mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
34 lines
1.5 KiB
Python
34 lines
1.5 KiB
Python
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):
|
|
model_box_count = tensor_output.shape[2]
|
|
model_class_count = tensor_output.shape[1] - 4
|
|
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 > 0.30)
|
|
class_ids = all_ids[mask]
|
|
confidences = all_confidences[mask]
|
|
cx, cy, w, h = all_boxes[mask].T
|
|
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:
|
|
detections = detections[np.argpartition(detections[:,1], -box_count)[-box_count:]]
|
|
detections.resize((box_count, 6))
|
|
return detections
|
|
|