2021广东工业智造创新大赛-瓷砖瑕疵检测方案

该方案针对2021广东工业智造创新大赛瓷砖瑕疵检测任务，基于Paddle2.2及PaddleDetection套件的FasterRCNN模型实现。处理初赛白板瓷砖数据（含15230张训练图、1762张测试图），将标注转为COCO格式，划分训练集与验证集，经训练、评估后，生成符合竞赛要求的预测提交文件，以实现瓷砖瑕疵的定位与分类。

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天池大赛-2021广东工业智造创新大赛—智能算法赛FasterRCNN方案

赛题背景

佛山作为国内最大的瓷砖生产制造基地之一，拥有众多瓷砖厂家和品牌。经前期调研，瓷砖生产环节一般（不同类型砖工艺不一样，这里以抛釉砖为例）经过原材料混合研磨、脱水、压胚、喷墨印花、淋釉、烧制、抛光，最后进行质量检测和包装。得益于产业自动化的发展，目前生产环节已基本实现无人化。而质量检测环节仍大量依赖人工完成。一般来说，一条产线需要配2～6名质检工，长时间在高光下观察瓷砖表面寻找瑕疵。这样导致质检效率低下、质检质量层次不齐且成本居高不下。瓷砖表检是瓷砖行业生产和质量管理的重要环节，也是困扰行业多年的技术瓶颈。

本赛场聚焦瓷砖表面瑕疵智能检测，要求选手开发出高效可靠的计算机视觉算法，提升瓷砖表面瑕疵质检的效果和效率，降低对大量人工的依赖。要求算法尽可能快与准确的给出瓷砖疵点具体的位置和类别，主要考察疵点的定位和分类能力。

赛题数据说明： 大赛深入到佛山瓷砖知名企业，在产线上架设专业拍摄设备，实地采集生产过程真实数据，解决企业真实的痛点需求。大赛数据覆盖到了瓷砖产线所有常见瑕疵，包括粉团、角裂、滴釉、断墨、滴墨、B孔、落脏、边裂、缺角、砖渣、白边等。实拍图示例如下：

针对某些缺陷在特定视角下的才能拍摄到，每块砖拍摄了三张图，包括低角度光照黑白图、高角度光照黑白图、彩色图，示例如下：

数据主要分为两种：

白板瓷砖。花色简单，数量总共约12000张，包含训练集和测试集，用于初赛。复杂瓷砖。花色相对复杂，并提供相应的模板图片（同花色且无瑕疵图片），数量总共约12000张，包含训练集和测试集，用于复赛。

初赛数据集： 白板数据包含有瑕疵图片、无瑕疵图片和标注数据。标注数据标注瑕疵位置和类别信息。训练集共15230张，测试集A共1762张

└── dataset    ├── Readme.md    ├── train_annos.json    └── train_imgs

环境准备

In [1]

!pip install scikit-image

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#安装至全局，如果重启项目，这几个依赖和库需要重新安装%cd /home/aistudio/cocoapi/PythonAPI!make install%cd ../..

   In [4]

#解压paddle的目标检测套件源码!unzip /home/aistudio/data/data113827/PaddleDetection-release-2.2_tile.zip -d work/

   In [5]

#安装依赖%cd /home/aistudio/work/PaddleDetection-release-2.2!pip install -r requirements.txt!python setup.py install

   In [6]

#解压数据集，训练集先放work路径下，后面划分验证集时候在弄到paddledetection下，测试集直接放过去# 该过程需要3mins多钟!unzip /home/aistudio/data/data66771/tile_round1_train_20201231.zip -d /home/aistudio/work/dataset!unzip /home/aistudio/data/data66771/tile_round1_testA_20201231.zip -d /home/aistudio/work/PaddleDetection-release-2.2/dataset/coco

   In [7]

#注意路径要正确，大部分已经改成绝对路径%cd ..

/home/aistudio/work

分析数据

类别不均衡，类别： {‘0’: 0, ‘1’: 576, ‘2’: 2151, ‘3’: 2174, ‘4’: 1112, ‘5’: 8886, ‘6’: 331}图片像素尺寸比较大：图片平均高5562.212738017071,图片平均宽7474.7293499671705In [ ]

#调用一些需要的第三方库import numpy as npimport pandas as pdimport shutilimport jsonimport osimport cv2import globfrom PIL import Image

   In [ ]

path = "/home/aistudio/work/dataset/tile_round1_train_20201231/train_imgs/220_146_t20201124140430373_CAM1.jpg"img1=cv2.imread(path)print(img1.shape)img2=Image.open(path)print(img2.size)

(6000, 8192, 3)(8192, 6000)

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#统计一下类别path = "/home/aistudio/work/dataset/tile_round1_train_20201231/train_annos.json"dict_class = {    "0":0,    "1":0,    "2":0,    "3":0,    "4":0,    "5":0,    "6":0}id_s = 0image_width,image_height = 0,0with open(path,"r") as f:    files = json.load(f)    #遍历标注文件    for file_img in files:        id_s += 1        #统计类别        file_class = file_img["category"]        dict_class[str(file_class)] += 1        #统计图片平均像素        image_height += file_img["image_height"]        image_width += file_img["image_width"]        if id_s % 1000 is 0:            print("处理到第{}个标注".format(id_s))print("类别：",dict_class)print("图片平均高{},图片平均宽{}".format(image_height/id_s,image_width/id_s))

将数据标注转为coco格式

paddledetection支持coco格式和voc格式，原数据标注不是这2种格式其中0类也就是背景类是没有数据的，所以可以去了，类别是1-6，共6个类别，当然如果用rcnn需要加上背景类，用yolo就不需要In [ ]

class Fabric2COCO:    def __init__(self,            is_mode = "train"            ):        self.images = []        self.annotations = []        self.categories = []        self.img_id = 0        self.ann_id = 0        self.is_mode = is_mode        if not os.path.exists("/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/{}".format(self.is_mode)):            os.makedirs("/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/{}".format(self.is_mode))    def to_coco(self, anno_file,img_dir):        self._init_categories()        anno_result= pd.read_json(open(anno_file,"r"))                if self.is_mode == "train":            anno_result = anno_result.head(int(anno_result['name'].count()*0.9))#取数据集前百分之90        elif self.is_mode == "val":            anno_result = anno_result.tail(int(anno_result['name'].count()*0.1))         name_list=anno_result["name"].unique()#返回唯一图片名字        for img_name in name_list:            img_anno = anno_result[anno_result["name"] == img_name]#取出此图片的所有标注            bboxs = img_anno["bbox"].tolist()#返回list            defect_names = img_anno["category"].tolist()            assert img_anno["name"].unique()[0] == img_name            img_path=os.path.join(img_dir,img_name)            #img =cv2.imread(img_path)            #h,w,c=img.shape            #这种读取方法更快            img = Image.open(img_path)            w, h = img.size            #h,w=6000,8192            self.images.append(self._image(img_path,h, w))            self._cp_img(img_path)#复制文件路径            if self.img_id % 200 is 0:                print("处理到第{}张图片".format(self.img_id))            for bbox, label in zip(bboxs, defect_names):                annotation = self._annotation(label, bbox)                self.annotations.append(annotation)                self.ann_id += 1            self.img_id += 1        instance = {}        instance['info'] = 'fabric defect'        instance['license'] = ['none']        instance['images'] = self.images        instance['annotations'] = self.annotations        instance['categories'] = self.categories        return instance    def _init_categories(self):        #1，2，3，4，5，6个类别        for v in range(1,7):            print(v)            category = {}            category['id'] = v            category['name'] = str(v)            category['supercategory'] = 'defect_name'            self.categories.append(category)    def _image(self, path,h,w):        image = {}        image['height'] = h        image['width'] = w        image['id'] = self.img_id        image['file_name'] = os.path.basename(path)#返回path最后的文件名        return image    def _annotation(self,label,bbox):        area=(bbox[2]-bbox[0])*(bbox[3]-bbox[1])        points=[[bbox[0],bbox[1]],[bbox[2],bbox[1]],[bbox[2],bbox[3]],[bbox[0],bbox[3]]]        annotation = {}        annotation['id'] = self.ann_id        annotation['image_id'] = self.img_id        annotation['category_id'] = label        annotation['segmentation'] = []# np.asarray(points).flatten().tolist()        annotation['bbox'] = self._get_box(points)        annotation['iscrowd'] = 0        annotation["ignore"] = 0        annotation['area'] = area        return annotation    def _cp_img(self, img_path):        shutil.copy(img_path, os.path.join("/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/{}".format(self.is_mode), os.path.basename(img_path)))    def _get_box(self, points):        min_x = min_y = np.inf        max_x = max_y = 0        for x, y in points:            min_x = min(min_x, x)            min_y = min(min_y, y)            max_x = max(max_x, x)            max_y = max(max_y, y)        '''coco,[x,y,w,h]'''        return [min_x, min_y, max_x - min_x, max_y - min_y]    def save_coco_json(self, instance, save_path):        import json        with open(save_path, 'w') as fp:            json.dump(instance, fp, indent=1, separators=(',', ': '))#缩进设置为1，元素之间用逗号隔开 ， key和内容之间 用冒号隔开

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'''转换有瑕疵的样本为coco格式'''#训练集,划分90%做为训练集img_dir = "/home/aistudio/work/dataset/tile_round1_train_20201231/train_imgs"anno_dir="/home/aistudio/work/dataset/tile_round1_train_20201231/train_annos.json"fabric2coco = Fabric2COCO()train_instance = fabric2coco.to_coco(anno_dir,img_dir)if not os.path.exists("/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/"):    os.makedirs("/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/")fabric2coco.save_coco_json(train_instance, "/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/"+'instances_{}.json'.format("train"))

   In [ ]

'''转换有瑕疵的样本为coco格式'''#验证集，划分10%做为验证集img_dir = "/home/aistudio/work/dataset/tile_round1_train_20201231/train_imgs"anno_dir="/home/aistudio/work/dataset/tile_round1_train_20201231/train_annos.json"fabric2coco = Fabric2COCO(is_mode = "val")train_instance = fabric2coco.to_coco(anno_dir,img_dir)if not os.path.exists("/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/"):    os.makedirs("/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/")fabric2coco.save_coco_json(train_instance, "/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/"+'instances_{}.json'.format("val"))

模型训练

配置文件已经放在work下，work/faster_rcnn_r50_fpn_2x.yml。 –eval参数表示在训练过程中在验证集上验证模型。

在某个模型基础上继续训练加上如 -r output/faster_rcnn_r50_fpn_2x/12.pdparams的参数。

In [8]

#开始训练%cd /home/aistudio/work/PaddleDetection-release-2.2/#%env CUDA_VISIBLE_DEVICES=0!python tools/train.py     -c /home/aistudio/work/faster_rcnn_r50_fpn_2x.yml     -r /home/aistudio/work/PaddleDetection-release-2.2/output/faster_rcnn_r50_fpn_2x/12.pdparams --eval

模型评估

模型评估需要指定被评估模型，如-o weights=output/faster_rcnn_r50_fpn_2x/best_model.pdparams：

In [9]

#模型评估%cd /home/aistudio/work/PaddleDetection-release-2.2/!python tools/eval.py     -c /home/aistudio/work/faster_rcnn_r50_fpn_2x.yml     -o weights=/home/aistudio/work/PaddleDetection-release-2.2/output/faster_rcnn_r50_fpn_2x/best_model.pdparams

模型预测

模型预测调用tools/infer.py文件，需要指定模型路径、被预测的图像路径如–infer_img=dataset/coco/val/235_7_t20201127123214965_CAM2.jpg、预测结果输出目录如–output_dir=infer_output/等：

预测结果会直接画在图像上保存在output_dir目录下。

In [11]

#模型预测%cd /home/aistudio/work/PaddleDetection-release-2.2/!python -u tools/infer.py     -c /home/aistudio/work/faster_rcnn_r50_fpn_2x.yml     --output_dir=infer_output/     --save_txt=True     -o  weights=/home/aistudio/work/PaddleDetection-release-2.2/output/faster_rcnn_r50_fpn_2x/best_model.pdparams     --infer_img=/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/val/235_7_t20201127123214965_CAM2.jpg

生成提交文件(json文件)

注意：该处是用eval生成的，PaddleDetection配置文件中读取验证集需要先读取img和标注（这里把img路径改成测试集的，标注随便弄个json文件，将save_prediction_only=True）

将测试集生成标注文件代码如下，生成的测试集标注文件存放在dataset/coco/annotations/instances_test.json：

In [38]

#生成个test标注文件（无效的）import os, sys, zipfileimport urllib.requestimport shutilimport numpy as npimport skimage.io as ioimport pandasimport matplotlib.pyplot as pltimport pylabimport jsonimport tqdmfrom PIL import Image#generate test def test_from_dir(pic_path):     pics = os.listdir(pic_path)     meta = {}     images = []     annotations = []     categories=[]    for v in range(1,7):        category = {}        category['id'] = v        category['name'] = str(v)        category['supercategory'] = 'defect_name'        categories.append(category)    num = 0     for im in pics:         num += 1         annotation =  {            "area": 326.1354999999996,            "iscrowd": 0,            "image_id": num,            "bbox": [                1654.76,                2975,                22,                15            ],            "category_id": 5,            "ignore": 0,            "segmentation": [],            "id": num        }        img = os.path.join(pic_path,im)        img = Image.open(img)        images_anno = {}         images_anno['file_name'] = im        images_anno['width'] = img.size[0]        images_anno['height'] = img.size[1]        images_anno['id'] = num        images.append(images_anno)         annotations.append(annotation)    meta['images'] = images    meta['categories'] = categories     meta['annotations'] = annotations    print(len(annotations))    json.dump(meta,open('/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/instances_test.json','w'),indent=4, ensure_ascii=False)

   In [39]

#生成测试集标注（伪标注，无效的）pic_path='/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/tile_round1_testA_20201231/testA_imgs'test_from_dir(pic_path)

1762

运行模型评估，数据集指向测试集。本案例为大家提供了修改完数据配置的配置文件faster_rcnn_r50_fpn_2x_test.yml：

In [40]

#模型评估（预测），标注在生成的bbox.json文件中，注意要把read配置文件中的eval部分的batch_size改成1，不然会报错，这边已经改好了# 该过程大概需要一个半小时%cd /home/aistudio/work/PaddleDetection-release-2.2!python tools/eval.py     -c /home/aistudio/work/faster_rcnn_r50_fpn_2x_test.yml     -o  weights=/home/aistudio/work/PaddleDetection-release-2.2/output/faster_rcnn_r50_fpn_2x/best_model.pdparams     save_prediction_only=True

   In [41]

#将评估预测的结果按照我们生成的测试集伪标注格式保存bbox = '/home/aistudio/work/PaddleDetection-release-2.2/bbox.json'test_path = '/home/aistudio/work/PaddleDetection-release-2.2/dataset/coco/annotations/instances_test.json'sub_path = '/home/aistudio/work/PaddleDetection-release-2.2/results.json'def make_submittion(sub_path,bbox,test_path):     meta = {}    with open(bbox) as f:        bbox = json.load(f)    with open(test_path) as f:        test_ann = json.load(f)    meta['images'] = test_ann['images']    meta['annotations'] = bbox    json.dump(meta,open(sub_path,'w'),indent=4) make_submittion(sub_path,bbox,test_path)

按照标注提交格式生成预测结果final_results.json

In [42]

final_path = '/home/aistudio/work/PaddleDetection-release-2.2/final_results.json'results_path = '/home/aistudio/work/PaddleDetection-release-2.2/results.json'def final_result(final_path, results_path):     with open(results_path,"r") as f:        test_result = json.load(f)    #获取图片id对应的图片名字字典    imgs = test_result["images"]    dict_img = {}    for img in imgs:        img_name = img["file_name"]        img_id = img["id"]        dict_img[str(img_id)] = img_name    #print("*******",dict_img)    #按照提交格式对应字段    final_results = []    annotations = test_result["annotations"]    for ann in annotations:        dict_ann = {}        #设置图片name        #将图片id对应为name        ann_name_id = str(ann["image_id"])        dict_ann["name"] = dict_img[ann_name_id]        #设置类别category        dict_ann["category"] = ann["category_id"]        #设置bbox        #之前预测的bbox中格式为【左上角横坐标x，左上角纵坐标y，框的高h，框的宽w】        #提交格式要求的bbox格式为【左上角横坐标，左上角纵坐标，右下角横坐标，右下角纵坐标】        bbox = ann["bbox"]        bbox = [bbox[0],bbox[1],bbox[0]+bbox[2],bbox[1]+bbox[3]]        dict_ann["bbox"] = bbox        #设置置信度score        dict_ann["score"] = ann["score"]        final_results.append(dict_ann)            #print(final_results[0])    json.dump(final_results,open(final_path,'w'),indent=4) final_result(final_path, results_path)

通过上面的代码得到了竞赛提交文件final_results.json。

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