网上很少有mosaic图像增强后的 图片与XML，本文直接提供你所需要的代码，可视化和具体修改见链接。
from PIL import Image, ImageDraw
import numpy as np
from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
import math
import xml.dom.minidom
import numpy as np
import sys
import cv2
import glob
import os
import xml.dom.minidom
import argparse
import random
from xml.etree.ElementTree import ElementTree,Element,parse
from xml.dom import minidom
import xml.etree.ElementTree as ET
import xml.dom.minidom as DOC
image_path = “你的图片路径”
path_origin_xml = “你的XML路径”
out_root_path （img或者XML 自己定义）= “输出路径”
def readxml(image_file):
# file_path = os.path.join(xml_path, xmlFile)
# dom = parse(xml_path)
# root = dom.getroot()
#xmls_list = os.listdir(path_origin_xml)
#nums = len(xmls_list)
#coords = list() #for i in range(nums): #xml_path = os.path.join(path_origin_xml, image_file.replace('jpg', 'xml')) if image_file.split(".")[1] == 'png': xml_path = os.path.join(path_origin_xml, image_file.replace('png', 'xml')) else: xml_path = os.path.join(path_origin_xml, image_file.replace('jpg', 'xml')) root = ET.parse(xml_path).getroot() bb = [] for obj in root.iter('object'): # 获取object节点中的name子节点 bbox = obj.find('bndbox') xmin = int(float(bbox.find('xmin').text.strip())) ymin = int(float(bbox.find('ymin').text.strip())) xmax = int(float(bbox.find('xmax').text.strip())) ymax = int(float(bbox.find('ymax').text.strip()))
####-----------------非常重要----------------######## 下面的1需要修改 具体修改见链接
bb.append(np.array([xmin, ymin, xmax, ymax, 1]))
return np.array(bb)
def CreatXml(imgPath, results, xmlPath):
img = cv2.imread(imgPath)
imgSize = img.shape
imgName = imgPath.split(‘/’)[-1]
impl = xml.dom.minidom.getDOMImplementation() dom = impl.createDocument(None, 'annotation', None) root = dom.documentElement folder = dom.createElement('folder') root.appendChild(folder) name_folfer = dom.createTextNode('Unknown') folder.appendChild(name_folfer) filename = dom.createElement('filename') root.appendChild(filename) name_img = dom.createTextNode(os.path.splitext(imgName)[0]) filename.appendChild(name_img) filepath = dom.createElement('path') root.appendChild(filepath) path_img = dom.createTextNode(imgPath) filepath.appendChild(path_img) source = dom.createElement('source') root.appendChild(source) database = dom.createElement('database') database_name = dom.createTextNode('Unknown') database.appendChild(database_name) source.appendChild(database) img_size = dom.createElement('size') root.appendChild(img_size) width = dom.createElement('width') width_num = dom.createTextNode(str(int(imgSize[1]))) width.appendChild(width_num) height = dom.createElement('height') height_num = dom.createTextNode(str(int(imgSize[0]))) height.appendChild(height_num) depth = dom.createElement('depth') depth_num = dom.createTextNode(str(int(imgSize[2]))) depth.appendChild(depth_num) img_size.appendChild(width) img_size.appendChild(height) img_size.appendChild(depth) segmented = dom.createElement('segmented') root.appendChild(segmented) segmented_num = dom.createTextNode('0') segmented.appendChild(segmented_num) for i in range(len(results)): img_object = dom.createElement('object') root.appendChild(img_object) label_name = dom.createElement('name') namecls = dom.createTextNode(results[i]['name']) label_name.appendChild(namecls) pose = dom.createElement('pose') pose_name = dom.createTextNode('Unspecified') pose.appendChild(pose_name) truncated = dom.createElement('truncated') truncated_num = dom.createTextNode('0') truncated.appendChild(truncated_num) difficult = dom.createElement('difficult') difficult_num = dom.createTextNode('0') difficult.appendChild(difficult_num) bndbox = dom.createElement('bndbox') xmin = dom.createElement('xmin') xmin_num = dom.createTextNode(str(int(results[i]['bbox'][0]))) xmin.appendChild(xmin_num) ymin = dom.createElement('ymin') ymin_num = dom.createTextNode(str(int(results[i]['bbox'][1]))) ymin.appendChild(ymin_num) xmax = dom.createElement('xmax') xmax_num = dom.createTextNode(str(int(results[i]['bbox'][2]))) xmax.appendChild(xmax_num) ymax = dom.createElement('ymax') ymax_num = dom.createTextNode(str(int(results[i]['bbox'][3]))) ymax.appendChild(ymax_num) bndbox.appendChild(xmin) bndbox.appendChild(ymin) bndbox.appendChild(xmax) bndbox.appendChild(ymax) img_object.appendChild(label_name) img_object.appendChild(pose) img_object.appendChild(truncated) img_object.appendChild(difficult) img_object.appendChild(bndbox) f = open(xmlPath, 'w') dom.writexml(f, addindent=' ', newl='\n') f.close()
def get_result(box_data):
classes = [‘object’, ‘scissors’, ‘lighter’, ‘zippooil’, ‘pressure’,
‘slingshot’, ‘handcuffs’, ‘nailpolish’, ‘powerbank’,
‘firecrackers’]
results = []
for obj in box_data:
result = {}
obj = [int(i) for i in obj]
box = obj[:4]
name = classes[obj[-1]]
result[“name”] = name
result[“bbox”] = box
results.append(result)
return results
def rand(a=0, b=1):
return np.random.rand()*(b-a) + a
def merge_bboxes(bboxes, cutx, cuty):
merge_bbox = [] for i in range(len(bboxes)): for box in bboxes[i]: tmp_box = [] x1,y1,x2,y2 = box[0], box[1], box[2], box[3] if i == 0: if y1 > cuty or x1 > cutx: continue if y2 >= cuty and y1 <= cuty: y2 = cuty if y2-y1 < 5: continue if x2 >= cutx and x1 <= cutx: x2 = cutx if x2-x1 < 5: continue if i == 1: if y2 < cuty or x1 > cutx: continue if y2 >= cuty and y1 <= cuty: y1 = cuty if y2-y1 < 5: continue if x2 >= cutx and x1 <= cutx: x2 = cutx if x2-x1 < 5: continue if i == 2: if y2 < cuty or x2 < cutx: continue if y2 >= cuty and y1 <= cuty: y1 = cuty if y2-y1 < 5: continue if x2 >= cutx and x1 <= cutx: x1 = cutx if x2-x1 < 5: continue if i == 3: if y1 > cuty or x2 < cutx: continue if y2 >= cuty and y1 <= cuty: y2 = cuty if y2-y1 < 5: continue if (x2 >= cutx) and (x1 <= cutx): x1 = cutx if x2-x1 < 5: continue tmp_box.append(x1) tmp_box.append(y1) tmp_box.append(x2) tmp_box.append(y2) tmp_box.append(box[-1]) merge_bbox.append(tmp_box) return merge_bbox
def get_random_data(annotation_line, input_shape, random=True, hue=.1, sat=1.5, val=1.5, proc_img=True):
‘’‘random preprocessing for real-time data augmentation’‘’
print(“image:”,annotation_line)
h, w = input_shape
min_offset_x = 0.4
min_offset_y = 0.4
scale_low = 1-min(min_offset_x,min_offset_y)
scale_high = scale_low+0.2
image_datas = [] box_datas = [] index = 0 place_x = [0,0,int(w*min_offset_x),int(w*min_offset_x)] place_y = [0,int(h*min_offset_y),int(w*min_offset_y),0] for line in annotation_line: # 每一行进行分割 #line_content = line.split() # 打开图片 image = Image.open(os.path.join(image_path,line)) image = image.convert("RGB") # 图片的大小 iw, ih = image.size box = readxml(line) # 保存框的位置 #box = np.array([np.array(list(map(int,box.split(',')))) for box in line_content[1:]]) # image.save(str(index)+".jpg") # 是否翻转图片 flip = rand()<.5 if flip and len(box)>0: image = image.transpose(Image.FLIP_LEFT_RIGHT) box[:, [0,2]] = iw - box[:, [2,0]] # 对输入进来的图片进行缩放 new_ar = w/h scale = rand(scale_low, scale_high) if new_ar < 1: nh = int(scale*h) nw = int(nh*new_ar) else: nw = int(scale*w) nh = int(nw/new_ar) image = image.resize((nw,nh), Image.BICUBIC) # 进行色域变换 hue = rand(-hue, hue) sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat) val = rand(1, val) if rand()<.5 else 1/rand(1, val) x = rgb_to_hsv(np.array(image)/255.) x[..., 0] += hue x[..., 0][x[..., 0]>1] -= 1 x[..., 0][x[..., 0]<0] += 1 x[..., 1] *= sat x[..., 2] *= val x[x>1] = 1 x[x<0] = 0 image = hsv_to_rgb(x) image = Image.fromarray((image*255).astype(np.uint8)) # 将图片进行放置，分别对应四张分割图片的位置 dx = place_x[index] dy = place_y[index] new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image_data = np.array(new_image)/255 index = index + 1 box_data = [] # 对box进行重新处理 if len(box)>0: np.random.shuffle(box) box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy box[:, 0:2][box[:, 0:2]<0] = 0 box[:, 2][box[:, 2]>w] = w box[:, 3][box[:, 3]>h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w>1, box_h>1)] box_data = np.zeros((len(box),5)) box_data[:len(box)] = box image_datas.append(image_data) box_datas.append(box_data) img = Image.fromarray((image_data*255).astype(np.uint8)) #for j in range(len(box_data)): # thickness = 3 # left, top, right, bottom = box_data[j][0:4] # draw = ImageDraw.Draw(img) # for i in range(thickness): # draw.rectangle([left + i, top + i, right - i, bottom - i],outline=(255,255,255)) #img.show() # 将图片分割，放在一起 cutx = np.random.randint(int(w*min_offset_x), int(w*(1 - min_offset_x))) cuty = np.random.randint(int(h*min_offset_y), int(h*(1 - min_offset_y))) print("cutx:",cutx) print("cuty:",cuty) print("h:",h) print("w:",w) new_image = np.zeros([h,w,3]) new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :] new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :] new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :] new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :] # 对框进行进一步的处理 new_boxes = merge_bboxes(box_datas, cutx, cuty) return new_image, new_boxes
def normal_(annotation_line, input_shape):
‘’‘random preprocessing for real-time data augmentation’‘’
line = annotation_line.split()
image = Image.open(line[0])
box = np.array([np.array(list(map(int,box.split(‘,’)))) for box in line[1:]])
iw, ih = image.size image = image.transpose(Image.FLIP_LEFT_RIGHT) box[:, [0,2]] = iw - box[:, [2,0]] return image, box
if name == “main”:
lines = [] for filename in os.listdir(os.path.join(image_path, '')): if filename.endswith(".jpg") or filename.endswith(".JPG") or filename.endswith(".png"): lines.append(filename) print(lines) list1 = list(range(0,len(lines))) print("list1:",list1) for j in range(5000): #a = np.random.randint(0,len(lines)) #line = lines[a:a+4] tem = [] for i in random.sample(list1, 4): tem.append(lines[i]) line = tem #try: image_data, box_data = get_random_data(line,[1000,1000]) img = Image.fromarray((image_data*255).astype(np.uint8)) img_path = "你的保存图像路径" % j img.save(img_path) results = get_result(box_data) xml_path = "你的保存XML路径l" % j CreatXml(img_path, results, xml_path) #except: #continue
