# Copyright (c) 2022 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 yaml import glob from functools import reduce import time import cv2 import numpy as np import math import paddle import sys parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 3))) sys.path.insert(0, parent_path) from python.infer import get_test_images from python.preprocess import preprocess, NormalizeImage, Permute, Resize_Mult32 from pphuman.ppvehicle.vehicle_plateutils import create_predictor, get_infer_gpuid, get_rotate_crop_image, draw_boxes from pphuman.ppvehicle.vehicleplate_postprocess import build_post_process from pphuman.pipe_utils import merge_cfg, print_arguments, argsparser class PlateDetector(object): def __init__(self, args, cfg): self.args = args self.pre_process_list = { 'Resize_Mult32': { 'limit_side_len': cfg['det_limit_side_len'], 'limit_type': cfg['det_limit_type'], }, 'NormalizeImage': { 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'is_scale': True, }, 'Permute': {} } postprocess_params = {} postprocess_params['name'] = 'DBPostProcess' postprocess_params["thresh"] = 0.3 postprocess_params["box_thresh"] = 0.6 postprocess_params["max_candidates"] = 1000 postprocess_params["unclip_ratio"] = 1.5 postprocess_params["use_dilation"] = False postprocess_params["score_mode"] = "fast" self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = create_predictor( args, cfg, 'det') def preprocess(self, image_list): preprocess_ops = [] for op_type, new_op_info in self.pre_process_list.items(): preprocess_ops.append(eval(op_type)(**new_op_info)) input_im_lst = [] input_im_info_lst = [] for im_path in image_list: im, im_info = preprocess(im_path, preprocess_ops) input_im_lst.append(im) input_im_info_lst.append(im_info['im_shape'] / im_info['scale_factor']) return np.stack(input_im_lst, axis=0), input_im_info_lst 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)] diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[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: 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: 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_image(self, img_list): st = time.time() img, shape_list = self.preprocess(img_list) if img is None: return None, 0 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) preds = {} preds['maps'] = outputs[0] #self.predictor.try_shrink_memory() post_result = self.postprocess_op(preds, shape_list) dt_batch_boxes = [] for idx in range(len(post_result)): org_shape = img_list[idx].shape dt_boxes = post_result[idx]['points'] dt_boxes = self.filter_tag_det_res(dt_boxes, org_shape) dt_batch_boxes.append(dt_boxes) et = time.time() return dt_batch_boxes, et - st class TextRecognizer(object): def __init__(self, args, cfg, use_gpu=True): self.rec_image_shape = cfg['rec_image_shape'] self.rec_batch_num = cfg['rec_batch_num'] self.rec_algorithm = cfg['rec_algorithm'] word_dict_path = cfg['word_dict_path'] use_space_char = True postprocess_params = { 'name': 'CTCLabelDecode', "character_dict_path": word_dict_path, "use_space_char": use_space_char } if self.rec_algorithm == "SRN": postprocess_params = { 'name': 'SRNLabelDecode', "character_dict_path": word_dict_path, "use_space_char": use_space_char } elif self.rec_algorithm == "RARE": postprocess_params = { 'name': 'AttnLabelDecode', "character_dict_path": word_dict_path, "use_space_char": use_space_char } elif self.rec_algorithm == 'NRTR': postprocess_params = { 'name': 'NRTRLabelDecode', "character_dict_path": word_dict_path, "use_space_char": use_space_char } elif self.rec_algorithm == "SAR": postprocess_params = { 'name': 'SARLabelDecode', "character_dict_path": word_dict_path, "use_space_char": use_space_char } self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = \ create_predictor(args, cfg, 'rec') self.use_onnx = False def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape if self.rec_algorithm == 'NRTR': img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # return padding_im image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize([100, 32], Image.ANTIALIAS) img = np.array(img) norm_img = np.expand_dims(img, -1) norm_img = norm_img.transpose((2, 0, 1)) return norm_img.astype(np.float32) / 128. - 1. assert imgC == img.shape[2] imgW = int((imgH * max_wh_ratio)) if self.use_onnx: w = self.input_tensor.shape[3:][0] if 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_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_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 predict_text(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() 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 max_wh_ratio = imgW / imgH # max_wh_ratio = 0 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) for ino in range(beg_img_no, end_img_no): if self.rec_algorithm == "SAR": norm_img, _, _, valid_ratio = self.resize_norm_img_sar( img_list[indices[ino]], self.rec_image_shape) norm_img = norm_img[np.newaxis, :] valid_ratio = np.expand_dims(valid_ratio, axis=0) valid_ratios = [] valid_ratios.append(valid_ratio) norm_img_batch.append(norm_img) elif self.rec_algorithm == "SRN": norm_img = self.process_image_srn( img_list[indices[ino]], self.rec_image_shape, 8, 25) encoder_word_pos_list = [] gsrm_word_pos_list = [] gsrm_slf_attn_bias1_list = [] gsrm_slf_attn_bias2_list = [] encoder_word_pos_list.append(norm_img[1]) gsrm_word_pos_list.append(norm_img[2]) gsrm_slf_attn_bias1_list.append(norm_img[3]) gsrm_slf_attn_bias2_list.append(norm_img[4]) norm_img_batch.append(norm_img[0]) elif self.rec_algorithm == "SVTR": norm_img = self.resize_norm_img_svtr(img_list[indices[ino]], self.rec_image_shape) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) else: 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.rec_algorithm == "SRN": encoder_word_pos_list = np.concatenate(encoder_word_pos_list) gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list) gsrm_slf_attn_bias1_list = np.concatenate( gsrm_slf_attn_bias1_list) gsrm_slf_attn_bias2_list = np.concatenate( gsrm_slf_attn_bias2_list) inputs = [ norm_img_batch, encoder_word_pos_list, gsrm_word_pos_list, gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list, ] if self.use_onnx: input_dict = {} input_dict[self.input_tensor.name] = norm_img_batch outputs = self.predictor.run(self.output_tensors, input_dict) preds = {"predict": outputs[2]} else: input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle( input_names[i]) input_tensor.copy_from_cpu(inputs[i]) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = {"predict": outputs[2]} elif self.rec_algorithm == "SAR": valid_ratios = np.concatenate(valid_ratios) inputs = [ norm_img_batch, valid_ratios, ] if 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] else: input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle( input_names[i]) input_tensor.copy_from_cpu(inputs[i]) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = outputs[0] else: if 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] else: self.input_tensor.copy_from_cpu(norm_img_batch) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) if len(outputs) != 1: preds = outputs else: preds = outputs[0] rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): rec_res[indices[beg_img_no + rno]] = rec_result[rno] return rec_res, time.time() - st class PlateRecognizer(object): def __init__(self, args, cfg): use_gpu = args.device.lower() == "gpu" self.platedetector = PlateDetector(args, cfg) self.textrecognizer = TextRecognizer(args, cfg, use_gpu=use_gpu) def get_platelicense(self, image_list): plate_text_list = [] plateboxes, det_time = self.platedetector.predict_image(image_list) for idx, boxes_pcar in enumerate(plateboxes): plate_pcar_list = [] for box in boxes_pcar: plate_images = get_rotate_crop_image(image_list[idx], box) plate_texts = self.textrecognizer.predict_text([plate_images]) plate_pcar_list.append(plate_texts) plate_text_list.append(plate_pcar_list) return self.check_plate(plate_text_list) def check_plate(self, text_list): simcode = [ '浙', '粤', '京', '津', '冀', '晋', '蒙', '辽', '黑', '沪', '吉', '苏', '皖', '赣', '鲁', '豫', '鄂', '湘', '桂', '琼', '渝', '川', '贵', '云', '藏', '陕', '甘', '青', '宁' ] plate_all = {"plate": []} for text_pcar in text_list: platelicense = "" for text_info in text_pcar: text = text_info[0][0][0] if len(text) > 2 and text[0] in simcode and len(text) < 10: platelicense = text plate_all["plate"].append(platelicense) return plate_all def main(): cfg = merge_cfg(FLAGS) print_arguments(cfg) vehicleplate_cfg = cfg['VEHICLE_PLATE'] detector = PlateRecognizer(FLAGS, vehicleplate_cfg) # predict from image img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file) for img in img_list: image = cv2.imread(img) results = detector.get_platelicense([image]) print(results) if __name__ == '__main__': paddle.enable_static() parser = argsparser() FLAGS = parser.parse_args() FLAGS.device = FLAGS.device.upper() assert FLAGS.device in ['CPU', 'GPU', 'XPU' ], "device should be CPU, GPU or XPU" main()