Tensorflow + ResNet101 + fasterRcnn 训练自己的模型 数据（一）
一、数据准备：
1、PASCAL VOC数据集格式
2、数据扩充：做了旋转【0， 90，180，270】（备注：这里可以不做那么多许旋转，fasterrcnn在训练的时候要做图片的镜像变换）、降采样
降采样：
import os
import cv2
import numpy as np
try:
import xml.etree.cElementTree as ET
except ImportEroor:
import xml.etree.ElementTree as ET
import copy,random
def _image_downsampling(folder,Savefolder,scale=0.5):
JPEGImages = os.path.join(folder, 'JPEGImages')
saveJPEGImages = os.path.join(Savefolder, 'JPEGImages')
if not os.path.exists(saveJPEGImages):
os.makedirs(saveJPEGImages)
for imgfile in os.listdir(JPEGImages):
print(imgfile)
img = cv2.imread(os.path.join(JPEGImages,imgfile))
img_downsampled = cv2.resize(img,(int(scale*img.shape[1]),int(scale*img.shape[0])))
new_name = imgfile[:-4] + '_downsampled' + str(scale) + '.jpg'
cv2.imwrite(os.path.join(saveJPEGImages,new_name),img_downsampled)
# cv2.imshow('name',downsampled_img)
# key = cv2.waitKey(5000)
# if key == 27:
# quit()
# cv2.destroyWindow('name')
def _rewrite_txt(folder,Savefolder,scale):
Annotations = os.path.join(folder, 'Annotations/labels')
saveAnnotations = os.path.join(Savefolder, 'Annotations/labels')
if not os.path.exists(saveAnnotations):
os.makedirs(saveAnnotations)
for txtfile in os.listdir(Annotations):
print(txtfile)
ff = open(os.path.join(Annotations,txtfile),'r')
f = open(os.path.join(saveAnnotations,txtfile[:-4] + '_downsampled' + str(scale) + '.txt'),'at')
for line in ff.readlines():
[filename, cls, x1, y1, x2, y2] = line.strip().split(' ')
x1 = int(int(x1) * scale)
y1 = int(int(y1) * scale)
x2 = int(int(x2) * scale)
y2 = int(int(y2) * scale)
new_name = filename[:-4] + '_downsampled' + str(scale) + '.jpg'
nline = new_name + ' ' + cls + ' ' + str(x1) + ' ' + str(y1) + ' ' + str(x2) + ' ' + str(y2) + '\n'
f.write(nline)
f.close()
ff.close()
def _rewrite_xml(folder,Savefolder,scale):
Annotations = os.path.join(folder, 'Annotations')
saveAnnotations = os.path.join(Savefolder, 'Annotations')
if not os.path.exists(saveAnnotations):
os.makedirs(saveAnnotations)
for xmlfile in os.listdir(Annotations):
print(xmlfile)
# if xmlfile[:-4] != '.xml':
# continue
new_name = xmlfile[:-4] + '_downsampled' + str(scale) + '.jpg'
tree = ET.parse(os.path.join(Annotations,xmlfile))
root = tree.getroot()
for obj in root.findall('object'):
bndbox = obj.find('bndbox')
bndbox[0].text = str(int(float(bndbox[0].text)*scale))
bndbox[1].text = str(int(float(bndbox[1].text)*scale))
bndbox[2].text = str(int(float(bndbox[2].text)*scale))
bndbox[3].text = str(int(float(bndbox[3].text)*scale))
size = tree.find('size')
height = size.find('height')
width = size.find('width')
height.text = str(int(float(height.text)*scale))
width.text = str(int(float(width.text)*scale))
filename = tree.find('filename')
filename.text = new_name
print(new_name)
tree.write(os.path.join(saveAnnotations,new_name[:-4]+'.xml'))
def _rewrite_trainval_and_test(folder,Savefolder,scales):
if not os.path.exists(os.path.join(Savefolder,'ImageSets', 'Main')):
os.makedirs(os.path.join(Savefolder,'ImageSets', 'Main'))
# folder = '../enhancement/' + cate + '/'
# Savefolder = '../downsampled/'+ cate + '_downsampled'
trainval = open(folder + 'ImageSets/Main/train.txt','r')
print 'trainval:', trainval
test = open(folder + 'ImageSets/Main/val.txt','r')
print 'test:',test
save_trainval = open(Savefolder + '/ImageSets/Main/train.txt','a')
print 'save_trainval:', save_trainval
save_test = open(Savefolder + '/ImageSets/Main/val.txt','a')
print 'save_test:', save_test
for line in trainval.readlines():
for i in range(len(scales)):
nline = line.strip() + '_downsampled' + str(i)
save_trainval.write(nline + '\n')
save_trainval.close()
for line in test.readlines():
for i in range(len(scales)):
nline = line.strip() + '_downsampled' + str(i)
save_test.write(nline + '\n')
save_test.close()
def main(folder,scale = 0.5):
#images
_image_downsampling(folder,Savefolder,scale)
#annotations
if annotations_file is 'txt':
_rewrite_txt(folder,Savefolder,scale)
elif annotations_file is 'xml':
_rewrite_xml(folder,Savefolder,scale)
#trainval,test
_rewrite_trainval_and_test(folder,Savefolder,[scale])
def main2():
JPEGImages = os.path.join(folder, 'JPEGImages')
saveJPEGImages = os.path.join(Savefolder, 'JPEGImages')
if not os.path.exists(saveJPEGImages):
os.makedirs(saveJPEGImages)
Annotations = os.path.join(folder, 'Annotations')
saveAnnotations = os.path.join(Savefolder, 'Annotations')
if not os.path.exists(saveAnnotations):
os.makedirs(saveAnnotations)
scales = None
for imgfile in os.listdir(JPEGImages):
#images
img = cv2.imread(os.path.join(JPEGImages,imgfile))
scales = [random.uniform(2,15)*0.1 for _ in range(2)]
# scales = [random.uniform(5,10)*0.1,random.uniform(10,15)]
for i,scale in enumerate(scales):
img_downsampled = cv2.resize(img,(int(scale*img.shape[1]),int(scale*img.shape[0])))
new_name = imgfile[:-4] + '_downsampled' + str(i)+'.jpg'
print(new_name)
cv2.imwrite(os.path.join(saveJPEGImages,new_name),img_downsampled)
#annotations
xmlfile = imgfile[:-4] + '.xml'
tree = ET.parse(os.path.join(Annotations,xmlfile))
root = tree.getroot()
for obj in root.findall('object'):
bndbox = obj.find('bndbox')
xmin = bndbox.find('xmin').text
ymin = bndbox.find('ymin').text
xmax = bndbox.find('xmax').text
ymax = bndbox.find('ymax').text
bndbox.find('xmin').text = str(int(int(xmin)*scale))
bndbox.find('ymin').text = str(int(int(ymin)*scale))
bndbox.find('xmax').text = str(int(int(xmax)*scale))
bndbox.find('ymax').text = str(int(int(ymax)*scale))
assert(((int(xmax) - int(xmin))*scale)>0)
assert(((int(ymax) - int(ymin))*scale)>0)
filename = tree.find('filename')
filename.text = new_name
size = tree.find('size')
height = size.find('height')
width = size.find('width')
height.text = str(int(float(height.text)*scale))
width.text = str(int(float(width.text)*scale))
tree.write(os.path.join(saveAnnotations,new_name[:-4]+'.xml'))
#trainval,test
_rewrite_trainval_and_test(folder,Savefolder,scales)
if __name__=='__main__':
global annotations_file,folder, Savefolder
annotations_file = 'xml'#xml
catelist = ['hydropower', 'thermalpower', 'tower', 'windpower']
for cate in catelist:
folder = '../enhancement/' + cate + '/'
Savefolder = '../downsampled/'+ cate + '_downsampled'
main2()
# _rewrite_trainval_and_test(folder,Savefolder,[0,0])
旋转：
import cv2
import os
import numpy as np
import math
import copy
try:
import xml.etree.cElementTree as ET
except ImportError:
import xml.etree.ElementTree as ET
def _write_(f,ff):
for lines in ff.readlines():
for angle in rotation_angle:
line1 = lines.strip()+'_rotated_' + str(angle)
# line2 = lines.strip()+'_rotated_' + str(angle) + '_mirror'
f.write(line1+'\n')
# f.write(line2+'\n')
f.close()
ff.close()
def rotate_about_center(src, angle, scale=1.):
w = src.shape[1]
h = src.shape[0]
rangle = np.deg2rad(angle) #angle in radians
nw = (abs(np.sin(rangle)*h)+abs(np.cos(rangle)*w))*scale
nh = (abs(np.cos(rangle)*h)+abs(np.sin(rangle)*w))*scale
rot_mat = cv2.getRotationMatrix2D((nw*0.5,nh*0.5), angle, scale)# rotate with center
rot_move = np.dot(rot_mat,np.array([(nw-w)*0.5,(nh-h)*0.5,0]))
rot_mat[0,2] += rot_move[0]
rot_mat[1,2] += rot_move[1]
return cv2.warpAffine(src,rot_mat,(int(math.ceil(nw)),int(math.ceil(nh))),flags = cv2.INTER_LANCZOS4)
def enhancement_using_rotation(ImagePath,AnnotationsPath):
print '============'
print ImagePath
for imgfile in os.listdir(ImagePath):
print imgfile[:-4]
if not os.path.isfile(os.path.join(AnnotationsPath,imgfile[:-4]+'.xml')):
continues
img = cv2.imread(os.path.join(ImagePath,imgfile))
#rotation
for angle in rotation_angle:
new_name = imgfile[:-4] + '_rotated_'+ str(angle) + '.jpg'
# print '\nnew JPEGImage:', new_name
rotate_img = rotate_about_center(img,angle)
cv2.imwrite(os.path.join(ImageSavePath,new_name),rotate_img)
center_x = img.shape[1]/2
center_y = img.shape[0]/2
new_center_x = rotate_img.shape[1]/2
new_center_y = rotate_img.shape[0]/2
if annotation_file is 'txt':
ff = open(os.path.join(AnnotationsPath,'labels',imgfile[:-4]+'.txt'),'r')
f = open(os.path.join(AnnotationsSavePath,'labels',new_name[:-4] +'.txt'),'a')
for line in ff.readlines():
[filename, cls, x1,y1,x2,y2,t] = line.split(' ')
final_x1, final_y1, final_x2, final_y2 = \
_rotated_location(x1,y1,x2,y2,center_x,center_y,new_center_x,new_center_y)
assert(final_y2-final_y1>0) & (final_x2-final_x1>0)
# print 'rotated:',final_x1, final_y1, final_x2, final_y2
if ifshow == 1:
cv2.rectangle(rotate_img,(final_x1,final_y1),(final_x2,final_y2),(0,0,255),2)
cv2.putText(rotate_img, cls, (int(final_x1),int(final_y1)),0,1.2,(0,0,255),2)
nline = new_name + ' ' + cls + ' ' + str(final_x1) + ' ' + str(final_y1) + ' ' + str(final_x2) + ' ' + str(final_y2) + '\n'
f.write(nline)
f.close()
ff.close()
elif annotation_file is 'xml':
Annotations = os.path.join(AnnotationsPath,imgfile[:-4]+'.xml')
saveAnnotations = os.path.join(AnnotationsSavePath,new_name[:-4]+'.xml')
tree = ET.parse(Annotations)
filename = tree.find('filename')
filename.text = new_name
size = tree.find('size')
height = size.find('height')
height.text = str(rotate_img.shape[0])
width = size.find('width')
width.text = str(rotate_img.shape[1])
root = tree.getroot()
for obj in tree.findall('object'):
class_node = obj.find('name')
bndbox = obj.find('bndbox')
x1 = bndbox.find('xmin').text
y1 = bndbox.find('ymin').text
x2 = bndbox.find('xmax').text
y2 = bndbox.find('ymax').text
if not(int(y2)-int(y1)>0) & (int(x2)-int(x1)>0):
print(Annotations)
root.remove(obj)
continue
assert(int(y2)-int(y1)>0) & (int(x2)-int(x1)>0)
# cls = class_node.text
# if cls == 'airplane':
# class_node.text = 'aircraft'
# print(class_node.text,cls,'11')
# if cls == 'car':
# class_node.text = 'vehicle'
# print(class_node.text,cls,'22')
# print(imgfile)
# root.remove(obj)
# continue
# if cls == 'copy of helicopter' or cls =='Copy of helicopter':
# class_node.text = 'helicopter'
# print(class_node.text,cls,'33')
# print(imgfile)
final_x1, final_y1, final_x2, final_y2 = \
_rotated_location(x1,y1,x2,y2,center_x,center_y,new_center_x,new_center_y,angle)
assert(final_y2-final_y1>0)
assert(final_x2-final_x1>0)
assert(final_x1>=0)
assert(final_y1>=0)
assert(final_x2<=width.text)
assert(final_y2<=height.text)
# print 'rotated:',final_x1, final_y1, final_x2, final_y2
if ifshow == 1:
cv2.rectangle(rotate_img,(final_x1,final_y1),(final_x2,final_y2),(0,0,255),2)
cv2.putText(rotate_img, cls, (int(final_x1),int(final_y1)),0,1.2,(0,0,255),2)
bndbox.find('xmin').text = str(final_x1)
bndbox.find('ymin').text = str(final_y1)
bndbox.find('xmax').text = str(final_x2)
bndbox.find('ymax').text = str(final_y2)
tree.write(saveAnnotations)
# tree.clear()
if ifshow == 1:
cv2.imshow(new_name,rotate_img)
key = cv2.waitKey(500)
if key ==27:
quit()
cv2.destroyWindow(new_name)
def _rotated_location(x1,y1,x2,y2,center_x,center_y,new_center_x,new_center_y,angle):
x1 = float(x1) - center_x
y1 = -(float(y1) - center_y)
x2 = float(x2) - center_x
y2 = -(float(y2) - center_y)
rangle = np.deg2rad(angle)
rotated_x1 = np.cos(rangle)*x1 - np.sin(rangle)*y1
rotated_y1 = np.cos(rangle)*y1 + np.sin(rangle)*x1
rotated_x2 = np.cos(rangle)*x2 - np.sin(rangle)*y2
rotated_y2 = np.cos(rangle)*y2 + np.sin(rangle)*x2
rotated_x1 = int(rotated_x1 + new_center_x)
rotated_y1 = int(-rotated_y1 + new_center_y)
rotated_x2 = int(rotated_x2 + new_center_x)
rotated_y2 = int(-rotated_y2 + new_center_y)
final_x1 = int(min(rotated_x1,rotated_x2))
final_y1 = int(min(rotated_y1,rotated_y2))
final_x2 = int(max(rotated_x1,rotated_x2))
final_y2 = int(max(rotated_y1,rotated_y2))
return final_x1, final_y1, final_x2, final_y2
def enhancement_using_mirror(ImagePath,AnnotationsPath):
#mirror
for imgfile in os.listdir(ImagePath):
img = cv2.imread(os.path.join(ImagePath,imgfile))
new_name = imgfile[:-4] + '_mirror' + '.jpg'
# print(new_name)
mirror_img = mirroir_hierachically(img)
cv2.imwrite(os.path.join(ImageSavePath,new_name),mirror_img)
w = img.shape[1]
if annotation_file is 'txt':
ff = open(os.path.join(txtpath,imgfile[:-4]+'.txt'),'r')
f = open(os.path.join(txtpath,new_name[:-4]+'.txt'),'a')
for line in ff.readlines():
[filename,cls,x1,y1,x2,y2] = line.strip().split(' ')
x2 = abs(int(x1)-w)
x1 = abs(int(x2)-w)
final_x1 = min(x1,x2)
final_x2 = max(x1,x2)
assert(final_x2-final_x1>0)
nline = new_name + ' ' + cls + ' ' + str(final_x1) + ' ' + y1 + ' ' + str(final_x2) + ' ' + y2 + '\n'
f.write(nline)
if ifshow == 1:
cv2.rectangle(mirror_img,(final_x1,int(y1)),(final_x2,int(y2)),(0,0,255),2)
cv2.putText(mirror_img, cls, (final_x1,int(y1)),0,1.2,(0,0,255),2)
f.close()
ff.close()
elif annotation_file is 'xml':
Annotations = os.path.join(AnnotationsPath, imgfile[:-4]+'.xml')
saveAnnotations = os.path.join(AnnotationsSavePath,new_name[:-4]+'.xml')
tree = ET.parse(Annotations)
filename = tree.find('filename')
filename.text = new_name
for obj in tree.findall('object'):
bndbox = obj.find('bndbox')
x1 = bndbox.find('xmin').text
y1 = bndbox.find('ymin').text
x2 = bndbox.find('xmax').text
y2 = bndbox.find('ymax').text
new_x1 = abs(int(x2)-w)
new_x2 = abs(int(x1)-w)
final_x1 = int(min(new_x1,new_x2))
final_x2 = int(max(new_x1,new_x2))
assert(final_x2-final_x1>0)
cls = obj.find('name').text
if ifshow == 1:
cv2.rectangle(mirror_img,(final_x1,int(y1)),(final_x2,int(y2)),(0,0,255),2)
cv2.putText(mirror_img, cls, (int(final_x1),int(y1)),0,1.2,(0,0,255),2)
bndbox.find('xmin').text = str(final_x1)
bndbox.find('xmax').text = str(final_x2)
tree.write(saveAnnotations)
# tree.clear()
if ifshow == 1:
cv2.imshow(new_name,mirror_img)
key = cv2.waitKey(500)
if key ==27:
quit()
cv2.destroyWindow(new_name)
def mirroir_hierachically(src):
w = src.shape[1]
h = src.shape[0]
ll = src.shape[2]
mirror_img = copy.deepcopy(src)
for wi in xrange(w):
mirror_img[:,w-wi-1] = src[:,wi]
return mirror_img
if __name__ == '__main__':
global annotation_file, rotation_angle, ifshow, ImageSavePath,AnnotationsSavePath,AnnotationsSavePath
annotation_file = 'xml'#'txt',xml'
rotation_angle=[0,90,180,270]
ifshow = 0
catelist = ['hydropower', 'thermalpower', 'tower', 'windpower']
# ##################################### PART 1 ########################################################
for cate in catelist:
savepath = '../enhancement/' + cate
ImageSavePath = savepath + '/JPEGImages/'
AnnotationsSavePath = savepath + '/Annotations/'
ImageSetsSavePath = savepath + '/ImageSets/Main/'
if not os.path.exists(ImageSavePath):
os.makedirs(ImageSavePath)
if not os.path.exists(AnnotationsSavePath):
os.makedirs(AnnotationsSavePath)
if not os.path.exists(ImageSetsSavePath):
os.makedirs(ImageSetsSavePath)
# ##################################### PART 2 ########################################################
#augument trainning data
print('rotating...')
# path = '../TrainData/' + cate
path = '../TrainData/' + cate
ImagePath = path + '/JPEG/'
AnnotationsPath = path + '/XML/'
enhancement_using_rotation(ImagePath,AnnotationsPath)
##################################### PART 3 ########################################################
# trainval.txt/test.txt
print('writing trainval / test txt...')
path = '../TrainData/' + cate + '/ImageSets/Main/'
f1 = open(path + 'train.txt','r')
f2 = open(path + 'val.txt','r')
trainval_txt = open('../enhancement/'+cate +'/ImageSets/Main/train.txt','a')
test_txt = open( '../enhancement/'+cate +'/ImageSets/Main/val.txt','a')
_write_(trainval_txt,f1)
_write_(test_txt,f2)3、训练中数据出现的问题：
（1）此次训练了4类目标，但是数据里面实际存在了其他类的数据，
（2）图片边界问题：x,y不能为负数； anchor一致
（3）一些参数问题要好好看下论文
（4）cache文件删除（数据初始化将train_val和test写入，因为没有删除 导致在test是出现keyerror。训练的时候是先初始化，数据放在cache文件pkl文件中）