mirror of
				https://github.com/blakeblackshear/frigate.git
				synced 2025-10-27 10:52:11 +01:00 
			
		
		
		
	check plasma store and consolidate frame drawing
This commit is contained in:
		
							parent
							
								
									569e07949f
								
							
						
					
					
						commit
						80b9652f7a
					
				@ -80,6 +80,11 @@ def main():
 | 
			
		||||
    # start plasma store
 | 
			
		||||
    plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
 | 
			
		||||
    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
 | 
			
		||||
    time.sleep(1)
 | 
			
		||||
    rc = plasma_process.poll()
 | 
			
		||||
    if rc is not None:
 | 
			
		||||
        raise RuntimeError("plasma_store exited unexpectedly with "
 | 
			
		||||
                            "code %d" % (rc,))
 | 
			
		||||
 | 
			
		||||
    ##
 | 
			
		||||
    # Setup config defaults for cameras
 | 
			
		||||
@ -95,6 +100,7 @@ def main():
 | 
			
		||||
    # Start the shared tflite process
 | 
			
		||||
    tflite_process = EdgeTPUProcess(MODEL_PATH)
 | 
			
		||||
 | 
			
		||||
    # start the camera processes
 | 
			
		||||
    camera_processes = []
 | 
			
		||||
    camera_stats_values = {}
 | 
			
		||||
    for name, config in CONFIG['cameras'].items():
 | 
			
		||||
@ -167,9 +173,13 @@ def main():
 | 
			
		||||
        while True:
 | 
			
		||||
            # max out at 1 FPS
 | 
			
		||||
            time.sleep(1)
 | 
			
		||||
            frame = object_processor.current_frame_with_objects(camera_name)
 | 
			
		||||
            frame = object_processor.get_current_frame(camera_name)
 | 
			
		||||
            if frame is None:
 | 
			
		||||
                frame = np.zeros((720,1280,3), np.uint8)
 | 
			
		||||
            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
 | 
			
		||||
            ret, jpg = cv2.imencode('.jpg', frame)
 | 
			
		||||
            yield (b'--frame\r\n'
 | 
			
		||||
                b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
 | 
			
		||||
                b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
 | 
			
		||||
 | 
			
		||||
    app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
 | 
			
		||||
 | 
			
		||||
 | 
			
		||||
@ -41,50 +41,8 @@ class TrackedObjectProcessor(threading.Thread):
 | 
			
		||||
        else:
 | 
			
		||||
            return None
 | 
			
		||||
    
 | 
			
		||||
    def get_frame(self, config, camera, obj):
 | 
			
		||||
        object_id_hash = hashlib.sha1(str.encode(f"{camera}{obj['frame_time']}"))
 | 
			
		||||
        object_id_bytes = object_id_hash.digest()
 | 
			
		||||
        object_id = plasma.ObjectID(object_id_bytes)
 | 
			
		||||
        best_frame = self.plasma_client.get(object_id)
 | 
			
		||||
        box = obj['box']
 | 
			
		||||
        draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}")
 | 
			
		||||
        # print a timestamp
 | 
			
		||||
        if config['snapshots']['show_timestamp']:
 | 
			
		||||
            time_to_show = datetime.datetime.fromtimestamp(obj['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
 | 
			
		||||
            cv2.putText(best_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
 | 
			
		||||
        return best_frame
 | 
			
		||||
    
 | 
			
		||||
    def current_frame_with_objects(self, camera):
 | 
			
		||||
        frame_time = self.camera_data[camera]['current_frame']
 | 
			
		||||
        object_id_hash = hashlib.sha1(str.encode(f"{camera}{frame_time}"))
 | 
			
		||||
        object_id_bytes = object_id_hash.digest()
 | 
			
		||||
        object_id = plasma.ObjectID(object_id_bytes)
 | 
			
		||||
        current_frame = self.plasma_client.get(object_id)
 | 
			
		||||
            
 | 
			
		||||
        tracked_objects = copy.deepcopy(self.camera_data[camera]['tracked_objects'])
 | 
			
		||||
 | 
			
		||||
        # draw the bounding boxes on the screen
 | 
			
		||||
        for obj in tracked_objects.values():
 | 
			
		||||
            thickness = 2
 | 
			
		||||
            color = COLOR_MAP[obj['label']]
 | 
			
		||||
            
 | 
			
		||||
            if obj['frame_time'] != frame_time:
 | 
			
		||||
                thickness = 1
 | 
			
		||||
                color = (255,0,0)
 | 
			
		||||
 | 
			
		||||
            box = obj['box']
 | 
			
		||||
            draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
 | 
			
		||||
        
 | 
			
		||||
        # # print fps
 | 
			
		||||
        # cv2.putText(frame, str(self.fps.eps())+'FPS', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
 | 
			
		||||
 | 
			
		||||
        # convert to BGR
 | 
			
		||||
        frame = cv2.cvtColor(current_frame, cv2.COLOR_RGB2BGR)
 | 
			
		||||
 | 
			
		||||
        # encode the image into a jpg
 | 
			
		||||
        ret, jpg = cv2.imencode('.jpg', frame)
 | 
			
		||||
 | 
			
		||||
        return jpg.tobytes()
 | 
			
		||||
    def get_current_frame(self, camera):
 | 
			
		||||
        return self.camera_data[camera]['current_frame']
 | 
			
		||||
 | 
			
		||||
    def run(self):
 | 
			
		||||
        while True:
 | 
			
		||||
@ -94,21 +52,56 @@ class TrackedObjectProcessor(threading.Thread):
 | 
			
		||||
            best_objects = self.camera_data[camera]['best_objects']
 | 
			
		||||
            current_object_status = self.camera_data[camera]['object_status']
 | 
			
		||||
            self.camera_data[camera]['tracked_objects'] = tracked_objects
 | 
			
		||||
            self.camera_data[camera]['current_frame'] = frame_time
 | 
			
		||||
 | 
			
		||||
            ###
 | 
			
		||||
            # Draw tracked objects on the frame
 | 
			
		||||
            ###
 | 
			
		||||
            object_id_hash = hashlib.sha1(str.encode(f"{camera}{frame_time}"))
 | 
			
		||||
            object_id_bytes = object_id_hash.digest()
 | 
			
		||||
            object_id = plasma.ObjectID(object_id_bytes)
 | 
			
		||||
            current_frame = self.plasma_client.get(object_id)
 | 
			
		||||
 | 
			
		||||
            # draw the bounding boxes on the frame
 | 
			
		||||
            for obj in tracked_objects.values():
 | 
			
		||||
                thickness = 2
 | 
			
		||||
                color = COLOR_MAP[obj['label']]
 | 
			
		||||
                
 | 
			
		||||
                if obj['frame_time'] != frame_time:
 | 
			
		||||
                    thickness = 1
 | 
			
		||||
                    color = (255,0,0)
 | 
			
		||||
 | 
			
		||||
                # draw the bounding boxes on the frame
 | 
			
		||||
                box = obj['box']
 | 
			
		||||
                draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
 | 
			
		||||
                # draw the regions on the frame
 | 
			
		||||
                region = obj['region']
 | 
			
		||||
                cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
 | 
			
		||||
            
 | 
			
		||||
            if config['snapshots']['show_timestamp']:
 | 
			
		||||
                time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
 | 
			
		||||
                cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
 | 
			
		||||
 | 
			
		||||
            ###
 | 
			
		||||
            # Set the current frame as ready
 | 
			
		||||
            ###
 | 
			
		||||
            self.camera_data[camera]['current_frame'] = current_frame
 | 
			
		||||
            
 | 
			
		||||
            ###
 | 
			
		||||
            # Maintain the highest scoring recent object and frame for each label
 | 
			
		||||
            ###
 | 
			
		||||
            for obj in tracked_objects.values():
 | 
			
		||||
                # if the object wasn't seen on the current frame, skip it
 | 
			
		||||
                if obj['frame_time'] != frame_time:
 | 
			
		||||
                    continue
 | 
			
		||||
                if obj['label'] in best_objects:
 | 
			
		||||
                    now = datetime.datetime.now().timestamp()
 | 
			
		||||
                    # if the object is a higher score than the current best score 
 | 
			
		||||
                    # or the current object is more than 1 minute old, use the new object
 | 
			
		||||
                    if obj['score'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
 | 
			
		||||
                        obj['frame'] = self.get_frame(config, camera, obj)
 | 
			
		||||
                        obj['frame'] = np.copy(current_frame)
 | 
			
		||||
                        best_objects[obj['label']] = obj
 | 
			
		||||
                else:
 | 
			
		||||
                    obj['frame'] = self.get_frame(config, camera, obj)
 | 
			
		||||
                    obj['frame'] = np.copy(current_frame)
 | 
			
		||||
                    best_objects[obj['label']] = obj
 | 
			
		||||
 | 
			
		||||
            ###
 | 
			
		||||
 | 
			
		||||
@ -9,7 +9,7 @@ import numpy as np
 | 
			
		||||
import multiprocessing as mp
 | 
			
		||||
from collections import defaultdict
 | 
			
		||||
from scipy.spatial import distance as dist
 | 
			
		||||
from frigate.util import draw_box_with_label, LABELS, calculate_region
 | 
			
		||||
from frigate.util import draw_box_with_label, calculate_region
 | 
			
		||||
 | 
			
		||||
# class ObjectCleaner(threading.Thread):
 | 
			
		||||
#     def __init__(self, camera):
 | 
			
		||||
 | 
			
		||||
@ -16,7 +16,7 @@ import copy
 | 
			
		||||
import itertools
 | 
			
		||||
import json
 | 
			
		||||
from collections import defaultdict
 | 
			
		||||
from frigate.util import tonumpyarray, LABELS, draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond
 | 
			
		||||
from frigate.util import tonumpyarray, draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond
 | 
			
		||||
# from frigate.object_detection import RegionPrepper, RegionRequester
 | 
			
		||||
from frigate.objects import ObjectTracker
 | 
			
		||||
# from frigate.mqtt import MqttObjectPublisher
 | 
			
		||||
 | 
			
		||||
		Loading…
	
		Reference in New Issue
	
	Block a user