学习前言

哈哈哈！我又来数据增强了！

什么是Mosaic数据增强方法

Yolov4的mosaic数据增强参考了CutMix数据增强方式，理论上具有一定的相似性！
CutMix数据增强方式利用两张图片进行拼接。

但是mosaic利用了四张图片，根据论文所说其拥有一个巨大的优点是丰富检测物体的背景！且在BN计算的时候一下子会计算四张图片的数据！
就像下图这样：

实现思路

1、每次读取四张图片。

2、分别对四张图片进行翻转、缩放、色域变化等，并且按照四个方向位置摆好。




3、进行图片的组合和框的组合

全部代码

全部代码构成如下：

from PIL import Image, ImageDraw import numpy as np from matplotlib.colors import rgb_to_hsv, hsv_to_rgb import math 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'''     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(line_content[0])         image = image.convert("RGB") # 图片的大小         iw, ih = image.size         # 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:              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 # Image.fromarray((image_data*255).astype(np.uint8)).save(str(index)+"distort.jpg")                  index = index + 1 # 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)))      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__": with open("2007_train.txt") as f:         lines = f.readlines()     a = np.random.randint(0,len(lines)) # index = 0 # line_all = lines[a:a+4] # for line in line_all: #     image_data, box_data = normal_(line,[416,416]) #     img = image_data #     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() #     # img.save(str(index)+"box.jpg") #     index = index+1              line = lines[a:a+4]     image_data, box_data = get_random_data(line,[416,416])     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() # img.save("box_all.jpg")