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use a queue instead
This commit is contained in:
blakeblackshear
parent
7d3027e056
commit
bca4e78e9a
@ -6,6 +6,7 @@ import datetime
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import ctypes
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import logging
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import multiprocessing as mp
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import queue
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import threading
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import json
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from contextlib import closing
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@ -19,7 +20,7 @@ from frigate.mqtt import MqttMotionPublisher, MqttObjectPublisher
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from frigate.objects import ObjectParser, ObjectCleaner, BestPersonFrame
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from frigate.motion import detect_motion
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from frigate.video import fetch_frames, FrameTracker
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from frigate.object_detection import prep_for_detection, detect_objects
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from frigate.object_detection import FramePrepper, PreppedQueueProcessor, detect_objects
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RTSP_URL = os.getenv('RTSP_URL')
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@ -82,10 +83,20 @@ def main():
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frame_ready = mp.Condition()
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# Condition for notifying that motion status changed globally
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motion_changed = mp.Condition()
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prepped_frame_array = mp.Array(ctypes.c_uint8, 300*300*3)
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# create shared value for storing the frame_time
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prepped_frame_time = mp.Value('d', 0.0)
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# Event for notifying that object detection needs a new frame
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prepped_frame_grabbed = mp.Event()
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prepped_frame_ready = mp.Event()
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# Condition for notifying that objects were parsed
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objects_parsed = mp.Condition()
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# Queue for detected objects
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object_queue = mp.Queue()
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# Queue for prepped frames
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prepped_frame_queue = queue.Queue()
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prepped_frame_box = mp.Array(ctypes.c_uint16, 3)
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# shape current frame so it can be treated as an image
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frame_arr = tonumpyarray(shared_arr).reshape(frame_shape)
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@ -96,21 +107,18 @@ def main():
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capture_process.daemon = True
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# for each region, start a separate process for motion detection and object detection
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detection_prep_processes = []
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detection_prep_threads = []
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motion_processes = []
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for region in regions:
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# possibly try putting these on threads and putting prepped
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# frames in a queue
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detection_prep_process = mp.Process(target=prep_for_detection, args=(shared_arr,
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detection_prep_threads.append(FramePrepper(
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frame_arr,
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shared_frame_time,
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frame_lock, frame_ready,
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frame_ready,
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frame_lock,
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region['motion_detected'],
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frame_shape,
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region['size'], region['x_offset'], region['y_offset'],
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region['prepped_frame_array'], region['prepped_frame_time'],
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region['prepped_frame_lock']))
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detection_prep_process.daemon = True
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detection_prep_processes.append(detection_prep_process)
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prepped_frame_queue
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))
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motion_process = mp.Process(target=detect_motion, args=(shared_arr,
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shared_frame_time,
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@ -124,13 +132,25 @@ def main():
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motion_process.daemon = True
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motion_processes.append(motion_process)
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prepped_queue_processor = PreppedQueueProcessor(
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prepped_frame_array,
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prepped_frame_time,
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prepped_frame_ready,
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prepped_frame_grabbed,
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prepped_frame_box,
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prepped_frame_queue
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)
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prepped_queue_processor.start()
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# create a process for object detection
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# if the coprocessor is doing the work, can this run as a thread
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# since it is waiting for IO?
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detection_process = mp.Process(target=detect_objects, args=(
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[region['prepped_frame_array'] for region in regions],
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[region['prepped_frame_time'] for region in regions],
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[region['prepped_frame_lock'] for region in regions],
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[[region['size'], region['x_offset'], region['y_offset']] for region in regions],
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motion_changed, [region['motion_detected'] for region in regions],
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prepped_frame_array,
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prepped_frame_time,
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prepped_frame_ready,
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prepped_frame_grabbed,
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prepped_frame_box,
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object_queue, DEBUG
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))
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detection_process.daemon = True
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@ -181,9 +201,8 @@ def main():
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print("capture_process pid ", capture_process.pid)
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# start the object detection prep processes
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for detection_prep_process in detection_prep_processes:
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detection_prep_process.start()
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print("detection_prep_process pid ", detection_prep_process.pid)
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for detection_prep_thread in detection_prep_threads:
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detection_prep_thread.start()
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detection_process.start()
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print("detection_process pid ", detection_process.pid)
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@ -256,8 +275,8 @@ def main():
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app.run(host='0.0.0.0', debug=False)
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capture_process.join()
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for detection_prep_process in detection_prep_processes:
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detection_prep_process.join()
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for detection_prep_thread in detection_prep_threads:
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detection_prep_thread.join()
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for motion_process in motion_processes:
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motion_process.join()
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detection_process.join()
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@ -1,5 +1,6 @@
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import datetime
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import cv2
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import threading
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import numpy as np
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from edgetpu.detection.engine import DetectionEngine
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from . util import tonumpyarray
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@ -19,9 +20,11 @@ def ReadLabelFile(file_path):
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ret[int(pair[0])] = pair[1].strip()
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return ret
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def detect_objects(prepped_frame_arrays, prepped_frame_times, prepped_frame_locks,
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prepped_frame_boxes, motion_changed, motion_regions, object_queue, debug):
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prepped_frame_nps = [tonumpyarray(prepped_frame_array) for prepped_frame_array in prepped_frame_arrays]
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def detect_objects(prepped_frame_array, prepped_frame_time,
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prepped_frame_ready, prepped_frame_grabbed,
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prepped_frame_box, object_queue, debug):
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prepped_frame_np = tonumpyarray(prepped_frame_array)
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# Load the edgetpu engine and labels
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engine = DetectionEngine(PATH_TO_CKPT)
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labels = ReadLabelFile(PATH_TO_LABELS)
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@ -29,85 +32,124 @@ def detect_objects(prepped_frame_arrays, prepped_frame_times, prepped_frame_lock
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frame_time = 0.0
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region_box = [0,0,0]
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while True:
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# while there is motion
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while len([r for r in motion_regions if r.is_set()]) > 0:
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# loop over all the motion regions and look for objects
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for i, motion_region in enumerate(motion_regions):
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# skip the region if no motion
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if not motion_region.is_set():
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continue
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# wait until a frame is ready
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prepped_frame_grabbed.clear()
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prepped_frame_ready.wait()
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# make a copy of the cropped frame
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with prepped_frame_locks[i]:
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prepped_frame_copy = prepped_frame_nps[i].copy()
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frame_time = prepped_frame_times[i].value
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region_box[:] = prepped_frame_boxes[i]
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prepped_frame_copy = prepped_frame_np.copy()
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frame_time = prepped_frame_time.value
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region_box[:] = prepped_frame_box
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# Actual detection.
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objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
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# print(engine.get_inference_time())
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# put detected objects in the queue
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if objects:
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for obj in objects:
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box = obj.bounding_box.flatten().tolist()
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object_queue.put({
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'frame_time': frame_time,
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'name': str(labels[obj.label_id]),
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'score': float(obj.score),
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'xmin': int((box[0] * region_box[0]) + region_box[1]),
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'ymin': int((box[1] * region_box[0]) + region_box[2]),
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'xmax': int((box[2] * region_box[0]) + region_box[1]),
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'ymax': int((box[3] * region_box[0]) + region_box[2])
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})
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else:
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object_queue.put({
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'frame_time': frame_time,
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'name': 'dummy',
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'score': 0.99,
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'xmin': int(0 + region_box[1]),
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'ymin': int(0 + region_box[2]),
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'xmax': int(10 + region_box[1]),
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'ymax': int(10 + region_box[2])
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})
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# wait for the global motion flag to change
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with motion_changed:
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motion_changed.wait()
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prepped_frame_grabbed.set()
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def prep_for_detection(shared_whole_frame_array, shared_frame_time, frame_lock, frame_ready,
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motion_detected, frame_shape, region_size, region_x_offset, region_y_offset,
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prepped_frame_array, prepped_frame_time, prepped_frame_lock):
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# shape shared input array into frame for processing
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shared_whole_frame = tonumpyarray(shared_whole_frame_array).reshape(frame_shape)
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# Actual detection.
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objects = engine.DetectWithInputTensor(prepped_frame_copy, threshold=0.5, top_k=3)
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# print(engine.get_inference_time())
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# put detected objects in the queue
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if objects:
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for obj in objects:
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box = obj.bounding_box.flatten().tolist()
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object_queue.put({
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'frame_time': frame_time,
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'name': str(labels[obj.label_id]),
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'score': float(obj.score),
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'xmin': int((box[0] * region_box[0]) + region_box[1]),
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'ymin': int((box[1] * region_box[0]) + region_box[2]),
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'xmax': int((box[2] * region_box[0]) + region_box[1]),
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'ymax': int((box[3] * region_box[0]) + region_box[2])
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})
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else:
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object_queue.put({
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'frame_time': frame_time,
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'name': 'dummy',
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'score': 0.99,
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'xmin': int(0 + region_box[1]),
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'ymin': int(0 + region_box[2]),
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'xmax': int(10 + region_box[1]),
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'ymax': int(10 + region_box[2])
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})
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shared_prepped_frame = tonumpyarray(prepped_frame_array).reshape((1,300,300,3))
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class PreppedQueueProcessor(threading.Thread):
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def __init__(self, prepped_frame_array,
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prepped_frame_time,
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prepped_frame_ready,
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prepped_frame_grabbed,
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prepped_frame_box,
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prepped_frame_queue):
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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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threading.Thread.__init__(self)
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self.prepped_frame_array = prepped_frame_array
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self.prepped_frame_time = prepped_frame_time
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self.prepped_frame_ready = prepped_frame_ready
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self.prepped_frame_grabbed = prepped_frame_grabbed
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self.prepped_frame_box = prepped_frame_box
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self.prepped_frame_queue = prepped_frame_queue
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# wait until motion is detected
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motion_detected.wait()
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def run(self):
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prepped_frame_np = tonumpyarray(self.prepped_frame_array)
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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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print(self.prepped_frame_queue.qsize())
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prepped_frame_np[:] = frame['frame']
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self.prepped_frame_time.value = frame['frame_time']
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self.prepped_frame_box[0] = frame['region_size']
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self.prepped_frame_box[1] = frame['region_x_offset']
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self.prepped_frame_box[2] = frame['region_y_offset']
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self.prepped_frame_ready.set()
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self.prepped_frame_grabbed.wait()
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self.prepped_frame_ready.clear()
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with 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 shared_frame_time.value == frame_time or (now - shared_frame_time.value) > 0.5:
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frame_ready.wait()
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# make a copy of the cropped frame
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with frame_lock:
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cropped_frame = shared_whole_frame[region_y_offset:region_y_offset+region_size, region_x_offset:region_x_offset+region_size].copy()
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frame_time = shared_frame_time.value
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# convert to RGB
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cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
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# Resize to 300x300 if needed
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if cropped_frame_rgb.shape != (300, 300, 3):
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cropped_frame_rgb = cv2.resize(cropped_frame_rgb, 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_rgb, axis=0)
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# copy the prepped frame to the shared output array
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with prepped_frame_lock:
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shared_prepped_frame[:] = frame_expanded
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prepped_frame_time.value = frame_time
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# should this be a region class?
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class FramePrepper(threading.Thread):
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def __init__(self, shared_frame, frame_time, frame_ready,
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frame_lock, motion_detected,
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region_size, region_x_offset, region_y_offset,
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prepped_frame_queue):
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threading.Thread.__init__(self)
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self.shared_frame = shared_frame
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self.frame_time = frame_time
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self.frame_ready = frame_ready
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self.frame_lock = frame_lock
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self.motion_detected = motion_detected
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self.region_size = region_size
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self.region_x_offset = region_x_offset
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self.region_y_offset = region_y_offset
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self.prepped_frame_queue = prepped_frame_queue
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def run(self):
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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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# wait until motion is detected
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self.motion_detected.wait()
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with self.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.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
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self.frame_ready.wait()
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# make a copy of the cropped frame
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with self.frame_lock:
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cropped_frame = self.shared_frame[self.region_y_offset:self.region_y_offset+self.region_size, self.region_x_offset:self.region_x_offset+self.region_size].copy()
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frame_time = self.frame_time.value
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# convert to RGB
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cropped_frame_rgb = cv2.cvtColor(cropped_frame, cv2.COLOR_BGR2RGB)
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# Resize to 300x300 if needed
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if cropped_frame_rgb.shape != (300, 300, 3):
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cropped_frame_rgb = cv2.resize(cropped_frame_rgb, 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_rgb, axis=0)
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# add the frame to the queue
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self.prepped_frame_queue.put({
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'frame_time': frame_time,
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'frame': frame_expanded.flatten().copy(),
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'region_size': self.region_size,
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'region_x_offset': self.region_x_offset,
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'region_y_offset': self.region_y_offset
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})
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