郑州网站优化公司排名,wordpress程序重装,上海企业名录大全黄页,吉林平台网站建设推荐首先将文件夹~/rknpu2/runtime/RK3588/Linux/librknn_api/aarch64/下的文件librknnrt.so复制到文件夹/usr/lib/下#xff08;该文件夹下原有的文件librknnrt.so是用来测试resnet50模型的#xff0c;所以要替换成yolo模型的librknnrt.so#xff09;#xff0c;如下图所示该文件夹下原有的文件librknnrt.so是用来测试resnet50模型的所以要替换成yolo模型的librknnrt.so如下图所示 然后在文件夹/home/rpdzkj/rknn-toolkit-lit2-examples/inference_with_lite/下放入以下3个文件
bus.jpg best.rknn des.py 其中bus.jpg是要检测的图片best.rknn是我们转换的yolov5的rknn模型des.py是要运行的py代码其代码如下
# -*- coding: utf-8 -*-
# coding:utf-8import os
import urllib
import traceback
import time
import sys
import numpy as np
import cv2
import platform
from rknnlite.api import RKNNLite
from PIL import Image# Model from https://github.com/airockchip/rknn_model_zoo
RKNN_MODEL best.rknn
IMG_PATH ./bus.jpg
DATASET ./dataset.txtQUANTIZE_ON True
BOX_THRESH 0.5
OBJ_THRESH 0.45
NMS_THRESH 0.65
IMG_SIZE 640CLASSES (car, moto, persons)# decice tree for rk356x/rk3588
DEVICE_COMPATIBLE_NODE /proc/device-tree/compatibledef get_host():# get platform and device typesystem platform.system()machine platform.machine()os_machine system - machineif os_machine Linux-aarch64:try:with open(DEVICE_COMPATIBLE_NODE) as f:device_compatible_str f.read()host RK3588except IOError:print(Read device node {} failed..format(DEVICE_COMPATIBLE_NODE))exit(-1)else:host os_machinereturn hostINPUT_SIZE 640RK3588_RKNN_MODEL best.rknndef 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 xy[:, 1] x[:, 1] - x[:, 3] / 2 # top left yy[:, 2] x[:, 0] x[:, 2] / 2 # bottom right xy[:, 3] x[:, 1] x[:, 3] / 2 # bottom right yreturn ydef process(input, mask, anchors):anchors [anchors[i] for i in mask]grid_h, grid_w map(int, input.shape[0:2])box_confidence sigmoid(input[..., 4])box_confidence np.expand_dims(box_confidence, axis-1)box_class_probs sigmoid(input[..., 5:])box_xy sigmoid(input[..., :2]) * 2 - 0.5col 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 gridbox_xy * int(IMG_SIZE/grid_h)box_wh pow(sigmoid(input[..., 2:4]) * 2, 2)box_wh box_wh * anchorsbox np.concatenate((box_xy, box_wh), axis-1)return box, box_confidence, box_class_probsdef filter_boxes(boxes, box_confidences, box_class_probs):Filter boxes with box threshold. Its a bit different with origin yolov5 post process!# Argumentsboxes: ndarray, boxes of objects.box_confidences: ndarray, confidences of objects.box_class_probs: ndarray, class_probs of objects.# Returnsboxes: 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)boxes boxes[_class_pos]classes classes[_class_pos]scores (class_max_score* box_confidences)[_class_pos]return boxes, classes, scoresdef nms_boxes(boxes, scores):Suppress non-maximal boxes.# Argumentsboxes: ndarray, boxes of objects.scores: ndarray, scores of objects.# Returnskeep: ndarray, index of effective boxes.x boxes[:, 0]y boxes[:, 1]w boxes[:, 2] - boxes[:, 0]h boxes[:, 3] - boxes[:, 1]areas w * horder 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 * h1ovr inter / (areas[i] areas[order[1:]] - inter)inds np.where(ovr NMS_THRESH)[0]order order[inds 1]keep np.array(keep)return keepdef yolov5_post_process(input_data):masks [[0, 1, 2], [3, 4, 5], [6, 7, 8]]anchors [[15,20], [20, 75], [28, 25], [31,136], [44,42],[53,215], [75,76], [98,421], [148,226]]#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, Noneboxes np.concatenate(nboxes)classes np.concatenate(nclasses)scores np.concatenate(nscores)return boxes, classes, scoresdef draw(image, boxes, scores, classes):Draw the boxes on the image.# Argument:image: original image.boxes: ndarray, boxes of objects.classes: ndarray, classes of objects.scores: ndarray, scores of objects.all_classes: all classes name.for box, score, cl in zip(boxes, scores, classes):top, left, right, bottom boxprint(class: {}, score: {}.format(CLASSES[cl], score))print(box coordinate left,top,right,down: [{}, {}, {}, {}].format(top, left, right, bottom))top int(top)left int(left)right int(right)bottom int(bottom)cv2.rectangle(image, (top, left), (right, 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)):# Resize and pad image while meeting stride-multiple constraintsshape im.shape[:2] # current shape [height, width]if isinstance(new_shape, int):new_shape (new_shape, new_shape)# Scale ratio (new / old)r min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingratio r, r # width, height ratiosnew_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 paddingdw / 2 # divide padding into 2 sidesdh / 2if shape[::-1] ! new_unpad: # resizeim cv2.resize(im, new_unpad, interpolationcv2.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, valuecolor) # add borderreturn im, ratio, (dw, dh)if __name__ __main__:host_name get_host()rknn_model RK3588_RKNN_MODELrknn_lite RKNNLite()# load RKNN modelprint(-- Load RKNN model)ret rknn_lite.load_rknn(rknn_model)if ret ! 0:print(Load RKNN model failed)exit(ret)print(done)#####ori_img cv2.imread(./bus.jpg)######img cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)# init runtime environmentprint(-- Init runtime environment)# run on RK356x/RK3588 with Debian OS, do not need specify target.ret rknn_lite.init_runtime(core_maskRKNNLite.NPU_CORE_0)if ret ! 0:print(Init runtime environment failed)exit(ret)print(done)# Set inputsimg cv2.imread(IMG_PATH)img, ratio, (dw, dh) letterbox(img, new_shape(IMG_SIZE, IMG_SIZE))img cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img cv2.resize(img, (IMG_SIZE, IMG_SIZE))# Inferenceprint(-- Running model)outputs rknn_lite.inference(inputs[img])#np.save(./onnx_yolov5_0.npy, outputs[0])#np.save(./onnx_yolov5_1.npy, outputs[1])#np.save(./onnx_yolov5_2.npy, outputs[2])print(done)# post processinput0_data outputs[0]input1_data outputs[1]input2_data outputs[2]input0_data input0_data.reshape([3, -1]list(input0_data.shape[-2:]))input1_data input1_data.reshape([3, -1]list(input1_data.shape[-2:]))input2_data input2_data.reshape([3, -1]list(input2_data.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_1 cv2.cvtColor(img, cv2.COLOR_RGB2BGR)if boxes is not None:draw(img_1, boxes, scores, classes)cv2.imwrite(result.jpg, img_1)rknn_lite.release()
代码运行结果如下 这里没有出车是我训练的模型问题。 大家对以上代码是不是很熟悉其实这部分代码的很多函数都是将onnx文件转换为rknn文件的代码只是把它的from rknn.api import RKNN修改为from rknnlite.api import RKNNLite并且将载入rknn模型的内容修改即可数据的处理完全没变。 用这个代码的好处是我们以后对相机进行检测目标时只需要下载一次rknn模型后续只需要处理图片即可。 二、下面再结合lidar数据的获取我们可以同时获取雷达数据和检测rknn模型代码如下
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# coding:utf-8
import cv2import rospy
from sensor_msgs.msg import PointCloud2
import sensor_msgs.point_cloud2 as pc2
from std_msgs.msg import Header
from visualization_msgs.msg import Marker, MarkerArray
from geometry_msgs.msg import Point#import torch
import numpy as np
import sys
import time,datetime
print(sys.version)
#from recon_barriers_model import recon_barriers
#from pclpy import pcl
from queue import Queue
import open3d as o3dimport matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#%matplotlib#from create_date_file import data_time
import threading
import time,datetime
import os#先初始化ros再from rknnlite.api import RKNNLite否则会报错
rospy.init_node(lidar_node)import os
import urllib
import traceback
import sys
import numpy as np
import platform
from rknnlite.api import RKNNLite
from PIL import Image#######################################################################################################
#1.rknn模型检测#
#######################################################################################################QUANTIZE_ON True
BOX_THRESH 0.5
OBJ_THRESH 0.45
NMS_THRESH 0.65
IMG_SIZE 640CLASSES (car, moto, persons)# decice tree for rk356x/rk3588
DEVICE_COMPATIBLE_NODE /proc/device-tree/compatibledef get_host():# get platform and device typesystem platform.system()machine platform.machine()os_machine system - machineif os_machine Linux-aarch64:try:with open(DEVICE_COMPATIBLE_NODE) as f:device_compatible_str f.read()host RK3588except IOError:print(Read device node {} failed..format(DEVICE_COMPATIBLE_NODE))exit(-1)else:host os_machinereturn hostINPUT_SIZE 640RK3588_RKNN_MODEL best.rknndef 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 xy[:, 1] x[:, 1] - x[:, 3] / 2 # top left yy[:, 2] x[:, 0] x[:, 2] / 2 # bottom right xy[:, 3] x[:, 1] x[:, 3] / 2 # bottom right yreturn ydef process(input, mask, anchors):anchors [anchors[i] for i in mask]grid_h, grid_w map(int, input.shape[0:2])box_confidence sigmoid(input[..., 4])box_confidence np.expand_dims(box_confidence, axis-1)box_class_probs sigmoid(input[..., 5:])box_xy sigmoid(input[..., :2]) * 2 - 0.5col 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 gridbox_xy * int(IMG_SIZE/grid_h)box_wh pow(sigmoid(input[..., 2:4]) * 2, 2)box_wh box_wh * anchorsbox np.concatenate((box_xy, box_wh), axis-1)return box, box_confidence, box_class_probsdef filter_boxes(boxes, box_confidences, box_class_probs):Filter boxes with box threshold. Its a bit different with origin yolov5 post process!# Argumentsboxes: ndarray, boxes of objects.box_confidences: ndarray, confidences of objects.box_class_probs: ndarray, class_probs of objects.# Returnsboxes: 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)boxes boxes[_class_pos]classes classes[_class_pos]scores (class_max_score* box_confidences)[_class_pos]return boxes, classes, scoresdef nms_boxes(boxes, scores):Suppress non-maximal boxes.# Argumentsboxes: ndarray, boxes of objects.scores: ndarray, scores of objects.# Returnskeep: ndarray, index of effective boxes.x boxes[:, 0]y boxes[:, 1]w boxes[:, 2] - boxes[:, 0]h boxes[:, 3] - boxes[:, 1]areas w * horder 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 * h1ovr inter / (areas[i] areas[order[1:]] - inter)inds np.where(ovr NMS_THRESH)[0]order order[inds 1]keep np.array(keep)return keepdef yolov5_post_process(input_data):masks [[0, 1, 2], [3, 4, 5], [6, 7, 8]]anchors [[15,20], [20, 75], [28, 25], [31,136], [44,42],[53,215], [75,76], [98,421], [148,226]]#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, Noneboxes np.concatenate(nboxes)classes np.concatenate(nclasses)scores np.concatenate(nscores)return boxes, classes, scoresdef draw(image, boxes, scores, classes):Draw the boxes on the image.# Argument:image: original image.boxes: ndarray, boxes of objects.classes: ndarray, classes of objects.scores: ndarray, scores of objects.all_classes: all classes name.for box, score, cl in zip(boxes, scores, classes):top, left, right, bottom boxprint(class: {}, score: {}.format(CLASSES[cl], score))print(box coordinate left,top,right,down: [{}, {}, {}, {}].format(top, left, right, bottom))top int(top)left int(left)right int(right)bottom int(bottom)cv2.rectangle(image, (top, left), (right, 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)):# Resize and pad image while meeting stride-multiple constraintsshape im.shape[:2] # current shape [height, width]if isinstance(new_shape, int):new_shape (new_shape, new_shape)# Scale ratio (new / old)r min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingratio r, r # width, height ratiosnew_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 paddingdw / 2 # divide padding into 2 sidesdh / 2if shape[::-1] ! new_unpad: # resizeim cv2.resize(im, new_unpad, interpolationcv2.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, valuecolor) # add borderreturn im, ratio, (dw, dh)host_name get_host()
rknn_model RK3588_RKNN_MODELrknn_lite RKNNLite()# load RKNN model
print(-- Load RKNN model)
ret rknn_lite.load_rknn(rknn_model)
if ret ! 0:print(Load RKNN model failed)exit(ret)
print(done)# init runtime environment
print(-- Init runtime environment)# run on RK356x/RK3588 with Debian OS, do not need specify target.
ret rknn_lite.init_runtime(core_maskRKNNLite.NPU_CORE_0)
if ret ! 0:print(Init runtime environment failed)exit(ret)
print(done)#######################################################################################################
#1.rknn模型检测#
##############################################################################################################################################################################################################
#2.雷达数据获取#
########################################################################################################2.打开相机
cap cv2.VideoCapture(/dev/video61)#1.根据时间自动创建文件夹
def data_time(root_pathimg_lidar_save/):# 1.鑾峰彇褰撳墠鏃堕棿瀛楃涓叉垨鏃堕棿鎴筹紙閮藉彲绮剧‘鍒板井绉掞級start_timedatetime.datetime.now().strftime(f%Y-%m-%d %H:%M:%S{r.%f})timesstart_time.split( )# 2.data_files锛氭牴鎹棩鏈熻幏鍙栬鍒涘缓鐨勬枃浠跺す鍚嶇О锛屾瘮濡備粖澶╂槸2023_12_07data_filestimes[0]#3.鑾峰彇鏂囦欢澶硅矾寰勶細img_lidar_save/2023_12_07file_pathroot_pathdata_filescamera_file file_path / camera_datalidar_file file_path / lidar_data#4.濡傛灉浠婂ぉ杩樻病鏈夋枃浠跺す锛屽垯鍒涘缓鏂囦欢澶?鏂囦欢澶瑰悕绉颁负 2023_12_07if not os.path.exists(file_path):os.makedirs(file_path)#5.寤虹珛camera鍜宭idar鏂囦欢澶癸紝瀛樺彇鍚勮嚜鐨勬暟鎹?if not os.path.exists(camera_file):os.makedirs(camera_file)if not os.path.exists(lidar_file):os.makedirs(lidar_file)#6.寤虹珛鍚勮嚜鐨勫瓨鍙栧浘鐗囧拰瑙嗛鐨勬枃浠跺すimg_filecamera_file / imagevedioscamera_file / vedioslidar_videoslidar_file / vedioslidar_pcdlidar_file / imageif not os.path.exists(img_file):os.makedirs(img_file)if not os.path.exists(vedios):os.makedirs(vedios)if not os.path.exists(lidar_videos):os.makedirs(lidar_videos)if not os.path.exists(lidar_pcd):os.makedirs(lidar_pcd)return img_file,vedios,lidar_videos,lidar_pcd#1.聚类的数据处理
def cluster(points, radius0.2):points: pointcloudradius: max cluster rangeitems []while len(points)1:item np.array([points[0]])base points[0]points np.delete(points, 0, 0)distance (points[:,0]-base[0])**2(points[:,1]-base[1])**2(points[:,2]-base[2])**2infected_points np.where(distance radius**2)item np.append(item, points[infected_points], axis0)border_points points[infected_points]points np.delete(points, infected_points, 0)while len(border_points) 0:border_base border_points[0]border_points np.delete(border_points, 0, 0)border_distance (points[:,0]-border_base[0])**2(points[:,1]-border_base[1])**2border_infected_points np.where(border_distance radius**2)item np.append(item, points[border_infected_points], axis0)border_points points[border_infected_points]points np.delete(points, border_infected_points, 0)items.append(item)return items#2.保存点云
def save_pointcloud(pointcloud_np, file_namepointcloud.pcd):point_cloud_o3d o3d.geometry.PointCloud()point_cloud_o3d.points o3d.utility.Vector3dVector(pointcloud_np[:, 0:3])o3d.io.write_point_cloud(file_name, point_cloud_o3d, write_asciiFalse, compressedTrue)#3.点云数据的处理
def lidars(msg,pcd_path):#4.点云数据的获取pcl_msg pc2.read_points(msg, skip_nansFalse, field_names(x, y, z, intensity,ring))#5.点云数据的过滤np_p_2 np.array(list(pcl_msg), dtypenp.float32)#print(np_p_2)#6.将过滤后的点云数据保存为pcd文件#print(pcd_path)save_pointcloud(np_p_2, file_namepcd_path)#7.根据条件过滤点云数据ssnp.where([s[0]2 and s[1]3 and s[-1]-3 and s[2]-0.5 for s in np_p_2])ansnp_p_2[ss]#8.点云的聚类算法 itemcluster(ans, radius0.2)m_item[]#9.求每个类的均值之后与目标进行匹配#hhnp.where([s.shape[0]20 for s in item])#print(//,len(hh),len(item))for items in item:#print(..............,items.shape)#x,y,zint(items[:,:1].sum().mean()) x,y,z,ritems[:,:1].mean(),items[:,1:2].mean(),items[:,2:3].mean(),items[:,3:4].mean()m_item.append([x,y,z]) #4.相机图片数据的处理
def images(frame,jpg_path):#1.保存jpg文件cv2.imwrite(jpg_path,frame)#5.对相机图片和点云数据的汇总处理
def velo_callback(msg):#1.自动生成当天保存文件的文件夹及保存文件的路径#img_file:存放图片的路径jpg或者png文件#vedios:存放相机视频的路径mp4文件#lidar_videos:存放雷达视频的路径bag文件#lidar_pcd:存放点云数据的pcd文件的视频pcd文件img_file,vedios,lidar_videos,lidar_pcddata_time(root_pathimg_lidar_save/)vedio_timeday_time()vedio_pathvedios/vedio_time.avilidar_pathlidar_videos/vedio_time.bagret, frame cap.read()frame cv2.rotate(frame, 0, dstNone) # 视频是倒着的要对视频进行两次90度的翻转frame cv2.rotate(frame, 0, dstNone) # 视频是倒着的要对视频进行两次90度的翻转########################################################################################################1.rknn模型检测########################################################################################################img, ratio, (dw, dh) letterbox(frame, new_shape(IMG_SIZE, IMG_SIZE))img cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img cv2.resize(img, (IMG_SIZE, IMG_SIZE))# Inferenceprint(-- Running model)outputs rknn_lite.inference(inputs[img])#np.save(./onnx_yolov5_0.npy, outputs[0])#np.save(./onnx_yolov5_1.npy, outputs[1])#np.save(./onnx_yolov5_2.npy, outputs[2])print(done)# post processinput0_data outputs[0]input1_data outputs[1]input2_data outputs[2]input0_data input0_data.reshape([3, -1]list(input0_data.shape[-2:]))input1_data input1_data.reshape([3, -1]list(input1_data.shape[-2:]))input2_data input2_data.reshape([3, -1]list(input2_data.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_1 cv2.cvtColor(img, cv2.COLOR_RGB2BGR)if boxes is not None:draw(img_1, boxes, scores, classes)cv2.imwrite(result.jpg, img_1)cv2.imshow(src_image, img_1)cv2.waitKey(1)########################################################################################################1.rknn模型检测#########################################################################################################显示视频#cv2.imshow(src_image, frame)#cv2.waitKey(1)#3.获取实时时间作为保存jpg和pcd文件的名称now_timeday_time()print(------,now_time)#4.获得存取pcd文件和jpg文件的路径pcd_pathlidar_pcd/now_time.pcdjpg_pathimg_file/now_time.jpg#4.lidar和images数据的处理及保存,两个函数放在两个线程同时运行lidars(msg,pcd_path)#lidar数据的处理及保存images(frame,jpg_path)#images数据的处理及保存#根据时间给jpg和pcd文件命名
def day_time():start_timedatetime.datetime.now().strftime(f%Y-%m-%d %H:%M:%S{r.%f})timesstart_time.split( )minstimes[1].split(:)day_namesmins[0]_mins[1]_mins[2][:2]_mins[2][3:5]return day_namesif __name__ __main__:sub_ rospy.Subscriber(livox/lidar, PointCloud2,velo_callback)print(ros_node has started!)rospy.spin()rknn_lite.release() 注意在该文件的同栏目需要建立一个文件夹 img_lidar_save这个文件夹下面保存相机的jpg文件和pcd文件。效果如下 以上是根据当天的实际时间自动保存的jig和pcd文件。下面是rknn模型检测的结果。
