20220621 modify camera queue

.ipynb_checkpoints/gstreamer camera improve-checkpoint.ipynb

@@ -7,7 +7,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import cv2"
+    "import cv2\n",
+    "import time\n",
+    "import subprocess"
    ]
   },
   {
@@ -18,7 +20,7 @@
    "outputs": [],
    "source": [
     "source = \"rtsp://192.168.5.218/txg/01\"\n",
-    "framerate = 60"
+    "framerate = 15"
    ]
   },
   {
@@ -29,7 +31,7 @@
    "outputs": [],
    "source": [
     "gst_format = f'rtspsrc location={source} latency=0 protocols=tcp ! ' \n",
-    "gst_format += 'rtph265depay ! h265parse ! nvv4l2decoder skip-frames=1 ! nvvidconv ! '\n",
+    "gst_format += 'rtph265depay ! h265parse ! nvv4l2decoder skip-frames=1 enable-max-performance=1 enable-frame-type-reporting=1 ! nvvidconv ! '\n",
     "gst_format += f' video/x-raw,format=BGRx ! videorate ! video/x-raw,framerate={framerate}/1 ! videoconvert ! video/x-raw,format=BGR ! '\n",
     "gst_format += 'appsink sync=false'"
    ]
@@ -51,11 +53,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "last_frame = dev.read()[1]\n",
     "while(dev.isOpened()):\n",
+    "    stime = time.time()\n",
     "    ret,frame = dev.read()\n",
+    "    print(\"read frame spend\", time.time() - stime)\n",
     "    if(ret):\n",
+    "        # 前後景比較\n",
+    "        \n",
+    "        stime = time.time()\n",
+    "        diff = (last_frame - frame).sum()\n",
+    "        print(\"前後景平均差\",diff, \"spend\", time.time() - stime)\n",
+    "        last_frame = frame\n",
+    "        \n",
+    "        # 拉普拉斯模糊計算\n",
+    "        stime = time.time()\n",
+    "        fuzzyValue = getImageVar(frame)\n",
+    "        print(\"模糊度\", fuzzyValue, \"spend\", time.time() - stime)\n",
     "        cv2.imshow(\"frame\", frame)\n",
-    "        if(cv2.waitKey(1000//framerate) == ord('q')):\n",
+    "#         if(cv2.waitKey(1000//framerate) == ord('q')):\n",
+    "        if(cv2.waitKey(1) == ord('q')):\n",
     "            break\n",
     "    else:\n",
     "        break\n",
@@ -78,7 +95,13 @@
    "id": "f6c5d62c",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# 返回指定路径图像的拉普拉斯算子边缘模糊程度值\n",
+    "def getImageVar(image):\n",
+    "    img2gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
+    "    imageVar = cv2.Laplacian(img2gray, cv2.CV_64F).var()\n",
+    "    return imageVar"
+   ]
   },
   {
    "cell_type": "code",

AlprTritonClient/EZLPR.py

@@ -31,7 +31,7 @@ class TrtRetinaPlateThread(threading.Thread):
     main thread is the consumer.
 
     """
-    def __init__(self, condition, vehicles_q, plates_q, conf_th=0.7, budget=10):
+    def __init__(self, condition, vehicles_q, plates_q, conf_th=0.9, budget=10):
         """__init__
         # Arguments
             condition: the condition variable used to notify main

AlprTritonClient/EZLPRDataCollection.py

@@ -153,7 +153,9 @@ class TrtCTCOCRThread(threading.Thread):
             
             resultInfo = ResultInfo(info.track_id, plate, info.vechile_img, info.plate_img)
             
-            if(resultInfo.plate is not None and resultInfo.plate_img is not None):
+            plate_w, plate_h = resultInfo.plate_img.shape[:2]
+            
+            if(resultInfo.plate is not None and resultInfo.plate_img is not None and plate_w*plate_h >= 50*30):
                 fullscreen_save_path = os.path.join(self.save_path, "fullscreen", "{}_{}.jpg".format(resultInfo.track_id, resultInfo.plate))
                 vehicle_save_path = os.path.join(self.save_path, "vehicles", "{}_{}.jpg".format(resultInfo.track_id, resultInfo.plate))
                 plate_save_path = os.path.join(self.save_path, "plates", "{}_{}.jpg".format(resultInfo.track_id, resultInfo.plate))

PlanB_test0613.py

@@ -2,6 +2,7 @@ from AlprTritonClient.yolo import TrtYOLO
 from AlprTritonClient.yolo_shared_memory import TrtYOLOSHM
 
 from AlprTritonClient.EZLPR import EZLPR
+# from AlprTritonClient.EZLPRDataCollection import EZLPR
 
 from VideoAnalysis.SystemModule import DisplayMeta
 from VideoAnalysis.SystemModule import TrafficFlowMeta
@@ -34,13 +35,13 @@ def time_span(stime):
 yolo = TrtYOLOSHM()
 easyLPR=EZLPR()
 
-#source="/home/tx2/Videos/20220504173000.mp4"
+# source="/home/aaeon/20220504173000.mp4"
 # source="/home/aaeon/Kevin_Car.MOV"
-#source = "../Ch17-20220314-190000.mp4"
+# source = "../Ch17-20220314-190000.mp4"
 
 source="rtsp://192.168.5.218/txg/01"
 # source="rtsp://140.120.65.31/live.sdp"
-cam=Camera(1,source,encoder = "h265",width=1920,height=1080, use_gstr=True, framerate=60, skip_frames=1)
+cam=Camera(1,source,encoder = "h265",width=1920,height=1080, use_gstr=True, framerate=15, skip_frames=0)
 # cam=Camera(1,source,encoder = "h264",width=1920,height=1080)
 
 camera_id=1
@@ -69,11 +70,11 @@ exist_track_obj = []
 # to run GUI event loop
 plt.ion()
 fig_scale = 2
-fig, ax_list = plt.subplots(maxSize, 2, figsize=(4*fig_scale, maxSize*fig_scale))
+fig, ax_list = plt.subplots(maxSize, 2, figsize=(2*fig_scale, maxSize*fig_scale))
 ax_cache_imgs = []
 ax_cache_backgrounds = []
 ax_list = ax_list.ravel()
-fake_img = np.zeros((640,480))
+fake_img = np.zeros((480,640))
 update_count = 0
 
 for i in range(len(ax_list)):
@@ -164,6 +165,7 @@ try:
                         # Process EZLPR
                         if obj.object_id in LPR_target and not obj.lock_plate_num:
                             obj.update_lpr_time()
+#                             easyLPR.put(obj.ID, frame.copy(), frame[y1:y2,x1:x2,:].copy())
                             easyLPR.put(obj.ID,frame[y1:y2,x1:x2,:].copy()) 
 
                 InputLPR_span,stime=time_span(stime) # !
@@ -195,8 +197,9 @@ try:
                             ax_list[ax_index*2+1].title.set_text("plate num {}".format(platNum))
                             
                             # 設置顯示圖片
-                            ax_cache_imgs[ax_index*2].set_data(vechile_img)
-                            ax_cache_imgs[ax_index*2+1].set_data(plate_img)
+                            # bgr 2 rgb
+                            ax_cache_imgs[ax_index*2].set_data(vechile_img[:, :, ::-1])
+                            ax_cache_imgs[ax_index*2+1].set_data(plate_img[:, :, ::-1])
                             
                             # restore background
                             fig.canvas.restore_region(ax_cache_backgrounds[ax_index*2])

VideoAnalysis/Camera_Jetson.py

@@ -112,11 +112,11 @@ class Camera(object):
             if self.encoder=="h265":
                 print("h265")
                 return (
-                   f'rtspsrc location={source} latency=0 protocols=tcp ! ' +
+                   f'rtspsrc location={source} latency=0 protocols=tcp ! queue ! ' +
 #                    'watchdog timeout=10000 !'+
                    f'rtph265depay ! h265parse ! nvv4l2decoder skip-frames={skip_frames} ! nvvidconv ! '+
                    f' video/x-raw,format=BGRx ! videorate ! video/x-raw,framerate={framerate}/1 ! videoconvert ! video/x-raw,format=BGR ! '+
-                   'appsink sync=false'
+                   'queue ! appsink sync=false max-buffers=1 drop=True'
                )
             elif self.encoder=="h264":
                 print("h264")

gstreamer camera improve.ipynb

@@ -7,7 +7,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import cv2"
+    "import cv2\n",
+    "import time\n",
+    "import subprocess"
    ]
   },
   {
@@ -18,7 +20,7 @@
    "outputs": [],
    "source": [
     "source = \"rtsp://192.168.5.218/txg/01\"\n",
-    "framerate = 60"
+    "framerate = 15"
    ]
   },
   {
@@ -29,7 +31,7 @@
    "outputs": [],
    "source": [
     "gst_format = f'rtspsrc location={source} latency=0 protocols=tcp ! ' \n",
-    "gst_format += 'rtph265depay ! h265parse ! nvv4l2decoder skip-frames=2 ! nvvidconv ! '\n",
+    "gst_format += 'rtph265depay ! h265parse ! nvv4l2decoder skip-frames=1 enable-max-performance=1 enable-frame-type-reporting=1 ! nvvidconv ! '\n",
     "gst_format += f' video/x-raw,format=BGRx ! videorate ! video/x-raw,framerate={framerate}/1 ! videoconvert ! video/x-raw,format=BGR ! '\n",
     "gst_format += 'appsink sync=false'"
    ]
@@ -51,11 +53,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "last_frame = dev.read()[1]\n",
     "while(dev.isOpened()):\n",
+    "    stime = time.time()\n",
     "    ret,frame = dev.read()\n",
+    "    print(\"read frame spend\", time.time() - stime)\n",
     "    if(ret):\n",
+    "        # 前後景比較\n",
+    "        \n",
+    "        stime = time.time()\n",
+    "        diff = (last_frame - frame).sum()\n",
+    "        print(\"前後景平均差\",diff, \"spend\", time.time() - stime)\n",
+    "        last_frame = frame\n",
+    "        \n",
+    "        # 拉普拉斯模糊計算\n",
+    "        stime = time.time()\n",
+    "        fuzzyValue = getImageVar(frame)\n",
+    "        print(\"模糊度\", fuzzyValue, \"spend\", time.time() - stime)\n",
     "        cv2.imshow(\"frame\", frame)\n",
-    "        if(cv2.waitKey(1000//framerate) == ord('q')):\n",
+    "#         if(cv2.waitKey(1000//framerate) == ord('q')):\n",
+    "        if(cv2.waitKey(1) == ord('q')):\n",
     "            break\n",
     "    else:\n",
     "        break\n",
@@ -78,7 +95,13 @@
    "id": "f6c5d62c",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# 返回指定路径图像的拉普拉斯算子边缘模糊程度值\n",
+    "def getImageVar(image):\n",
+    "    img2gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
+    "    imageVar = cv2.Laplacian(img2gray, cv2.CV_64F).var()\n",
+    "    return imageVar"
+   ]
   },
   {
    "cell_type": "code",