forked from Github/frigate
cleanup old code
This commit is contained in:
Blake Blackshear
@@ -1,139 +0,0 @@
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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 copy
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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, calculate_region
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class PreppedQueueProcessor(threading.Thread):
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def __init__(self, cameras, prepped_frame_queue, fps):
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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.avg_inference_speed = 10
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def run(self):
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prctl.set_name(self.__class__.__name__)
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# process queue...
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while True:
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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.detect_with_input_tensor(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(self.__class__.__name__)
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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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# grab the current tracked objects
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with self.camera.object_tracker.tracked_objects_lock:
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tracked_objects = copy.deepcopy(self.camera.object_tracker.tracked_objects).values()
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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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self.camera.regions_in_process[frame_time] += len(tracked_objects)
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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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# request a region for tracked objects
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for tracked_object in tracked_objects:
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box = tracked_object['box']
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# calculate a new region that will hopefully get the entire object
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(size, x_offset, y_offset) = calculate_region(self.camera.frame_shape,
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box['xmin'], box['ymin'],
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box['xmax'], box['ymax'])
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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': -1,
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'size': size,
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'x_offset': x_offset,
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'y_offset': y_offset
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})
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class RegionPrepper(threading.Thread):
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def __init__(self, camera, frame_cache, resize_request_queue, prepped_frame_queue):
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threading.Thread.__init__(self)
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self.camera = camera
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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(self.__class__.__name__)
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while True:
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resize_request = self.resize_request_queue.get()
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# if the queue is over 100 items long, only prep dynamic regions
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if resize_request['region_id'] != -1 and self.prepped_frame_queue.qsize() > 100:
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with self.camera.regions_in_process_lock:
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self.camera.regions_in_process[resize_request['frame_time']] -= 1
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if self.camera.regions_in_process[resize_request['frame_time']] == 0:
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del self.camera.regions_in_process[resize_request['frame_time']]
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self.camera.skipped_region_tracker.update()
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continue
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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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with self.camera.regions_in_process_lock:
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self.camera.regions_in_process[resize_request['frame_time']] -= 1
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if self.camera.regions_in_process[resize_request['frame_time']] == 0:
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del self.camera.regions_in_process[resize_request['frame_time']]
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self.camera.skipped_region_tracker.update()
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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)
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