import time import datetime import threading import cv2 from object_detection.utils import visualization_utils as vis_util class ObjectParser(threading.Thread): def __init__(self, cameras, object_queue, detected_objects, regions): threading.Thread.__init__(self) self.cameras = cameras self.object_queue = object_queue self.regions = regions def run(self): # frame_times = {} while True: obj = self.object_queue.get() self.cameras[obj['camera_name']].add_object(obj) class ObjectCleaner(threading.Thread): def __init__(self, objects_parsed, detected_objects): threading.Thread.__init__(self) self._objects_parsed = objects_parsed self._detected_objects = detected_objects def run(self): while True: # wait a bit before checking for expired frames time.sleep(0.2) # expire the objects that are more than 1 second old now = datetime.datetime.now().timestamp() # look for the first object found within the last second # (newest objects are appended to the end) detected_objects = self._detected_objects.copy() #print([round(now-obj['frame_time'],2) for obj in detected_objects]) num_to_delete = 0 for obj in detected_objects: if now-obj['frame_time']<2: break num_to_delete += 1 if num_to_delete > 0: del self._detected_objects[:num_to_delete] # notify that parsed objects were changed with self._objects_parsed: self._objects_parsed.notify_all() # Maintains the frame and person with the highest score from the most recent # motion event class BestPersonFrame(threading.Thread): def __init__(self, objects_parsed, recent_frames, detected_objects): threading.Thread.__init__(self) self.objects_parsed = objects_parsed self.recent_frames = recent_frames self.detected_objects = detected_objects self.best_person = None self.best_frame = None def run(self): while True: # wait until objects have been parsed with self.objects_parsed: self.objects_parsed.wait() # make a copy of detected objects detected_objects = self.detected_objects.copy() detected_people = [obj for obj in detected_objects if obj['name'] == 'person'] # make a copy of the recent frames recent_frames = self.recent_frames.copy() # get the highest scoring person new_best_person = max(detected_people, key=lambda x:x['score'], default=self.best_person) # if there isnt a person, continue if new_best_person is None: continue # if there is no current best_person if self.best_person is None: self.best_person = new_best_person # if there is already a best_person else: now = datetime.datetime.now().timestamp() # if the new best person is a higher score than the current best person # or the current person is more than 1 minute old, use the new best person if new_best_person['score'] > self.best_person['score'] or (now - self.best_person['frame_time']) > 60: self.best_person = new_best_person if not self.best_person is None and self.best_person['frame_time'] in recent_frames: best_frame = recent_frames[self.best_person['frame_time']] best_frame = cv2.cvtColor(best_frame, cv2.COLOR_BGR2RGB) # draw the bounding box on the frame vis_util.draw_bounding_box_on_image_array(best_frame, self.best_person['ymin'], self.best_person['xmin'], self.best_person['ymax'], self.best_person['xmax'], color='red', thickness=2, display_str_list=["{}: {}%".format(self.best_person['name'],int(self.best_person['score']*100))], use_normalized_coordinates=False) # convert back to BGR self.best_frame = cv2.cvtColor(best_frame, cv2.COLOR_RGB2BGR)