update ppvehicle_plate docs (#6625)

* add ppvehicle_plate

* replace cn codes

* update docs

* update mot model

deploy/pipeline/config/infer_cfg_ppvehicle.yml

@@ -14,8 +14,8 @@ MOT:
 
 VEHICLE_PLATE:
   det_model_dir: output_inference/ch_PP-OCRv3_det_infer/
-  det_limit_side_len: 480
-  det_limit_type: "max"
+  det_limit_side_len: 736
+  det_limit_type: "min"
   rec_model_dir: output_inference/ch_PP-OCRv3_rec_infer/
   rec_image_shape: [3, 48, 320]
   rec_batch_num: 6

deploy/pipeline/docs/tutorials/ppvehicle_plate.md

 
 # PP-Vehicle车牌识别模块
 
-【应用介绍】
+车牌识别，在车辆应用场景中有着非常广泛的应用，起到车辆身份识别的作用，比如车辆出入口自动闸机。PP-Vehicle中提供了车辆的跟踪及其车牌识别的功能，并提供模型下载：
 
-【模型下载】
+| 任务                 | 算法 | 精度 | 预测速度(ms) |预测模型下载链接                                                                               |
+|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
+| 车辆检测/跟踪 |  PP-YOLOE-l | mAP: 63.9 | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) |
+| 车牌检测模型    |  ch_PP-OCRv3_det  |  hmean: 0.979  | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz) |
+| 车牌识别模型    |  ch_PP-OCRv3_rec  |  acc: 0.773  | - | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) |
+1. 跟踪模型使用PPVehicle数据集（整合了BDD100K-MOT和UA-DETRAC），是将BDD100K-MOT中的car, truck, bus, van和UA-DETRAC中的car, bus, van都合并为1类vehicle(1)后的数据集。
+2. 车牌检测、识别模型使用PP-OCRv3模型在CCPD2019、CCPD2020混合车牌数据集上fine-tune得到。
 
 ## 使用方法
 
-【配置项说明】
+1. 从上表链接中下载模型并解压到```PaddleDetection/output_inference```路径下，并修改配置文件中模型路径，也可默认自动下载模型。设置```deploy/pipeline/config/infer_cfg_ppvehicle.yml```中`VEHICLE_PLATE`的enable: True
 
-【使用命令】
+`infer_cfg_ppvehicle.yml`中配置项说明：
+```
+VEHICLE_PLATE:                                                            #模块名称
+  det_model_dir: output_inference/ch_PP-OCRv3_det_infer/                  #车牌检测模型路径
+  det_limit_side_len: 480                                                 #检测模型单边输入尺寸
+  det_limit_type: "max"                                                   #检测模型输入尺寸长短边选择，"max"表示长边
+  rec_model_dir: output_inference/ch_PP-OCRv3_rec_infer/                  #车牌识别模型路径
+  rec_image_shape: [3, 48, 320]                                           #车牌识别模型输入尺寸
+  rec_batch_num: 6                                                        #车牌识别batchsize
+  word_dict_path: deploy/pipeline/ppvehicle/rec_word_dict.txt             #OCR模型查询字典
+  basemode: "idbased"                                                     #流程类型，'idbased'表示基于跟踪模型
+  enable: False                                                           #功能是否开启
+```
+
+2. 图片输入时，启动命令如下(更多命令参数说明，请参考[快速开始-参数说明](./PPVehicle_QUICK_STARTED.md#41-参数说明))。
+```python
+#单张图片
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --image_file=test_image.jpg \
+                                                   --device=gpu \
+
+#图片文件夹
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --image_dir=images/ \
+                                                   --device=gpu \
+
+```
+
+3. 视频输入时，启动命令如下
+```python
+#单个视频文件
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --video_file=test_video.mp4 \
+                                                   --device=gpu \
+
+#视频文件夹
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --video_dir=test_videos/ \
+                                                   --device=gpu \
+```
+
+4. 若修改模型路径，有以下两种方式：
+
+    - 方法一：```./deploy/pipeline/config/infer_cfg_ppvehicle.yml```下可以配置不同模型路径，车牌识别模型修改`VEHICLE_PLATE`字段下配置
+    - 方法二：命令行中--config配置项后面增加`-o VEHICLE_PLATE.det_model_dir=[YOUR_DETMODEL_PATH] VEHICLE_PLATE.rec_model_dir=[YOUR_RECMODEL_PATH]`修改模型路径。
+
+
+测试效果如下：
+
+<div width="1000" align="center">
+  <img src="../images/ppvehicleplate.jpg"/>
+</div>
 
-【效果展示】
 
 ## 方案说明
 
-【实现方案及特色】
+1. 目标检测/多目标跟踪获取图片/视频输入中的车辆检测框，模型方案为PP-YOLOE，详细文档参考[PP-YOLOE](../../../configs/ppyoloe/README_cn.md)
+2. 通过车辆检测框的坐标在输入图像中截取每个车辆
+3. 使用车牌检测模型在每张车辆截图中识别车牌所在位置，同理截取车牌区域，模型方案为PP-OCRv3_det模型，经CCPD数据集在车牌场景fine-tune得到。
+4. 使用字符识别模型识别车牌中的字符。模型方案为PP-OCRv3_rec模型，经CCPD数据集在车牌场景fine-tune得到。
+
+**性能优化措施：**
+
+1. 使用跳帧策略，每10帧做一次车牌检测，避免每帧做车牌检测的算力消耗。
+2. 车牌结果稳定策略，避免单帧结果的波动，利用同一个id的历史所有车牌识别结果进行投票，得到该id最大可能的正确结果。
+
+## 参考资料
 
-## 参考文献
+1. PaddeDetection特色检测模型[PP-YOLOE](../../../../configs/ppyoloe)。
+2. Paddle字符识别模型库[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)。

deploy/pipeline/pipeline.py

@@ -364,6 +364,36 @@ class PipePredictor(object):
         # auto download inference model
         model_dir_dict = get_model_dir(self.cfg)
 
+        if self.with_vehicleplate:
+            vehicleplate_cfg = self.cfg['VEHICLE_PLATE']
+            self.vehicleplate_detector = PlateRecognizer(args, vehicleplate_cfg)
+            basemode = self.basemode['VEHICLE_PLATE']
+            self.modebase[basemode] = True
+
+        if self.with_human_attr:
+            attr_cfg = self.cfg['ATTR']
+            model_dir = model_dir_dict['ATTR']
+            batch_size = attr_cfg['batch_size']
+            basemode = self.basemode['ATTR']
+            self.modebase[basemode] = True
+            self.attr_predictor = AttrDetector(
+                model_dir, device, run_mode, batch_size, trt_min_shape,
+                trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
+                enable_mkldnn)
+
+        if self.with_vehicle_attr:
+            vehicleattr_cfg = self.cfg['VEHICLE_ATTR']
+            model_dir = model_dir_dict['VEHICLE_ATTR']
+            batch_size = vehicleattr_cfg['batch_size']
+            color_threshold = vehicleattr_cfg['color_threshold']
+            type_threshold = vehicleattr_cfg['type_threshold']
+            basemode = self.basemode['VEHICLE_ATTR']
+            self.modebase[basemode] = True
+            self.vehicle_attr_predictor = VehicleAttr(
+                model_dir, device, run_mode, batch_size, trt_min_shape,
+                trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
+                enable_mkldnn, color_threshold, type_threshold)
+
         if not is_video:
             det_cfg = self.cfg['DET']
             model_dir = model_dir_dict['DET']
@@ -372,41 +402,8 @@ class PipePredictor(object):
                 model_dir, device, run_mode, batch_size, trt_min_shape,
                 trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
                 enable_mkldnn)
-            if self.with_human_attr:
-                attr_cfg = self.cfg['ATTR']
-                model_dir = model_dir_dict['ATTR']
-                batch_size = attr_cfg['batch_size']
-                basemode = self.basemode['ATTR']
-                self.modebase[basemode] = True
-                self.attr_predictor = AttrDetector(
-                    model_dir, device, run_mode, batch_size, trt_min_shape,
-                    trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
-                    enable_mkldnn)
-
-            if self.with_vehicle_attr:
-                vehicleattr_cfg = self.cfg['VEHICLE_ATTR']
-                model_dir = model_dir_dict['VEHICLE_ATTR']
-                batch_size = vehicleattr_cfg['batch_size']
-                color_threshold = vehicleattr_cfg['color_threshold']
-                type_threshold = vehicleattr_cfg['type_threshold']
-                basemode = self.basemode['VEHICLE_ATTR']
-                self.modebase[basemode] = True
-                self.vehicle_attr_predictor = VehicleAttr(
-                    model_dir, device, run_mode, batch_size, trt_min_shape,
-                    trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
-                    enable_mkldnn, color_threshold, type_threshold)
 
         else:
-            if self.with_human_attr:
-                attr_cfg = self.cfg['ATTR']
-                model_dir = model_dir_dict['ATTR']
-                batch_size = attr_cfg['batch_size']
-                basemode = self.basemode['ATTR']
-                self.modebase[basemode] = True
-                self.attr_predictor = AttrDetector(
-                    model_dir, device, run_mode, batch_size, trt_min_shape,
-                    trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
-                    enable_mkldnn)
             if self.with_idbased_detaction:
                 idbased_detaction_cfg = self.cfg['ID_BASED_DETACTION']
                 model_dir = model_dir_dict['ID_BASED_DETACTION']
@@ -502,26 +499,6 @@ class PipePredictor(object):
                         use_dark=False)
                     self.kpt_buff = KeyPointBuff(skeleton_action_frames)
 
-            if self.with_vehicleplate:
-                vehicleplate_cfg = self.cfg['VEHICLE_PLATE']
-                self.vehicleplate_detector = PlateRecognizer(args,
-                                                             vehicleplate_cfg)
-                basemode = self.basemode['VEHICLE_PLATE']
-                self.modebase[basemode] = True
-
-            if self.with_vehicle_attr:
-                vehicleattr_cfg = self.cfg['VEHICLE_ATTR']
-                model_dir = model_dir_dict['VEHICLE_ATTR']
-                batch_size = vehicleattr_cfg['batch_size']
-                color_threshold = vehicleattr_cfg['color_threshold']
-                type_threshold = vehicleattr_cfg['type_threshold']
-                basemode = self.basemode['VEHICLE_ATTR']
-                self.modebase[basemode] = True
-                self.vehicle_attr_predictor = VehicleAttr(
-                    model_dir, device, run_mode, batch_size, trt_min_shape,
-                    trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads,
-                    enable_mkldnn, color_threshold, type_threshold)
-
             if self.with_mtmct:
                 reid_cfg = self.cfg['REID']
                 model_dir = model_dir_dict['REID']
@@ -661,6 +638,20 @@ class PipePredictor(object):
                 attr_res = {'output': vehicle_attr_res_list}
                 self.pipeline_res.update(attr_res, 'vehicle_attr')
 
+            if self.with_vehicleplate:
+                if i > self.warmup_frame:
+                    self.pipe_timer.module_time['vehicleplate'].start()
+                crop_inputs = crop_image_with_det(batch_input, det_res)
+                platelicenses = []
+                for crop_input in crop_inputs:
+                    platelicense = self.vehicleplate_detector.get_platelicense(
+                        crop_input)
+                    platelicenses.extend(platelicense['plate'])
+                if i > self.warmup_frame:
+                    self.pipe_timer.module_time['vehicleplate'].end()
+                vehicleplate_res = {'vehicleplate': platelicenses}
+                self.pipeline_res.update(vehicleplate_res, 'vehicleplate')
+
             self.pipe_timer.img_num += len(batch_input)
             if i > self.warmup_frame:
                 self.pipe_timer.total_time.end()
@@ -1078,6 +1069,7 @@ class PipePredictor(object):
         det_res = result.get('det')
         human_attr_res = result.get('attr')
         vehicle_attr_res = result.get('vehicle_attr')
+        vehicleplate_res = result.get('vehicleplate')
 
         for i, (im_file, im) in enumerate(zip(im_files, images)):
             if det_res is not None:
@@ -1088,7 +1080,7 @@ class PipePredictor(object):
                 im = visualize_box_mask(
                     im,
                     det_res_i,
-                    labels=['person'],
+                    labels=['target'],
                     threshold=self.cfg['crop_thresh'])
                 im = np.ascontiguousarray(np.copy(im))
                 im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
@@ -1100,6 +1092,11 @@ class PipePredictor(object):
                 vehicle_attr_res_i = vehicle_attr_res['output'][
                     start_idx:start_idx + boxes_num_i]
                 im = visualize_attr(im, vehicle_attr_res_i, det_res_i['boxes'])
+            if vehicleplate_res is not None:
+                plates = vehicleplate_res['vehicleplate']
+                det_res_i['boxes'][:, 4:6] = det_res_i[
+                    'boxes'][:, 4:6] - det_res_i['boxes'][:, 2:4]
+                im = visualize_vehicleplate(im, plates, det_res_i['boxes'])
 
             img_name = os.path.split(im_file)[-1]
             if not os.path.exists(self.output_dir):

deploy/pipeline/ppvehicle/vehicle_plate.py

@@ -258,21 +258,54 @@ class PlateRecognizer(object):
         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 len(text) < 10:
-                    platelicense = text
+                    platelicense = self.replace_cn_code(text)
             plate_all["plate"].append(platelicense)
         return plate_all
 
+    def replace_cn_code(self, text):
+        simcode = {
+            '浙': 'ZJ-',
+            '粤': 'GD-',
+            '京': 'BJ-',
+            '津': 'TJ-',
+            '冀': 'HE-',
+            '晋': 'SX-',
+            '蒙': 'NM-',
+            '辽': 'LN-',
+            '黑': 'HLJ-',
+            '沪': 'SH-',
+            '吉': 'JL-',
+            '苏': 'JS-',
+            '皖': 'AH-',
+            '赣': 'JX-',
+            '鲁': 'SD-',
+            '豫': 'HA-',
+            '鄂': 'HB-',
+            '湘': 'HN-',
+            '桂': 'GX-',
+            '琼': 'HI-',
+            '渝': 'CQ-',
+            '川': 'SC-',
+            '贵': 'GZ-',
+            '云': 'YN-',
+            '藏': 'XZ-',
+            '陕': 'SN-',
+            '甘': 'GS-',
+            '青': 'QH-',
+            '宁': 'NX-',
+            '·': ' '
+        }
+        for _char in text:
+            if _char in simcode:
+                text = text.replace(_char, simcode[_char])
+        return text
+
 
 def main():
     cfg = merge_cfg(FLAGS)

deploy/python/visualize.py

@@ -419,7 +419,7 @@ def visualize_vehicleplate(im, results, boxes=None):
 
     im_h, im_w = im.shape[:2]
     text_scale = max(1.0, im.shape[0] / 1600.)
-    text_thickness = 1
+    text_thickness = 2
 
     line_inter = im.shape[0] / 40.
     for i, res in enumerate(results):
@@ -436,7 +436,7 @@ def visualize_vehicleplate(im, results, boxes=None):
             text_loc = (text_w, text_h)
             cv2.putText(
                 im,
-                text,
+                "LP: " + text,
                 text_loc,
                 cv2.FONT_ITALIC,
                 text_scale, (0, 255, 255),