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	check plasma store and consolidate frame drawing
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				| @ -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 | ||||
|  | ||||
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