Basic pakages:
sudo apt-get update
sudo apt-get install -y python3 python3-dev python3-pip gcc
sudo apt-get install -y python3-opencv
sudo apt-get install -y python3-numpy
sudo apt-get install git
sudo apt-get install wget
sudo apt-get install python3-setuptools
FOR 3588
used v1.5.2 version!
#choose own version
wget https://github.com/rockchip-linux/rknpu2/blob/master/runtime/RK3588/Linux/librknn_api/aarch64/librknnrt.so
sudo mv librknnrt.so /usr/lib/librknnrt.so
# choose correct version rknn-toolkit (look on your python3 version , in my case 3.10)
python3 --version
wget https://github.com/airockchip/rknn-toolkit2/blob/master/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.0.0b0-cp310-cp310-linux_aarch64.whl
pip3 install rknn_toolkit_lite2-2.0.0b0-cp310-cp310-linux_aarch64.whl
sudo ln -s librknnrt.so librknn_api.so
To test it:
Download repsitory
https://github.com/airockchip/rknn-toolkit2/tree/master/rknn-toolkit-lite2/examples/resnet18
and run test.py
install ananconda
conda create -n rknn_env python=3.10
conda activate rknn_env
install package (depend of python3 version) for PC with using pip3 install
https://github.com/ViktorPavlovA/rknn-toolkit2-fork-1.5.2/tree/v1.5.2/packages
git clone https://github.com/airockchip/ultralytics_yolov8
cd ultralytics_yolov8
in requirement.txt uncomment string with onnx
pip3 install -r requirement.txt
python3 setup.py install
create file convert.py
from ultralytics import YOLO
model = YOLO('best.pt') # exaple
path = model.export(format='rknn')
After all action we get best.onnx
git clone https://github.com/airockchip/rknn_model_zoo.git
Copy best.onnx in:
rknn_model_zoo/examples/yolov8/python/
python3 convert.py best.onnx rk3588
if process will be successes you model will laying in the rknn_model_zoo/examples/yolov8/model/
Thanks Oleg-milantiev
"""
Usage example
"""
import cv2
import numpy as np
from rknnlite.api import RKNNLite
from time import time
class Coco:
CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light",
"fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant",
"bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
"baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ",
"spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa",
"pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop ", "mouse ", "remote ", "keyboard ", "cell phone", "microwave ",
"oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ")
class Yolov8_rknn:
def __init__(self,model:str,classes=Coco.CLASSES,imgsz=(640,640),obj_threshold = 0.5,nms_threshold = 0.65 ) -> None:
self.model = None
#init model
self.model = self.__load_rknn(model)
self.frame = None
self.w = imgsz[0]
self.h = imgsz[1]
self.obj_threshold = obj_threshold
self.nms_threshold = nms_threshold
def __load_rknn(self,link_model:str):
"""Load engine rknn file
"""
print("Init RKNNLite --->")
self.model = RKNNLite()
print("Init yolov8 model --->")
ret = self.model.load_rknn(link_model)
ret = self.model.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
return self.model
def filter_boxes(self,boxes, box_confidences, box_class_probs):
"""Filter boxes with object threshold.
"""
box_confidences = box_confidences.reshape(-1)
candidate, class_num = box_class_probs.shape
class_max_score = np.max(box_class_probs, axis=-1)
classes = np.argmax(box_class_probs, axis=-1)
_class_pos = np.where(class_max_score* box_confidences >= self.obj_threshold)
scores = (class_max_score* box_confidences)[_class_pos]
boxes = boxes[_class_pos]
classes = classes[_class_pos]
return boxes, classes, scores
def nms_boxes(self,boxes, scores):
"""Suppress non-maximal boxes.
# Returns
keep: ndarray, index of effective boxes.
"""
x = boxes[:, 0]
y = boxes[:, 1]
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
areas = w * h
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x[i], x[order[1:]])
yy1 = np.maximum(y[i], y[order[1:]])
xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
inter = w1 * h1
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= self.nms_threshold)[0]
order = order[inds + 1]
keep = np.array(keep)
return keep
def dfl(self, position):
x = np.array(position)
n, c, h, w = x.shape
p_num = 4
mc = c // p_num
y = x.reshape(n, p_num, mc, h, w)
max_values = np.max(y, axis=2, keepdims=True)
exp_values = np.exp(y - max_values)
y = exp_values / np.sum(exp_values, axis=2, keepdims=True)
acc_matrix = np.arange(mc, dtype=np.float32).reshape(1, 1, mc, 1, 1)
y = np.sum(y * acc_matrix, axis=2)
return y
def box_process(self, position):
grid_h, grid_w = position.shape[2:4]
col, row = np.meshgrid(np.arange(0, grid_w), np.arange(0, grid_h))
col = col.reshape(1, 1, grid_h, grid_w)
row = row.reshape(1, 1, grid_h, grid_w)
grid = np.concatenate((col, row), axis=1)
stride = np.array([self.h//grid_h, self.w//grid_w]).reshape(1,2,1,1)
position = self.dfl(position)
box_xy = grid +0.5 -position[:,0:2,:,:]
box_xy2 = grid +0.5 +position[:,2:4,:,:]
xyxy = np.concatenate((box_xy*stride, box_xy2*stride), axis=1)
return xyxy
def post_process(self, input_data):
boxes, scores, classes_conf = [], [], []
defualt_branch=3
pair_per_branch = len(input_data)//defualt_branch
# Python 忽略 score_sum 输出
for i in range(defualt_branch):
boxes.append(self.box_process(input_data[pair_per_branch*i]))
classes_conf.append(input_data[pair_per_branch*i+1])
scores.append(np.ones_like(input_data[pair_per_branch*i+1][:,:1,:,:], dtype=np.float32))
def sp_flatten(_in):
ch = _in.shape[1]
_in = _in.transpose(0,2,3,1)
return _in.reshape(-1, ch)
boxes = [sp_flatten(_v) for _v in boxes]
classes_conf = [sp_flatten(_v) for _v in classes_conf]
scores = [sp_flatten(_v) for _v in scores]
boxes = np.concatenate(boxes)
classes_conf = np.concatenate(classes_conf)
scores = np.concatenate(scores)
# filter according to threshold
boxes, classes, scores = self.filter_boxes(boxes, scores, classes_conf)
# nms
nboxes, nclasses, nscores = [], [], []
for c in set(classes):
inds = np.where(classes == c)
b = boxes[inds]
c = classes[inds]
s = scores[inds]
keep = self.nms_boxes(b, s)
if len(keep) != 0:
nboxes.append(b[keep])
nclasses.append(c[keep])
nscores.append(s[keep])
if not nclasses and not nscores:
return None, None, None
boxes = np.concatenate(nboxes)
classes = np.concatenate(nclasses)
scores = np.concatenate(nscores)
return boxes, classes, scores
def __call__(self,img:np.ndarray):
outputs = self.model.inference(inputs=[img])
boxes, classes, scores = self.post_process(outputs)
if boxes is not None:
for box in boxes:
box = box.astype(int)
img = cv2.rectangle(img,(box[0],box[1]),(box[2],box[3]),(0,255,0),2)
return img
else:
print("Not found box")
return img
def realse(self):
self.model.release()
if __name__ == "__main__":
model = Yolov8_rknn(model='yolov8.rknn')
cap = cv2.VideoCapture(0)
time_start = 0
while True:
ret,img = cap.read()
img = cv2.resize(img,(640,640))
img = model(img)
cv2.imshow("img",img)
if cv2.waitKey(2) & 0xFF == ord('q'):
break
fps = 1/(time() - time_start)
print(fps)
time_start = time()
cv2.waitKey(0)
cv2.destroyAllWindows()
model.release()
FPS +- 18 640x640
