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https://github.com/MarcA711/frigate-rockchip.git
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102 lines
4.4 KiB
Python
102 lines
4.4 KiB
Python
import datetime
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import cv2
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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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def detect_objects(prepped_frame_array, prepped_frame_time, prepped_frame_lock,
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prepped_frame_ready, 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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frame_time = 0.0
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region_box = [0,0,0,0]
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while True:
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with prepped_frame_ready:
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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_lock:
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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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# assumes square
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region_size = region_box[2]-region_box[0]
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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_size) + region_box[0]),
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'ymin': int((box[1] * region_size) + region_box[1]),
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'xmax': int((box[2] * region_size) + region_box[0]),
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'ymax': int((box[3] * region_size) + region_box[1])
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})
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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_ready, prepped_frame_lock,
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prepped_frame_box):
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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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shared_prepped_frame = tonumpyarray(prepped_frame_array).reshape((1,300,300,3))
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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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motion_detected.wait()
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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 = frame_time
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prepped_frame_box[:] = [region_x_offset, region_y_offset, region_x_offset+region_size, region_y_offset+region_size]
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# signal that a prepped frame is ready
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with prepped_frame_ready:
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prepped_frame_ready.notify_all() |