Merge branch 'develop' into fix_infer
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xiaoting
GitHub
commit
f8515609f3
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@ -15,7 +15,7 @@ EvalReader:
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TestReader:
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reader_function: ppocr.data.det.dataset_traversal,EvalTestReader
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process_function: ppocr.data.det.db_process,DBProcessTest
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single_img_path:
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infer_img:
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img_set_dir: ./train_data/icdar2015/text_localization/
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label_file_path: ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
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test_image_shape: [736, 1280]
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@ -17,7 +17,7 @@ EvalReader:
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TestReader:
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reader_function: ppocr.data.det.dataset_traversal,EvalTestReader
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process_function: ppocr.data.det.east_process,EASTProcessTest
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single_img_path:
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infer_img:
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img_set_dir: ./train_data/icdar2015/text_localization/
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label_file_path: ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
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do_eval: True
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@ -10,4 +10,4 @@ EvalReader:
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TestReader:
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reader_function: ppocr.data.rec.dataset_traversal,LMDBReader
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lmdb_sets_dir: ./train_data/data_lmdb_release/evaluation/
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infer_img:
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infer_img: ./infer_img
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@ -79,10 +79,10 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml -o Global.checkpoints="./ou
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测试单张图像的检测效果
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```
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python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.single_img_path="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
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python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
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```
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测试文件夹下所有图像的检测效果
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```
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python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.single_img_path="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
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python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o TestReader.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
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```
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@ -200,7 +200,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model
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如果想尝试使用其他检测算法或者识别算法，请参考上述文本检测模型推理和文本识别模型推理，更新相应配置和模型，下面给出基于EAST文本检测和STAR-Net文本识别执行命令：
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```
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/rec/" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
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python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
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```
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执行命令后，识别结果图像如下：
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@ -84,7 +84,7 @@ class EvalTestReader(object):
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img_path = os.path.join(img_set_dir, img_name)
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img_list.append(img_path)
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else:
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img_path = self.params['single_img_path']
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img_path = self.params['infer_img']
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img_list = get_image_file_list(img_path)
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def batch_iter_reader():
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@ -78,6 +78,7 @@ def main():
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'fetch_name_list':eval_fetch_name_list,\
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'fetch_varname_list':eval_fetch_varname_list}
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metrics = eval_det_run(exe, config, eval_info_dict, "eval")
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print("Eval result", metrics)
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else:
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reader_type = config['Global']['reader_yml']
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if "benchmark" not in reader_type:
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@ -34,6 +34,7 @@ import json
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from copy import deepcopy
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import cv2
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from ppocr.data.reader_main import reader_main
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import os
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def cal_det_res(exe, config, eval_info_dict):
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@ -43,6 +44,8 @@ def cal_det_res(exe, config, eval_info_dict):
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postprocess_params.update(global_params)
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postprocess = create_module(postprocess_params['function']) \
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(params=postprocess_params)
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if not os.path.exists(os.path.dirname(save_res_path)):
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os.makedirs(os.path.dirname(save_res_path))
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with open(save_res_path, "wb") as fout:
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tackling_num = 0
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for data in eval_info_dict['reader']():
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@ -93,7 +96,7 @@ def load_label_infor(label_file_path, do_ignore=False):
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if text == "###" and do_ignore:
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ignore = True
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bbox_infor[bno]['ignore'] = ignore
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img_name_label_dict[substr[0]] = bbox_infor
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img_name_label_dict[os.path.basename(substr[0])] = bbox_infor
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return img_name_label_dict
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@ -31,6 +31,7 @@ class TextRecognizer(object):
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image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.rec_image_shape = image_shape
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self.character_type = args.rec_char_type
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self.rec_batch_num = args.rec_batch_num
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char_ops_params = {}
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char_ops_params["character_type"] = args.rec_char_type
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char_ops_params["character_dict_path"] = args.rec_char_dict_path
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@ -59,8 +60,8 @@ class TextRecognizer(object):
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def __call__(self, img_list):
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img_num = len(img_list)
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batch_num = 30
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rec_res = []
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batch_num = self.rec_batch_num
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predict_time = 0
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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@ -89,7 +89,7 @@ def sorted_boxes(dt_boxes):
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sorted boxes(array) with shape [4, 2]
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"""
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num_boxes = dt_boxes.shape[0]
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sorted_boxes = sorted(dt_boxes, key=lambda x: x[0][1])
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sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
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_boxes = list(sorted_boxes)
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for i in range(num_boxes - 1):
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@ -56,6 +56,7 @@ def parse_args():
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parser.add_argument("--rec_model_dir", type=str)
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parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
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parser.add_argument("--rec_char_type", type=str, default='ch')
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parser.add_argument("--rec_batch_num", type=int, default=30)
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parser.add_argument(
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"--rec_char_dict_path",
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type=str,
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@ -172,7 +173,8 @@ def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
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continue
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font = ImageFont.truetype(
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"./doc/simfang.ttf", font_size, encoding="utf-8")
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new_txt = str(count) + ': ' + txt + ' ' + '%.3f' % (scores[count])
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new_txt = str(count) + ': ' + txt + ' ' + '%.3f' % (
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scores[count])
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draw_txt.text(
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(20, gap * (count + 1)), new_txt, txt_color, font=font)
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count += 1
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@ -106,7 +106,6 @@ def main():
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with open(save_res_path, "wb") as fout:
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test_reader = reader_main(config=config, mode='test')
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# image_file_list = get_image_file_list(args.image_dir)
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tackling_num = 0
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for data in test_reader():
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img_num = len(data)
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@ -135,7 +134,7 @@ def main():
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elif config['Global']['algorithm'] == 'DB':
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dic = {'maps': outs[0]}
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else:
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raise Exception("only support algorithm: ['EAST', 'BD']")
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raise Exception("only support algorithm: ['EAST', 'DB']")
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dt_boxes_list = postprocess(dic, ratio_list)
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for ino in range(img_num):
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dt_boxes = dt_boxes_list[ino]
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