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深度学习分类问题、目标检测问题数据预处理、数据增强总结_基于分类算法的学习失败预警读取数据,数据预处理
作者：天景科技苑 | 2024-07-25 03:30:59
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基于分类算法的学习失败预警读取数据,数据预处理
分类问题
这里记一下keras的预处理、数据增强方法，想看pytorch的移步博主另一篇博客
https://blog.csdn.net/qq_36852276/article/details/94588656
多分类问题
使用keras自带的类ImageDataGenerator定义一个对象，这个对象在定义的时候可以指定对每张图像进行的操作
#ImageDataGenerator的例子 #加载ImageDataGenerator类 from keras.preprocessing.image import ImageDataGenerator #加载预加载模型提供的图像归一化方法 preprocess_input，帮你做了去中心化、标准差归一化等操作，不用这个的话也可以只除以某个值，例如利用rescale参数等，也可以利用自定义函数 from keras.applications.inception_v3 import InceptionV3, preprocess_input #定义对象，参数就是可设置的各种操作，实现数据增强，详细参数自己可以查一下 train_datagen = ImageDataGenerator( preprocessing_function = preprocess_input, #图像预处理 horizontal_flip = True, #垂直翻转 vertical_flip = True, #水平翻转 rotation_range=30, width_shift_range = 0.2, height_shift_range = 0.2, zoom_range = 0.1 )
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然后调用该对象的方法flow()或flow_from_directory()
flow()方法要求输入图像X和Y的矩阵，然后根据batch_size输出X和Y，这个方法我不常用因为我的工作数据量一般较大，无法直接全部加载到内存里
常用的是flow_from_directory()，它可以迭代的输出X和对应的标签，输入是一个路径，保存格式要求每个类别的图片放在不同的文件夹中，然后会自动建立文件夹名对类名的映射
这是图片放置的格式，train_set是输入的路径，里面包含了n_class个文件夹，每个文件夹都是一类，里面存放了该类的所有图片
看一下生成迭代器的例子
#生成迭代器
train_generator = train_datagen.flow_from_directory(
directory = TRAIN_DIR,
target_size = (IN_SIZE, IN_SIZE),
batch_size=BATCH_SIZE,
class_mode='categorical',
shuffle=True
)
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同时mark两个方法
print(train_generator.class_indices) # 输出对应的标签文件夹
print(train_generator.filenames) # 按顺序输出文件的名字
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多标签问题
一张图片有多个标签的时候无法简单的把每张图分到每一类，而且一般的保存格式是把类别放在CSV里，例如这样
CSV保存了图片名，第二列是对应的类别，用分号隔开，读取的思路是自己写迭代器，每次返回输入的X和标签Y
先记一下CSV文件的读取和写入过程
#CSV的读取过程 #制作形如[[1.jpg,cls],[2.jpg,cls]...]的列表，并打乱，用来制作训练集和验证集 input_path = "/home/xxx/df_cloud/" train_data = pd.read_csv(input_path+"xxx.csv") cls_list = [] source_path = "/home/xxx/df_cloud/train/" num = 0 for index, row in train_data.iterrows(): temp = [] filename = row["FileName"] cls = row["Code"] for item in cls.split(";"):#这里处理分号隔开的数据 temp.append(filename) temp.append(item) cls_list.append(temp) temp = [] num+=1 print(num) # num+=1 import random random.shuffle(cls_list)
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#CSV写入过程 root_path = '/home/xxx/df_cloud/test/' path_list = os.listdir(root_path) INT_HEIGHT = 299 IN_WIDTH = 299 filenames = [] result = [] for filename in path_list: img = load_img(root_path + filename, target_size=(INT_HEIGHT, IN_WIDTH, 3)) img = img_to_array(img) img = preprocess_input(img) img = np.expand_dims(img, axis=0) prediction = model_2.predict(img)[0] index = list(prediction).index(np.max(prediction)) res = index_2_cls[index] filenames.append(filename) result.append(res) print(filename) print(len(filenames)) print(len(result)) import pandas as pd dataframe = pd.DataFrame({'FileName': filenames, 'type': result}) dataframe.to_csv('/home/xxxf/df_cloud/baseline_1_result_2/submit/baseline_1_2.csv', index=False, header=True)
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接着预处理，这里发现keras为我们提供了好用的数据预处理、数据增强函数
想看这些函数的源码可以看这位博主的博客
https://blog.csdn.net/wyx100/article/details/81459083
#数据读取、预处理
from keras.preprocessing.image import img_to_array, load_img
#数据增强方法 大量！！！
from keras.preprocessing.image import random_rotation, random_shift, random_shear, random_zoom, random_channel_shift
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这样直接写迭代器就好了（这部分待更新）
线下数据增强方法
还是利用ImageDataGenerator，每次读取一张图，随机数据增强，然后保存进硬盘即可（待更新）
目标检测问题
利用现成的框架来实现目标检测，常规的方法就是把自己的数据整理成VOC、COCO数据集的格式，然后就符合了框架输入的需求，所以问题就在于如何把数据集整理成这些标准格式，我常用的是VOC数据集格式
使用labelme进行数据标注
使用的时候常用3个参数labelme --nodata --output --autosave
使用 labelme -h 可以查看这些参数作用
labelme生成的是json文件，那么就要把json转成需要的xml文件
记一下如何生成xml文件
import os from lxml.etree import Element, SubElement, tostring from xml.dom.minidom import parseString from PIL import Image #保存xml文件函数的核心实现，输入为图片名称image_name,分类category（一个列表，元素与bbox对应），bbox(一个列表，与分类对应)，保存路径save_dir ，通道数channel def save_xml(image_name, category,bbox, file_dir = '/home/xbw/wurenting/dataset_3/',save_dir='/home/xxx/voc_dataset/Annotations/',channel=3): file_path = file_dir img = Image.open(file_path + image_name) width = img.size[0] height = img.size[1] node_root = Element('annotation') node_folder = SubElement(node_root, 'folder') node_folder.text = 'VOC2007' node_filename = SubElement(node_root, 'filename') node_filename.text = image_name node_size = SubElement(node_root, 'size') node_width = SubElement(node_size, 'width') node_width.text = '%s' % width node_height = SubElement(node_size, 'height') node_height.text = '%s' % height node_depth = SubElement(node_size, 'depth') node_depth.text = '%s' % channel for i in range(len(bbox)): left, top, right, bottom = bbox[i][0],bbox[i][1],bbox[i][2], bbox[i][3] node_object = SubElement(node_root, 'object') node_name = SubElement(node_object, 'name') node_name.text = category[i] node_difficult = SubElement(node_object, 'difficult') node_difficult.text = '0' node_bndbox = SubElement(node_object, 'bndbox') node_xmin = SubElement(node_bndbox, 'xmin') node_xmin.text = '%s' % left node_ymin = SubElement(node_bndbox, 'ymin') node_ymin.text = '%s' % top node_xmax = SubElement(node_bndbox, 'xmax') node_xmax.text = '%s' % right node_ymax = SubElement(node_bndbox, 'ymax') node_ymax.text = '%s' % bottom xml = tostring(node_root, pretty_print=True) dom = parseString(xml) save_xml = os.path.join(save_dir, image_name.replace('jpg', 'xml')) with open(save_xml, 'wb') as f: f.write(xml) return
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只要把json文件保存的类别、bbox读出来，输入函数就可以了
json的读取
with open(file,'r') as obj:
res = json.load(obj)
res['xxx'] = xxx
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labelme生成的json文件如何保存成xml，这是我使用的脚本，就是把json文件读取出来，然后取出类别和bbox，利用上面的函数写入即可
import os from lxml.etree import Element, SubElement, tostring from xml.dom.minidom import parseString from PIL import Image import json json_dir = '/home/xxx/label/label_5/' img_dir = '/home/xxx/dataset_5/' save_dir='/home/xxx/xml/label_5_xml/' json_list = os.listdir(json_dir) for image_name in json_list: with open(json_dir+image_name) as obj: nums = json.load(obj) labels = [] bboxes = [] for i in nums['shapes']: labels.append(i['label']) bboxes.append([min(i['points'][0][0],i['points'][1][0]),min(i['points'][0][1],i['points'][1][1]),max(i['points'][0][0],i['points'][1][0]),max(i['points'][0][1],i['points'][1][1])]) save_xml(image_name[:-5]+'.jpg',labels,bboxes,file_dir = img_dir,save_dir=save_dir)
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线下数据增强
这个直接参考了这位博主的实现https://blog.csdn.net/Mmm_Zzz/article/details/81784758
代码放在https://github.com/maozezhong/CV_ToolBox/blob/master/DataAugForObjectDetection/DataAugmentForObejctDetection.py
# -*- coding=utf-8 -*- ############################################################## # description: # data augmentation for obeject detection # author: # maozezhong 2018-6-27 ############################################################## # 包括: # 1. 裁剪(需改变bbox) # 2. 平移(需改变bbox) # 3. 改变亮度 # 4. 加噪声 # 5. 旋转角度(需要改变bbox) # 6. 镜像(需要改变bbox) # 7. cutout # 注意: # random.seed(),相同的seed,产生的随机数是一样的!! import time import random import cv2 import os import math import numpy as np from skimage.util import random_noise from skimage import exposure def show_pic(img, bboxes=None): ''' 输入: img:图像array bboxes:图像的所有boudning box list, 格式为[[x_min, y_min, x_max, y_max]....] names:每个box对应的名称 ''' # cv2.imwrite('./1.jpg', img) # img = cv2.imread('./1.jpg') img=img/255 for i in range(len(bboxes)): bbox = bboxes[i] x_min = bbox[0] y_min = bbox[1] x_max = bbox[2] y_max = bbox[3] cv2.rectangle(img,(int(x_min),int(y_min)),(int(x_max),int(y_max)),(0,255,0),3) cv2.namedWindow('pic', 0) # 1表示原图 cv2.moveWindow('pic', 0, 0) cv2.resizeWindow('pic', 1200,800) # 可视化的图片大小 cv2.imshow('pic', img) if cv2.waitKey(0)==ord('q'): cv2.destroyAllWindows() return cv2.destroyAllWindows() # os.remove('./1.jpg') # 图像均为cv2读取 class DataAugmentForObjectDetection(): def __init__(self, rotation_rate=0.5, max_rotation_angle=5, crop_rate=0.5, shift_rate=0.5, change_light_rate=0.5, add_noise_rate=0.5, flip_rate=0.5, cutout_rate=0.5, cut_out_length=50, cut_out_holes=1, cut_out_threshold=0.5): self.rotation_rate = rotation_rate self.max_rotation_angle = max_rotation_angle self.crop_rate = crop_rate self.shift_rate = shift_rate self.change_light_rate = change_light_rate self.add_noise_rate = add_noise_rate self.flip_rate = flip_rate self.cutout_rate = cutout_rate self.cut_out_length = cut_out_length self.cut_out_holes = cut_out_holes self.cut_out_threshold = cut_out_threshold # 加噪声 def _addNoise(self, img): ''' 输入: img:图像array 输出: 加噪声后的图像array,由于输出的像素是在[0,1]之间,所以得乘以255 ''' # random.seed(int(time.time())) # return random_noise(img, mode='gaussian', seed=int(time.time()), clip=True)*255 return random_noise(img, mode='gaussian', clip=True)*255 # 调整亮度 def _changeLight(self, img): # random.seed(int(time.time())) flag = random.uniform(0.5, 1.5) #flag>1为调暗,小于1为调亮 return exposure.adjust_gamma(img, flag) # cutout def _cutout(self, img, bboxes, length=100, n_holes=1, threshold=0.5): ''' 原版本：https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py Randomly mask out one or more patches from an image. Args: img : a 3D numpy array,(h,w,c) bboxes : 框的坐标 n_holes (int): Number of patches to cut out of each image. length (int): The length (in pixels) of each square patch. ''' def cal_iou(boxA, boxB): ''' boxA, boxB为两个框，返回iou boxB为bouding box ''' # determine the (x, y)-coordinates of the intersection rectangle xA = max(boxA[0], boxB[0]) yA = max(boxA[1], boxB[1]) xB = min(boxA[2], boxB[2]) yB = min(boxA[3], boxB[3]) if xB <= xA or yB <= yA: return 0.0 # compute the area of intersection rectangle interArea = (xB - xA + 1) * (yB - yA + 1) # compute the area of both the prediction and ground-truth # rectangles boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) # compute the intersection over union by taking the intersection # area and dividing it by the sum of prediction + ground-truth # areas - the interesection area # iou = interArea / float(boxAArea + boxBArea - interArea) iou = interArea / float(boxBArea) # return the intersection over union value return iou # 得到h和w if img.ndim == 3: h,w,c = img.shape else: _,h,w,c = img.shape mask = np.ones((h,w,c), np.float32) for n in range(n_holes): chongdie = True #看切割的区域是否与box重叠太多 while chongdie: y = np.random.randint(h) x = np.random.randint(w) y1 = np.clip(y - length // 2, 0, h) #numpy.clip(a, a_min, a_max, out=None), clip这个函数将将数组中的元素限制在a_min, a_max之间，大于a_max的就使得它等于 a_max，小于a_min,的就使得它等于a_min y2 = np.clip(y + length // 2, 0, h) x1 = np.clip(x - length // 2, 0, w) x2 = np.clip(x + length // 2, 0, w) chongdie = False for box in bboxes: if cal_iou([x1,y1,x2,y2], box) > threshold: chongdie = True break mask[y1: y2, x1: x2, :] = 0. # mask = np.expand_dims(mask, axis=0) img = img * mask return img # 旋转 def _rotate_img_bbox(self, img, bboxes, angle=5, scale=1.): ''' 参考:https://blog.csdn.net/u014540717/article/details/53301195crop_rate 输入: img:图像array,(h,w,c) bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 angle:旋转角度 scale:默认1 输出: rot_img:旋转后的图像array rot_bboxes:旋转后的boundingbox坐标list ''' #---------------------- 旋转图像 ---------------------- w = img.shape[1] h = img.shape[0] # 角度变弧度 rangle = np.deg2rad(angle) # angle in radians # now calculate new image width and height nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale # ask OpenCV for the rotation matrix rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale) # calculate the move from the old center to the new center combined # with the rotation rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0])) # the move only affects the translation, so update the translation # part of the transform rot_mat[0,2] += rot_move[0] rot_mat[1,2] += rot_move[1] # 仿射变换 rot_img = cv2.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4) #---------------------- 矫正bbox坐标 ---------------------- # rot_mat是最终的旋转矩阵 # 获取原始bbox的四个中点，然后将这四个点转换到旋转后的坐标系下 rot_bboxes = list() for bbox in bboxes: xmin = bbox[0] ymin = bbox[1] xmax = bbox[2] ymax = bbox[3] point1 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymin, 1])) point2 = np.dot(rot_mat, np.array([xmax, (ymin+ymax)/2, 1])) point3 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymax, 1])) point4 = np.dot(rot_mat, np.array([xmin, (ymin+ymax)/2, 1])) # 合并np.array concat = np.vstack((point1, point2, point3, point4)) # 改变array类型 concat = concat.astype(np.int32) # 得到旋转后的坐标 rx, ry, rw, rh = cv2.boundingRect(concat) rx_min = rx ry_min = ry rx_max = rx+rw ry_max = ry+rh # 加入list中 rot_bboxes.append([rx_min, ry_min, rx_max, ry_max]) return rot_img, rot_bboxes # 裁剪 def _crop_img_bboxes(self, img, bboxes): ''' 裁剪后的图片要包含所有的框 输入: img:图像array bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 输出: crop_img:裁剪后的图像array crop_bboxes:裁剪后的bounding box的坐标list ''' #---------------------- 裁剪图像 ---------------------- w = img.shape[1] h = img.shape[0] x_min = w #裁剪后的包含所有目标框的最小的框 x_max = 0 y_min = h y_max = 0 for bbox in bboxes: x_min = min(x_min, bbox[0]) y_min = min(y_min, bbox[1]) x_max = max(x_max, bbox[2]) y_max = max(y_max, bbox[3]) d_to_left = x_min #包含所有目标框的最小框到左边的距离 d_to_right = w - x_max #包含所有目标框的最小框到右边的距离 d_to_top = y_min #包含所有目标框的最小框到顶端的距离 d_to_bottom = h - y_max #包含所有目标框的最小框到底部的距离 #随机扩展这个最小框 crop_x_min = int(x_min - random.uniform(0, d_to_left)) crop_y_min = int(y_min - random.uniform(0, d_to_top)) crop_x_max = int(x_max + random.uniform(0, d_to_right)) crop_y_max = int(y_max + random.uniform(0, d_to_bottom)) # 随机扩展这个最小框 , 防止别裁的太小 # crop_x_min = int(x_min - random.uniform(d_to_left//2, d_to_left)) # crop_y_min = int(y_min - random.uniform(d_to_top//2, d_to_top)) # crop_x_max = int(x_max + random.uniform(d_to_right//2, d_to_right)) # crop_y_max = int(y_max + random.uniform(d_to_bottom//2, d_to_bottom)) #确保不要越界 crop_x_min = max(0, crop_x_min) crop_y_min = max(0, crop_y_min) crop_x_max = min(w, crop_x_max) crop_y_max = min(h, crop_y_max) crop_img = img[crop_y_min:crop_y_max, crop_x_min:crop_x_max] #---------------------- 裁剪boundingbox ---------------------- #裁剪后的boundingbox坐标计算 crop_bboxes = list() for bbox in bboxes: crop_bboxes.append([bbox[0]-crop_x_min, bbox[1]-crop_y_min, bbox[2]-crop_x_min, bbox[3]-crop_y_min]) return crop_img, crop_bboxes # 平移 def _shift_pic_bboxes(self, img, bboxes): ''' 参考:https://blog.csdn.net/sty945/article/details/79387054 平移后的图片要包含所有的框 输入: img:图像array bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 输出: shift_img:平移后的图像array shift_bboxes:平移后的bounding box的坐标list ''' #---------------------- 平移图像 ---------------------- w = img.shape[1] h = img.shape[0] x_min = w #裁剪后的包含所有目标框的最小的框 x_max = 0 y_min = h y_max = 0 for bbox in bboxes: x_min = min(x_min, bbox[0]) y_min = min(y_min, bbox[1]) x_max = max(x_max, bbox[2]) y_max = max(y_max, bbox[3]) d_to_left = x_min #包含所有目标框的最大左移动距离 d_to_right = w - x_max #包含所有目标框的最大右移动距离 d_to_top = y_min #包含所有目标框的最大上移动距离 d_to_bottom = h - y_max #包含所有目标框的最大下移动距离 x = random.uniform(-(d_to_left-1) / 3, (d_to_right-1) / 3) y = random.uniform(-(d_to_top-1) / 3, (d_to_bottom-1) / 3) M = np.float32([[1, 0, x], [0, 1, y]]) #x为向左或右移动的像素值,正为向右负为向左; y为向上或者向下移动的像素值,正为向下负为向上 shift_img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0])) #---------------------- 平移boundingbox ---------------------- shift_bboxes = list() for bbox in bboxes: shift_bboxes.append([bbox[0]+x, bbox[1]+y, bbox[2]+x, bbox[3]+y]) return shift_img, shift_bboxes # 镜像 def _filp_pic_bboxes(self, img, bboxes): ''' 参考:https://blog.csdn.net/jningwei/article/details/78753607 平移后的图片要包含所有的框 输入: img:图像array bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 输出: flip_img:平移后的图像array flip_bboxes:平移后的bounding box的坐标list ''' # ---------------------- 翻转图像 ---------------------- import copy flip_img = copy.deepcopy(img) if random.random() < 0.5: #0.5的概率水平翻转，0.5的概率垂直翻转 horizon = True else: horizon = False h,w,_ = img.shape if horizon: #水平翻转 flip_img = cv2.flip(flip_img, 1) #1是水平，-1是水平垂直 else: flip_img = cv2.flip(flip_img, 0) # ---------------------- 调整boundingbox ---------------------- flip_bboxes = list() for box in bboxes: x_min = box[0] y_min = box[1] x_max = box[2] y_max = box[3] if horizon: flip_bboxes.append([w-x_max, y_min, w-x_min, y_max]) else: flip_bboxes.append([x_min, h-y_max, x_max, h-y_min]) return flip_img, flip_bboxes def dataAugment(self, img, bboxes): ''' 图像增强 输入: img:图像array bboxes:该图像的所有框坐标 输出: img:增强后的图像 bboxes:增强后图片对应的box ''' change_num = 0 #改变的次数 print('------') while change_num < 1: #默认至少有一种数据增强生效 if random.random() < self.crop_rate: #裁剪 print('裁剪') change_num += 1 img, bboxes = self._crop_img_bboxes(img, bboxes) if random.random() > self.rotation_rate: #旋转 print('旋转') change_num += 1 # angle = random.uniform(-self.max_rotation_angle, self.max_rotation_angle) angle = random.sample([90, 180, 270],1)[0] scale = random.uniform(0.7, 0.8) img, bboxes = self._rotate_img_bbox(img, bboxes, angle, scale) if random.random() < self.shift_rate: #平移 print('平移') change_num += 1 img, bboxes = self._shift_pic_bboxes(img, bboxes) if random.random() > self.change_light_rate: #改变亮度 print('亮度') change_num += 1 img = self._changeLight(img) if random.random() < self.add_noise_rate: #加噪声 print('加噪声') change_num += 1 img = self._addNoise(img) if random.random() < self.cutout_rate: #cutout print('cutout') change_num += 1 img = self._cutout(img, bboxes, length=self.cut_out_length, n_holes=self.cut_out_holes, threshold=self.cut_out_threshold) if random.random() < self.flip_rate: #翻转 print('翻转') change_num += 1 img, bboxes = self._filp_pic_bboxes(img, bboxes) print('\n') # print('------') return img, bboxes
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# -*- coding=utf-8 -*- import xml.etree.ElementTree as ET import xml.dom.minidom as DOC # 从xml文件中提取bounding box信息, 格式为[[x_min, y_min, x_max, y_max, name]] def parse_xml(xml_path): ''' 输入： xml_path: xml的文件路径 输出： 从xml文件中提取bounding box信息, 格式为[[x_min, y_min, x_max, y_max, name]] ''' tree = ET.parse(xml_path) root = tree.getroot() objs = root.findall('object') coords = list() for ix, obj in enumerate(objs): name = obj.find('name').text box = obj.find('bndbox') x_min = int(box[0].text) y_min = int(box[1].text) x_max = int(box[2].text) y_max = int(box[3].text) coords.append([x_min, y_min, x_max, y_max, name]) return coords
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#测试 import shutil need_aug_num = 1 dataAug = DataAugmentForObjectDetection() source_pic_root_path = '/home/xbw/faster-rcnn.pytorch/beifen/VOC2007/JPEGImages' source_xml_root_path = '/home/xbw/faster-rcnn.pytorch/beifen/VOC2007/Annotations' for parent, _, files in os.walk(source_pic_root_path): for file in files: cnt = 0 while cnt < need_aug_num: pic_path = os.path.join(parent, file) xml_path = os.path.join(source_xml_root_path, file[:-4]+'.xml') coords = parse_xml(xml_path) #解析得到box信息，格式为[[x_min,y_min,x_max,y_max,name]] coords = [coord[:4] for coord in coords] img = cv2.imread(pic_path) show_pic(img, coords) # 原图 auged_img, auged_bboxes = dataAug.dataAugment(img, coords) cnt += 1 show_pic(auged_img, auged_bboxes) # 强化后的图
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线下数据增强的实例脚本
# -*- coding=utf-8 -*- # 包括: # 1. 裁剪(需改变bbox) # 2. 平移(需改变bbox) # 3. 改变亮度 # 4. 加噪声 # 5. 旋转角度(需要改变bbox) # 6. 镜像(需要改变bbox) # 7. cutout # 注意: # random.seed(),相同的seed,产生的随机数是一样的!! import time import random import cv2 import os import math import numpy as np from skimage.util import random_noise from skimage import exposure import sys #显示带标签显示的图片 def show_pic(img, bboxes=None,labels=None): ''' 输入: img:图像array bboxes:图像的所有boudning box list, 格式为[[x_min, y_min, x_max, y_max]....] names:每个box对应的名称 ''' # cv2.imwrite('./1.jpg', img) # img = cv2.imread('./1.jpg') img=img/255 for i in range(len(bboxes)): bbox = bboxes[i] x_min = bbox[0] y_min = bbox[1] x_max = bbox[2] y_max = bbox[3] cv2.rectangle(img,(int(x_min),int(y_min)),(int(x_max),int(y_max)),(0,255,0),3) cv2.putText(img,labels[i],(int(x_min),int(y_min)),cv2.FONT_HERSHEY_SIMPLEX,0.8,(0,0,255),2) cv2.namedWindow('pic', 0) # 1表示原图 cv2.moveWindow('pic', 0, 0) cv2.resizeWindow('pic', 1200,800) # 可视化的图片大小 cv2.imshow('pic', img) if cv2.waitKey(1)==ord('q'): cv2.destroyAllWindows() sys.exit() # cv2.destroyAllWindows() # os.remove('./1.jpg') # 图像均为cv2读取 class DataAugmentForObjectDetection(): def __init__(self, rotation_rate=0.5, max_rotation_angle=30, crop_rate=0.5, shift_rate=0.5, change_light_rate=0.5, add_noise_rate=0.5, flip_rate=0.5, cutout_rate=0.5, cut_out_length=50, cut_out_holes=1, cut_out_threshold=0.5): self.rotation_rate = rotation_rate self.max_rotation_angle = max_rotation_angle self.crop_rate = crop_rate self.shift_rate = shift_rate self.change_light_rate = change_light_rate self.add_noise_rate = add_noise_rate self.flip_rate = flip_rate self.cutout_rate = cutout_rate self.cut_out_length = cut_out_length self.cut_out_holes = cut_out_holes self.cut_out_threshold = cut_out_threshold # 加噪声 def _addNoise(self, img): ''' 输入: img:图像array 输出: 加噪声后的图像array,由于输出的像素是在[0,1]之间,所以得乘以255 ''' # random.seed(int(time.time())) # return random_noise(img, mode='gaussian', seed=int(time.time()), clip=True)*255 return random_noise(img, mode='gaussian', clip=True)*255 # 调整亮度 def _changeLight(self, img): # random.seed(int(time.time())) flag = random.uniform(0.5, 1.5) #flag>1为调暗,小于1为调亮 return exposure.adjust_gamma(img, flag) # cutout def _cutout(self, img, bboxes, length=100, n_holes=1, threshold=0.5): ''' 原版本：https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py Randomly mask out one or more patches from an image. Args: img : a 3D numpy array,(h,w,c) bboxes : 框的坐标 n_holes (int): Number of patches to cut out of each image. length (int): The length (in pixels) of each square patch. ''' def cal_iou(boxA, boxB): ''' boxA, boxB为两个框，返回iou boxB为bouding box ''' # determine the (x, y)-coordinates of the intersection rectangle xA = max(boxA[0], boxB[0]) yA = max(boxA[1], boxB[1]) xB = min(boxA[2], boxB[2]) yB = min(boxA[3], boxB[3]) if xB <= xA or yB <= yA: return 0.0 # compute the area of intersection rectangle interArea = (xB - xA + 1) * (yB - yA + 1) # compute the area of both the prediction and ground-truth # rectangles boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) # compute the intersection over union by taking the intersection # area and dividing it by the sum of prediction + ground-truth # areas - the interesection area # iou = interArea / float(boxAArea + boxBArea - interArea) iou = interArea / float(boxBArea) # return the intersection over union value return iou # 得到h和w if img.ndim == 3: h,w,c = img.shape else: _,h,w,c = img.shape mask = np.ones((h,w,c), np.float32) for n in range(n_holes): chongdie = True #看切割的区域是否与box重叠太多 while chongdie: y = np.random.randint(h) x = np.random.randint(w) y1 = np.clip(y - length // 2, 0, h) #numpy.clip(a, a_min, a_max, out=None), clip这个函数将将数组中的元素限制在a_min, a_max之间，大于a_max的就使得它等于 a_max，小于a_min,的就使得它等于a_min y2 = np.clip(y + length // 2, 0, h) x1 = np.clip(x - length // 2, 0, w) x2 = np.clip(x + length // 2, 0, w) chongdie = False for box in bboxes: if cal_iou([x1,y1,x2,y2], box) > threshold: chongdie = True break mask[y1: y2, x1: x2, :] = 0. # mask = np.expand_dims(mask, axis=0) img = img * mask return img # 旋转 def _rotate_img_bbox(self, img, bboxes, angle=5, scale=1.): ''' 参考:https://blog.csdn.net/u014540717/article/details/53301195crop_rate 输入: img:图像array,(h,w,c) bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 angle:旋转角度 scale:默认1 输出: rot_img:旋转后的图像array rot_bboxes:旋转后的boundingbox坐标list ''' #---------------------- 旋转图像 ---------------------- w = img.shape[1] h = img.shape[0] # 角度变弧度 rangle = np.deg2rad(angle) # angle in radians # now calculate new image width and height nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale # ask OpenCV for the rotation matrix rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale) # calculate the move from the old center to the new center combined # with the rotation rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0])) # the move only affects the translation, so update the translation # part of the transform rot_mat[0,2] += rot_move[0] rot_mat[1,2] += rot_move[1] # 仿射变换 rot_img = cv2.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4) #---------------------- 矫正bbox坐标 ---------------------- # rot_mat是最终的旋转矩阵 # 获取原始bbox的四个中点，然后将这四个点转换到旋转后的坐标系下 rot_bboxes = list() for bbox in bboxes: xmin = bbox[0] ymin = bbox[1] xmax = bbox[2] ymax = bbox[3] point1 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymin, 1])) point2 = np.dot(rot_mat, np.array([xmax, (ymin+ymax)/2, 1])) point3 = np.dot(rot_mat, np.array([(xmin+xmax)/2, ymax, 1])) point4 = np.dot(rot_mat, np.array([xmin, (ymin+ymax)/2, 1])) # 合并np.array concat = np.vstack((point1, point2, point3, point4)) # 改变array类型 concat = concat.astype(np.int32) # 得到旋转后的坐标 rx, ry, rw, rh = cv2.boundingRect(concat) rx_min = rx ry_min = ry rx_max = rx+rw ry_max = ry+rh # 加入list中 rot_bboxes.append([rx_min, ry_min, rx_max, ry_max]) return rot_img, rot_bboxes # 裁剪 def _crop_img_bboxes(self, img, bboxes): ''' 裁剪后的图片要包含所有的框 输入: img:图像array bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 输出: crop_img:裁剪后的图像array crop_bboxes:裁剪后的bounding box的坐标list ''' #---------------------- 裁剪图像 ---------------------- w = img.shape[1] h = img.shape[0] x_min = w #裁剪后的包含所有目标框的最小的框 x_max = 0 y_min = h y_max = 0 for bbox in bboxes: x_min = min(x_min, bbox[0]) y_min = min(y_min, bbox[1]) x_max = max(x_max, bbox[2]) y_max = max(y_max, bbox[3]) d_to_left = x_min #包含所有目标框的最小框到左边的距离 d_to_right = w - x_max #包含所有目标框的最小框到右边的距离 d_to_top = y_min #包含所有目标框的最小框到顶端的距离 d_to_bottom = h - y_max #包含所有目标框的最小框到底部的距离 #随机扩展这个最小框 crop_x_min = int(x_min - random.uniform(0, d_to_left)) crop_y_min = int(y_min - random.uniform(0, d_to_top)) crop_x_max = int(x_max + random.uniform(0, d_to_right)) crop_y_max = int(y_max + random.uniform(0, d_to_bottom)) # 随机扩展这个最小框 , 防止别裁的太小 # crop_x_min = int(x_min - random.uniform(d_to_left//2, d_to_left)) # crop_y_min = int(y_min - random.uniform(d_to_top//2, d_to_top)) # crop_x_max = int(x_max + random.uniform(d_to_right//2, d_to_right)) # crop_y_max = int(y_max + random.uniform(d_to_bottom//2, d_to_bottom)) #确保不要越界 crop_x_min = max(0, crop_x_min) crop_y_min = max(0, crop_y_min) crop_x_max = min(w, crop_x_max) crop_y_max = min(h, crop_y_max) crop_img = img[crop_y_min:crop_y_max, crop_x_min:crop_x_max] #---------------------- 裁剪boundingbox ---------------------- #裁剪后的boundingbox坐标计算 crop_bboxes = list() for bbox in bboxes: crop_bboxes.append([bbox[0]-crop_x_min, bbox[1]-crop_y_min, bbox[2]-crop_x_min, bbox[3]-crop_y_min]) return crop_img, crop_bboxes # 平移 def _shift_pic_bboxes(self, img, bboxes): ''' 参考:https://blog.csdn.net/sty945/article/details/79387054 平移后的图片要包含所有的框 输入: img:图像array bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 输出: shift_img:平移后的图像array shift_bboxes:平移后的bounding box的坐标list ''' #---------------------- 平移图像 ---------------------- w = img.shape[1] h = img.shape[0] x_min = w #裁剪后的包含所有目标框的最小的框 x_max = 0 y_min = h y_max = 0 for bbox in bboxes: x_min = min(x_min, bbox[0]) y_min = min(y_min, bbox[1]) x_max = max(x_max, bbox[2]) y_max = max(y_max, bbox[3]) d_to_left = x_min #包含所有目标框的最大左移动距离 d_to_right = w - x_max #包含所有目标框的最大右移动距离 d_to_top = y_min #包含所有目标框的最大上移动距离 d_to_bottom = h - y_max #包含所有目标框的最大下移动距离 x = random.uniform(-(d_to_left-1) / 3, (d_to_right-1) / 3) y = random.uniform(-(d_to_top-1) / 3, (d_to_bottom-1) / 3) M = np.float32([[1, 0, x], [0, 1, y]]) #x为向左或右移动的像素值,正为向右负为向左; y为向上或者向下移动的像素值,正为向下负为向上 shift_img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0])) #---------------------- 平移boundingbox ---------------------- shift_bboxes = list() for bbox in bboxes: shift_bboxes.append([bbox[0]+x, bbox[1]+y, bbox[2]+x, bbox[3]+y]) return shift_img, shift_bboxes # 镜像 def _filp_pic_bboxes(self, img, bboxes): ''' 参考:https://blog.csdn.net/jningwei/article/details/78753607 平移后的图片要包含所有的框 输入: img:图像array bboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值 输出: flip_img:平移后的图像array flip_bboxes:平移后的bounding box的坐标list ''' # ---------------------- 翻转图像 ---------------------- import copy flip_img = copy.deepcopy(img) if random.random() < 0.5: #0.5的概率水平翻转，0.5的概率垂直翻转 horizon = True else: horizon = False h,w,_ = img.shape if horizon: #水平翻转 flip_img = cv2.flip(flip_img, 1) #1是水平，-1是水平垂直 else: flip_img = cv2.flip(flip_img, 0) # ---------------------- 调整boundingbox ---------------------- flip_bboxes = list() for box in bboxes: x_min = box[0] y_min = box[1] x_max = box[2] y_max = box[3] if horizon: flip_bboxes.append([w-x_max, y_min, w-x_min, y_max]) else: flip_bboxes.append([x_min, h-y_max, x_max, h-y_min]) return flip_img, flip_bboxes def dataAugment(self, img, bboxes): ''' 图像增强 输入: img:图像array bboxes:该图像的所有框坐标 输出: img:增强后的图像 bboxes:增强后图片对应的box ''' change_num = 0 #改变的次数 print('------') while change_num < 1: #默认至少有一种数据增强生效 if random.random() < self.crop_rate: #裁剪 print('裁剪') change_num += 1 img, bboxes = self._crop_img_bboxes(img, bboxes) if random.random() > self.rotation_rate: #旋转 print('旋转') change_num += 1 angle = random.uniform(-self.max_rotation_angle, self.max_rotation_angle) # angle = random.sample([90, 180, 270],1)[0] scale = random.uniform(0.7, 0.8) img, bboxes = self._rotate_img_bbox(img, bboxes, angle, scale) if random.random() < self.shift_rate: #平移 print('平移') change_num += 1 img, bboxes = self._shift_pic_bboxes(img, bboxes) if random.random() > self.change_light_rate: #改变亮度 print('亮度') change_num += 1 img = self._changeLight(img) if random.random() < self.add_noise_rate: #加噪声 print('加噪声') change_num += 1 img = self._addNoise(img) if random.random() < self.cutout_rate: #cutout print('cutout') change_num += 1 img = self._cutout(img, bboxes, length=self.cut_out_length, n_holes=self.cut_out_holes, threshold=self.cut_out_threshold) # if random.random() < self.flip_rate: #翻转 # print('翻转') # change_num += 1 # img, bboxes = self._filp_pic_bboxes(img, bboxes) print('\n') # print('------') return img, bboxes
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# -*- coding=utf-8 -*- import xml.etree.ElementTree as ET import xml.dom.minidom as DOC # 从xml文件中提取bounding box信息, 格式为[[x_min, y_min, x_max, y_max, name]] def parse_xml(xml_path): ''' 输入： xml_path: xml的文件路径 输出： 从xml文件中提取bounding box信息, 格式为[[x_min, y_min, x_max, y_max, name]] ''' tree = ET.parse(xml_path) root = tree.getroot() objs = root.findall('object') coords = list() for ix, obj in enumerate(objs): name = obj.find('name').text box = obj.find('bndbox') x_min = int(float(box[0].text)) y_min = int(float(box[1].text)) x_max = int(float(box[2].text)) y_max = int(float(box[3].text)) coords.append([x_min, y_min, x_max, y_max, name]) return coords
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import os from lxml.etree import Element, SubElement, tostring from xml.dom.minidom import parseString from PIL import Image #保存xml文件函数的核心实现，输入为图片名称image_name,分类category（一个列表，元素与bbox对应），bbox(一个列表，与分类对应)，保存路径save_dir ，通道数channel def save_xml(image_name, category,bbox, file_dir = '/home/xbw/wurenting/dataset_3/',save_dir='/home/xxx/voc_dataset/Annotations/',channel=3): file_path = file_dir img = Image.open(file_path + image_name) width = img.size[0] height = img.size[1] node_root = Element('annotation') node_folder = SubElement(node_root, 'folder') node_folder.text = 'VOC2007' node_filename = SubElement(node_root, 'filename') node_filename.text = image_name node_size = SubElement(node_root, 'size') node_width = SubElement(node_size, 'width') node_width.text = '%s' % width node_height = SubElement(node_size, 'height') node_height.text = '%s' % height node_depth = SubElement(node_size, 'depth') node_depth.text = '%s' % channel for i in range(len(bbox)): left, top, right, bottom = bbox[i][0],bbox[i][1],bbox[i][2], bbox[i][3] node_object = SubElement(node_root, 'object') node_name = SubElement(node_object, 'name') node_name.text = category[i] node_difficult = SubElement(node_object, 'difficult') node_difficult.text = '0' node_bndbox = SubElement(node_object, 'bndbox') node_xmin = SubElement(node_bndbox, 'xmin') node_xmin.text = '%s' % left node_ymin = SubElement(node_bndbox, 'ymin') node_ymin.text = '%s' % top node_xmax = SubElement(node_bndbox, 'xmax') node_xmax.text = '%s' % right node_ymax = SubElement(node_bndbox, 'ymax') node_ymax.text = '%s' % bottom xml = tostring(node_root, pretty_print=True) dom = parseString(xml) save_xml = os.path.join(save_dir, image_name.replace('jpg', 'xml')) with open(save_xml, 'wb') as f: f.write(xml) return
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import shutil need_aug_num = 1 dataAug = DataAugmentForObjectDetection() source_pic_root_path = '/home/xbw/wurenting/dataset/' source_xml_root_path = '/home/xbw/wurenting/labels/' img_save_path = '/home/xbw/wurenting/argdataset/' save_dir = '/home/xbw/wurenting/arglabels/' for parent, _, files in os.walk(source_pic_root_path): for file in files: cnt = 0 while cnt < need_aug_num: pic_path = os.path.join(parent, file) xml_path = os.path.join(source_xml_root_path, file[:-4]+'.xml') coords = parse_xml(xml_path) #解析得到box信息，格式为[[x_min,y_min,x_max,y_max,name]] coordss = [coord[:4] for coord in coords] labels = [coord[4] for coord in coords] img = cv2.imread(pic_path) show_pic(img, coordss,labels) # 原图 auged_img, auged_bboxes = dataAug.dataAugment(img, coordss) cnt += 1 cv2.imwrite(img_save_path+file[:-4]+'_arg.jpg',auged_img) save_xml(file[:-4]+'_arg.jpg',labels,auged_bboxes,file_dir = img_save_path,save_dir=save_dir) show_pic(auged_img, auged_bboxes,labels) # 强化后的图
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#测试label是否正确 import shutil need_aug_num = 1 dataAug = DataAugmentForObjectDetection() source_pic_root_path = '/home/xbw/wurenting/dataset/' source_xml_root_path = '/home/xbw/wurenting/labels/' for parent, _, files in os.walk(source_pic_root_path): for file in files: cnt = 0 while cnt < need_aug_num: pic_path = os.path.join(parent, file) xml_path = os.path.join(source_xml_root_path, file[:-4]+'.xml') coords = parse_xml(xml_path) #解析得到box信息，格式为[[x_min,y_min,x_max,y_max,name]] coordss = [coord[:4] for coord in coords] labels = [coord[4] for coord in coords] img = cv2.imread(pic_path) show_pic(img, coordss,labels) # 原图 cnt += 1 cv2.destroyAllWindows()
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使用imgaug进行数据增强
import imgaug as ia from imgaug import augmenters as iaa seq = iaa.Sequential([ iaa.Fliplr(0.5), # 0.5的概率水平翻转 iaa.Crop(percent=(0, 0.1)), # random crops #sigma在0~0.5间随机高斯模糊，且每张图纸生效的概率是0.5 iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 0.5)) ), # 增大或减小每张图像的对比度 iaa.ContrastNormalization((0.75, 1.5)), # 高斯噪点 iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # 给每个像素乘上0.8-1.2之间的数来使图片变暗或变亮 #20%的图片在每个channel上乘以不同的因子 iaa.Multiply((0.8, 1.2), per_channel=0.2), # 对每张图片进行仿射变换，包括缩放、平移、旋转、修剪等 iaa.Affine( scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, rotate=(-25, 25), shear=(-8, 8) ) ], random_order=True) # 随机应用以上的图片增强方法
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配合生成器，写出数据增强的生成器，具体就是使用augment_images（np.nparray）方法即可
def data_generator(root_path,batch_size): while True: class_nums = len(glob.glob('/home/xbw/siamese/classes/*')) x_batchs_1 = [] x_batchs_2 = [] y_batchs = [] for i in range(class_nums): #文件夹要以0开头 jpg_nums = len(glob.glob(root_path + str(i) + '/' +'*.jpg')) path_list = os.listdir(root_path + str(i)) random.shuffle(path_list) for j in range(jpg_nums-1): if len(y_batchs)>=batch_size: yield [[seq.augment_images(np.array(x_batchs_1))/255,seq.augment_images(np.array(x_batchs_2))/255],np.array(y_batchs)] x_batchs_1 = [] x_batchs_2 = [] y_batchs = [] #增加相同的 jpg_path1 = root_path + str(i) + '/'+ path_list[j] z1 = cv2.imread(jpg_path1) z1 = cv2.cvtColor(z1, cv2.COLOR_BGR2RGB) z1 = cv2.resize(z1,(32,32),interpolation=cv2.INTER_AREA) jpg_path2 = root_path + str(i) + '/'+ path_list[j+1] z2 = cv2.imread(jpg_path2) z2 = cv2.cvtColor(z2, cv2.COLOR_BGR2RGB) z2 = cv2.resize(z2,(32,32),interpolation=cv2.INTER_AREA) x_batchs_1.append(z1) x_batchs_2.append(z2) y_batchs.append(1) #增加不同的 inc = random.randrange(1,class_nums) random_num = (i + inc)%class_nums temp_list = os.listdir(root_path + str(random_num)) random.shuffle(temp_list) jpg_path3 = root_path + str(random_num) + '/'+ temp_list[0] z3 = cv2.imread(jpg_path3) z3 = cv2.cvtColor(z3, cv2.COLOR_BGR2RGB) z3 = cv2.resize(z3,(32,32),interpolation=cv2.INTER_AREA) x_batchs_1.append(z1) x_batchs_2.append(z3) y_batchs.append(0)
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