mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
104 lines
4.0 KiB
Python
104 lines
4.0 KiB
Python
import datetime
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import time
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import cv2
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import threading
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import prctl
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import numpy as np
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from edgetpu.detection.engine import DetectionEngine
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from frigate.util import tonumpyarray, LABELS, PATH_TO_CKPT
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class PreppedQueueProcessor(threading.Thread):
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def __init__(self, cameras, prepped_frame_queue, fps, queue_full):
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threading.Thread.__init__(self)
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self.cameras = cameras
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self.prepped_frame_queue = prepped_frame_queue
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# Load the edgetpu engine and labels
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self.engine = DetectionEngine(PATH_TO_CKPT)
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self.labels = LABELS
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self.fps = fps
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self.queue_full = queue_full
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self.avg_inference_speed = 10
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def run(self):
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prctl.set_name("PreppedQueueProcessor")
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# process queue...
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while True:
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if self.prepped_frame_queue.full():
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self.queue_full.update()
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frame = self.prepped_frame_queue.get()
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# Actual detection.
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frame['detected_objects'] = self.engine.DetectWithInputTensor(frame['frame'], threshold=0.2, top_k=5)
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self.fps.update()
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self.avg_inference_speed = (self.avg_inference_speed*9 + self.engine.get_inference_time())/10
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self.cameras[frame['camera_name']].detected_objects_queue.put(frame)
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class RegionRequester(threading.Thread):
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def __init__(self, camera):
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threading.Thread.__init__(self)
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self.camera = camera
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def run(self):
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prctl.set_name("RegionRequester")
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frame_time = 0.0
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while True:
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now = datetime.datetime.now().timestamp()
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with self.camera.frame_ready:
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# if there isnt a frame ready for processing or it is old, wait for a new frame
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if self.camera.frame_time.value == frame_time or (now - self.camera.frame_time.value) > 0.5:
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self.camera.frame_ready.wait()
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# make a copy of the frame_time
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frame_time = self.camera.frame_time.value
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with self.camera.regions_in_process_lock:
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self.camera.regions_in_process[frame_time] = len(self.camera.config['regions'])
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for index, region in enumerate(self.camera.config['regions']):
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self.camera.resize_queue.put({
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'camera_name': self.camera.name,
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'frame_time': frame_time,
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'region_id': index,
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'size': region['size'],
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'x_offset': region['x_offset'],
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'y_offset': region['y_offset']
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})
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class RegionPrepper(threading.Thread):
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def __init__(self, frame_cache, resize_request_queue, prepped_frame_queue):
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threading.Thread.__init__(self)
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self.frame_cache = frame_cache
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self.resize_request_queue = resize_request_queue
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self.prepped_frame_queue = prepped_frame_queue
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def run(self):
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prctl.set_name("RegionPrepper")
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while True:
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resize_request = self.resize_request_queue.get()
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frame = self.frame_cache.get(resize_request['frame_time'], None)
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if frame is None:
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print("RegionPrepper: frame_time not in frame_cache")
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continue
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# make a copy of the region
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cropped_frame = frame[resize_request['y_offset']:resize_request['y_offset']+resize_request['size'], resize_request['x_offset']:resize_request['x_offset']+resize_request['size']].copy()
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# Resize to 300x300 if needed
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if cropped_frame.shape != (300, 300, 3):
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# TODO: use Pillow-SIMD?
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cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
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# Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
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frame_expanded = np.expand_dims(cropped_frame, axis=0)
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# add the frame to the queue
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resize_request['frame'] = frame_expanded.flatten().copy()
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self.prepped_frame_queue.put(resize_request) |