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stop using pycoral libs for efficiency
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@ -9,7 +9,6 @@ from abc import ABC, abstractmethod
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from typing import Dict
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from typing import Dict
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import numpy as np
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import numpy as np
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from pycoral.adapters import detect
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import tflite_runtime.interpreter as tflite
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import tflite_runtime.interpreter as tflite
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from setproctitle import setproctitle
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from setproctitle import setproctitle
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from tflite_runtime.interpreter import load_delegate
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from tflite_runtime.interpreter import load_delegate
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@ -103,19 +102,25 @@ class LocalObjectDetector(ObjectDetector):
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self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
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self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
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self.interpreter.invoke()
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self.interpreter.invoke()
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objects = detect.get_objects(self.interpreter, 0.4)
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boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
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class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
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scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
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count = int(
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self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
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)
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detections = np.zeros((20, 6), np.float32)
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detections = np.zeros((20, 6), np.float32)
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for i, obj in enumerate(objects):
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if i == 20:
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for i in range(count):
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if scores[i] < 0.4 or i == 20:
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break
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break
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detections[i] = [
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detections[i] = [
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obj.id,
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class_ids[i],
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obj.score,
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float(scores[i]),
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obj.bbox.ymin,
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boxes[i][0],
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obj.bbox.xmin,
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boxes[i][1],
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obj.bbox.ymax,
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boxes[i][2],
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obj.bbox.xmax,
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boxes[i][3],
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]
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]
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return detections
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return detections
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@ -112,7 +112,7 @@ class EventCleanup(threading.Thread):
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def expire(self, media_type):
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def expire(self, media_type):
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## Expire events from unlisted cameras based on the global config
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## Expire events from unlisted cameras based on the global config
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if media_type == "clips":
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if media_type == "clips":
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retain_config = self.config.clips.retain
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retain_config = self.config.record.events.retain
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file_extension = "mp4"
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file_extension = "mp4"
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update_params = {"has_clip": False}
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update_params = {"has_clip": False}
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else:
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else:
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@ -163,7 +163,7 @@ class EventCleanup(threading.Thread):
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## Expire events from cameras based on the camera config
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## Expire events from cameras based on the camera config
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for name, camera in self.config.cameras.items():
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for name, camera in self.config.cameras.items():
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if media_type == "clips":
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if media_type == "clips":
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retain_config = camera.clips.retain
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retain_config = camera.record.events.retain
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else:
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else:
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retain_config = camera.snapshots.retain
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retain_config = camera.snapshots.retain
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# get distinct objects in database for this camera
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# get distinct objects in database for this camera
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@ -413,17 +413,16 @@ def detect(
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object_detector, frame, model_shape, region, objects_to_track, object_filters
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object_detector, frame, model_shape, region, objects_to_track, object_filters
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):
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):
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tensor_input = create_tensor_input(frame, model_shape, region)
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tensor_input = create_tensor_input(frame, model_shape, region)
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scale = float(region[2] - region[0]) / model_shape[0]
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detections = []
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detections = []
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region_detections = object_detector.detect(tensor_input)
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region_detections = object_detector.detect(tensor_input)
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for d in region_detections:
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for d in region_detections:
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box = d[2]
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box = d[2]
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size = region[2] - region[0]
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size = region[2] - region[0]
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x_min = int(max(0, box[1]) * scale + region[0])
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x_min = int((box[1] * size) + region[0])
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y_min = int(max(0, box[0]) * scale + region[1])
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y_min = int((box[0] * size) + region[1])
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x_max = int(min(frame.shape[1], box[3]) * scale + region[0])
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x_max = int((box[3] * size) + region[0])
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y_max = int(min(frame.shape[0], box[2]) * scale + region[1])
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y_max = int((box[2] * size) + region[1])
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det = (
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det = (
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d[0],
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d[0],
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d[1],
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d[1],
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