目标检测中的数据增强方法及其实现
不同于图像分类中的数据增强，在目标检测中，由于涉及到了边界框（bounding box），导致有一些常用的图像增强不能用,在图像变化的时候同时要考虑到方框坐标点的变化。下面我们总结一些常用的包装好的数据增强函数。
1、图像数组翻转
def random_flip(self, im, boxes):
if random.random() < 0.5:
im_lr = np.fliplr(im).copy()
h,w,_ = im.shape
xmin = w - boxes[:,2]
xmax = w - boxes[:,0]
boxes[:,0] = xmin
boxes[:,2] = xmax
return im_lr, boxes
return im, boxes
2、图像缩放
def randomScale(self,img,boxes):
#固定住高度，以0.8-1.2伸缩宽度，做图像形变
if random.random() < 0.5:
scale = random.uniform(0.8,1.2)
height,width,c = img.shape
bgr = cv2.resize(img,(int(width*scale),height))
scale_tensor = torch.FloatTensor([[scale,1,scale,1]]).expand_as(boxes)
boxes = boxes * scale_tensor
return img,boxes
return img,boxes
3、图像模糊
def randomBlur(self,img):
if random.random()<0.5:
bgr = cv2.blur(img,(5,5))
return bgr
4、随机光照、饱和度
def RandomBrightness(self,bgr):
if random.random() < 0.5:
hsv = self.BGR2HSV(bgr)
h,s,v = cv2.split(hsv)
adjust = random.choice([0.5,1.5])
v = v*adjust
v = np.clip(v, 0, 255).astype(hsv.dtype)
hsv = cv2.merge((h,s,v))
bgr = self.HSV2BGR(hsv)
return bgr
def RandomHue(self,bgr):
if random.random() < 0.5:
hsv = self.BGR2HSV(bgr)
h,s,v = cv2.split(hsv)
adjust = random.choice([0.5,1.5])
h = h*adjust
h = np.clip(h, 0, 255).astype(hsv.dtype)
hsv = cv2.merge((h,s,v))
bgr = self.HSV2BGR(hsv)
return bgr
def RandomSaturation(self,bgr):
if random.random() < 0.5:
hsv = self.BGR2HSV(bgr)
h,s,v = cv2.split(hsv)
adjust = random.choice([0.5,1.5])
s = s*adjust
s = np.clip(s, 0, 255).astype(hsv.dtype)
hsv = cv2.merge((h,s,v))
bgr = self.HSV2BGR(hsv)
return bgr
5、平移变换
def randomShift(self,bgr,boxes,labels):
#平移变换
center = (boxes[:,2:]+boxes[:,:2])/2
if random.random() <0.5:
height,width,c = bgr.shape
after_shfit_image = np.zeros((height,width,c),dtype=bgr.dtype)
after_shfit_image[:,:,:] = (104,117,123) #bgr
shift_x = random.uniform(-width*0.2,width*0.2)
shift_y = random.uniform(-height*0.2,height*0.2)
#print(bgr.shape,shift_x,shift_y)
#原图像的平移
if shift_x>=0 and shift_y>=0:
after_shfit_image[int(shift_y):,int(shift_x):,:] = bgr[:height-int(shift_y),:width-int(shift_x),:]
elif shift_x>=0 and shift_y<0:
after_shfit_image[:height+int(shift_y),int(shift_x):,:] = bgr[-int(shift_y):,:width-int(shift_x),:]
elif shift_x <0 and shift_y >=0:
after_shfit_image[int(shift_y):,:width+int(shift_x),:] = bgr[:height-int(shift_y),-int(shift_x):,:]
elif shift_x<0 and shift_y<0:
after_shfit_image[:height+int(shift_y),:width+int(shift_x),:] = bgr[-int(shift_y):,-int(shift_x):,:]
shift_xy = torch.FloatTensor([[int(shift_x),int(shift_y)]]).expand_as(center)
center = center + shift_xy
mask1 = (center[:,0] >0) & (center[:,0] < width)
mask2 = (center[:,1] >0) & (center[:,1] < height)
mask = (mask1 & mask2).view(-1,1)
boxes_in = boxes[mask.expand_as(boxes)].view(-1,4)
if len(boxes_in) == 0:
return bgr,boxes,labels
box_shift = torch.FloatTensor([[int(shift_x),int(shift_y),int(shift_x),int(shift_y)]]).expand_as(boxes_in)
boxes_in = boxes_in+box_shift
labels_in = labels[mask.view(-1)]
return after_shfit_image,boxes_in,labels_in
return bgr,boxes,labels
6、随机裁剪
def randomCrop(self,bgr,boxes,labels):
if random.random() < 0.5:
center = (boxes[:,2:]+boxes[:,:2])/2
height,width,c = bgr.shape
h = random.uniform(0.6*height,height)
w = random.uniform(0.6*width,width)
x = random.uniform(0,width-w)
y = random.uniform(0,height-h)
x,y,h,w = int(x),int(y),int(h),int(w)
center = center - torch.FloatTensor([[x,y]]).expand_as(center)
mask1 = (center[:,0]>0) & (center[:,0]0) & (center[:,1] 7、减去均值
def subMean(self,bgr,mean):
mean = np.array(mean, dtype=np.float32)
bgr = bgr - mean
return bgr
8、随机亮度
def random_bright(self, im, delta=16):
alpha = random.random()
if alpha > 0.3:
im = im * alpha + random.randrange(-delta,delta)
im = im.clip(min=0,max=255).astype(np.uint8)
return im
9、通道转换
def BGR2RGB(self,img):
return cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
def BGR2HSV(self,img):
return cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
def HSV2BGR(self,img):
return cv2.cvtColor(img,cv2.COLOR_HSV2BGR)