mirror of
https://github.com/blakeblackshear/frigate.git
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123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
import logging
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from typing import Literal
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import cv2
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import cv2.dnn
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import numpy as np
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try:
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from hide_warnings import hide_warnings
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except: # noqa: E722
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def hide_warnings(func):
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pass
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from pydantic import Field
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "rknn"
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class RknnDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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score_thresh: float = Field(
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default=0.5, ge=0, le=1, title="Minimal confidence for detection."
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)
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nms_thresh: float = Field(
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default=0.45, ge=0, le=1, title="IoU threshold for non-maximum suppression."
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)
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class Rknn(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, config: RknnDetectorConfig):
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self.height = config.model.height
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self.width = config.model.width
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self.score_thresh = config.score_thresh
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self.nms_thresh = config.nms_thresh
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self.model_path = config.model.path or "/models/yolov8n-320x320.rknn"
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from rknnlite.api import RKNNLite
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self.rknn = RKNNLite(verbose=False)
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if self.rknn.load_rknn(self.model_path) != 0:
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logger.error("Error initializing rknn model.")
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if self.rknn.init_runtime() != 0:
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logger.error("Error initializing rknn runtime.")
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def __del__(self):
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self.rknn.release()
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def postprocess(self, results):
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"""
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Processes yolov8 output.
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Args:
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results: array with shape: (1, 84, n, 1) where n depends on yolov8 model size (for 320x320 model n=2100)
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Returns:
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detections: array with shape (20, 6) with 20 rows of (class, confidence, y_min, x_min, y_max, x_max)
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"""
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results = np.transpose(results[0, :, :, 0]) # array shape (2100, 84)
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classes = np.argmax(
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results[:, 4:], axis=1
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) # array shape (2100,); index of class with max confidence of each row
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scores = np.max(
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results[:, 4:], axis=1
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) # array shape (2100,); max confidence of each row
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# array shape (2100, 4); bounding box of each row
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boxes = np.transpose(
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np.vstack(
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(
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results[:, 0] - 0.5 * results[:, 2],
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results[:, 1] - 0.5 * results[:, 3],
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results[:, 2],
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results[:, 3],
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)
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)
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)
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# indices of rows with confidence > SCORE_THRESH with Non-maximum Suppression (NMS)
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result_boxes = cv2.dnn.NMSBoxes(
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boxes, scores, self.score_thresh, self.nms_thresh, 0.5
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)
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detections = np.zeros((20, 6), np.float32)
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for i in range(len(result_boxes)):
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if i >= 20:
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break
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index = result_boxes[i]
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detections[i] = [
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classes[index],
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scores[index],
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(boxes[index][1]) / self.height,
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(boxes[index][0]) / self.width,
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(boxes[index][1] + boxes[index][3]) / self.height,
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(boxes[index][0] + boxes[index][2]) / self.width,
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]
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return detections
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@hide_warnings
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def inference(self, tensor_input):
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return self.rknn.inference(inputs=tensor_input)
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def detect_raw(self, tensor_input):
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output = self.inference(
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[
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tensor_input,
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]
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)
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return self.postprocess(output[0])
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