545 lines
21 KiB
Python
545 lines
21 KiB
Python
# -*- coding=utf-8 -*-
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import time
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import random
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import copy
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import cv2
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import os
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import math
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import numpy as np
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from skimage.util import random_noise
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from lxml import etree, objectify
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import xml.etree.ElementTree as ET
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import argparse
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# 显示图片
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def show_pic(img, bboxes=None):
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'''
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输入:
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img:图像array
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bboxes:图像的所有boudning box list, 格式为[[x_min, y_min, x_max, y_max]....]
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names:每个box对应的名称
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'''
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for i in range(len(bboxes)):
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bbox = bboxes[i]
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x_min = bbox[0]
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y_min = bbox[1]
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x_max = bbox[2]
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y_max = bbox[3]
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cv2.rectangle(img, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 3)
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cv2.namedWindow('pic', 0) # 1表示原图
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cv2.moveWindow('pic', 0, 0)
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cv2.resizeWindow('pic', 640, 640) # 可视化的图片大小
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cv2.imshow('pic', img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# 图像均为cv2读取
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class DataAugmentForObjectDetection():
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def __init__(self, rotation_rate=0.5, max_rotation_angle=5,
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crop_rate=0.5, shift_rate=0.5, change_light_rate=0.5,
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add_noise_rate=0.5, flip_rate=0.5,
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cutout_rate=0.5, cut_out_length=50, cut_out_holes=1, cut_out_threshold=0.5,
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is_addNoise=True, is_changeLight=True, is_cutout=True, is_rotate_img_bbox=True,
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is_crop_img_bboxes=True, is_shift_pic_bboxes=True, is_filp_pic_bboxes=True):
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# 配置各个操作的属性
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self.rotation_rate = rotation_rate
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self.max_rotation_angle = max_rotation_angle
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self.crop_rate = crop_rate
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self.shift_rate = shift_rate
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self.change_light_rate = change_light_rate
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self.add_noise_rate = add_noise_rate
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self.flip_rate = flip_rate
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self.cutout_rate = cutout_rate
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self.cut_out_length = cut_out_length
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self.cut_out_holes = cut_out_holes
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self.cut_out_threshold = cut_out_threshold
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# 是否使用某种增强方式
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self.is_addNoise = is_addNoise
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self.is_changeLight = is_changeLight
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self.is_cutout = is_cutout
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self.is_rotate_img_bbox = is_rotate_img_bbox
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self.is_crop_img_bboxes = is_crop_img_bboxes
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self.is_shift_pic_bboxes = is_shift_pic_bboxes
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self.is_filp_pic_bboxes = is_filp_pic_bboxes
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# ----1.加噪声---- #
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def _addNoise(self, img):
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'''
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输入:
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img:图像array
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输出:
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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
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'''
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# return cv2.GaussianBlur(img, (11, 11), 0)
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return random_noise(img, mode='gaussian', seed=int(time.time()), clip=True) * 255
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# ---2.调整亮度--- #
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def _changeLight(self, img):
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alpha = random.uniform(0.35, 1)
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blank = np.zeros(img.shape, img.dtype)
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return cv2.addWeighted(img, alpha, blank, 1 - alpha, 0)
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# ---3.cutout--- #
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def _cutout(self, img, bboxes, length=100, n_holes=1, threshold=0.5):
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'''
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原版本：https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py
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Randomly mask out one or more patches from an image.
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Args:
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img : a 3D numpy array,(h,w,c)
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bboxes : 框的坐标
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n_holes (int): Number of patches to cut out of each image.
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length (int): The length (in pixels) of each square patch.
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'''
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def cal_iou(boxA, boxB):
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'''
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boxA, boxB为两个框，返回iou
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boxB为bouding box
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'''
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# determine the (x, y)-coordinates of the intersection rectangle
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xA = max(boxA[0], boxB[0])
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yA = max(boxA[1], boxB[1])
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xB = min(boxA[2], boxB[2])
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yB = min(boxA[3], boxB[3])
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if xB <= xA or yB <= yA:
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return 0.0
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# compute the area of intersection rectangle
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interArea = (xB - xA + 1) * (yB - yA + 1)
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# compute the area of both the prediction and ground-truth
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# rectangles
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boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
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boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
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iou = interArea / float(boxBArea)
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return iou
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# 得到h和w
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if img.ndim == 3:
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h, w, c = img.shape
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else:
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_, h, w, c = img.shape
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mask = np.ones((h, w, c), np.float32)
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for n in range(n_holes):
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chongdie = True # 看切割的区域是否与box重叠太多
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while chongdie:
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y = np.random.randint(h)
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x = np.random.randint(w)
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y1 = np.clip(y - length // 2, 0,
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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
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y2 = np.clip(y + length // 2, 0, h)
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x1 = np.clip(x - length // 2, 0, w)
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x2 = np.clip(x + length // 2, 0, w)
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chongdie = False
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for box in bboxes:
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if cal_iou([x1, y1, x2, y2], box) > threshold:
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chongdie = True
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break
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mask[y1: y2, x1: x2, :] = 0.
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img = img * mask
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return img
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# ---4.旋转--- #
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def _rotate_img_bbox(self, img, bboxes, angle=5, scale=1.):
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'''
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参考:https://blog.csdn.net/u014540717/article/details/53301195crop_rate
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输入:
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img:图像array,(h,w,c)
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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
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angle:旋转角度
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scale:默认1
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输出:
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rot_img:旋转后的图像array
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rot_bboxes:旋转后的boundingbox坐标list
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'''
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# 旋转图像
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w = img.shape[1]
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h = img.shape[0]
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# 角度变弧度
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rangle = np.deg2rad(angle) # angle in radians
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# now calculate new image width and height
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nw = (abs(np.sin(rangle) * h) + abs(np.cos(rangle) * w)) * scale
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nh = (abs(np.cos(rangle) * h) + abs(np.sin(rangle) * w)) * scale
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# ask OpenCV for the rotation matrix
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rot_mat = cv2.getRotationMatrix2D((nw * 0.5, nh * 0.5), angle, scale)
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# calculate the move from the old center to the new center combined
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# with the rotation
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rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))
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# the move only affects the translation, so update the translation
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rot_mat[0, 2] += rot_move[0]
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rot_mat[1, 2] += rot_move[1]
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# 仿射变换
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rot_img = cv2.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
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# 矫正bbox坐标
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# rot_mat是最终的旋转矩阵
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# 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
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rot_bboxes = list()
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for bbox in bboxes:
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xmin = bbox[0]
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ymin = bbox[1]
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xmax = bbox[2]
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ymax = bbox[3]
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point1 = np.dot(rot_mat, np.array([(xmin + xmax) / 2, ymin, 1]))
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point2 = np.dot(rot_mat, np.array([xmax, (ymin + ymax) / 2, 1]))
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point3 = np.dot(rot_mat, np.array([(xmin + xmax) / 2, ymax, 1]))
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point4 = np.dot(rot_mat, np.array([xmin, (ymin + ymax) / 2, 1]))
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# 合并np.array
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concat = np.vstack((point1, point2, point3, point4))
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# 改变array类型
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concat = concat.astype(np.int32)
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# 得到旋转后的坐标
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rx, ry, rw, rh = cv2.boundingRect(concat)
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rx_min = rx
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ry_min = ry
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rx_max = rx + rw
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ry_max = ry + rh
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# 加入list中
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rot_bboxes.append([rx_min, ry_min, rx_max, ry_max])
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return rot_img, rot_bboxes
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# ---5.裁剪--- #
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def _crop_img_bboxes(self, img, bboxes):
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'''
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裁剪后的图片要包含所有的框
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输入:
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img:图像array
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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
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输出:
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crop_img:裁剪后的图像array
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crop_bboxes:裁剪后的bounding box的坐标list
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'''
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# 裁剪图像
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w = img.shape[1]
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h = img.shape[0]
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x_min = w # 裁剪后的包含所有目标框的最小的框
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x_max = 0
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y_min = h
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y_max = 0
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for bbox in bboxes:
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x_min = min(x_min, bbox[0])
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y_min = min(y_min, bbox[1])
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x_max = max(x_max, bbox[2])
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y_max = max(y_max, bbox[3])
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d_to_left = x_min # 包含所有目标框的最小框到左边的距离
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d_to_right = w - x_max # 包含所有目标框的最小框到右边的距离
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d_to_top = y_min # 包含所有目标框的最小框到顶端的距离
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d_to_bottom = h - y_max # 包含所有目标框的最小框到底部的距离
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# 随机扩展这个最小框
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crop_x_min = int(x_min - random.uniform(0, d_to_left))
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crop_y_min = int(y_min - random.uniform(0, d_to_top))
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crop_x_max = int(x_max + random.uniform(0, d_to_right))
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crop_y_max = int(y_max + random.uniform(0, d_to_bottom))
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# 随机扩展这个最小框 , 防止别裁的太小
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# crop_x_min = int(x_min - random.uniform(d_to_left//2, d_to_left))
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# crop_y_min = int(y_min - random.uniform(d_to_top//2, d_to_top))
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# crop_x_max = int(x_max + random.uniform(d_to_right//2, d_to_right))
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# crop_y_max = int(y_max + random.uniform(d_to_bottom//2, d_to_bottom))
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# 确保不要越界
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crop_x_min = max(0, crop_x_min)
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crop_y_min = max(0, crop_y_min)
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crop_x_max = min(w, crop_x_max)
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crop_y_max = min(h, crop_y_max)
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crop_img = img[crop_y_min:crop_y_max, crop_x_min:crop_x_max]
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# 裁剪boundingbox
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# 裁剪后的boundingbox坐标计算
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crop_bboxes = list()
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for bbox in bboxes:
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crop_bboxes.append([bbox[0] - crop_x_min, bbox[1] - crop_y_min, bbox[2] - crop_x_min, bbox[3] - crop_y_min])
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return crop_img, crop_bboxes
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# ---6.平移--- #
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def _shift_pic_bboxes(self, img, bboxes):
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'''
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平移后的图片要包含所有的框
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输入:
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img:图像array
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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
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输出:
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shift_img:平移后的图像array
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shift_bboxes:平移后的bounding box的坐标list
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'''
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# 平移图像
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w = img.shape[1]
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h = img.shape[0]
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x_min = w # 裁剪后的包含所有目标框的最小的框
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x_max = 0
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y_min = h
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y_max = 0
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for bbox in bboxes:
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x_min = min(x_min, bbox[0])
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y_min = min(y_min, bbox[1])
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x_max = max(x_max, bbox[2])
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y_max = max(y_max, bbox[3])
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d_to_left = x_min # 包含所有目标框的最大左移动距离
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d_to_right = w - x_max # 包含所有目标框的最大右移动距离
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d_to_top = y_min # 包含所有目标框的最大上移动距离
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d_to_bottom = h - y_max # 包含所有目标框的最大下移动距离
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x = random.uniform(-(d_to_left - 1) / 3, (d_to_right - 1) / 3)
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y = random.uniform(-(d_to_top - 1) / 3, (d_to_bottom - 1) / 3)
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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
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shift_img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))
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# 平移boundingbox
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shift_bboxes = list()
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for bbox in bboxes:
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shift_bboxes.append([bbox[0] + x, bbox[1] + y, bbox[2] + x, bbox[3] + y])
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return shift_img, shift_bboxes
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# ---7.镜像--- #
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def _filp_pic_bboxes(self, img, bboxes):
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'''
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平移后的图片要包含所有的框
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输入:
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img:图像array
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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
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输出:
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flip_img:平移后的图像array
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flip_bboxes:平移后的bounding box的坐标list
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'''
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# 翻转图像
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flip_img = copy.deepcopy(img)
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h, w, _ = img.shape
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sed = random.random()
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if 0 < sed < 0.33: # 0.33的概率水平翻转，0.33的概率垂直翻转,0.33是对角反转
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flip_img = cv2.flip(flip_img, 0) # _flip_x
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inver = 0
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elif 0.33 < sed < 0.66:
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flip_img = cv2.flip(flip_img, 1) # _flip_y
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inver = 1
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else:
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flip_img = cv2.flip(flip_img, -1) # flip_x_y
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inver = -1
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# 调整boundingbox
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flip_bboxes = list()
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for box in bboxes:
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x_min = box[0]
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y_min = box[1]
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x_max = box[2]
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y_max = box[3]
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if inver == 0:
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# 0：垂直翻转
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flip_bboxes.append([x_min, h - y_max, x_max, h - y_min])
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elif inver == 1:
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# 1：水平翻转
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flip_bboxes.append([w - x_max, y_min, w - x_min, y_max])
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elif inver == -1:
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# -1：水平垂直翻转
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flip_bboxes.append([w - x_max, h - y_max, w - x_min, h - y_min])
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return flip_img, flip_bboxes
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# 图像增强方法
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def dataAugment(self, img, bboxes):
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'''
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图像增强
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输入:
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img:图像array
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bboxes:该图像的所有框坐标
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输出:
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img:增强后的图像
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bboxes:增强后图片对应的box
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'''
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change_num = 0 # 改变的次数
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# print('------')
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while change_num < 1: # 默认至少有一种数据增强生效
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if self.is_rotate_img_bbox:
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if random.random() > self.rotation_rate: # 旋转
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change_num += 1
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angle = random.uniform(-self.max_rotation_angle, self.max_rotation_angle)
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scale = random.uniform(0.7, 0.8)
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img, bboxes = self._rotate_img_bbox(img, bboxes, angle, scale)
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if self.is_shift_pic_bboxes:
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if random.random() < self.shift_rate: # 平移
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change_num += 1
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img, bboxes = self._shift_pic_bboxes(img, bboxes)
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if self.is_changeLight:
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if random.random() > self.change_light_rate: # 改变亮度
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change_num += 1
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img = self._changeLight(img)
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if self.is_addNoise:
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if random.random() < self.add_noise_rate: # 加噪声
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change_num += 1
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img = self._addNoise(img)
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if self.is_cutout:
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if random.random() < self.cutout_rate: # cutout
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change_num += 1
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img = self._cutout(img, bboxes, length=self.cut_out_length, n_holes=self.cut_out_holes,
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threshold=self.cut_out_threshold)
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if self.is_filp_pic_bboxes:
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if random.random() < self.flip_rate: # 翻转
|
||
change_num += 1
|
||
img, bboxes = self._filp_pic_bboxes(img, bboxes)
|
||
|
||
return img, bboxes
|
||
|
||
|
||
# xml解析工具
|
||
class ToolHelper():
|
||
# 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(self, 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(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
|
||
|
||
# 保存图片结果
|
||
def save_img(self, file_name, save_folder, img):
|
||
cv2.imwrite(os.path.join(save_folder, file_name), img)
|
||
|
||
# 保持xml结果
|
||
def save_xml(self, file_name, save_folder, img_info, height, width, channel, bboxs_info):
|
||
'''
|
||
:param file_name:文件名
|
||
:param save_folder:#保存的xml文件的结果
|
||
:param height:图片的信息
|
||
:param width:图片的宽度
|
||
:param channel:通道
|
||
:return:
|
||
'''
|
||
folder_name, img_name = img_info # 得到图片的信息
|
||
|
||
E = objectify.ElementMaker(annotate=False)
|
||
|
||
anno_tree = E.annotation(
|
||
E.folder(folder_name),
|
||
E.filename(img_name),
|
||
E.path(os.path.join(folder_name, img_name)),
|
||
E.source(
|
||
E.database('Unknown'),
|
||
),
|
||
E.size(
|
||
E.width(width),
|
||
E.height(height),
|
||
E.depth(channel)
|
||
),
|
||
E.segmented(0),
|
||
)
|
||
|
||
labels, bboxs = bboxs_info # 得到边框和标签信息
|
||
for label, box in zip(labels, bboxs):
|
||
anno_tree.append(
|
||
E.object(
|
||
E.name(label),
|
||
E.pose('Unspecified'),
|
||
E.truncated('0'),
|
||
E.difficult('0'),
|
||
E.bndbox(
|
||
E.xmin(box[0]),
|
||
E.ymin(box[1]),
|
||
E.xmax(box[2]),
|
||
E.ymax(box[3])
|
||
)
|
||
))
|
||
|
||
etree.ElementTree(anno_tree).write(os.path.join(save_folder, file_name), pretty_print=True)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
|
||
need_aug_num = 5 # 每张图片需要增强的次数
|
||
|
||
is_endwidth_dot = True # 文件是否以.jpg或者png结尾
|
||
|
||
dataAug = DataAugmentForObjectDetection() # 数据增强工具类
|
||
|
||
toolhelper = ToolHelper() # 工具
|
||
|
||
# 获取相关参数
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument('--source_img_path', type=str, default=r'D:\1BNS_projects\yolo_safehat_num_v8\datasets\2m')
|
||
parser.add_argument('--source_xml_path', type=str, default=r'D:\1BNS_projects\yolo_safehat_num_v8\datasets\labels')
|
||
parser.add_argument('--save_img_path', type=str, default=r'D:\1BNS_projects\yolo_safehat_num_v8\datasets\2m1')
|
||
parser.add_argument('--save_xml_path', type=str, default=r'D:\1BNS_projects\yolo_safehat_num_v8\datasets\labels1')
|
||
args = parser.parse_args()
|
||
source_img_path = args.source_img_path # 图片原始位置
|
||
source_xml_path = args.source_xml_path # xml的原始位置
|
||
|
||
save_img_path = args.save_img_path # 图片增强结果保存文件
|
||
save_xml_path = args.save_xml_path # xml增强结果保存文件
|
||
|
||
# 如果保存文件夹不存在就创建
|
||
if not os.path.exists(save_img_path):
|
||
os.mkdir(save_img_path)
|
||
|
||
if not os.path.exists(save_xml_path):
|
||
os.mkdir(save_xml_path)
|
||
|
||
for parent, _, files in os.walk(source_img_path):
|
||
files.sort()
|
||
for file in files:
|
||
cnt = 0
|
||
pic_path = os.path.join(parent, file)
|
||
xml_path = os.path.join(source_xml_path, file[:-4] + '.xml')
|
||
values = toolhelper.parse_xml(xml_path) # 解析得到box信息，格式为[[x_min,y_min,x_max,y_max,name]]
|
||
coords = [v[:4] for v in values] # 得到框
|
||
labels = [v[-1] for v in values] # 对象的标签
|
||
|
||
# 如果图片是有后缀的
|
||
if is_endwidth_dot:
|
||
# 找到文件的最后名字
|
||
dot_index = file.rfind('.')
|
||
_file_prefix = file[:dot_index] # 文件名的前缀
|
||
_file_suffix = file[dot_index:] # 文件名的后缀
|
||
img = cv2.imread(pic_path)
|
||
|
||
# show_pic(img, coords) # 显示原图
|
||
while cnt < need_aug_num: # 继续增强
|
||
auged_img, auged_bboxes = dataAug.dataAugment(img, coords)
|
||
auged_bboxes_int = np.array(auged_bboxes).astype(np.int32)
|
||
height, width, channel = auged_img.shape # 得到图片的属性
|
||
img_name = '{}_{}{}'.format(_file_prefix, cnt + 1, _file_suffix) # 图片保存的信息
|
||
toolhelper.save_img(img_name, save_img_path,
|
||
auged_img) # 保存增强图片
|
||
|
||
toolhelper.save_xml('{}_{}.xml'.format(_file_prefix, cnt + 1),
|
||
save_xml_path, (save_img_path, img_name), height, width, channel,
|
||
(labels, auged_bboxes_int)) # 保存xml文件
|
||
# show_pic(auged_img, auged_bboxes) # 强化后的图
|
||
print(img_name)
|
||
cnt += 1 # 继续增强下一张
|