YoloV4 Mosaic数据增强
Bubbliiiing 人气:0什么是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 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 # Image.fromarray((image_data*255).astype(np.uint8)).save(str(index)+"distort.jpg") 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))) 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")
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