multi_thread & multi_rtsp input (#6834)

* multi_thread & multi_rtsp input; test=document_fix

* update rtsp docs; test=document_fix

* update savename,add threadid to name;test=document_fix

deploy/pipeline/cfg_utils.py

@@ -69,6 +69,12 @@ def argsparser():
         type=str,
         default=None,
         help="Dir of video file, `video_file` has a higher priority.")
+    parser.add_argument(
+        "--rtsp",
+        type=str,
+        nargs='+',
+        default=None,
+        help="list of rtsp inputs, for one or multiple rtsp input.")
     parser.add_argument(
         "--camera_id",
         type=int,

deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md

@@ -123,10 +123,13 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml -o SKELETON_ACTION.enbale=True --video_file=test_video.mp4 --device=gpu
 ```
 
-3. 对rtsp流的支持，video_file后面的视频地址更换为rtsp流地址，示例如下：
+3. 对rtsp流的支持，使用--rtsp RTSP [RTSP ...]参数指定一路或者多路rtsp视频流，如果是多路地址中间用空格隔开。(或者video_file后面的视频地址直接更换为rtsp流地址)，示例如下：
 ```
-# 例：行人属性识别，指定配置文件路径和测试视频
-python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_human_attr.yml -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu
+# 例：行人属性识别，单路视频流
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_human_attr.yml -o visual=False --rtsp rtsp://[YOUR_RTSP_SITE]  --device=gpu
+
+# 例：行人属性识别，多路视频流
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_human_attr.yml -o visual=False --rtsp rtsp://[YOUR_RTSP_SITE1]  rtsp://[YOUR_RTSP_SITE2] --device=gpu
 ```
 
 ### Jetson部署说明
@@ -149,7 +152,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 | -o | Option | 覆盖配置文件中对应的配置  |
 | --image_file | Option | 需要预测的图片 |
 | --image_dir  | Option |  要预测的图片文件夹路径   |
-| --video_file | Option | 需要预测的视频，或者rtsp流地址 |
+| --video_file | Option | 需要预测的视频，或者rtsp流地址（推荐使用rtsp参数） |
+| --rtsp | Option | rtsp视频流地址，支持一路或者多路同时输入 |
 | --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4|
 | --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
 | --output_dir | Option|可视化结果保存的根目录，默认为output/|

deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md

@@ -129,10 +129,13 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 
 ```
 
-3. 对rtsp流的支持，video_file后面的视频地址更换为rtsp流地址，示例如下：
+3. 对rtsp流的支持，使用--rtsp RTSP [RTSP ...]参数指定一路或者多路rtsp视频流，如果是多路地址中间用空格隔开。(或者video_file后面的视频地址直接更换为rtsp流地址)，示例如下：
 ```
-# 例：车辆属性识别，指定配置文件路径和测试视频
-python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_vehicle_attr.yml  -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu
+# 例：行人属性识别，单路视频流
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_vehicle_attr.yml -o visual=False --rtsp rtsp://[YOUR_RTSP_SITE]  --device=gpu
+
+# 例：行人属性识别，多路视频流
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_vehicle_attr.yml -o visual=False --rtsp rtsp://[YOUR_RTSP_SITE1]  rtsp://[YOUR_RTSP_SITE2] --device=gpu
 ```
 
 ### Jetson部署说明
@@ -156,6 +159,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 | --image_file | Option | 需要预测的图片 |
 | --image_dir  | Option |  要预测的图片文件夹路径   |
 | --video_file | Option | 需要预测的视频，或者rtsp流地址 |
+| --rtsp | Option | rtsp视频流地址，支持一路或者多路同时输入 |
 | --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4|
 | --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
 | --output_dir | Option|可视化结果保存的根目录，默认为output/|

deploy/pipeline/pipeline.py

@@ -73,7 +73,7 @@ class Pipeline(object):
         self.vis_result = cfg['visual']
         self.input = self._parse_input(args.image_file, args.image_dir,
                                        args.video_file, args.video_dir,
-                                       args.camera_id)
+                                       args.camera_id, args.rtsp)
         if self.multi_camera:
             self.predictor = []
             for name in self.input:
@@ -85,10 +85,10 @@ class Pipeline(object):
         else:
             self.predictor = PipePredictor(args, cfg, self.is_video)
             if self.is_video:
-                self.predictor.set_file_name(args.video_file)
+                self.predictor.set_file_name(self.input)
 
     def _parse_input(self, image_file, image_dir, video_file, video_dir,
-                     camera_id):
+                     camera_id, rtsp):
 
         # parse input as is_video and multi_camera
 
@@ -115,6 +115,16 @@ class Pipeline(object):
                 input = videof[0]
             self.is_video = True
 
+        elif rtsp is not None:
+            if len(rtsp) > 1:
+                rtsp = [rtsp_item for rtsp_item in rtsp if 'rtsp' in rtsp_item]
+                self.multi_camera = True
+                input = rtsp
+            else:
+                self.multi_camera = False
+                input = rtsp[0]
+            self.is_video = True
+
         elif camera_id != -1:
             self.multi_camera = False
             input = camera_id
@@ -127,6 +137,37 @@ class Pipeline(object):
 
         return input
 
+    def run_multithreads(self):
+        import threading
+        if self.multi_camera:
+            multi_res = []
+            threads = []
+            for idx, (predictor,
+                      input) in enumerate(zip(self.predictor, self.input)):
+                thread = threading.Thread(
+                    name=str(idx).zfill(3),
+                    target=predictor.run,
+                    args=(input, idx))
+                threads.append(thread)
+
+            for thread in threads:
+                thread.start()
+
+            for predictor, thread in zip(self.predictor, threads):
+                thread.join()
+                collector_data = predictor.get_result()
+                multi_res.append(collector_data)
+
+            if self.enable_mtmct:
+                mtmct_process(
+                    multi_res,
+                    self.input,
+                    mtmct_vis=self.vis_result,
+                    output_dir=self.output_dir)
+
+        else:
+            self.predictor.run(self.input)
+
     def run(self):
         if self.multi_camera:
             multi_res = []
@@ -437,9 +478,9 @@ class PipePredictor(object):
     def get_result(self):
         return self.collector.get_res()
 
-    def run(self, input):
+    def run(self, input, thread_idx=0):
         if self.is_video:
-            self.predict_video(input)
+            self.predict_video(input, thread_idx=thread_idx)
         else:
             self.predict_image(input)
         self.pipe_timer.info()
@@ -525,14 +566,15 @@ class PipePredictor(object):
             if self.cfg['visual']:
                 self.visualize_image(batch_file, batch_input, self.pipeline_res)
 
-    def predict_video(self, video_file):
+    def predict_video(self, video_file, thread_idx=0):
         # mot
         # mot -> attr
         # mot -> pose -> action
         capture = cv2.VideoCapture(video_file)
         video_out_name = 'output.mp4' if self.file_name is None else self.file_name
         if "rtsp" in video_file:
-            video_out_name = video_out_name + "_rtsp.mp4"
+            video_out_name = video_out_name + "_t" + str(thread_idx).zfill(
+                2) + "_rtsp.mp4"
 
         # Get Video info : resolution, fps, frame count
         width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
@@ -593,7 +635,7 @@ class PipePredictor(object):
 
         while (1):
             if frame_id % 10 == 0:
-                print('frame id: ', frame_id)
+                print('Thread: {}; frame id: {}'.format(thread_idx, frame_id))
 
             ret, frame = capture.read()
             if not ret:
@@ -621,6 +663,10 @@ class PipePredictor(object):
                     self.pipe_timer.module_time['mot'].end()
                     self.pipe_timer.track_num += len(mot_res['boxes'])
 
+                if frame_id % 10 == 0:
+                    print("Thread: {}; trackid number: {}".format(
+                        thread_idx, len(mot_res['boxes'])))
+
                 # flow_statistic only support single class MOT
                 boxes, scores, ids = res[0]  # batch size = 1 in MOT
                 mot_result = (frame_id + 1, boxes[0], scores[0],
@@ -1021,7 +1067,8 @@ def main():
     print_arguments(cfg)
 
     pipeline = Pipeline(FLAGS, cfg)
-    pipeline.run()
+    # pipeline.run()
+    pipeline.run_multithreads()
 
 
 if __name__ == '__main__':

deploy/pptracking/python/mot/utils.py

@@ -325,7 +325,7 @@ def flow_statistic(result,
         info += ", Count during {} secs: {}".format(secs_interval,
                                                     curr_interval_count)
         interval_id_set.clear()
-    print(info)
+    # print(info)
     info += "\n"
     records.append(info)