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HDMI输入直至Yolov5识别全流程代码

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# 简介
* 使用多线程异步操作rknn模型, 提高rk3588/rk3588s的NPU使用率, 进而提高推理帧数(rk3568之类修改后应该也能使用, 但是作者本人并没有rk3568开发板......)
* 此分支使用模型[yolov5s_relu_tk2_RK3588_i8.rknn](https://github.com/airockchip/rknn_model_zoo), 将yolov5s模型的激活函数silu修改为为relu,在损失一点精度的情况下获得较大性能提升,详情见于[rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo)
* 此项目的[c++](https://github.com/leafqycc/rknn-cpp-Multithreading)实现
# 更新说明
* 无
# 使用说明
### 演示
* 将仓库拉取至本地, 并将Releases中的演示视频放于项目根目录下, 运行main.py查看演示示例
* 切换至root用户运行performance.sh可以进行定频操作(约等于开启性能模式)
* 运行rkcat.sh可以查看当前温度与NPU占用
### 部署应用
* 修改main.py下的modelPath为你自己的模型所在路径
* 修改main.py下的cap为你想要运行的视频/摄像头
* 修改main.py下的TPEs为你想要的线程数, 具体可参考下表
* 修改func.py为你自己需要的推理函数, 具体可查看myFunc函数
# 多线程模型帧率测试
* 使用performance.sh进行CPU/NPU定频尽量减少误差
* 测试模型为[yolov5s_relu_tk2_RK3588_i8.rknn](https://github.com/airockchip/rknn_model_zoo)
* 测试视频见于Releases
| 模型\线程数 | 1 | 2 | 3 | 4 | 5 | 6 |
| ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| yolov5s | 27.4491 | 49.0747 | 65.3673 | 63.3204 | 71.8407 | 72.0590 |
# 补充
* 多线程下CPU, NPU占用较高, **核心温度相应增高**, 请做好散热。推荐开1, 2, 3线程, 实测小铜片散热下运行三分钟温度约为56°, 64°, 69°
# Acknowledgements
* https://github.com/ultralytics/yolov5
* https://github.com/rockchip-linux/rknn-toolkit2
* https://github.com/airockchip/rknn_model_zoo

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#以下代码改自https://github.com/rockchip-linux/rknn-toolkit2/tree/master/examples/onnx/yolov5
import cv2
import numpy as np
OBJ_THRESH, NMS_THRESH, IMG_SIZE = 0.25, 0.45, 640
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 ")
#
# def sigmoid(x):
# return 1 / (1 + np.exp(-x))
def xywh2xyxy(x):
# Convert [x, y, w, h] to [x1, y1, x2, y2]
y = np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
return y
def process(input, mask, anchors):
anchors = [anchors[i] for i in mask]
grid_h, grid_w = map(int, input.shape[0:2])
box_confidence = input[..., 4]
box_confidence = np.expand_dims(box_confidence, axis=-1)
box_class_probs = input[..., 5:]
box_xy = input[..., :2] *2 - 0.5
col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
grid = np.concatenate((col, row), axis=-1)
box_xy += grid
box_xy *= int(IMG_SIZE/grid_h)
box_wh = pow(input[..., 2:4] *2, 2)
box_wh = box_wh * anchors
return np.concatenate((box_xy, box_wh), axis=-1), box_confidence, box_class_probs
def filter_boxes(boxes, box_confidences, box_class_probs):
"""Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
# Arguments
boxes: ndarray, boxes of objects.
box_confidences: ndarray, confidences of objects.
box_class_probs: ndarray, class_probs of objects.
# Returns
boxes: ndarray, filtered boxes.
classes: ndarray, classes for boxes.
scores: ndarray, scores for boxes.
"""
boxes = boxes.reshape(-1, 4)
box_confidences = box_confidences.reshape(-1)
box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
_box_pos = np.where(box_confidences >= OBJ_THRESH)
boxes = boxes[_box_pos]
box_confidences = box_confidences[_box_pos]
box_class_probs = box_class_probs[_box_pos]
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 >= OBJ_THRESH)
return boxes[_class_pos], classes[_class_pos], (class_max_score * box_confidences)[_class_pos]
def nms_boxes(boxes, scores):
"""Suppress non-maximal boxes.
# Arguments
boxes: ndarray, boxes of objects.
scores: ndarray, scores of objects.
# 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 <= NMS_THRESH)[0]
order = order[inds + 1]
return np.array(keep)
def yolov5_post_process(input_data):
masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]]
boxes, classes, scores = [], [], []
for input, mask in zip(input_data, masks):
b, c, s = process(input, mask, anchors)
b, c, s = filter_boxes(b, c, s)
boxes.append(b)
classes.append(c)
scores.append(s)
boxes = np.concatenate(boxes)
boxes = xywh2xyxy(boxes)
classes = np.concatenate(classes)
scores = np.concatenate(scores)
nboxes, nclasses, nscores = [], [], []
for c in set(classes):
inds = np.where(classes == c)
b = boxes[inds]
c = classes[inds]
s = scores[inds]
keep = nms_boxes(b, s)
nboxes.append(b[keep])
nclasses.append(c[keep])
nscores.append(s[keep])
if not nclasses and not nscores:
return None, None, None
return np.concatenate(nboxes), np.concatenate(nclasses), np.concatenate(nscores)
def draw(image, boxes, scores, classes):
for box, score, cl in zip(boxes, scores, classes):
top, left, right, bottom = box
# print('class: {}, score: {}'.format(CLASSES[cl], score))
# print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
top = int(top)
left = int(left)
cv2.rectangle(image, (top, left), (int(right), int(bottom)), (255, 0, 0), 2)
cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
(top, left - 6),
cv2.FONT_HERSHEY_SIMPLEX,
0.6, (0, 0, 255), 2)
def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - \
new_unpad[1] # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right,
cv2.BORDER_CONSTANT, value=color) # add border
return im
# return im, ratio, (dw, dh)
def myFunc(rknn_lite, IMG):
IMG = cv2.cvtColor(IMG, cv2.COLOR_BGR2RGB)
# 等比例缩放
# IMG = letterbox(IMG)
# 强制放缩
IMG = cv2.resize(IMG, (IMG_SIZE, IMG_SIZE))
outputs = rknn_lite.inference(inputs=[IMG])
input0_data = outputs[0].reshape([3, -1]+list(outputs[0].shape[-2:]))
input1_data = outputs[1].reshape([3, -1]+list(outputs[1].shape[-2:]))
input2_data = outputs[2].reshape([3, -1]+list(outputs[2].shape[-2:]))
input_data = list()
input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
boxes, classes, scores = yolov5_post_process(input_data)
IMG = cv2.cvtColor(IMG, cv2.COLOR_RGB2BGR)
if boxes is not None:
draw(IMG, boxes, scores, classes)
return IMG

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import cv2
import time
from rknnpool import rknnPoolExecutor
# 图像处理函数，实际应用过程中需要自行修改
from func import myFunc
cap = cv2.VideoCapture('./test.mp4')
# cap = cv2.VideoCapture(0)
modelPath = "./rknnModel/yolov5s_relu_tk2_RK3588_i8.rknn"
# 线程数, 增大可提高帧率
TPEs = 3
# 初始化rknn池
pool = rknnPoolExecutor(
rknnModel=modelPath,
TPEs=TPEs,
func=myFunc)
# 初始化异步所需要的帧
if (cap.isOpened()):
for i in range(TPEs + 1):
ret, frame = cap.read()
if not ret:
cap.release()
del pool
exit(-1)
pool.put(frame)
frames, loopTime, initTime = 0, time.time(), time.time()
while (cap.isOpened()):
frames += 1
ret, frame = cap.read()
if not ret:
break
pool.put(frame)
frame, flag = pool.get()
if flag == False:
break
cv2.imshow('test', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if frames % 30 == 0:
print("30帧平均帧率:\t", 30 / (time.time() - loopTime), "帧")
loopTime = time.time()
print("总平均帧率\t", frames / (time.time() - initTime))
# 释放cap和rknn线程池
cap.release()
cv2.destroyAllWindows()
pool.release()

60
NpuYoloV5/05_rknn-multi-threaded-nosigmoid/main_hdmi_in.py Normal file
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import cv2
import time
import numpy as np
from rknnpool import rknnPoolExecutor
# 图像处理函数，实际应用过程中需要自行修改
from func import myFunc
cap = cv2.VideoCapture('/dev/video10')
# cap = cv2.VideoCapture(0)
modelPath = "./rknnModel/yolov5s_relu_tk2_RK3588_i8.rknn"
# 线程数, 增大可提高帧率
TPEs = 3
# 初始化rknn池
pool = rknnPoolExecutor(
rknnModel=modelPath,
TPEs=TPEs,
func=myFunc)
# 初始化异步所需要的帧
if (cap.isOpened()):
for i in range(TPEs + 1):
ret, frame = cap.read()
# 检查帧数据是否需要重塑
if frame.size == 1280 * 720 * 3: # 检查是否为扁平化数据
frame = frame.reshape((720, 1280, 3)).astype(np.uint8)
else:
print(f"Unexpected frame shape: {frame.shape}")
if not ret:
cap.release()
del pool
exit(-1)
pool.put(frame)
frames, loopTime, initTime = 0, time.time(), time.time()
while (cap.isOpened()):
frames += 1
ret, frame = cap.read()
# 检查帧数据是否需要重塑
if frame.size == 1280 * 720 * 3: # 检查是否为扁平化数据
frame = frame.reshape((720, 1280, 3)).astype(np.uint8)
else:
print(f"Unexpected frame shape: {frame.shape}")
if not ret:
break
pool.put(frame)
frame, flag = pool.get()
if flag == False:
break
cv2.imshow('test', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if frames % 30 == 0:
print("30帧平均帧率:\t", 30 / (time.time() - loopTime), "帧")
loopTime = time.time()
print("总平均帧率\t", frames / (time.time() - initTime))
# 释放cap和rknn线程池
cap.release()
cv2.destroyAllWindows()
pool.release()

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NpuYoloV5/05_rknn-multi-threaded-nosigmoid/performance.sh Normal file
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# 请切换到root用户
# CPU定频
echo "CPU0-3 可用频率:"
sudo cat /sys/devices/system/cpu/cpufreq/policy0/scaling_available_frequencies
sudo echo userspace > /sys/devices/system/cpu/cpufreq/policy0/scaling_governor
sudo echo 1800000 > /sys/devices/system/cpu/cpufreq/policy0/scaling_setspeed
echo "CPU0-3 当前频率:"
sudo cat /sys/devices/system/cpu/cpufreq/policy0/cpuinfo_cur_freq
echo "CPU4-5 可用频率:"
sudo cat /sys/devices/system/cpu/cpufreq/policy4/scaling_available_frequencies
sudo echo userspace > /sys/devices/system/cpu/cpufreq/policy4/scaling_governor
sudo echo 2400000 > /sys/devices/system/cpu/cpufreq/policy4/scaling_setspeed
echo "CPU4-5 当前频率:"
sudo cat /sys/devices/system/cpu/cpufreq/policy4/cpuinfo_cur_freq
echo "CPU6-7 可用频率:"
sudo cat /sys/devices/system/cpu/cpufreq/policy6/scaling_available_frequencies
sudo echo userspace > /sys/devices/system/cpu/cpufreq/policy6/scaling_governor
sudo echo 2400000 > /sys/devices/system/cpu/cpufreq/policy6/scaling_setspeed
echo "CPU6-7 当前频率:"
sudo cat /sys/devices/system/cpu/cpufreq/policy6/cpuinfo_cur_freq
# NPU定频
echo "NPU 可用频率:"
sudo cat /sys/class/devfreq/fdab0000.npu/available_frequencies
sudo echo userspace > /sys/class/devfreq/fdab0000.npu/governor
sudo echo 1000000000 > /sys/class/devfreq/fdab0000.npu/userspace/set_freq
echo "NPU 当前频率:"
sudo cat /sys/class/devfreq/fdab0000.npu/cur_freq

5
NpuYoloV5/05_rknn-multi-threaded-nosigmoid/rkcat.sh Normal file
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# 查看温度
sensors
# 查看NPU占用
echo "当前NPU占用:"
sudo cat /sys/kernel/debug/rknpu/load

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NpuYoloV5/05_rknn-multi-threaded-nosigmoid/rknnModel/yolov5s_relu_tk2_RK3588_i8.rknn Normal file
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NpuYoloV5/05_rknn-multi-threaded-nosigmoid/rknnpool.py Normal file
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from queue import Queue
from rknnlite.api import RKNNLite
from concurrent.futures import ThreadPoolExecutor, as_completed
def initRKNN(rknnModel="./rknnModel/yolov5s.rknn", id=0):
rknn_lite = RKNNLite()
ret = rknn_lite.load_rknn(rknnModel)
if ret != 0:
print("Load RKNN rknnModel failed")
exit(ret)
if id == 0:
ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
elif id == 1:
ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_1)
elif id == 2:
ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_2)
elif id == -1:
ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0_1_2)
else:
ret = rknn_lite.init_runtime()
if ret != 0:
print("Init runtime environment failed")
exit(ret)
print(rknnModel, "\t\tdone")
return rknn_lite
def initRKNNs(rknnModel="./rknnModel/yolov5s.rknn", TPEs=1):
rknn_list = []
for i in range(TPEs):
rknn_list.append(initRKNN(rknnModel, i % 3))
return rknn_list
class rknnPoolExecutor():
def __init__(self, rknnModel, TPEs, func):
self.TPEs = TPEs
self.queue = Queue()
self.rknnPool = initRKNNs(rknnModel, TPEs)
self.pool = ThreadPoolExecutor(max_workers=TPEs)
self.func = func
self.num = 0
def put(self, frame):
self.queue.put(self.pool.submit(
self.func, self.rknnPool[self.num % self.TPEs], frame))
self.num += 1
def get(self):
if self.queue.empty():
return None, False
fut = self.queue.get()
return fut.result(), True
def release(self):
self.pool.shutdown()
for rknn_lite in self.rknnPool:
rknn_lite.release()