Sets up code for generating metrics and profiles for deciding sampling rate.

.gitignore

@@ -0,0 +1,121 @@
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src/schedule_1.py

@@ -0,0 +1,11 @@
+import os
+import argparse
+
+parser = argparse.ArgumentParser(description="Job scheduler for sampling decision")
+parser.add_argument("--jump",type=int,default=1,help = 'integer value for jumping frames')
+args = parser.parse_args()
+for i in range (1,11):
+    videoname = "sample_video_{}.mp4".format(i)
+    command = "python src/trafficApp.py --video ./resources/{} --saveoutput true  --jump {} ".format(videoname,args.jump)
+    os.system(command)
+    

src/trafficApp.py

@@ -1,11 +1,12 @@
 import argparse
-
+import ntpath
 import cv2 as cv
 import numpy as np
 from tqdm import tqdm
 import os
 os.environ['DISPLAY'] = ':0'
 
+
 from config.config import PARAMS
 from src.numberPlateRoiDetection import NumberPlateROIDetection
 from src.objectDetection import ObjectDetection
@@ -16,7 +17,7 @@ from src.trackingManager import TrackerManager
 class TrafficApp(object):
     def __init__(self,args):
         self.args = args
-
+        
         #get Object Detection Up
         self.objectDetection = ObjectDetection(debug=args.debug,target=args.target)
         self.numberPlateDetection = NumberPlateROIDetection(args= args,algoType='NumberPlate')
@@ -31,6 +32,8 @@ class TrafficApp(object):
 
     def runVideoFlow(self):
         frame_count = 0
+        successfulDetections=0
+
         if args.video is not None:
             try:
                 videoObj = cv.VideoCapture(args.video)
@@ -67,11 +70,11 @@ class TrafficApp(object):
                     print('Frames-{},Height-{}, Width-{}'.format(totalFrames,imgH,imgW))
 
                 if self.args.saveoutput and (imgH > 0 and imgW > 0):
-                    self.vid_writer = cv.VideoWriter(self.args.outputfile,
-                                                      cv.VideoWriter_fourcc(*"MJPG"), 30,
-                                                      (round(imgW),round(imgH)))
+                    outputfile = "output-{}-{}.mp4".format(ntpath.basename(args.video)[0:-4],args.jump)
+                    self.vid_writer = cv.VideoWriter(outputfile,cv.VideoWriter_fourcc(*"MJPG"), 30,(round(imgW),round(imgH)))
                 
                 progress_bar=tqdm(total = totalFrames)
+                total_frame_processed = 0
                 # start reading frame
                 while True:
                     grabbed, frame = videoObj.read()
@@ -80,11 +83,10 @@ class TrafficApp(object):
                     if not grabbed:
                         break
                     frame_count +=1
-
                     #print('Frame_count-',frame_count)
                     #Use jump argument to skip frames.
                     if (frame_count % self.args.jump == 0):
-                        
+                        total_frame_processed+=1
                         # get object detection on this frame
                         img_objectMarking, boxes, confidences, classids, idxs,status = self.objectDetection.run_object_detection(frame.copy(),imageH=imgH,imageW=imgW)
                         '''Assign Trcakers'''
@@ -108,11 +110,15 @@ class TrafficApp(object):
                         ''' Get Number plate OCR '''
                         number_plate_ocr_dict = get_number_plate_ocr_from_rois(frame.copy(),detected_np_info, False)
 
+                        if any(x > PARAMS._YOLOV3_OD_CONFIDENCE for x in confidences):
+                            successfulDetections+=1
+
                         #Display frame
                         displayFrame = self.displayFrame(frame.copy(),detected_np_info,number_plate_ocr_dict,object_detect_info)
 
                         winName = 'YOLOV3 Object Detection'
-                        cv.namedWindow(winName, cv.WINDOW_NORMAL)
+                        
+                        #cv.namedWindow(winName, cv.WINDOW_NORMAL)
                         #cv.imshow(winName, displayFrame)
                         #cv.resizeWindow('objectDetection',680,420)
                         if self.vid_writer:
@@ -123,6 +129,13 @@ class TrafficApp(object):
                             videoObj.release()
                             break
                         progress_bar.close()
+                
+                with open("{}-{}-metric.txt".format(ntpath.basename(args.video)[0:-4],args.jump),"w") as f:
+                    line = "Successful detection = {} total_frame_processed = {}".format(successfulDetections,total_frame_processed)
+                    f.write(line)
+                f.close()
+
+
     def parseObjDetectInfo(self,object_roi_info):
         boxes, confidences, classids, idxs, status = object_roi_info
         #[[list of bbox ][list of conf and labels]]
@@ -256,12 +269,11 @@ if __name__ == '__main__':
     parser.add_argument('--realtime', help='Camera Connected Input')
     parser.add_argument('--target', type=str,default = 'CPU',help='Target for CNN to run')
     parser.add_argument('--saveoutput',type=bool,default=True, help='save video or not')
-    parser.add_argument('--outputfile',type=str,default='./result.avi', help='save video path')
     parser.add_argument('--debug',type=bool,default=False, help='print time taken by function')
     parser.add_argument('--jump',type=int,default=1,help='integer value for jumping frames')
     args = parser.parse_args()
     app_profiler.enable()
     app = TrafficApp(args = args)
     app_profiler.disable()
-    profile_name = str('profile_info-{}.prof'.format(args.jump))
+    profile_name = str('{}-{}.prof'.format(ntpath.basename(args.video)[0:-4],args.jump))
     app_profiler.dump_stats(profile_name)