blakeblackshear.frigate/frigate/detectors/yolo_utils.py

import logging

import numpy as np
import cv2

logger = logging.getLogger(__name__)

def yolov8_preprocess(tensor_input, model_input_shape):
    # tensor_input must be nhwc
    assert tensor_input.shape[3] == 3
    if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]):
        logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
    # cv2.dnn.blobFromImage is faster than numpying it
    return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)

def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20, score_threshold = 0.3, nms_threshold = 0.5):
    model_box_count = tensor_output.shape[2]
    probs = tensor_output[0, 4:, :]
    all_ids = np.argmax(probs, axis=0)
    all_confidences = probs.T[np.arange(model_box_count), all_ids]
    all_boxes = tensor_output[0, 0:4, :].T
    mask = (all_confidences > score_threshold)
    class_ids = all_ids[mask]
    confidences = all_confidences[mask]
    cx, cy, w, h = all_boxes[mask].T

    if model_input_shape[3] == 3:
        scale_y, scale_x = 1 / model_input_shape[1], 1 / model_input_shape[2]
    else:
        scale_y, scale_x = 1 / model_input_shape[2], 1 / model_input_shape[3]
    detections = np.stack((class_ids, confidences, scale_y * (cy - h / 2), scale_x * (cx - w / 2), scale_y * (cy + h / 2), scale_x * (cx + w / 2)), axis=1)
    if detections.shape[0] > box_count:
        # if too many detections, do nms filtering to suppress overlapping boxes
        boxes = np.stack((cx - w / 2, cy - h / 2, w, h), axis=1)
        indexes = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold, nms_threshold)
        detections = detections[indexes]
        # if still too many, trim the rest by confidence
        if detections.shape[0] > box_count:
            detections = detections[np.argpartition(detections[:,1], -box_count)[-box_count:]]
        detections = detections.copy()
    detections.resize((box_count, 6))
    return detections