From a61f6915e4ed5e1416d7d7d11e5812b8e9a0fd93 Mon Sep 17 00:00:00 2001 From: zhiboniu <31800336+zhiboniu@users.noreply.github.com> Date: Tue, 16 Aug 2022 19:01:01 +0800 Subject: [PATCH] vehicleplate self train docs (#6660) --- deploy/pipeline/tools/ccpd2ocr_all.py | 167 ++++++++++++++ .../customization/ppvehicle_plate.md | 215 ++++++++++++++++++ 2 files changed, 382 insertions(+) create mode 100644 deploy/pipeline/tools/ccpd2ocr_all.py create mode 100644 docs/advanced_tutorials/customization/ppvehicle_plate.md diff --git a/deploy/pipeline/tools/ccpd2ocr_all.py b/deploy/pipeline/tools/ccpd2ocr_all.py new file mode 100644 index 000000000..2f0d168e4 --- /dev/null +++ b/deploy/pipeline/tools/ccpd2ocr_all.py @@ -0,0 +1,167 @@ +import cv2 +import os +import json +from tqdm import tqdm +import numpy as np + +provinces = [ + "皖", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "京", "闽", "赣", + "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", + "新", "警", "学", "O" +] +alphabets = [ + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', + 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'O' +] +ads = [ + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', + 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '0', '1', '2', '3', '4', '5', + '6', '7', '8', '9', 'O' +] + + +def make_label_2020(img_dir, save_gt_folder, phase): + crop_img_save_dir = os.path.join(save_gt_folder, phase, 'crop_imgs') + os.makedirs(crop_img_save_dir, exist_ok=True) + + f_det = open( + os.path.join(save_gt_folder, phase, 'det.txt'), 'w', encoding='utf-8') + f_rec = open( + os.path.join(save_gt_folder, phase, 'rec.txt'), 'w', encoding='utf-8') + + i = 0 + for filename in tqdm(os.listdir(os.path.join(img_dir, phase))): + str_list = filename.split('-') + if len(str_list) < 5: + continue + coord_list = str_list[3].split('_') + txt_list = str_list[4].split('_') + boxes = [] + for coord in coord_list: + boxes.append([int(x) for x in coord.split("&")]) + boxes = [boxes[2], boxes[3], boxes[0], boxes[1]] + lp_number = provinces[int(txt_list[0])] + alphabets[int(txt_list[ + 1])] + ''.join([ads[int(x)] for x in txt_list[2:]]) + + # det + det_info = [{'points': boxes, 'transcription': lp_number}] + f_det.write('{}\t{}\n'.format( + os.path.join("CCPD2020/ccpd_green", phase, filename), + json.dumps( + det_info, ensure_ascii=False))) + + # rec + boxes = np.float32(boxes) + img = cv2.imread(os.path.join(img_dir, phase, filename)) + # crop_img = img[int(boxes[:,1].min()):int(boxes[:,1].max()),int(boxes[:,0].min()):int(boxes[:,0].max())] + crop_img = get_rotate_crop_image(img, boxes) + crop_img_save_filename = '{}_{}.jpg'.format(i, '_'.join(txt_list)) + crop_img_save_path = os.path.join(crop_img_save_dir, + crop_img_save_filename) + cv2.imwrite(crop_img_save_path, crop_img) + f_rec.write('{}/{}/crop_imgs/{}\t{}\n'.format( + "CCPD2020/PPOCR", phase, crop_img_save_filename, lp_number)) + i += 1 + f_det.close() + f_rec.close() + + +def make_label_2019(list_dir, save_gt_folder, phase): + crop_img_save_dir = os.path.join(save_gt_folder, phase, 'crop_imgs') + os.makedirs(crop_img_save_dir, exist_ok=True) + + f_det = open( + os.path.join(save_gt_folder, phase, 'det.txt'), 'w', encoding='utf-8') + f_rec = open( + os.path.join(save_gt_folder, phase, 'rec.txt'), 'w', encoding='utf-8') + + with open(os.path.join(list_dir, phase + ".txt"), 'r') as rf: + imglist = rf.readlines() + + i = 0 + for idx, filename in enumerate(imglist): + if idx % 1000 == 0: + print("{}/{}".format(idx, len(imglist))) + filename = filename.strip() + str_list = filename.split('-') + if len(str_list) < 5: + continue + coord_list = str_list[3].split('_') + txt_list = str_list[4].split('_') + boxes = [] + for coord in coord_list: + boxes.append([int(x) for x in coord.split("&")]) + boxes = [boxes[2], boxes[3], boxes[0], boxes[1]] + lp_number = provinces[int(txt_list[0])] + alphabets[int(txt_list[ + 1])] + ''.join([ads[int(x)] for x in txt_list[2:]]) + + # det + det_info = [{'points': boxes, 'transcription': lp_number}] + f_det.write('{}\t{}\n'.format( + os.path.join("CCPD2019", filename), + json.dumps( + det_info, ensure_ascii=False))) + + # rec + boxes = np.float32(boxes) + imgpath = os.path.join(list_dir[:-7], filename) + img = cv2.imread(imgpath) + # crop_img = img[int(boxes[:,1].min()):int(boxes[:,1].max()),int(boxes[:,0].min()):int(boxes[:,0].max())] + crop_img = get_rotate_crop_image(img, boxes) + crop_img_save_filename = '{}_{}.jpg'.format(i, '_'.join(txt_list)) + crop_img_save_path = os.path.join(crop_img_save_dir, + crop_img_save_filename) + cv2.imwrite(crop_img_save_path, crop_img) + f_rec.write('{}/{}/crop_imgs/{}\t{}\n'.format( + "CCPD2019/PPOCR", phase, crop_img_save_filename, lp_number)) + i += 1 + f_det.close() + f_rec.close() + + +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 + + +img_dir = './CCPD2020/ccpd_green' +save_gt_folder = './CCPD2020/PPOCR' +# phase = 'train' # change to val and test to make val dataset and test dataset +for phase in ['train', 'val', 'test']: + make_label_2020(img_dir, save_gt_folder, phase) + +list_dir = './CCPD2019/splits/' +save_gt_folder = './CCPD2019/PPOCR' + +for phase in ['train', 'val', 'test']: + make_label_2019(list_dir, save_gt_folder, phase) diff --git a/docs/advanced_tutorials/customization/ppvehicle_plate.md b/docs/advanced_tutorials/customization/ppvehicle_plate.md new file mode 100644 index 000000000..37694ff8f --- /dev/null +++ b/docs/advanced_tutorials/customization/ppvehicle_plate.md @@ -0,0 +1,215 @@ +简体中文 | [English](./ppvehicle_plate_en.md) + +# 车牌识别任务二次开发 + +车牌识别任务，采用PP-OCRv3模型在车牌数据集上进行fine-tune得到，过程参考[PaddleOCR车牌应用介绍](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/applications/%E8%BD%BB%E9%87%8F%E7%BA%A7%E8%BD%A6%E7%89%8C%E8%AF%86%E5%88%AB.md)在CCPD2019数据集上进行了拓展。 + +## 数据准备 + +1. 对于CCPD2019、CCPD2020数据集，我们提供了处理脚本[ccpd2ocr_all.py](../../../deploy/pipeline/tools/ccpd2ocr_all.py), 使用时跟CCPD2019、CCPD2020数据集文件夹放在同一目录下，然后执行脚本即可在CCPD2019/PPOCR、CCPD2020/PPOCR目录下得到检测、识别模型的训练标注文件。训练时可以整合到一起使用。 +2. 对于其他来源数据或者自标注数据，可以按如下格式整理训练列表文件： + +- **车牌检测标注** + +标注文件格式如下，中间用'\t'分隔： + +``` +" 图像文件路径 标注框标注信息" +CCPD2020/xxx.jpg [{"transcription": "京AD88888", "points": [[310, 104], [416, 141], [418, 216], [312, 179]]}, {...}] +``` + +标注框标注信息是包含多个字典的list，有多少个标注框就有多少个字典对应，字典中的 `points` 表示车牌框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。 `transcription` 表示当前文本框的文字，***当其内容为“###”时，表示该文本框无效，在训练时会跳过。*** + +- **车牌字符识别标注** + +标注文件的格式如下，txt文件中默认请将图片路径和图片标签用'\t'分割，如用其他方式分割将造成训练报错。其中图片是对车牌字符的截图。 + +``` +" 图像文件名 字符标注信息 " +CCPD2020/crop_imgs/xxx.jpg 京AD88888 +``` + +## 模型训练 + +首先执行以下命令clone PaddleOCR库代码到训练机器： +``` +git clone git@github.com:PaddlePaddle/PaddleOCR.git +``` + +下载预训练模型： +``` +#检测预训练模型： +mkdir models +cd models +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar +tar -xf ch_PP-OCRv3_det_distill_train.tar + +#识别预训练模型： +wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar +tar -xf ch_PP-OCRv3_rec_train.tar +cd .. +``` + +安装相关依赖环境： +``` +cd PaddleOCR +pip install -r requirements.txt +``` + +然后进行训练相关配置修改。 + +### 修改配置 + +**检测模型配置项** + +修改配置项包括以下3部分内容，可以在训练时以命令行修改，或者直接在配置文件`configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml`中修改： +1. 模型存储和训练相关: +- Global.pretrained_model: 指向前面下载的PP-OCRv3文本检测预训练模型地址 +- Global.eval_batch_step: 模型多少step评估一次，一般设置为一个epoch对应的step数，可以从训练开始的log中读取。此处以[0, 772]为例，第一个数字表示从第0各step开始算起。 +2. 优化器相关: +- Optimizer.lr.name: 学习率衰减器设为常量 Const +- Optimizer.lr.learning_rate: 做 fine-tune 实验，学习率需要设置的比较小，此处学习率设为配置文件中的0.05倍 +- Optimizer.lr.warmup_epoch: warmup_epoch设为0 +3. 数据集相关: +- Train.dataset.data_dir：指向训练集图片存放根目录 +- Train.dataset.label_file_list：指向训练集标注文件 +- Eval.dataset.data_dir：指向测试集图片存放根目录 +- Eval.dataset.label_file_list：指向测试集标注文件 + +**识别模型配置项** + +修改配置项包括以下3部分内容，可以在训练时以命令行修改，或者直接在配置文件`configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml`中修改： +1. 模型存储和训练相关: +- Global.pretrained_model: 指向PP-OCRv3文本识别预训练模型地址 +- Global.eval_batch_step: 模型多少step评估一次，一般设置为一个epoch对应的step数，可以从训练开始的log中读取。此处以[0, 90]为例，第一个数字表示从第0各step开始算起。 +2. 优化器相关 +- Optimizer.lr.name: 学习率衰减器设为常量 Const +- Optimizer.lr.learning_rate: 做 fine-tune 实验，学习率需要设置的比较小，此处学习率设为配置文件中的0.05倍 +- Optimizer.lr.warmup_epoch: warmup_epoch设为0 +3. 数据集相关 +- Train.dataset.data_dir：指向训练集图片存放根目录 +- Train.dataset.label_file_list：指向训练集标注文件 +- Eval.dataset.data_dir：指向测试集图片存放根目录 +- Eval.dataset.label_file_list：指向测试集标注文件 + +### 执行训练 + +然后运行以下命令开始训练。如果在配置文件中已经做了修改，可以省略`-o`及其后面的内容。 + +**检测模型训练命令** + +``` +#单卡训练 +python3 tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \ + Global.pretrained_model=models/ch_PP-OCRv3_det_distill_train/student.pdparams \ + Global.save_model_dir=output/CCPD/det \ + Global.eval_batch_step="[0, 772]" \ + Optimizer.lr.name=Const \ + Optimizer.lr.learning_rate=0.0005 \ + Optimizer.lr.warmup_epoch=0 \ + Train.dataset.data_dir=/home/aistudio/ccpd_data/ \ + Train.dataset.label_file_list=[/home/aistudio/ccpd_data/train/det.txt] + +#多卡训练 +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + tools/train.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \ + Global.pretrained_model=models/ch_PP-OCRv3_det_distill_train/student.pdparams \ + Global.save_model_dir=output/CCPD/det \ + Global.eval_batch_step="[0, 772]" \ + Optimizer.lr.name=Const \ + Optimizer.lr.learning_rate=0.0005 \ + Optimizer.lr.warmup_epoch=0 \ + Train.dataset.data_dir=/home/aistudio/ccpd_data/ \ + Train.dataset.label_file_list=[/home/aistudio/ccpd_data/train/det.txt] + +``` + +训练完成后可以执行以下命令进行性能评估： +``` +#单卡评估 +python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \ + Global.pretrained_model=output/CCPD/det/best_accuracy.pdparams \ + Eval.dataset.data_dir=/home/aistudio/ccpd_data/ \ + Eval.dataset.label_file_list=[/home/aistudio/ccpd_data/test/det.txt] +``` + +**识别模型训练命令** + +``` +#单卡训练 +python3 tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \ + Global.pretrained_model=models/ch_PP-OCRv3_rec_train/student.pdparams \ + Global.save_model_dir=output/CCPD/rec/ \ + Global.eval_batch_step="[0, 90]" \ + Optimizer.lr.name=Const \ + Optimizer.lr.learning_rate=0.0005 \ + Optimizer.lr.warmup_epoch=0 \ + Train.dataset.data_dir=/home/aistudio/ccpd_data \ + Train.dataset.label_file_list=[/home/aistudio/ccpd_data/train/rec.txt] \ + Eval.dataset.data_dir=/home/aistudio/ccpd_data \ + Eval.dataset.label_file_list=[/home/aistudio/ccpd_data/test/rec.txt] + + +#多卡训练 +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + tools/train.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \ + Global.pretrained_model=models/ch_PP-OCRv3_rec_train/student.pdparams \ + Global.save_model_dir=output/CCPD/rec/ \ + Global.eval_batch_step="[0, 90]" \ + Optimizer.lr.name=Const \ + Optimizer.lr.learning_rate=0.0005 \ + Optimizer.lr.warmup_epoch=0 \ + Train.dataset.data_dir=/home/aistudio/ccpd_data \ + Train.dataset.label_file_list=[/home/aistudio/ccpd_data/train/rec.txt] \ + Eval.dataset.data_dir=/home/aistudio/ccpd_data \ + Eval.dataset.label_file_list=[/home/aistudio/ccpd_data/test/rec.txt] + +``` + +训练完成后可以执行以下命令进行性能评估： +``` +#单卡评估 +python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \ + Global.pretrained_model=output/CCPD/rec/best_accuracy.pdparams \ + Eval.dataset.data_dir=/home/aistudio/ccpd_data/ \ + Eval.dataset.label_file_list=[/home/aistudio/ccpd_data/test/rec.txt] +``` + + +### 模型导出 + +使用下述命令将训练好的模型导出为预测部署模型。 + +**检测模型导出** + +``` +python tools/export_model.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o \ + Global.pretrained_model=output/CCPD/det/best_accuracy.pdparams \ + Global.save_inference_dir=output/det/infer +``` + +**识别模型导出** + +``` +python tools/export_model.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml -o \ + Global.pretrained_model=output/CCPD/rec/best_accuracy.pdparams \ + Global.save_inference_dir=output/CCPD/rec/infer +``` + + +使用时在PP-Vehicle中的配置文件`./deploy/pipeline/config/infer_cfg_ppvehicle.yml`中修改`VEHICLE_PLATE`模块中的`det_model_dir`、`rec_model_dir`项，并开启功能`enable: True`。 +``` +VEHICLE_PLATE: + det_model_dir: [YOUR_DET_INFERENCE_MODEL_PATH] #设置检测模型路径 + det_limit_side_len: 736 + det_limit_type: "max" + rec_model_dir: [YOUR_REC_INFERENCE_MODEL_PATH] #设置识别模型路径 + rec_image_shape: [3, 48, 320] + rec_batch_num: 6 + word_dict_path: deploy/pipeline/ppvehicle/rec_word_dict.txt + enable: True #开启功能 +``` + +然后可以使用-->至此即完成更新车牌识别模型任务。 -- GitLab