mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
112 lines
4.7 KiB
Python
112 lines
4.7 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 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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# Path to frozen detection graph. This is the actual model that is used for the object detection.
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PATH_TO_CKPT = '/frozen_inference_graph.pb'
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# List of the strings that is used to add correct label for each box.
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PATH_TO_LABELS = '/label_map.pbtext'
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# Function to read labels from text files.
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def ReadLabelFile(file_path):
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with open(file_path, 'r') as f:
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lines = f.readlines()
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ret = {}
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for line in lines:
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pair = line.strip().split(maxsplit=1)
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ret[int(pair[0])] = pair[1].strip()
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return ret
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class PreppedQueueProcessor(threading.Thread):
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def __init__(self, prepped_frame_queue, object_queue):
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threading.Thread.__init__(self)
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self.prepped_frame_queue = prepped_frame_queue
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self.object_queue = object_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 = ReadLabelFile(PATH_TO_LABELS)
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def run(self):
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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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# Actual detection.
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objects = self.engine.DetectWithInputTensor(frame['frame'], threshold=0.5, top_k=3)
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# time.sleep(0.1)
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# objects = []
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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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self.object_queue.put({
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'frame_time': frame['frame_time'],
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'name': str(self.labels[obj.label_id]),
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'score': float(obj.score),
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'xmin': int((box[0] * frame['region_size']) + frame['region_x_offset']),
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'ymin': int((box[1] * frame['region_size']) + frame['region_y_offset']),
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'xmax': int((box[2] * frame['region_size']) + frame['region_x_offset']),
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'ymax': int((box[3] * frame['region_size']) + frame['region_y_offset'])
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})
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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,
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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.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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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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# print("Prepped frame at " + str(self.region_x_offset) + "," + str(self.region_y_offset))
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# add the frame to the queue
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if not self.prepped_frame_queue.full():
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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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else:
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print("queue full. moving on")
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