网站 开发,北京市建设工程交易服务平台,c网站开发案例详解代码,建设网站文件夹的名字目录 1. 前言
2. 储备知识
3. 准备工作
4. 代码修改的地方
5.结果展示 1. 前言 之前一直在忙着写文档#xff0c;之前一直做分类#xff0c;检测和分割#xff0c;现在看到跟踪算法#xff0c;花了几天时间找代码调试#xff0c;看了看#xff0c;展示效果比单纯的检…目录 1. 前言
2. 储备知识
3. 准备工作
4. 代码修改的地方
5.结果展示 1. 前言 之前一直在忙着写文档之前一直做分类检测和分割现在看到跟踪算法花了几天时间找代码调试看了看展示效果比单纯的检测要更加的炸裂一点。
2. 储备知识 DeepSORTDeep Learning to Track Multi-Object in SORT是一种基于深度学习的多目标跟踪算法它结合了深度学习的目标检测和传统的轨迹跟踪方法旨在实现在复杂场景中准确和稳定地跟踪多个移动目标。以下是关于DeepSORT的检测思想、特点和应用方面的介绍 检测思想 DeepSORT的核心思想是结合深度学习目标检测和轨迹跟踪方法以实现多目标跟踪。首先利用深度学习目标检测模型如YOLO、Faster R-CNN等检测出每一帧图像中的所有目标物体并提取其特征。然后通过应用传统的轨迹跟踪算法如卡尔曼滤波器和轨迹关联等将目标在连续帧之间进行关联从而生成每个目标的运动轨迹。 特点
多目标跟踪 DeepSORT专注于同时跟踪多个目标适用于需要同时监测和追踪多个物体的场景如交通监控、人群管理等。深度特征 通过使用深度学习模型提取目标的特征DeepSORT可以更准确地表示目标从而提高跟踪的精度和鲁棒性。轨迹关联 DeepSORT使用传统的轨迹关联技术来连接不同帧之间的目标确保在物体出现、消失、重叠等情况下仍能准确跟踪。实时性能 DeepSORT设计用于实时应用可以在视频流中高效地进行目标跟踪适用于要求实时性能的应用场景。
需要了解的算法内容详细介绍
目前主流的目标跟踪算法都是基于Tracking-by-Detecton策略即基于目标检测的结果来进行目标跟踪。DeepSORT运用的就是这个策略上面的视频是DeepSORT对人群进行跟踪的结果每个bbox左上角的数字是用来标识某个人的唯一ID号。 这里就有个问题视频中不同时刻的同一个人位置发生了变化那么是如何关联上的呢答案就是匈牙利算法和卡尔曼滤波。 匈牙利算法可以告诉我们当前帧的某个目标是否与前一帧的某个目标相同。卡尔曼滤波可以基于目标前一时刻的位置来预测当前时刻的位置并且可以比传感器在目标跟踪中即目标检测器比如Yolo等更准确的估计目标的位置。
3. 准备工作 基础代码:黄老师的github,参考的是这位博主的我做了相应的修改
4. 代码修改的地方
具体需要修改的有两个py文件
(1) main.py文件里面的检测器yolo用onnx做推理onnx模型参考我的博文yolov5转rknn聪明的你应该会的
import cv2
import torch
import numpy as np
import onnxruntime as rtdef sigmoid(x):return 1 / (1 np.exp(-x))def 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 0.45)[0]order order[inds 1]keep np.array(keep)return keepdef 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.box_classes np.argmax(box_class_probs, axis-1)box_class_scores np.max(box_class_probs, axis-1)pos np.where(box_confidences[..., 0] 0.5)boxes boxes[pos]classes box_classes[pos]scores box_class_scores[pos]return boxes, classes, scoresdef 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 letterbox(img, new_shape(640, 640), color(114, 114, 114), autoTrue, scaleFillFalse, scaleupTrue, stride32):# Resize and pad image while meeting stride-multiple constraintsshape img.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])if not scaleup: # only scale down, do not scale up (for better test mAP)r min(r, 1.0)# 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 paddingif auto: # minimum rectangledw, dh np.mod(dw, stride), np.mod(dh, stride) # wh paddingelif scaleFill: # stretchdw, dh 0.0, 0.0new_unpad (new_shape[1], new_shape[0])ratio new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratiosdw / 2 # divide padding into 2 sidesdh / 2if shape[::-1] ! new_unpad: # resizeimg cv2.resize(img, 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))img cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, valuecolor) # add borderreturn img, ratio, (dw, dh)def clip_coords(boxes, img_shape):# Clip bounding xyxy bounding boxes to image shape (height, width)boxes[:, 0].clamp_(0, img_shape[1]) # x1boxes[:, 1].clamp_(0, img_shape[0]) # y1boxes[:, 2].clamp_(0, img_shape[1]) # x2boxes[:, 3].clamp_(0, img_shape[0]) # y2def xywh2xyxy(x):# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1top-left, xy2bottom-righty x.clone() if isinstance(x, torch.Tensor) else 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 yCLASSES [person, bicycle, car, motorcycle, airplane, 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, couch, potted plant, bed,dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink,refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush]def preprocess(img, img_size):img0 img.copy()img letterbox(img, new_shapeimg_size)[0]img img[:, :, ::-1].transpose(2, 0, 1)img np.ascontiguousarray(img).astype(np.float32)img torch.from_numpy(img)img / 255.0if img.ndimension() 3:img img.unsqueeze(0)return img0, imgdef 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 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)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 detect(im, img_size, sess, input_name, outputs_name):im0, img preprocess(im, img_size)input_data onnx_inference(img.numpy(), sess, input_name, outputs_name)boxes, classes, scores yolov5_post_process(input_data)if boxes is not None:draw(im, boxes, scores, classes)cv2.imshow(demo, im)cv2.waitKey(1)def onnx_inference(img, sess, input_name, outputs_name):# 模型推理:模型输出节点名模型输入节点名输入数据(注意节点名的格式)outputs sess.run(outputs_name, {input_name: img})input0_data outputs[0]input1_data outputs[1]input2_data outputs[2]input0_data input0_data.reshape([3, 80, 80, 85])input1_data input1_data.reshape([3, 40, 40, 85])input2_data input2_data.reshape([3, 20, 20, 85])input_data list()input_data.append(np.transpose(input0_data, (1, 2, 0, 3)))input_data.append(np.transpose(input1_data, (1, 2, 0, 3)))input_data.append(np.transpose(input2_data, (1, 2, 0, 3)))return input_datadef load_onnx_model():# onnx模型前向推理sess rt.InferenceSession(./weights/modified_yolov5s.onnx)# 模型的输入和输出节点名可以通过netron查看input_name imagesoutputs_name [396, 440, 484]return sess, input_name, outputs_nameif __name__ __main__:# create onnx_modelsess, input_name, outputs_name load_onnx_model()# input_model_sizeimg_size 640# read videovideo cv2.VideoCapture(./video/cut3.avi)print(Loaded video ...)frame_interval 2 # 间隔帧数例如每隔10帧获取一次frame_count 0while True:# 读取每帧图片_, im video.read()if frame_count % frame_interval 0:if im is None:break# 缩小尺寸1920x1080-960x540im cv2.resize(im, (640, 640))list_bboxs []# det_objectdetect(im, img_size, sess, input_name, outputs_name)frame_count 1video.release()cv2.destroyAllWindows()
(2) feature_extractor.py的修改:
这里有4种推理情况ckpt.t7是ReID( Re-identification利用算法),在图像库中找到要搜索的目标的技术所以它是属于图像检索的一个子问题。 (1) 动态的batch_size推理:由于检测到的目标是多个object在本项目的代码REID推理中会将目标通过torch.cat连接起来变成(n, 64, 128)的形状所以需要用动态的onnx模型 (2)那我就想要静态的怎么办安排思路就是将cat的拆分开就行了shape变成(1, 64 , 128)单个推理后将结果cat起来就行了easy的。
重要ckpt文件转onnx的代码
import os
import cv2
import time
import argparse
import torch
import numpy as np
from deep_sort import build_tracker
from utils.draw import draw_boxes
from utils.parser import get_config
from tqdm import tqdmif __name__ __main__:parser argparse.ArgumentParser()parser.add_argument(--config_deepsort, typestr, default./configs/deep_sort.yaml, helpConfigure tracker)parser.add_argument(--cpu, destuse_cuda, actionstore_false, defaultTrue, helpRun in CPU)args parser.parse_args()cfg get_config()cfg.merge_from_file(args.config_deepsort)use_cuda args.use_cuda and torch.cuda.is_available()torch.set_grad_enabled(False)model build_tracker(cfg, use_cudaFalse)model.reid Truemodel.extractor.net.eval()device cpuoutput_onnx deepsort.onnx# ------------------------ export -----------------------------print( Exporting model to ONNX format at {}.format(output_onnx))input_names [input]output_names [output]input_tensor torch.randn(1, 3, 128, 64, devicedevice)torch.onnx.export(model.extractor.net, input_tensor, output_onnx, export_paramsTrue, verboseFalse,input_namesinput_names, output_namesoutput_names, opset_version13,do_constant_foldingTrue)(3)但是要转rknn怎么办ckpt.t7转onnx后有一个ReduceL2,不支持量化我就转的fp16(在RK3588上是可以的rk1808不知道行不行)不过我尝试了将最后两个节点删除对结果好像没有什么影响(用的是cut后的onnx推理)有懂的朋友可以解释一下 (4) 就是rknn的推理这里就不展示了需要的私聊我吧
import torch
import torchvision.transforms as transforms
import numpy as np
import cv2
# import onnxruntime as rt
# from rknnlite.api import RKNNLiteclass Extractor(object):def __init__(self, model_path):self.model_path model_pathself.device cpuself.size (64, 128)self.norm transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),])def _preprocess(self, im_crops):TODO:1. to float with scale from 0 to 12. resize to (64, 128) as Market1501 dataset did3. concatenate to a numpy array3. to torch Tensor4. normalizedef _resize(im, size):return cv2.resize(im.astype(np.float32) / 255., size)im_batch torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim0).float()return im_batchdef __call__(self, im_crops):im_batch self._preprocess(im_crops)# sess rt.InferenceSession(self.model_path)# 模型的输入和输出节点名可以通过netron查看# input_name input# outputs_name [output]# (1)动态输出# features sess.run(outputs_name, {input_name: im_batch.numpy()})# print(features:, np.array(features)[0, :, :].shape)# return np.array(features)[0, :, :]# (2)静态态输出# sort_results []# n im_batch.numpy().shape[0]# for i in range(n):# img im_batch.numpy()[i, :, :].reshape(1, 3, 128, 64)# feature sess.run(outputs_name, {input_name: img})# feature np.array(feature)# sort_results.append(feature)# features np.concatenate(sort_results, axis1)[0, :, :]# print(features.shape)# return np.array(features)# (3)去掉onnx的最后两个节点的静态模型输出# input_name input# outputs_name [204]# sort_results []# n im_batch.numpy().shape[0]# for i in range(n):# img im_batch.numpy()[i, :, :].reshape(1, 3, 128, 64)# feature sess.run(outputs_name, {input_name: img})# feature np.array(feature)# sort_results.append(feature)# features np.concatenate(sort_results, axis1)[0, :, :]# print(features.shape)# return np.array(features)# (4 )rk模型修改# rknn_lite RKNNLite()# rknn_lite.load_rknn(./weights/ckpt_fp16.rknn)# ret rknn_lite.init_runtime(core_maskRKNNLite.NPU_CORE_0_1_2)# if ret ! 0:# print(Init runtime environment failed)# exit(ret)# print(done)# sort_results []# n im_batch.numpy().shape[0]# for i in range(n):# img im_batch.numpy()[i, :, :].reshape(1, 3, 128, 64)# feature self.model_path.inference(inputs[img])# feature np.array(feature)# sort_results.append(feature)# features np.concatenate(sort_results, axis1)[0, :, :]# print(features.shape)# return np.array(features)
5.结果展示 onnx的转换结果(测试视频地址) 检测结果
