diff --git a/kmeans_for_anchors.py b/kmeans_for_anchors.py index 2dcbbc0e3d15abc624580a296271b126186da848..999f04f3f6ad98e1e32af43ac40cebc03514497a 100644 --- a/kmeans_for_anchors.py +++ b/kmeans_for_anchors.py @@ -1,10 +1,13 @@ +#-------------------------------------------------------------------------------------------------# +# kmeans虽然会对数据集中的框进行聚类，但是很多数据集由于框的大小相近，聚类出来的9个框相差不大， +# 这样的框反而不利于模型的训练。因为不同的特征层适合不同大小的先验框，越浅的特征层适合越大的先验框 +# 原始网络的先验框已经按大中小比例分配好了，不进行聚类也会有非常好的效果。 +#-------------------------------------------------------------------------------------------------# import glob -import random import xml.etree.ElementTree as ET import numpy as np - def cas_iou(box,cluster): x = np.minimum(cluster[:,0],box[0]) y = np.minimum(cluster[:,1],box[1]) @@ -20,7 +23,6 @@ def cas_iou(box,cluster): def avg_iou(box,cluster): return np.mean([np.max(cas_iou(box[i],cluster)) for i in range(box.shape[0])]) - def kmeans(box,k): # 取出一共有多少框 row = box.shape[0] diff --git a/predict.py b/predict.py index 63d2b894e57027c050e54262bd60c9efb4aae8c6..65a8aa1c6651a9c8dd98a7baf3b2701cbc52ed95 100644 --- a/predict.py +++ b/predict.py @@ -1,27 +1,99 @@ -''' -predict.py有几个注意点 -1、该代码无法直接进行批量预测，如果想要批量预测，可以利用os.listdir()遍历文件夹，利用Image.open打开图片文件进行预测。 -具体流程可以参考get_dr_txt.py，在get_dr_txt.py即实现了遍历还实现了目标信息的保存。 -2、如果想要进行检测完的图片的保存，利用r_image.save("img.jpg")即可保存，直接在predict.py里进行修改即可。 -3、如果想要获得预测框的坐标，可以进入yolo.detect_image函数，在绘图部分读取top，left，bottom，right这四个值。 -4、如果想要利用预测框截取下目标，可以进入yolo.detect_image函数，在绘图部分利用获取到的top，left，bottom，right这四个值 -在原图上利用矩阵的方式进行截取。 -5、如果想要在预测图上写额外的字，比如检测到的特定目标的数量，可以进入yolo.detect_image函数，在绘图部分对predicted_class进行判断， -比如判断if predicted_class == 'car': 即可判断当前目标是否为车，然后记录数量即可。利用draw.text即可写字。 -''' +#----------------------------------------------------# +# 对视频中的predict.py进行了修改， +# 将单张图片预测、摄像头检测和FPS测试功能 +# 整合到了一个py文件中，通过指定mode进行模式的修改。 +#----------------------------------------------------# +import time + +import cv2 +import numpy as np from PIL import Image from yolo import YOLO -yolo = YOLO() +if __name__ == "__main__": + yolo = YOLO() + #-------------------------------------------------------------------------# + # mode用于指定测试的模式： + # 'predict'表示单张图片预测 + # 'video'表示视频检测 + # 'fps'表示测试fps + #-------------------------------------------------------------------------# + mode = "predict" + #-------------------------------------------------------------------------# + # video_path用于指定视频的路径，当video_path=0时表示检测摄像头 + # video_save_path表示视频保存的路径，当video_save_path=""时表示不保存 + # video_fps用于保存的视频的fps + # video_path、video_save_path和video_fps仅在mode='video'时有效 + # 保存视频时需要ctrl+c退出才会完成完整的保存步骤，不可直接结束程序。 + #-------------------------------------------------------------------------# + video_path = 0 + video_save_path = "" + video_fps = 25.0 + + if mode == "predict": + ''' + 1、该代码无法直接进行批量预测，如果想要批量预测，可以利用os.listdir()遍历文件夹，利用Image.open打开图片文件进行预测。 + 具体流程可以参考get_dr_txt.py，在get_dr_txt.py即实现了遍历还实现了目标信息的保存。 + 2、如果想要进行检测完的图片的保存，利用r_image.save("img.jpg")即可保存，直接在predict.py里进行修改即可。 + 3、如果想要获得预测框的坐标，可以进入yolo.detect_image函数，在绘图部分读取top，left，bottom，right这四个值。 + 4、如果想要利用预测框截取下目标，可以进入yolo.detect_image函数，在绘图部分利用获取到的top，left，bottom，right这四个值 + 在原图上利用矩阵的方式进行截取。 + 5、如果想要在预测图上写额外的字，比如检测到的特定目标的数量，可以进入yolo.detect_image函数，在绘图部分对predicted_class进行判断， + 比如判断if predicted_class == 'car': 即可判断当前目标是否为车，然后记录数量即可。利用draw.text即可写字。 + ''' + while True: + img = input('Input image filename:') + try: + image = Image.open(img) + except: + print('Open Error! Try again!') + continue + else: + r_image = yolo.detect_image(image) + r_image.show() + + elif mode == "video": + capture=cv2.VideoCapture(video_path) + if video_save_path!="": + fourcc = cv2.VideoWriter_fourcc(*'XVID') + size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))) + out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size) + + fps = 0.0 + while(True): + t1 = time.time() + # 读取某一帧 + ref,frame=capture.read() + # 格式转变，BGRtoRGB + frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB) + # 转变成Image + frame = Image.fromarray(np.uint8(frame)) + # 进行检测 + frame = np.array(yolo.detect_image(frame)) + # RGBtoBGR满足opencv显示格式 + frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR) + + fps = ( fps + (1./(time.time()-t1)) ) / 2 + print("fps= %.2f"%(fps)) + frame = cv2.putText(frame, "fps= %.2f"%(fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + + cv2.imshow("video",frame) + c= cv2.waitKey(1) & 0xff + if video_save_path!="": + out.write(frame) + + if c==27: + capture.release() + break + capture.release() + out.release() + cv2.destroyAllWindows() -while True: - img = input('Input image filename:') - try: - image = Image.open(img) - except: - print('Open Error! Try again!') - continue + elif mode == "fps": + test_interval = 100 + img = Image.open('img/street.jpg') + tact_time = yolo.get_FPS(img, test_interval) + print(str(tact_time) + ' seconds, ' + str(1/tact_time) + 'FPS, @batch_size 1') else: - r_image = yolo.detect_image(image) - r_image.show() + raise AssertionError("Please specify the correct mode: 'predict', 'video' or 'fps'.") diff --git a/train.py b/train.py index 99f169fe4a94b4c15f48c7b0cd68cca0c7ffe366..56a70e7e4b58ac5141a9108edfe8340f47e26669 100644 --- a/train.py +++ b/train.py @@ -9,7 +9,7 @@ from torch.utils.data import DataLoader from tqdm import tqdm from nets.yolo4 import YoloBody -from nets.yolo_training import YOLOLoss, LossHistory, weights_init +from nets.yolo_training import LossHistory, YOLOLoss, weights_init from utils.dataloader import YoloDataset, yolo_dataset_collate @@ -36,6 +36,8 @@ def get_lr(optimizer): def fit_one_epoch(net,yolo_loss,epoch,epoch_size,epoch_size_val,gen,genval,Epoch,cuda): + if Tensorboard: + global train_tensorboard_step, val_tensorboard_step total_loss = 0 val_loss = 0 @@ -72,18 +74,26 @@ def fit_one_epoch(net,yolo_loss,epoch,epoch_size,epoch_size_val,gen,genval,Epoch num_pos_all += num_pos loss = sum(losses) / num_pos_all + total_loss += loss.item() + #----------------------# # 反向传播 #----------------------# loss.backward() optimizer.step() - total_loss += loss.item() - + if Tensorboard: + # 将loss写入tensorboard，每一步都写 + writer.add_scalar('Train_loss', loss, train_tensorboard_step) + train_tensorboard_step += 1 + pbar.set_postfix(**{'total_loss': total_loss / (iteration + 1), 'lr' : get_lr(optimizer)}) pbar.update(1) + # 将loss写入tensorboard，下面注释的是每个世代保存一次 + # if Tensorboard: + # writer.add_scalar('Train_loss', total_loss/(iteration+1), epoch) net.eval() print('Start Validation') with tqdm(total=epoch_size_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar: @@ -110,9 +120,17 @@ def fit_one_epoch(net,yolo_loss,epoch,epoch_size,epoch_size_val,gen,genval,Epoch num_pos_all += num_pos loss = sum(losses) / num_pos_all val_loss += loss.item() + + # 将loss写入tensorboard, 下面注释的是每一步都写 + # if Tensorboard: + # writer.add_scalar('Val_loss', loss, val_tensorboard_step) + # val_tensorboard_step += 1 pbar.set_postfix(**{'total_loss': val_loss / (iteration + 1)}) pbar.update(1) + # 将loss写入tensorboard，每个世代保存一次 + if Tensorboard: + writer.add_scalar('Val_loss',val_loss / (epoch_size_val+1), epoch) loss_history.append_loss(total_loss/(epoch_size+1), val_loss/(epoch_size_val+1)) print('Finish Validation') print('Epoch:'+ str(epoch+1) + '/' + str(Epoch)) @@ -125,6 +143,10 @@ def fit_one_epoch(net,yolo_loss,epoch,epoch_size,epoch_size_val,gen,genval,Epoch # https://www.bilibili.com/video/BV1zE411u7Vw #----------------------------------------------------# if __name__ == "__main__": + #-------------------------------# + # 是否使用Tensorboard + #-------------------------------# + Tensorboard = False #-------------------------------# # 是否使用Cuda # 没有GPU可以设置成False @@ -141,20 +163,12 @@ if __name__ == "__main__": # 显存比较大可以使用608x608 #-------------------------------# input_shape = (416,416) - #----------------------------------------------------# # classes和anchor的路径，非常重要 # 训练前一定要修改classes_path，使其对应自己的数据集 #----------------------------------------------------# anchors_path = 'model_data/yolo_anchors.txt' classes_path = 'model_data/voc_classes.txt' - #----------------------------------------------------# - # 获取classes和anchor - #----------------------------------------------------# - class_names = get_classes(classes_path) - anchors = get_anchors(anchors_path) - num_classes = len(class_names) - #------------------------------------------------------# # Yolov4的tricks应用 # mosaic 马赛克数据增强 True or False @@ -166,6 +180,13 @@ if __name__ == "__main__": Cosine_lr = False smoooth_label = 0 + #----------------------------------------------------# + # 获取classes和anchor + #----------------------------------------------------# + class_names = get_classes(classes_path) + anchors = get_anchors(anchors_path) + num_classes = len(class_names) + #------------------------------------------------------# # 创建yolo模型 # 训练前一定要修改classes_path和对应的txt文件 @@ -213,7 +234,18 @@ if __name__ == "__main__": np.random.seed(None) num_val = int(len(lines)*val_split) num_train = len(lines) - num_val - + + if Tensorboard: + from tensorboardX import SummaryWriter + writer = SummaryWriter(log_dir='logs',flush_secs=60) + if Cuda: + graph_inputs = torch.randn(1,3,input_shape[0],input_shape[1]).type(torch.FloatTensor).cuda() + else: + graph_inputs = torch.randn(1,3,input_shape[0],input_shape[1]).type(torch.FloatTensor) + writer.add_graph(model, graph_inputs) + train_tensorboard_step = 1 + val_tensorboard_step = 1 + #------------------------------------------------------# # 主干特征提取网络特征通用，冻结训练可以加快训练速度 # 也可以在训练初期防止权值被破坏。 diff --git a/yolo.py b/yolo.py index 942d21e7efae347ae5f087729e53425cb91bcb6b..4b513dde39339b201d0af4d2a308c3756a43e49e 100644 --- a/yolo.py +++ b/yolo.py @@ -3,6 +3,7 @@ #-------------------------------------# import colorsys import os +import time import numpy as np import torch @@ -10,8 +11,8 @@ import torch.nn as nn from PIL import Image, ImageDraw, ImageFont from nets.yolo4 import YoloBody -from utils.utils import (DecodeBox, letterbox_image, - non_max_suppression, yolo_correct_boxes) +from utils.utils import (DecodeBox, letterbox_image, non_max_suppression, + yolo_correct_boxes) #--------------------------------------------# @@ -229,3 +230,90 @@ class YOLO(object): del draw return image + def get_FPS(self, image, test_interval): + # 调整图片使其符合输入要求 + image_shape = np.array(np.shape(image)[0:2]) + + #---------------------------------------------------------# + # 给图像增加灰条，实现不失真的resize + # 也可以直接resize进行识别 + #---------------------------------------------------------# + if self.letterbox_image: + crop_img = np.array(letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))) + else: + crop_img = image.convert('RGB') + crop_img = crop_img.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC) + photo = np.array(crop_img,dtype = np.float32) / 255.0 + photo = np.transpose(photo, (2, 0, 1)) + #---------------------------------------------------------# + # 添加上batch_size维度 + #---------------------------------------------------------# + images = [photo] + + with torch.no_grad(): + images = torch.from_numpy(np.asarray(images)) + if self.cuda: + images = images.cuda() + outputs = self.net(images) + output_list = [] + for i in range(3): + output_list.append(self.yolo_decodes[i](outputs[i])) + output = torch.cat(output_list, 1) + batch_detections = non_max_suppression(output, len(self.class_names), + conf_thres=self.confidence, + nms_thres=self.iou) + try: + batch_detections = batch_detections[0].cpu().numpy() + top_index = batch_detections[:,4]*batch_detections[:,5] > self.confidence + top_conf = batch_detections[top_index,4]*batch_detections[top_index,5] + top_label = np.array(batch_detections[top_index,-1],np.int32) + top_bboxes = np.array(batch_detections[top_index,:4]) + top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1) + + if self.letterbox_image: + boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape) + else: + top_xmin = top_xmin / self.model_image_size[1] * image_shape[1] + top_ymin = top_ymin / self.model_image_size[0] * image_shape[0] + top_xmax = top_xmax / self.model_image_size[1] * image_shape[1] + top_ymax = top_ymax / self.model_image_size[0] * image_shape[0] + boxes = np.concatenate([top_ymin,top_xmin,top_ymax,top_xmax], axis=-1) + + except: + pass + + t1 = time.time() + for _ in range(test_interval): + with torch.no_grad(): + outputs = self.net(images) + output_list = [] + for i in range(3): + output_list.append(self.yolo_decodes[i](outputs[i])) + output = torch.cat(output_list, 1) + batch_detections = non_max_suppression(output, len(self.class_names), + conf_thres=self.confidence, + nms_thres=self.iou) + try: + batch_detections = batch_detections[0].cpu().numpy() + top_index = batch_detections[:,4]*batch_detections[:,5] > self.confidence + top_conf = batch_detections[top_index,4]*batch_detections[top_index,5] + top_label = np.array(batch_detections[top_index,-1],np.int32) + top_bboxes = np.array(batch_detections[top_index,:4]) + top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1) + + if self.letterbox_image: + boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape) + else: + top_xmin = top_xmin / self.model_image_size[1] * image_shape[1] + top_ymin = top_ymin / self.model_image_size[0] * image_shape[0] + top_xmax = top_xmax / self.model_image_size[1] * image_shape[1] + top_ymax = top_ymax / self.model_image_size[0] * image_shape[0] + boxes = np.concatenate([top_ymin,top_xmin,top_ymax,top_xmax], axis=-1) + + except: + pass + + t2 = time.time() + tact_time = (t2 - t1) / test_interval + return tact_time +