mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-30 19:09:13 +01:00
71 lines
3.3 KiB
Python
71 lines
3.3 KiB
Python
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import datetime
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from unittest import TestCase, main
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from frigate.video import process_frames, start_or_restart_ffmpeg, capture_frames
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from frigate.util import DictFrameManager, EventsPerSecond, draw_box_with_label
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from frigate.motion import MotionDetector
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from frigate.edgetpu import LocalObjectDetector
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from frigate.objects import ObjectTracker
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import multiprocessing as mp
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import numpy as np
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import cv2
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from frigate.object_processing import COLOR_MAP
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class FalsePositiveTests(TestCase):
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def test_back_1594395958_675351_0(self):
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### load in frames
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frame_shape = (1080,1920,3)
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frame_manager = DictFrameManager()
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frame_queue = mp.Queue()
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fps = EventsPerSecond()
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skipped_fps = EventsPerSecond()
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stop_event = mp.Event()
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detection_frame = mp.Value('d', datetime.datetime.now().timestamp()+100000)
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ffmpeg_cmd = "ffmpeg -hide_banner -loglevel panic -i /debug/false_positives/back-1595647759.228381-0.mp4 -f rawvideo -pix_fmt rgb24 pipe:".split(" ")
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ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, frame_shape[0]*frame_shape[1]*frame_shape[2])
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capture_frames(ffmpeg_process, "back", frame_shape, frame_manager, frame_queue, 1, fps, skipped_fps, stop_event, detection_frame)
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ffmpeg_process.wait()
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ffmpeg_process.communicate()
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assert(frame_queue.qsize() > 0)
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### process frames
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mask = np.zeros((frame_shape[0], frame_shape[1], 1), np.uint8)
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mask[:] = 255
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motion_detector = MotionDetector(frame_shape, mask)
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object_detector = LocalObjectDetector(labels='/labelmap.txt')
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object_tracker = ObjectTracker(10)
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detected_objects_queue = mp.Queue()
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process_fps = EventsPerSecond()
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current_frame = mp.Value('d', 0.0)
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process_frames("back", frame_queue, frame_shape, frame_manager, motion_detector, object_detector, object_tracker, detected_objects_queue,
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process_fps, current_frame, ['person'], {}, mask, stop_event, exit_on_empty=True)
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assert(detected_objects_queue.qsize() > 0)
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### check result
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while(not detected_objects_queue.empty()):
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camera_name, frame_time, current_tracked_objects = detected_objects_queue.get()
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current_frame = frame_manager.get(f"{camera_name}{frame_time}")
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# draw the bounding boxes on the frame
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for obj in current_tracked_objects.values():
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thickness = 2
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color = COLOR_MAP[obj['label']]
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if obj['frame_time'] != frame_time:
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thickness = 1
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color = (255,0,0)
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# draw the bounding boxes on the frame
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box = obj['box']
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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)
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# draw the regions on the frame
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region = obj['region']
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draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', f"{region[2]-region[0]}", thickness=1, color=(0,255,0))
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cv2.imwrite(f"/debug/frames/{int(frame_time*1000000)}.jpg", cv2.cvtColor(current_frame, cv2.COLOR_RGB2BGR))
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if __name__ == '__main__':
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main()
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