diff --git a/detect_objects.py b/detect_objects.py index 447bffb59..caa29efc0 100644 --- a/detect_objects.py +++ b/detect_objects.py @@ -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) diff --git a/frigate/object_processing.py b/frigate/object_processing.py index c881d4a91..a2eb21f5d 100644 --- a/frigate/object_processing.py +++ b/frigate/object_processing.py @@ -40,51 +40,9 @@ class TrackedObjectProcessor(threading.Thread): return self.camera_data[camera]['best_objects'][label]['frame'] 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 ### diff --git a/frigate/objects.py b/frigate/objects.py index 4e99fc6c0..a74319c08 100644 --- a/frigate/objects.py +++ b/frigate/objects.py @@ -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): diff --git a/frigate/video.py b/frigate/video.py index 8b1b65a34..da5787394 100755 --- a/frigate/video.py +++ b/frigate/video.py @@ -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