mode 100644 index 0000000..84b885d --- /dev/null +++ b/src/models/thrid_party/paddleocr/infer/ppocr_keys_v1.txt @@ -0,0 +1,6623 @@ +' +疗 +绚 +诚 +娇 +溜 +题 +贿 +者 +廖 +更 +纳 +加 +奉 +公 +一 +就 +汴 +计 +与 +路 +房 +原 +妇 +2 +0 +8 +- +7 +其 +> +: +] +, +， +骑 +刈 +全 +消 +昏 +傈 +安 +久 +钟 +嗅 +不 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b/src/models/thrid_party/paddleocr/infer/predict_det.py @@ -0,0 +1,298 @@ +# 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 os +import sys + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../.."))) + +os.environ["FLAGS_allocator_strategy"] = "auto_growth" + +import cv2 +import numpy as np +import time +import sys + +# import tools.infer.utility as utility +import utility +from utility import get_logger + +from DBPostProcess import DBPostProcess +from operators import DetResizeForTest, KeepKeys, NormalizeImage, ToCHWImage + + +def transform(data, ops=None): + """transform""" + if ops is None: + ops = [] + for op in ops: + data = op(data) + if data is None: + return None + return data + +logger = get_logger() + + +class TextDetector(object): + def __init__(self, args): + self.args = args + self.det_algorithm = args.det_algorithm + self.use_onnx = args.use_onnx + postprocess_params = {} + assert self.det_algorithm == "DB" + postprocess_params["name"] = "DBPostProcess" + postprocess_params["thresh"] = args.det_db_thresh + postprocess_params["box_thresh"] = args.det_db_box_thresh + postprocess_params["max_candidates"] = 1000 + postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio + postprocess_params["use_dilation"] = args.use_dilation + postprocess_params["score_mode"] = args.det_db_score_mode + postprocess_params["box_type"] = args.det_box_type + + self.preprocess_op = [ + DetResizeForTest(limit_side_len=args.det_limit_side_len, limit_type=args.det_limit_type), + NormalizeImage(std= [0.229, 0.224, 0.225], mean= [0.485, 0.456, 0.406], scale= 1./255., order= "hwc"), + ToCHWImage(), + KeepKeys(keep_keys= ["image", "shape"]) + ] + self.postprocess_op = DBPostProcess(**postprocess_params) + ( + self.predictor, + self.input_tensor, + self.output_tensors, + self.config, + ) = utility.create_predictor(args, "det", logger) + + assert self.use_onnx + if self.use_onnx: + img_h, img_w = self.input_tensor.shape[2:] + if isinstance(img_h, str) or isinstance(img_w, str): + pass + elif img_h is not None and img_w is not None and img_h > 0 and img_w > 0: + self.preprocess_op[0] = DetResizeForTest(image_shape=[img_h, img_w]) + + + def order_points_clockwise(self, pts): + rect = np.zeros((4, 2), dtype="float32") + s = pts.sum(axis=1) + rect[0] = pts[np.argmin(s)] + rect[2] = pts[np.argmax(s)] + tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0) + diff = np.diff(np.array(tmp), axis=1) + rect[1] = tmp[np.argmin(diff)] + rect[3] = tmp[np.argmax(diff)] + return rect + + def clip_det_res(self, points, img_height, img_width): + for pno in range(points.shape[0]): + points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1)) + points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1)) + return points + + def filter_tag_det_res(self, dt_boxes, image_shape): + img_height, img_width = image_shape[0:2] + dt_boxes_new = [] + for box in dt_boxes: + if type(box) is list: + box = np.array(box) + box = self.order_points_clockwise(box) + box = self.clip_det_res(box, img_height, img_width) + rect_width = int(np.linalg.norm(box[0] - box[1])) + rect_height = int(np.linalg.norm(box[0] - box[3])) + if rect_width <= 3 or rect_height <= 3: + continue + dt_boxes_new.append(box) + dt_boxes = np.array(dt_boxes_new) + return dt_boxes + + def filter_tag_det_res_only_clip(self, dt_boxes, image_shape): + img_height, img_width = image_shape[0:2] + dt_boxes_new = [] + for box in dt_boxes: + if type(box) is list: + box = np.array(box) + box = self.clip_det_res(box, img_height, img_width) + dt_boxes_new.append(box) + dt_boxes = np.array(dt_boxes_new) + return dt_boxes + + def predict(self, img): + ori_im = img.copy() + data = {"image": img} + + st = time.time() + + if self.args.benchmark: + self.autolog.times.start() + + data = transform(data, self.preprocess_op) + img, shape_list = data + if img is None: + return None, 0 + img = np.expand_dims(img, axis=0) + shape_list = np.expand_dims(shape_list, axis=0) + img = img.copy() + + if self.args.benchmark: + self.autolog.times.stamp() + if self.use_onnx: + input_dict = {} + input_dict[self.input_tensor.name] = img + outputs = self.predictor.run(self.output_tensors, input_dict) + else: + self.input_tensor.copy_from_cpu(img) + self.predictor.run() + outputs = [] + for output_tensor in self.output_tensors: + output = output_tensor.copy_to_cpu() + outputs.append(output) + if self.args.benchmark: + self.autolog.times.stamp() + + preds = {} + if self.det_algorithm == "EAST": + preds["f_geo"] = outputs[0] + preds["f_score"] = outputs[1] + elif self.det_algorithm == "SAST": + preds["f_border"] = outputs[0] + preds["f_score"] = outputs[1] + preds["f_tco"] = outputs[2] + preds["f_tvo"] = outputs[3] + elif self.det_algorithm in ["DB", "PSE", "DB++"]: + preds["maps"] = outputs[0] + elif self.det_algorithm == "FCE": + for i, output in enumerate(outputs): + preds["level_{}".format(i)] = output + elif self.det_algorithm == "CT": + preds["maps"] = outputs[0] + preds["score"] = outputs[1] + else: + raise NotImplementedError + + post_result = self.postprocess_op(preds, shape_list) + dt_boxes = post_result[0]["points"] + + if self.args.det_box_type == "poly": + dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape) + else: + dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) + + if self.args.benchmark: + self.autolog.times.end(stamp=True) + et = time.time() + return dt_boxes, et - st + + def __call__(self, img): + # For image like poster with one side much greater than the other side, + # splitting recursively and processing with overlap to enhance performance. + MIN_BOUND_DISTANCE = 50 + dt_boxes = np.zeros((0, 4, 2), dtype=np.float32) + elapse = 0 + if ( + img.shape[0] / img.shape[1] > 2 + and img.shape[0] > self.args.det_limit_side_len + ): + start_h = 0 + end_h = 0 + while end_h <= img.shape[0]: + end_h = start_h + img.shape[1] * 3 // 4 + subimg = img[start_h:end_h, :] + if len(subimg) == 0: + break + sub_dt_boxes, sub_elapse = self.predict(subimg) + offset = start_h + # To prevent text blocks from being cut off, roll back a certain buffer area. + if ( + len(sub_dt_boxes) == 0 + or img.shape[1] - max([x[-1][1] for x in sub_dt_boxes]) + > MIN_BOUND_DISTANCE + ): + start_h = end_h + else: + sorted_indices = np.argsort(sub_dt_boxes[:, 2, 1]) + sub_dt_boxes = sub_dt_boxes[sorted_indices] + bottom_line = ( + 0 + if len(sub_dt_boxes) <= 1 + else int(np.max(sub_dt_boxes[:-1, 2, 1])) + ) + if bottom_line > 0: + start_h += bottom_line + sub_dt_boxes = sub_dt_boxes[ + sub_dt_boxes[:, 2, 1] <= bottom_line + ] + else: + start_h = end_h + if len(sub_dt_boxes) > 0: + if dt_boxes.shape[0] == 0: + dt_boxes = sub_dt_boxes + np.array( + [0, offset], dtype=np.float32 + ) + else: + dt_boxes = np.append( + dt_boxes, + sub_dt_boxes + np.array([0, offset], dtype=np.float32), + axis=0, + ) + elapse += sub_elapse + elif ( + img.shape[1] / img.shape[0] > 3 + and img.shape[1] > self.args.det_limit_side_len * 3 + ): + start_w = 0 + end_w = 0 + while end_w <= img.shape[1]: + end_w = start_w + img.shape[0] * 3 // 4 + subimg = img[:, start_w:end_w] + if len(subimg) == 0: + break + sub_dt_boxes, sub_elapse = self.predict(subimg) + offset = start_w + if ( + len(sub_dt_boxes) == 0 + or img.shape[0] - max([x[-1][0] for x in sub_dt_boxes]) + > MIN_BOUND_DISTANCE + ): + start_w = end_w + else: + sorted_indices = np.argsort(sub_dt_boxes[:, 2, 0]) + sub_dt_boxes = sub_dt_boxes[sorted_indices] + right_line = ( + 0 + if len(sub_dt_boxes) <= 1 + else int(np.max(sub_dt_boxes[:-1, 1, 0])) + ) + if right_line > 0: + start_w += right_line + sub_dt_boxes = sub_dt_boxes[sub_dt_boxes[:, 1, 0] <= right_line] + else: + start_w = end_w + if len(sub_dt_boxes) > 0: + if dt_boxes.shape[0] == 0: + dt_boxes = sub_dt_boxes + np.array( + [offset, 0], dtype=np.float32 + ) + else: + dt_boxes = np.append( + dt_boxes, + sub_dt_boxes + np.array([offset, 0], dtype=np.float32), + axis=0, + ) + elapse += sub_elapse + else: + dt_boxes, elapse = self.predict(img) + return dt_boxes, elapse + diff --git a/src/models/thrid_party/paddleocr/infer/predict_rec.py b/src/models/thrid_party/paddleocr/infer/predict_rec.py new file mode 100755 index 0000000..a2d4a47 --- /dev/null +++ b/src/models/thrid_party/paddleocr/infer/predict_rec.py @@ -0,0 +1,383 @@ +# 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 os +import sys +from PIL import Image + +__dir__ = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(__dir__) +sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../.."))) + +os.environ["FLAGS_allocator_strategy"] = "auto_growth" + +import cv2 +import numpy as np +import math +import time + +import utility +from utility import get_logger + +from CTCLabelDecode import CTCLabelDecode + +logger = get_logger() + + +class TextRecognizer(object): + def __init__(self, args): + self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")] + self.rec_batch_num = args.rec_batch_num + self.rec_algorithm = args.rec_algorithm + self.postprocess_op = CTCLabelDecode(character_dict_path=args.rec_char_dict_path, use_space_char=args.use_space_char) + ( + self.predictor, + self.input_tensor, + self.output_tensors, + self.config, + ) = utility.create_predictor(args, "rec", logger) + self.benchmark = args.benchmark + self.use_onnx = args.use_onnx + self.return_word_box = args.return_word_box + + def resize_norm_img(self, img, max_wh_ratio): + imgC, imgH, imgW = self.rec_image_shape + if self.rec_algorithm == "NRTR" or self.rec_algorithm == "ViTSTR": + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # return padding_im + image_pil = Image.fromarray(np.uint8(img)) + if self.rec_algorithm == "ViTSTR": + img = image_pil.resize([imgW, imgH], Image.BICUBIC) + else: + img = image_pil.resize([imgW, imgH], Image.Resampling.LANCZOS) + img = np.array(img) + norm_img = np.expand_dims(img, -1) + norm_img = norm_img.transpose((2, 0, 1)) + if self.rec_algorithm == "ViTSTR": + norm_img = norm_img.astype(np.float32) / 255.0 + else: + norm_img = norm_img.astype(np.float32) / 128.0 - 1.0 + return norm_img + elif self.rec_algorithm == "RFL": + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_CUBIC) + resized_image = resized_image.astype("float32") + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + resized_image -= 0.5 + resized_image /= 0.5 + return resized_image + + assert imgC == img.shape[2] + imgW = int((imgH * max_wh_ratio)) + if self.use_onnx: + w = self.input_tensor.shape[3:][0] + if isinstance(w, str): + pass + elif w is not None and w > 0: + imgW = w + h, w = img.shape[:2] + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + if self.rec_algorithm == "RARE": + if resized_w > self.rec_image_shape[2]: + resized_w = self.rec_image_shape[2] + imgW = self.rec_image_shape[2] + resized_image = cv2.resize(img, (resized_w, imgH)) + resized_image = resized_image.astype("float32") + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + return padding_im + + def resize_norm_img_vl(self, img, image_shape): + imgC, imgH, imgW = image_shape + img = img[:, :, ::-1] # bgr2rgb + resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) + resized_image = resized_image.astype("float32") + resized_image = resized_image.transpose((2, 0, 1)) / 255 + return resized_image + + def resize_norm_img_srn(self, img, image_shape): + imgC, imgH, imgW = image_shape + + img_black = np.zeros((imgH, imgW)) + im_hei = img.shape[0] + im_wid = img.shape[1] + + if im_wid <= im_hei * 1: + img_new = cv2.resize(img, (imgH * 1, imgH)) + elif im_wid <= im_hei * 2: + img_new = cv2.resize(img, (imgH * 2, imgH)) + elif im_wid <= im_hei * 3: + img_new = cv2.resize(img, (imgH * 3, imgH)) + else: + img_new = cv2.resize(img, (imgW, imgH)) + + img_np = np.asarray(img_new) + img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) + img_black[:, 0 : img_np.shape[1]] = img_np + img_black = img_black[:, :, np.newaxis] + + row, col, c = img_black.shape + c = 1 + + return np.reshape(img_black, (c, row, col)).astype(np.float32) + + def srn_other_inputs(self, image_shape, num_heads, max_text_length): + imgC, imgH, imgW = image_shape + feature_dim = int((imgH / 8) * (imgW / 8)) + + encoder_word_pos = ( + np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype("int64") + ) + gsrm_word_pos = ( + np.array(range(0, max_text_length)) + .reshape((max_text_length, 1)) + .astype("int64") + ) + + gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) + gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( + [-1, 1, max_text_length, max_text_length] + ) + gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]).astype( + "float32" + ) * [-1e9] + + gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( + [-1, 1, max_text_length, max_text_length] + ) + gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]).astype( + "float32" + ) * [-1e9] + + encoder_word_pos = encoder_word_pos[np.newaxis, :] + gsrm_word_pos = gsrm_word_pos[np.newaxis, :] + + return [ + encoder_word_pos, + gsrm_word_pos, + gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2, + ] + + def process_image_srn(self, img, image_shape, num_heads, max_text_length): + norm_img = self.resize_norm_img_srn(img, image_shape) + norm_img = norm_img[np.newaxis, :] + + [ + encoder_word_pos, + gsrm_word_pos, + gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2, + ] = self.srn_other_inputs(image_shape, num_heads, max_text_length) + + gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32) + gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32) + encoder_word_pos = encoder_word_pos.astype(np.int64) + gsrm_word_pos = gsrm_word_pos.astype(np.int64) + + return ( + norm_img, + encoder_word_pos, + gsrm_word_pos, + gsrm_slf_attn_bias1, + gsrm_slf_attn_bias2, + ) + + def resize_norm_img_sar(self, img, image_shape, width_downsample_ratio=0.25): + imgC, imgH, imgW_min, imgW_max = image_shape + h = img.shape[0] + w = img.shape[1] + valid_ratio = 1.0 + # make sure new_width is an integral multiple of width_divisor. + width_divisor = int(1 / width_downsample_ratio) + # resize + ratio = w / float(h) + resize_w = math.ceil(imgH * ratio) + if resize_w % width_divisor != 0: + resize_w = round(resize_w / width_divisor) * width_divisor + if imgW_min is not None: + resize_w = max(imgW_min, resize_w) + if imgW_max is not None: + valid_ratio = min(1.0, 1.0 * resize_w / imgW_max) + resize_w = min(imgW_max, resize_w) + resized_image = cv2.resize(img, (resize_w, imgH)) + resized_image = resized_image.astype("float32") + # norm + if image_shape[0] == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + resize_shape = resized_image.shape + padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32) + padding_im[:, :, 0:resize_w] = resized_image + pad_shape = padding_im.shape + + return padding_im, resize_shape, pad_shape, valid_ratio + + def resize_norm_img_spin(self, img): + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # return padding_im + img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC) + img = np.array(img, np.float32) + img = np.expand_dims(img, -1) + img = img.transpose((2, 0, 1)) + mean = [127.5] + std = [127.5] + mean = np.array(mean, dtype=np.float32) + std = np.array(std, dtype=np.float32) + mean = np.float32(mean.reshape(1, -1)) + stdinv = 1 / np.float32(std.reshape(1, -1)) + img -= mean + img *= stdinv + return img + + def resize_norm_img_svtr(self, img, image_shape): + imgC, imgH, imgW = image_shape + resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) + resized_image = resized_image.astype("float32") + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + return resized_image + + def resize_norm_img_cppd_padding( + self, img, image_shape, padding=True, interpolation=cv2.INTER_LINEAR + ): + imgC, imgH, imgW = image_shape + h = img.shape[0] + w = img.shape[1] + if not padding: + resized_image = cv2.resize(img, (imgW, imgH), interpolation=interpolation) + resized_w = imgW + else: + ratio = w / float(h) + if math.ceil(imgH * ratio) > imgW: + resized_w = imgW + else: + resized_w = int(math.ceil(imgH * ratio)) + resized_image = cv2.resize(img, (resized_w, imgH)) + resized_image = resized_image.astype("float32") + if image_shape[0] == 1: + resized_image = resized_image / 255 + resized_image = resized_image[np.newaxis, :] + else: + resized_image = resized_image.transpose((2, 0, 1)) / 255 + resized_image -= 0.5 + resized_image /= 0.5 + padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) + padding_im[:, :, 0:resized_w] = resized_image + + return padding_im + + def resize_norm_img_abinet(self, img, image_shape): + imgC, imgH, imgW = image_shape + + resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) + resized_image = resized_image.astype("float32") + resized_image = resized_image / 255.0 + + mean = np.array([0.485, 0.456, 0.406]) + std = np.array([0.229, 0.224, 0.225]) + resized_image = (resized_image - mean[None, None, ...]) / std[None, None, ...] + resized_image = resized_image.transpose((2, 0, 1)) + resized_image = resized_image.astype("float32") + + return resized_image + + def norm_img_can(self, img, image_shape): + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image + + if self.inverse: + img = 255 - img + + if self.rec_image_shape[0] == 1: + h, w = img.shape + _, imgH, imgW = self.rec_image_shape + if h < imgH or w < imgW: + padding_h = max(imgH - h, 0) + padding_w = max(imgW - w, 0) + img_padded = np.pad( + img, + ((0, padding_h), (0, padding_w)), + "constant", + constant_values=(255), + ) + img = img_padded + + img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w + img = img.astype("float32") + + return img + + def __call__(self, img_list): + img_num = len(img_list) + # Calculate the aspect ratio of all text bars + width_list = [] + for img in img_list: + width_list.append(img.shape[1] / float(img.shape[0])) + # Sorting can speed up the recognition process + indices = np.argsort(np.array(width_list)) + rec_res = [["", 0.0]] * img_num + batch_num = self.rec_batch_num + st = time.time() + if self.benchmark: + self.autolog.times.start() + for beg_img_no in range(0, img_num, batch_num): + end_img_no = min(img_num, beg_img_no + batch_num) + norm_img_batch = [] + imgC, imgH, imgW = self.rec_image_shape[:3] + max_wh_ratio = imgW / imgH + wh_ratio_list = [] + for ino in range(beg_img_no, end_img_no): + h, w = img_list[indices[ino]].shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + wh_ratio_list.append(wh_ratio) + for ino in range(beg_img_no, end_img_no): + norm_img = self.resize_norm_img( + img_list[indices[ino]], max_wh_ratio + ) + norm_img = norm_img[np.newaxis, :] + norm_img_batch.append(norm_img) + norm_img_batch = np.concatenate(norm_img_batch) + norm_img_batch = norm_img_batch.copy() + if self.benchmark: + self.autolog.times.stamp() + + assert self.use_onnx + input_dict = {} + input_dict[self.input_tensor.name] = norm_img_batch + outputs = self.predictor.run(self.output_tensors, input_dict) + preds = outputs[0] + rec_result = self.postprocess_op( + preds, + return_word_box=self.return_word_box, + wh_ratio_list=wh_ratio_list, + max_wh_ratio=max_wh_ratio, + ) + for rno in range(len(rec_result)): + rec_res[indices[beg_img_no + rno]] = rec_result[rno] + if self.benchmark: + self.autolog.times.end(stamp=True) + return rec_res, time.time() - st diff --git a/src/models/thrid_party/paddleocr/infer/utility.py b/src/models/thrid_party/paddleocr/infer/utility.py new file mode 100644 index 0000000..e92a77c --- /dev/null +++ b/src/models/thrid_party/paddleocr/infer/utility.py @@ -0,0 +1,713 @@ +# 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 argparse +import os +import sys +import functools +import logging +import cv2 +import numpy as np +import PIL +from PIL import Image, ImageDraw, ImageFont +import math +import random + + +def str2bool(v): + return v.lower() in ("true", "yes", "t", "y", "1") + + +def str2int_tuple(v): + return tuple([int(i.strip()) for i in v.split(",")]) + + +def init_args(): + parser = argparse.ArgumentParser() + # params for prediction engine + parser.add_argument("--use_gpu", type=str2bool, default=True) + parser.add_argument("--use_xpu", type=str2bool, default=False) + parser.add_argument("--use_npu", type=str2bool, default=False) + parser.add_argument("--use_mlu", type=str2bool, default=False) + parser.add_argument("--ir_optim", type=str2bool, default=True) + parser.add_argument("--use_tensorrt", type=str2bool, default=False) + parser.add_argument("--min_subgraph_size", type=int, default=15) + parser.add_argument("--precision", type=str, default="fp32") + parser.add_argument("--gpu_mem", type=int, default=500) + parser.add_argument("--gpu_id", type=int, default=0) + + # params for text detector + parser.add_argument("--image_dir", type=str) + parser.add_argument("--page_num", type=int, default=0) + parser.add_argument("--det_algorithm", type=str, default="DB") + parser.add_argument("--det_model_dir", type=str) + parser.add_argument("--det_limit_side_len", type=float, default=960) + parser.add_argument("--det_limit_type", type=str, default="max") + parser.add_argument("--det_box_type", type=str, default="quad") + + # DB parmas + parser.add_argument("--det_db_thresh", type=float, default=0.3) + parser.add_argument("--det_db_box_thresh", type=float, default=0.6) + parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5) + parser.add_argument("--max_batch_size", type=int, default=10) + parser.add_argument("--use_dilation", type=str2bool, default=False) + parser.add_argument("--det_db_score_mode", type=str, default="fast") + + # EAST parmas + parser.add_argument("--det_east_score_thresh", type=float, default=0.8) + parser.add_argument("--det_east_cover_thresh", type=float, default=0.1) + parser.add_argument("--det_east_nms_thresh", type=float, default=0.2) + + # SAST parmas + parser.add_argument("--det_sast_score_thresh", type=float, default=0.5) + parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2) + + # PSE parmas + parser.add_argument("--det_pse_thresh", type=float, default=0) + parser.add_argument("--det_pse_box_thresh", type=float, default=0.85) + parser.add_argument("--det_pse_min_area", type=float, default=16) + parser.add_argument("--det_pse_scale", type=int, default=1) + + # FCE parmas + parser.add_argument("--scales", type=list, default=[8, 16, 32]) + parser.add_argument("--alpha", type=float, default=1.0) + parser.add_argument("--beta", type=float, default=1.0) + parser.add_argument("--fourier_degree", type=int, default=5) + + # params for text recognizer + parser.add_argument("--rec_algorithm", type=str, default="SVTR_LCNet") + parser.add_argument("--rec_model_dir", type=str) + parser.add_argument("--rec_image_inverse", type=str2bool, default=True) + parser.add_argument("--rec_image_shape", type=str, default="3, 48, 320") + parser.add_argument("--rec_batch_num", type=int, default=6) + parser.add_argument("--max_text_length", type=int, default=25) + parser.add_argument( + "--rec_char_dict_path", type=str, default="./ppocr_keys_v1.txt" + ) + parser.add_argument("--use_space_char", type=str2bool, default=True) + parser.add_argument("--vis_font_path", type=str, default="./doc/fonts/simfang.ttf") + parser.add_argument("--drop_score", type=float, default=0.5) + + # params for e2e + parser.add_argument("--e2e_algorithm", type=str, default="PGNet") + parser.add_argument("--e2e_model_dir", type=str) + parser.add_argument("--e2e_limit_side_len", type=float, default=768) + parser.add_argument("--e2e_limit_type", type=str, default="max") + + # PGNet parmas + parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5) + parser.add_argument( + "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt" + ) + parser.add_argument("--e2e_pgnet_valid_set", type=str, default="totaltext") + parser.add_argument("--e2e_pgnet_mode", type=str, default="fast") + + # params for text classifier + parser.add_argument("--use_angle_cls", type=str2bool, default=False) + parser.add_argument("--cls_model_dir", type=str) + parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192") + parser.add_argument("--label_list", type=list, default=["0", "180"]) + parser.add_argument("--cls_batch_num", type=int, default=6) + parser.add_argument("--cls_thresh", type=float, default=0.9) + + parser.add_argument("--enable_mkldnn", type=str2bool, default=False) + parser.add_argument("--cpu_threads", type=int, default=10) + parser.add_argument("--use_pdserving", type=str2bool, default=False) + parser.add_argument("--warmup", type=str2bool, default=False) + + # SR parmas + parser.add_argument("--sr_model_dir", type=str) + parser.add_argument("--sr_image_shape", type=str, default="3, 32, 128") + parser.add_argument("--sr_batch_num", type=int, default=1) + + # + parser.add_argument("--draw_img_save_dir", type=str, default="./inference_results") + parser.add_argument("--save_crop_res", type=str2bool, default=False) + parser.add_argument("--crop_res_save_dir", type=str, default="./output") + + # multi-process + parser.add_argument("--use_mp", type=str2bool, default=False) + parser.add_argument("--total_process_num", type=int, default=1) + parser.add_argument("--process_id", type=int, default=0) + + parser.add_argument("--benchmark", type=str2bool, default=False) + parser.add_argument("--save_log_path", type=str, default="./log_output/") + + parser.add_argument("--show_log", type=str2bool, default=True) + parser.add_argument("--use_onnx", type=str2bool, default=False) + + # extended function + parser.add_argument( + "--return_word_box", + type=str2bool, + default=False, + help="Whether return the bbox of each word (split by space) or chinese character. Only used in ppstructure for layout recovery", + ) + + return parser + + +def parse_args(): + parser = init_args() + return parser.parse_args([]) + + +def create_predictor(args, mode, logger): + if mode == "det": + model_dir = args.det_model_dir + elif mode == "cls": + model_dir = args.cls_model_dir + elif mode == "rec": + model_dir = args.rec_model_dir + elif mode == "table": + model_dir = args.table_model_dir + elif mode == "ser": + model_dir = args.ser_model_dir + elif mode == "re": + model_dir = args.re_model_dir + elif mode == "sr": + model_dir = args.sr_model_dir + elif mode == "layout": + model_dir = args.layout_model_dir + else: + model_dir = args.e2e_model_dir + + if model_dir is None: + logger.info("not find {} model file path {}".format(mode, model_dir)) + sys.exit(0) + assert args.use_onnx + + import onnxruntime as ort + + model_file_path = model_dir + if not os.path.exists(model_file_path): + raise ValueError("not find model file path {}".format(model_file_path)) + if args.use_gpu: + sess = ort.InferenceSession( + model_file_path, providers=["CUDAExecutionProvider"] + ) + else: + sess = ort.InferenceSession(model_file_path) + return sess, sess.get_inputs()[0], None, None + + + +def get_output_tensors(args, mode, predictor): + output_names = predictor.get_output_names() + output_tensors = [] + if mode == "rec" and args.rec_algorithm in ["CRNN", "SVTR_LCNet", "SVTR_HGNet"]: + output_name = "softmax_0.tmp_0" + if output_name in output_names: + return [predictor.get_output_handle(output_name)] + else: + for output_name in output_names: + output_tensor = predictor.get_output_handle(output_name) + output_tensors.append(output_tensor) + else: + for output_name in output_names: + output_tensor = predictor.get_output_handle(output_name) + output_tensors.append(output_tensor) + return output_tensors + + +def draw_e2e_res(dt_boxes, strs, img_path): + src_im = cv2.imread(img_path) + for box, str in zip(dt_boxes, strs): + box = box.astype(np.int32).reshape((-1, 1, 2)) + cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2) + cv2.putText( + src_im, + str, + org=(int(box[0, 0, 0]), int(box[0, 0, 1])), + fontFace=cv2.FONT_HERSHEY_COMPLEX, + fontScale=0.7, + color=(0, 255, 0), + thickness=1, + ) + return src_im + + +def draw_text_det_res(dt_boxes, img): + for box in dt_boxes: + box = np.array(box).astype(np.int32).reshape(-1, 2) + cv2.polylines(img, [box], True, color=(255, 255, 0), thickness=2) + return img + + +def resize_img(img, input_size=600): + """ + resize img and limit the longest side of the image to input_size + """ + img = np.array(img) + im_shape = img.shape + im_size_max = np.max(im_shape[0:2]) + im_scale = float(input_size) / float(im_size_max) + img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale) + return img + + +def draw_ocr( + image, + boxes, + txts=None, + scores=None, + drop_score=0.5, + font_path="./doc/fonts/simfang.ttf", +): + """ + Visualize the results of OCR detection and recognition + args: + image(Image|array): RGB image + boxes(list): boxes with shape(N, 4, 2) + txts(list): the texts + scores(list): txxs corresponding scores + drop_score(float): only scores greater than drop_threshold will be visualized + font_path: the path of font which is used to draw text + return(array): + the visualized img + """ + if scores is None: + scores = [1] * len(boxes) + box_num = len(boxes) + for i in range(box_num): + if scores is not None and (scores[i] < drop_score or math.isnan(scores[i])): + continue + box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64) + image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2) + if txts is not None: + img = np.array(resize_img(image, input_size=600)) + txt_img = text_visual( + txts, + scores, + img_h=img.shape[0], + img_w=600, + threshold=drop_score, + font_path=font_path, + ) + img = np.concatenate([np.array(img), np.array(txt_img)], axis=1) + return img + return image + + +def draw_ocr_box_txt( + image, + boxes, + txts=None, + scores=None, + drop_score=0.5, + font_path="./doc/fonts/simfang.ttf", +): + h, w = image.height, image.width + img_left = image.copy() + img_right = np.ones((h, w, 3), dtype=np.uint8) * 255 + random.seed(0) + + draw_left = ImageDraw.Draw(img_left) + if txts is None or len(txts) != len(boxes): + txts = [None] * len(boxes) + 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) + img_right_text = draw_box_txt_fine((w, h), box, txt, font_path) + pts = np.array(box, np.int32).reshape((-1, 1, 2)) + cv2.polylines(img_right_text, [pts], True, color, 1) + img_right = cv2.bitwise_and(img_right, img_right_text) + img_left = Image.blend(image, img_left, 0.5) + img_show = Image.new("RGB", (w * 2, h), (255, 255, 255)) + img_show.paste(img_left, (0, 0, w, h)) + img_show.paste(Image.fromarray(img_right), (w, 0, w * 2, h)) + return np.array(img_show) + + +def draw_box_txt_fine(img_size, box, txt, font_path="./doc/fonts/simfang.ttf"): + box_height = int( + math.sqrt((box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][1]) ** 2) + ) + box_width = int( + math.sqrt((box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][1]) ** 2) + ) + + if box_height > 2 * box_width and box_height > 30: + img_text = Image.new("RGB", (box_height, box_width), (255, 255, 255)) + draw_text = ImageDraw.Draw(img_text) + if txt: + font = create_font(txt, (box_height, box_width), font_path) + draw_text.text([0, 0], txt, fill=(0, 0, 0), font=font) + img_text = img_text.transpose(Image.ROTATE_270) + else: + img_text = Image.new("RGB", (box_width, box_height), (255, 255, 255)) + draw_text = ImageDraw.Draw(img_text) + if txt: + font = create_font(txt, (box_width, box_height), font_path) + draw_text.text([0, 0], txt, fill=(0, 0, 0), font=font) + + pts1 = np.float32( + [[0, 0], [box_width, 0], [box_width, box_height], [0, box_height]] + ) + pts2 = np.array(box, dtype=np.float32) + M = cv2.getPerspectiveTransform(pts1, pts2) + + img_text = np.array(img_text, dtype=np.uint8) + img_right_text = cv2.warpPerspective( + img_text, + M, + img_size, + flags=cv2.INTER_NEAREST, + borderMode=cv2.BORDER_CONSTANT, + borderValue=(255, 255, 255), + ) + return img_right_text + + +def create_font(txt, sz, font_path="./doc/fonts/simfang.ttf"): + font_size = int(sz[1] * 0.99) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + if int(PIL.__version__.split(".")[0]) < 10: + length = font.getsize(txt)[0] + else: + length = font.getlength(txt) + + if length > sz[0]: + font_size = int(font_size * sz[0] / length) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + return font + + +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): + the number of Chinese characters + """ + import string + + count_zh = count_pu = 0 + s_len = len(s) + en_dg_count = 0 + for c in s: + if c in string.ascii_letters or c.isdigit() or c.isspace(): + en_dg_count += 1 + elif c.isalpha(): + count_zh += 1 + else: + count_pu += 1 + return s_len - math.ceil(en_dg_count / 2) + + +def text_visual( + texts, scores, img_h=400, img_w=600, threshold=0.0, font_path="./doc/simfang.ttf" +): + """ + create new blank img and draw txt on it + args: + texts(list): the text will be draw + scores(list|None): corresponding score of each txt + img_h(int): the height of blank img + 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( + scores + ), "The number of txts and corresponding scores must match" + + def create_blank_img(): + blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255 + blank_img[:, img_w - 1 :] = 0 + blank_img = Image.fromarray(blank_img).convert("RGB") + draw_txt = ImageDraw.Draw(blank_img) + return blank_img, draw_txt + + blank_img, draw_txt = create_blank_img() + + font_size = 20 + txt_color = (0, 0, 0) + font = ImageFont.truetype(font_path, font_size, encoding="utf-8") + + gap = font_size + 5 + txt_img_list = [] + count, index = 1, 0 + for idx, txt in enumerate(texts): + index += 1 + if scores[idx] < threshold or math.isnan(scores[idx]): + index -= 1 + continue + first_line = True + while str_count(txt) >= img_w // font_size - 4: + tmp = txt + txt = tmp[: img_w // font_size - 4] + if first_line: + new_txt = str(index) + ": " + txt + first_line = False + else: + new_txt = " " + txt + draw_txt.text((0, gap * count), new_txt, txt_color, font=font) + txt = tmp[img_w // font_size - 4 :] + if count >= img_h // gap - 1: + txt_img_list.append(np.array(blank_img)) + blank_img, draw_txt = create_blank_img() + count = 0 + count += 1 + if first_line: + new_txt = str(index) + ": " + txt + " " + "%.3f" % (scores[idx]) + else: + new_txt = " " + txt + " " + "%.3f" % (scores[idx]) + draw_txt.text((0, gap * count), new_txt, txt_color, font=font) + # whether add new blank img or not + if count >= img_h // gap - 1 and idx + 1 < len(texts): + txt_img_list.append(np.array(blank_img)) + blank_img, draw_txt = create_blank_img() + count = 0 + count += 1 + txt_img_list.append(np.array(blank_img)) + if len(txt_img_list) == 1: + blank_img = np.array(txt_img_list[0]) + else: + blank_img = np.concatenate(txt_img_list, axis=1) + return np.array(blank_img) + + +def base64_to_cv2(b64str): + import base64 + + data = base64.b64decode(b64str.encode("utf8")) + data = np.frombuffer(data, np.uint8) + data = cv2.imdecode(data, cv2.IMREAD_COLOR) + return data + + +def draw_boxes(image, boxes, scores=None, drop_score=0.5): + if scores is None: + scores = [1] * len(boxes) + for box, score in zip(boxes, scores): + if score < drop_score: + continue + box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64) + image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2) + return image + + +def get_rotate_crop_image(img, points): + """ + img_height, img_width = img.shape[0:2] + left = int(np.min(points[:, 0])) + right = int(np.max(points[:, 0])) + top = int(np.min(points[:, 1])) + bottom = int(np.max(points[:, 1])) + img_crop = img[top:bottom, left:right, :].copy() + points[:, 0] = points[:, 0] - left + points[:, 1] = points[:, 1] - top + """ + assert len(points) == 4, "shape of points must be 4*2" + img_crop_width = int( + max( + np.linalg.norm(points[0] - points[1]), np.linalg.norm(points[2] - points[3]) + ) + ) + img_crop_height = int( + max( + np.linalg.norm(points[0] - points[3]), np.linalg.norm(points[1] - points[2]) + ) + ) + pts_std = np.float32( + [ + [0, 0], + [img_crop_width, 0], + [img_crop_width, img_crop_height], + [0, img_crop_height], + ] + ) + M = cv2.getPerspectiveTransform(points, pts_std) + dst_img = cv2.warpPerspective( + img, + M, + (img_crop_width, img_crop_height), + borderMode=cv2.BORDER_REPLICATE, + flags=cv2.INTER_CUBIC, + ) + dst_img_height, dst_img_width = dst_img.shape[0:2] + if dst_img_height * 1.0 / dst_img_width >= 1.5: + dst_img = np.rot90(dst_img) + return dst_img + + +def get_minarea_rect_crop(img, points): + bounding_box = cv2.minAreaRect(np.array(points).astype(np.int32)) + points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) + + index_a, index_b, index_c, index_d = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_a = 0 + index_d = 1 + else: + index_a = 1 + index_d = 0 + if points[3][1] > points[2][1]: + index_b = 2 + index_c = 3 + else: + index_b = 3 + index_c = 2 + + box = [points[index_a], points[index_b], points[index_c], points[index_d]] + crop_img = get_rotate_crop_image(img, np.array(box)) + return crop_img + + +# def check_gpu(use_gpu): +# if use_gpu and ( +# not paddle.is_compiled_with_cuda() or paddle.device.get_device() == "cpu" +# ): +# use_gpu = False +# return use_gpu + + +def _check_image_file(path): + img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif", "pdf"} + return any([path.lower().endswith(e) for e in img_end]) + + +def get_image_file_list(img_file, infer_list=None): + imgs_lists = [] + if img_file is None or not os.path.exists(img_file): + raise Exception("not found any img file in {}".format(img_file)) + + if os.path.isfile(img_file) and _check_image_file(img_file): + imgs_lists.append(img_file) + elif os.path.isdir(img_file): + for single_file in os.listdir(img_file): + file_path = os.path.join(img_file, single_file) + if os.path.isfile(file_path) and _check_image_file(file_path): + imgs_lists.append(file_path) + + if len(imgs_lists) == 0: + raise Exception("not found any img file in {}".format(img_file)) + imgs_lists = sorted(imgs_lists) + return imgs_lists + + +logger_initialized = {} +@functools.lru_cache() +def get_logger(name="ppocr", log_file=None, log_level=logging.DEBUG): + """Initialize and get a logger by name. + If the logger has not been initialized, this method will initialize the + logger by adding one or two handlers, otherwise the initialized logger will + be directly returned. During initialization, a StreamHandler will always be + added. If `log_file` is specified a FileHandler will also be added. + Args: + name (str): Logger name. + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the logger. + log_level (int): The logger level. Note that only the process of + rank 0 is affected, and other processes will set the level to + "Error" thus be silent most of the time. + Returns: + logging.Logger: The expected logger. + """ + logger = logging.getLogger(name) + if name in logger_initialized: + return logger + for logger_name in logger_initialized: + if name.startswith(logger_name): + return logger + + formatter = logging.Formatter( + "[%(asctime)s] %(name)s %(levelname)s: %(message)s", datefmt="%Y/%m/%d %H:%M:%S" + ) + + stream_handler = logging.StreamHandler(stream=sys.stdout) + stream_handler.setFormatter(formatter) + logger.addHandler(stream_handler) + logger_initialized[name] = True + logger.propagate = False + return logger + + +def get_rotate_crop_image(img, points): + """ + img_height, img_width = img.shape[0:2] + left = int(np.min(points[:, 0])) + right = int(np.max(points[:, 0])) + top = int(np.min(points[:, 1])) + bottom = int(np.max(points[:, 1])) + img_crop = img[top:bottom, left:right, :].copy() + points[:, 0] = points[:, 0] - left + points[:, 1] = points[:, 1] - top + """ + assert len(points) == 4, "shape of points must be 4*2" + img_crop_width = int( + max( + np.linalg.norm(points[0] - points[1]), np.linalg.norm(points[2] - points[3]) + ) + ) + img_crop_height = int( + max( + np.linalg.norm(points[0] - points[3]), np.linalg.norm(points[1] - points[2]) + ) + ) + pts_std = np.float32( + [ + [0, 0], + [img_crop_width, 0], + [img_crop_width, img_crop_height], + [0, img_crop_height], + ] + ) + M = cv2.getPerspectiveTransform(points, pts_std) + dst_img = cv2.warpPerspective( + img, + M, + (img_crop_width, img_crop_height), + borderMode=cv2.BORDER_REPLICATE, + flags=cv2.INTER_CUBIC, + ) + dst_img_height, dst_img_width = dst_img.shape[0:2] + if dst_img_height * 1.0 / dst_img_width >= 1.5: + dst_img = np.rot90(dst_img) + return dst_img + + +def get_minarea_rect_crop(img, points): + bounding_box = cv2.minAreaRect(np.array(points).astype(np.int32)) + points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) + + index_a, index_b, index_c, index_d = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_a = 0 + index_d = 1 + else: + index_a = 1 + index_d = 0 + if points[3][1] > points[2][1]: + index_b = 2 + index_c = 3 + else: + index_b = 3 + index_c = 2 + + box = [points[index_a], points[index_b], points[index_c], points[index_d]] + crop_img = get_rotate_crop_image(img, np.array(box)) + return crop_img + + + +if __name__ == "__main__": + pass