# GrapeCounting **Repository Path**: shang_mou/GrapeCounting ## Basic Information - **Project Name**: GrapeCounting - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2020-07-22 - **Last Updated**: 2025-02-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # TSGYE: Two-stage Grape Yield Estimation Utilize YOLOv4 for grape cluster detection on WGISD dataset, and annotate 58 images of WGISD test set on Huawei ModelArts platform for evaluating grape counting methods. ### Dataset WGISD (Wine Grape Instance Segmentation Dataset): https://github.com/thsant/wgisd. we punctuate the grape berries in each image, and then write all coordinate information into xml files. GrapeCounting - raw_images # 58 images from WGISD test set - detect_images # cluster detecting results based on YOLOv4 - count_label # folder for counting label ### YOLOv4 Darknet: https://github.com/AlexeyAB/darknet. ### YOLOv4 grape cluster detection YOLOv4 - obj.data # the dataset config file for YOLOv4 training and evaluating - yolov4-obj-1.cfg # the config file of YOLOv4 training network - yolov4-obj-1_best.weights # load this network weights, you can detect cluster boxes on WGISD dataset accurately (download link ：https://pan.baidu.com/s/14zRQHkQJbEZU3PVXWi85Uw 提取码：nx9z) After you follow the Darknet guideline to deploy YOLO network. you can predict grape clusters by following code. ### Predict clusters ``` ./darknet detector test cfg/obj.data cfg/yolov4-obj-1.cfg weights/yolov4/yolov4-obj-1_best.weights ``` ## grape cluster detection ### 1如何用YOLOv4训练VOC数据集 #### 1.1准备YOLO编译环境 下载YOLO源码: ``` git clone https://github.com/AlexeyAB/darknet ``` 进入darknet目录 ``` cd darknet ``` 修改编译配置文件MakeFile ``` GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1 AVX=0 OPENMP=0 LIBSO=1 ZED_CAMERA=0 # ZED SDK 3.0 and above ZED_CAMERA_v2_8=0 # ZED SDK 2.X # set GPU=1 and CUDNN=1 to speedup on GPU # set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher # set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0) USE_CPP=0 DEBUG=1 ``` 编译运行配置文件 ``` make ``` 看到目录下生成的darknet文件，说明上述步骤成功 #### 1.2 VOC数据格式的组装 data/2007_train.txt data/2007_val.txt ``` VOCdevkit │ └───VOC2007 │ └───Annotations # 所有图片的xml标签数据 │ │ CDY_2015.xml │ │ ... │ └───ImageSets # 数据集的划分 │ │ │ └───Main # 所有图片的xml标签数据 │ │ test.txt # 测试集图片文件名集合 │ │ train.txt # 训练集图片文件名集合 │ │ val.txt # 验证集图片文件名集合 │ └───JPEGImages # 所有图片的jpg数据 │ │ CDY_2015.jpg │ │ ... │ └───labels # 所有图片的yolo标签 │ CDY_2015.txt │ ... ``` VOCdevkit - VOC2007 - Annotations # 所有图片的xml标签数据 - CDY_2015.xml - ImageSets # 数据集的划分 - Main - test.txt # 测试集图片文件名集合 - train.txt # 训练集图片文件名集合 - val.txt # 验证集图片文件名集合 - JPEGImages # 所有图片的jpg数据 - CDY_2015.jpg - labels # 所有图片的yolo标签 - CDY_2015.txt #### 1.3 准备obj.names ``` CDY # 0 CFR # 1 CSV # 2 SVB # 3 SYH # 4 ``` 放入cfg/路径下 #### 1.4 准备obj.data ``` classes= 5 # 数据集中的类别数 train = data/2007_train.txt # 训练数据图片文件名汇总 valid = data/2007_val.txt # 验证数据图片文件名汇总 names = cfg/obj.names # class中每一个编号对应的类别名称，需一一对应 backup = backup/ # 权重结果保存路径 ``` 放入cfg/路径下 #### 1.5 配置YOLOv4网络结构和训练配置文件 ``` cp cfg/yolov4-custom.cfg cfg/yolov4-obj.cfg vim cfg/yolov4-obj.cfg ``` 修改yolov4-obj.cfg的内容 Training部分 ``` batch=64 subdivisions=64 width=416 height=416 channels=3 momentum=0.949 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1 learning_rate=0.001 burn_in=1000 max_batches = 10000 # 迭代训练10000次 policy=steps steps=8000,9000 scales=.1,.1 #cutmix=1 mosaic=1 ``` 修改class和对应的convolutional层layer数 ``` /classes # 关键字搜索找到classes对应的地方 ``` 对上面的convolutional层修改layer数 ``` [convolutional] size=1 stride=1 pad=1 filters=30 # (classes + 5) * 3, 这里classes是5, 所以filter为30 activation=linear [yolo] mask = 0,1,2 anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 classes=5 num=9 ``` 需要上述这样操作的地方还有3处，因为有3个yolo层。 下载yolov4.conv.137预训练配置文件 下载链接:https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp 以上，yolov4训练自己数据集的准备工作已经做好了,需要以下几个部分 - VOC格式的数据 # 划分好训练集和测试集 - obj.names # 数据集中类别的名称 - obj.data # 数据集相关配置信息 - yolov4-obj.cfg # 网络配置和训练文件 - yolov4.conv.137 # 加载的预训练权重 ### 2 如何利用earlystopping选取表现最好的权重 ``` ./darknet detector train data/obj.data cfg/yolov4-obj.cfg weights/yolov4.conv.137 -dont_show -map ``` 训练产生的权重均会在backup目录下 ![backup](./pictures/back.png) 训练结束后也会产生整个训练过程的mAP随IoU变化的信息图 ![chart-box](./pictures/chart_yolov4-obj-1.png) ### 3 如何使用训练好的模型 #### 3.1 预测 利用训练好的模型进行预测 ``` ./darknet detector test cfg/obj.data cfg/yolov4-obj-1.cfg weights/yolov4/yolov4-obj-1_best.weights ``` 原图: ![backup](./pictures/CFR.jpg) 输入图片路径,预测结果为prediction.jpg ![backup](./pictures/predictions.jpg) #### 3.2 评分 检测训练好的模型在IoU阈值为0.3的条件下，mAP，以及各项准确率的多少。 ``` ./darknet detector map cfg/obj.data cfg/yolov4-obj-1.cfg weights/yolov4/yolov4-obj-1_best.weights -iou_thresh 0.30 -thresh 0.30 ``` ![backup](./pictures/mAP.png) ## grape berry counting ### 1 高斯核密度函数 matlab运行get_density_map_autogaussian.m 产生原图的高斯核密度图，作为groundtruth ### 2 去除方框以外的背景 利用YOLOv4对图片中的葡萄串进行方框定位，执行mask.m函数，将背景图中方框以外的部分去除 ``` path = 'raw_images\'; filename = strcat(path, 'SVB_1962.jpg'); img = imread(filename) ; data_box = importdata('SVB_1962.txt') ; mask_img = getMask(img, data_box); imshow(mask_img) ``` ![backup](./pictures/SVB_1962_mask.jpg) 将rgb图片转成hsv三色图形式 ``` newimg = rgb2hsv(mask_img); % imshow(newimg); ``` ![backup](./pictures/SVB_1962_hsv.jpg) ### 3 对方框以内的像素点进行Hough变换圆圈计数 ``` [centers, radiis] = segmentImage(newimg, 5, 30, 0.92); prefix = strsplit('SVB_1962.jpg', '.'); label = readxml( prefix{1}); ``` ![backup](./pictures/SVB_1962_segment.jpg)