目标检测中的数据增强方法及其实现_np.fliplr 数据增强

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
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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
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3、图像模糊

    def randomBlur(self,img):
        if random.random()<0.5:
            bgr = cv2.blur(img,(5,5))
        return bgr
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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
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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
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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
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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
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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]<w)
        mask2 = (center[:,1]>0) & (center[:,1]<h)
        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([[x,y,x,y]]).expand_as(boxes_in)

        boxes_in = boxes_in - box_shift
        boxes_in[:,0]=boxes_in[:,0].clamp_(min=0,max=w)
        boxes_in[:,2]=boxes_in[:,2].clamp_(min=0,max=w)
        boxes_in[:,1]=boxes_in[:,1].clamp_(min=0,max=h)
        boxes_in[:,3]=boxes_in[:,3].clamp_(min=0,max=h)

        labels_in = labels[mask.view(-1)]
        img_croped = bgr[y:y+h,x:x+w,:]
        return img_croped,boxes_in,labels_in
    return bgr,boxes,labels 
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7、减去均值

    def subMean(self,bgr,mean):
        mean = np.array(mean, dtype=np.float32)
        bgr = bgr - mean
        return bgr
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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
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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)
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