旅游电子商务网站建设中最重要的环节和内容是什么,校园网站建设平台,怎么建立一个网站推广,wordpress托管是什么意思三、将best.onnx转为RKNN格式 这一步就需要我们进入到Ubuntu20.04系统中了#xff0c;我的Ubuntu系统中已经下载好了anaconda#xff0c;使用anaconda的好处就是可以方便的安装一些库#xff0c;而且还可以利用conda来配置虚拟环境#xff0c;做到环境与环境之间相互独立。…三、将best.onnx转为RKNN格式 这一步就需要我们进入到Ubuntu20.04系统中了我的Ubuntu系统中已经下载好了anaconda使用anaconda的好处就是可以方便的安装一些库而且还可以利用conda来配置虚拟环境做到环境与环境之间相互独立。 对于我来说使用了以下命令创建了一个名为rknn_ceshi的虚拟环境
conda create -n rknn_ceshi python3.8之后点击GitHub - rockchip-linux/rknn-toolkit2把整个项目给下载下来解压后我们进入刚刚创立虚拟环境下安装rknn-toolkit2。进入doc目录后输入命令
pip install -r requirements_cp38-1.4.0.txt -i https://mirror.baidu.com/pypi/simple 这里一定要带上百度的镜像源要不然会报错这个坑折磨了我整整3小时。 安装完成后出现下面的界面就说明已经安装好了 接下来我们进入packages文件夹输入一下命令
pip install rknn_toolkit2-1.4.0_22dcfef4-cp38-cp38-linux_x86_64.whl 出现以下界面但是这里一定要注意有一个超级大坑超级大坑超级大坑后续我也是询问了大佬才解决的这个问题这个地方安装的是rknn-toolkit2-1.4.0-22dcfef4 出现上面那个界面后我们在终端输入python再输入以下命令如果没有报错则证明我们的环境已经搭载好了
from rknn.api import RKNN接下来我们要做的就是修改test.py里面的一些内容 我第一次做的时候报了错误当时提示的是没有这个2-1.4.0-22dcfef4版本我就跑到conda的环境包下把所有的2-1.4.0-22dcfef4版本改成了2-1.4.0才解决这个问题。 可是当我这一次在执行这个文件的时候就没报这个错误直接就跑起来了。 之后在我们的文件夹下出现了best.rknn这样就可以到香橙派5上部署了
四、香橙派5部署rknn实现NPU加速YOLOV5视频推理 这里给大家强调一下我使用的是RKNN的python版本来实现NPU加速的这里我们需要到Github上下载RKNN官方教程下载完成后进入该文件夹输入指令
cd /examples/onnx/yolov5 进入文件夹后创建一个名为demo.py的文件将以下代码复制即可我已经实现了实时视频为了保护隐私这里我依旧采用官方的yolov5s.rknn模型而没有用我自己的
import os
import urllib
import traceback
import time
import datetime as dt
import sys
import numpy as np
import cv2
from rknnlite.api import RKNNLiteRKNN_MODEL yolov5s.rknn
DATASET ./dataset.txtQUANTIZE_ON TrueOBJ_THRESH 0.25
NMS_THRESH 0.45
IMG_SIZE 640CLASSES (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 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 [[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, fps):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.fps: int.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)#
# 如下为改动部分主要就是去掉了官方 demo 中的模型转换代码直接加载 rknn 模型并将 RKNN 类换成了 rknn_toolkit2_lite 中的 RKNNLite 类
# rknn RKNNLite()# load RKNN model
print(-- Load RKNN model)
ret rknn.load_rknn(RKNN_MODEL)# Init runtime environment
print(-- Init runtime environment)
# use NPU core 0 1 2
ret rknn.init_runtime(core_maskRKNNLite.NPU_CORE_0_1_2)
if ret ! 0:print(Init runtime environment failed!)exit(ret)
print(done)# Create a VideoCapture object and read from input file
# If the input is the camera, pass 0 instead of the video file name
cap cv2.VideoCapture(0)# Check if camera opened successfully
if (cap.isOpened() False): print(Error opening video stream or file)# Read until video is completed
while(cap.isOpened()):start dt.datetime.utcnow()# Capture frame-by-frameret, img cap.read()if not ret:breakimg cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img cv2.resize(img, (IMG_SIZE, IMG_SIZE))# Inferenceprint(-- Running model)outputs rknn.inference(inputs[img])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)duration dt.datetime.utcnow() - startfps round(10000000 / duration.microseconds)# draw process result and fpsimg_1 cv2.cvtColor(img, cv2.COLOR_RGB2BGR)cv2.putText(img_1, ffps: {fps},(20, 20),cv2.FONT_HERSHEY_SIMPLEX,0.6, (0, 125, 125), 2)if boxes is not None:draw(img_1, boxes, scores, classes, fps)# show outputcv2.imshow(post process result, img_1)# Press Q on keyboard to exitif cv2.waitKey(25) 0xFF ord(q):break# When everything done, release the video capture object
cap.release()# Closes all the frames
cv2.destroyAllWindows()这里我开启了CPU定频可是并没有提高多少NPU的使用率干脆我也不再把代码放上来了等到以后有时间我再研究一下。 之后在终端中运行命令
python demo.py 效果我放到了B站感兴趣的小伙伴可以点进去看一下B站视频
