update data aug

JsonToCoco.py

@@ -0,0 +1,126 @@
+import os
+import json
+import numpy as np
+import shutil
+import pandas as pd
+
+# defect_name2label = {
+#     '破洞': 1, '水渍': 2, '油渍': 2, '污渍': 2, '三丝': 3, '结头': 4, '花板跳': 5, '百脚': 6, '毛粒': 7,
+#     '粗经': 8, '松经': 9, '断经': 10, '吊经': 11, '粗维': 12, '纬缩': 13, '浆斑': 14, '整经结': 15, '星跳': 16, '跳花': 16,
+#     '断氨纶': 17, '稀密档': 18, '浪纹档': 18, '色差档': 18, '磨痕': 19, '轧痕': 19, '修痕': 19, '烧毛痕': 19, '死皱': 20, '云织': 20,
+#     '双纬': 20, '双经': 20, '跳纱': 20, '筘路': 20, '纬纱不良': 20,
+# }
+defect_name2label = {
+    1: 1, 2: 2, 3: 3,4: 4, 5: 5, 6: 6, 7: 7,
+    8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15,
+    16:16,17:17,18:18,19:19,20:20 
+}
+
+class Fabric2COCO:
+
+    def __init__(self,mode="train"):
+        self.images = []
+        self.annotations = []
+        self.categories = []
+        self.img_id = 5912
+        self.ann_id = 5912
+        self.mode =mode
+        if not os.path.exists("coco/images/{}".format(self.mode)):
+            os.makedirs("coco/images/{}".format(self.mode))
+
+    def to_coco(self, anno_file,img_dir):
+        self._init_categories()
+        anno_result= pd.read_json(open(anno_file,"r"))
+        name_list=anno_result["name"].unique()
+        for img_name in name_list:
+            img_anno = anno_result[anno_result["name"] == img_name]
+            bboxs = img_anno["bbox"].tolist()
+            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
+            h,w=1000,2446
+            self.images.append(self._image(img_path,h, w))
+
+            self._cp_img(img_path)
+            # print(defect_names[0].type)
+            for bbox, defect_name in zip(bboxs, defect_names):
+                # print(defect_name)
+                label= defect_name2label[defect_name]
+                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):
+        for v in range(1,21):
+            print(v)
+            category = {}
+            category['id'] = v
+            category['name'] = str(v)
+            category['supercategory'] = 'defect_name'
+            self.categories.append(category)
+        # for k, v in defect_name2label.items():
+        #     category = {}
+        #     category['id'] = v
+        #     category['name'] = k
+        #     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)
+        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['area'] = area
+        return annotation
+
+    def _cp_img(self, img_path):
+        shutil.copy(img_path, os.path.join("coco/images/{}".format(self.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=(',', ': '))
+
+
+'''转换有瑕疵的样本为coco格式'''
+img_dir = "./normal_images_aug"
+anno_dir="./annotation_normal.json"
+fabric2coco = Fabric2COCO()
+train_instance = fabric2coco.to_coco(anno_dir,img_dir)
+if not os.path.exists("coco/annotations/"):
+    os.makedirs("coco/annotations/")
+fabric2coco.save_coco_json(train_instance, "coco/annotations/"+'instances_{}.json'.format("train"))

README.md

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 
 # MMDetection
 
@@ -147,4 +148,8 @@ If you use this toolbox or benchmark in your research, please cite this project.
 
 ## Contact
 
-This repo is currently maintained by Kai Chen ([@hellock](http://github.com/hellock)), Jiangmiao Pang ([@OceanPang](https://github.com/OceanPang)), Jiaqi Wang ([@myownskyW7](https://github.com/myownskyW7)) and Yuhang Cao ([@yhcao6](https://github.com/yhcao6)).
+This repo is currently maintained by Kai Chen ([@hellock](http://github.com/hellock)), Jiangmiao Pang ([@OceanPang](https://github.com/OceanPang)), Jiaqi Wang ([@myownskyW7](https://github.com/myownskyW7)) and Yuhang Cao ([@yhcao6](https://github.com/yhcao6)).
+=======
+# Tianchi_data_aug
+normal-data-augmentation
+>>>>>>> 07584541bbba36e48c7cc85c8e20a463b797d2f1

crop_object.py

@@ -0,0 +1,71 @@
+
+import os
+import cv2
+import re
+#原文：https://blog.csdn.net/angelbeats11/article/details/88427314 
+pattens = ['name','xmin','ymin','xmax','ymax']
+import matplotlib.pyplot as plt
+import numpy as np
+count=0
+def get_annotations(xml_path):
+    bbox = []
+    with open(xml_path,'r') as f:
+        text = f.read().replace('\n','return')
+        p1 = re.compile(r'(?<=<object>)(.*?)(?=</object>)')
+        result = p1.findall(text)
+        for obj in result:
+            tmp = []
+            for patten in pattens:
+                p = re.compile(r'(?<=<{}>)(.*?)(?=</{}>)'.format(patten,patten))
+                if patten == 'name':
+                    tmp.append(p.findall(obj)[0])
+                else:
+                    tmp.append(int(float(p.findall(obj)[0])))
+            bbox.append(tmp)
+    return bbox
+
+def save_viz_image(image_path,xml_path,save_path):
+    global count
+    bbox = get_annotations(xml_path)
+    image = cv2.imread(image_path)
+    if not os.path.exists(save_path):
+        os.mkdir(save_path)
+
+    for info in bbox:
+        # print(info[0])
+
+        count+=1
+        # print(os.path.join(save_path,str(count),image_path.split('/')[-1]))
+        # print(image[info[2]:info[4],info[1]:info[3]].shape)
+        # plt.imshow(image[info[2]:info[4],info[1]:info[3]])
+        # plt.show()
+
+        # print(info[1],info[2],info[3],info[4])
+        # cv2.rectangle(image,(info[1],info[2]),(info[3],info[4]),(0,255,0),thickness=2)
+        # cv2.putText(image,info[0],(info[1],info[2]),cv2.FONT_HERSHEY_PLAIN,2,(0,0,255),2)
+        # gray=cv2.cvtColor(image[info[2]:info[4],info[1]:info[3]],cv2.COLOR_BGR2GRAY)
+        # # ret2, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+        # im = np.float32(gray) / 255.0
+
+        # # Calculate gradient
+        # gx = cv2.Sobel(im, cv2.CV_32F, 1, 0, ksize=1)
+        # gy = cv2.Sobel(im, cv2.CV_32F, 0, 1, ksize=1)
+        # mag, angle = cv2.cartToPolar(gx, gy, angleInDegrees=True)
+        #
+        # plt.imshow(mag)
+        # plt.show()
+        # print(save_path+str(count)+'.jpg')
+        cv2.imwrite(save_path+"/"+str(count)+'_'+str(info[0])+'.jpg',image[info[2]:info[4],info[1]:info[3]])
+        #cv2.imwrite(save_path + str(count) + '.jpg', mag)
+
+if __name__ == '__main__':
+    image_dir = './VOC2020/JPEGImages'
+    xml_dir = './VOC2020/Annotations'
+    save_dir = './detect_images'
+    image_list = os.listdir(image_dir)
+    for i in  image_list:
+        image_path = os.path.join(image_dir,i)
+        xml_path = os.path.join(xml_dir,i.replace('.jpg','.xml'))
+        save_viz_image(image_path,xml_path,save_dir)
+
+

tianchi_aug_normal.py

@@ -0,0 +1,95 @@
+from skimage import io,data,color
+from matplotlib import pyplot as plt
+import numpy as np
+import cv2
+import os
+import random
+import skimage
+from skimage.exposure import histogram
+from skimage import feature
+import json
+detect_img_root='./detect_images/'
+normal_img_root='./normal_images/'
+aug_dir='./normal_images_aug/'
+
+detect_name=os.listdir(detect_img_root)
+normal_name=os.listdir(normal_img_root)
+# # for test
+# img=io.imread(img_root+file_name[0])
+# # img=np.array(img)
+# print(img.shape)
+# cv2.imshow('image',img)
+# cv2.waitKey(0)
+# random.shuffle(normal_name)
+# print(img.shape)
+# print(img.shape)     #显示尺寸
+# print(img.shape[0])  #图片高度
+# print(img.shape[1])  #图片宽度
+# print(img.shape[2])  #图片通道数
+# print(img.size)      #显示总像素个数
+# print(img.max())     #最大像素值
+# print(img.min())     #最小像素值
+# print(img.mean()) 
+
+random.seed(2019)#设定随机数种子
+# 正常样本扩增
+result=[]
+for i in range(len(normal_name)):
+	# print(normal_name[i])
+	img=cv2.imread(normal_img_root+normal_name[i])
+	# print(img.shape)
+	height,width=img.shape[0],img.shape[1]
+	# print(height,width)
+	defect_img=cv2.imread(detect_img_root+detect_name[i])
+	# print(detect_name[i].split('.')[0].split('_')[1])
+	label=detect_name[i].split('.')[0].split('_')[1]
+	defect_h,defect_w=defect_img.shape[0],defect_img.shape[1]
+	# print(defect_h)
+	# print((height-defect_h))
+	# h_range=abs(height-defect_h)
+	# print((width-defect_w))
+	# w_range=abs(width-defect_w)
+	for j in range(5):
+		xmin=random.randint(1,2446)
+		ymin=random.randint(1,1000)
+		xmax=xmin+defect_w
+		ymax=ymin+defect_h
+		print(xmin,ymin,xmax,ymax)
+		print(img[xmin:xmax,ymin:ymax].shape,defect_img.shape)
+		img=np.array(img)
+		defect_img=np.array(defect_img)
+		try:
+			img[ymin:ymax,xmin:xmax]=defect_img
+			result.append({'name': normal_name[i], 'category': label, 'bbox': [xmin,ymin,xmax,ymax]})
+			# cv2.rectangle(img, (xmin,ymin), (xmax,ymax), (0,255,0), 2)
+			# cv2.imshow('image',img)
+			# cv2.waitKey(0)
+		except:
+			continue
+	cv2.imwrite(aug_dir+normal_name[i],img)
+json_name='annotation_normal.json'
+with open(json_name,'w') as fp:
+        json.dump(result, fp, indent = 4, separators=(',', ': '))
+	# if (defect_w*defect_h)<(32*32):
+
+
+
+
+
+
+#带桌面数据处理，检测垂直边
+# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构
+# for i in range(len(normal_name)):
+# 	img=cv2.imread(normal_img_root+normal_name[i])
+# 	img_gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+# 	img=skimage.filters.sobel_v(img_gray)
+# 	img=cv2.dilate(img, kernel)
+# 	print(img.shape)
+# 	row_index=[]
+# 	for i in range(img.shape[1]):
+# 		count_list=list(img[:,i]*255>50)
+# 		row_index.append(count_list.count(True))
+# 	print(np.argmax(row_index))
+
+
+