数据增强 python实现Moaic数据增强 python实现Moaic数据增强
Moaic数据增强：对四张图片进行拼接，获得一张新的图片，同时获得这张图片对应的标签框。
主要原理：把4张图片，通过随机缩放、随机裁减、随机排布的方式进行拼接，再拼接到一张图上作为训练数据。yolov5之后加上了放射变换处理。
Mosaic有如下优点：
（1）丰富数据集，加强了小目标检测，加强了面对复杂环境的情况等等：随机使用4张图片，随机缩放，再随机分布进行拼接，丰富了检测数据集，随机缩放增加了很多小目标，让网络的鲁棒性更强；
（2） 减少对mini-batch的依赖，降低对GPU显存要求：直接计算4张图片的数据，使得Mini-batch大小并不需要很大就可以达到比较好的效果，四张图片拼接在一起变相提高了batch_size，在进行batch normalization（归一化）的时候也会计算四张图片。
import cv2
import torch
import random
import os.path
import numpy as np
import matplotlib.pyplot as plt
from camvid import get_bbox, draw_box
def load_mosaic(im_files, name_color_dict):
im_size = 640
s_mosaic = im_size * 2
mosaic_border = [-im_size // 2, -im_size // 2]
labels4, segments4, colors = [], [], []
# mosaic center x, y
y_c, x_c = (int(random.uniform(-x, s_mosaic + x)) for x in mosaic_border)
img4 = np.full((s_mosaic, s_mosaic, 3), 114, dtype=np.uint8)
seg4 = np.full((s_mosaic, s_mosaic), 0, dtype=np.uint8)
for i, im_file in enumerate(im_files):
# Load image
img = cv2.imread(im_file)
seg_file = im_file.replace('images', 'labels')
name = os.path.basename(seg_file).split('.')[0]
seg_file = os.path.join(os.path.dirname(seg_file), name + '_L.png')
seg, boxes, color = get_bbox(seg_file, names, name_color_dict)
colors += color
h, w, _ = np.shape(img)
# place img in img4
if i == 0: # top left
x1a, y1a, x2a, y2a = max(x_c - w, 0), max(y_c - h, 0), x_c, y_c
x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h
elif i == 1: # top right
x1a, y1a, x2a, y2a = x_c, max(y_c - h, 0), min(x_c + w, s_mosaic), y_c
x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
elif i == 2: # bottom left
x1a, y1a, x2a, y2a = max(x_c - w, 0), y_c, x_c, min(s_mosaic, y_c + h)
x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
elif i == 3: # bottom right
x1a, y1a, x2a, y2a = x_c, y_c, min(x_c + w, s_mosaic), min(s_mosaic, y_c + h)
x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]
# place seg in seg4
seg4[y1a:y2a, x1a:x2a] = seg[y1b:y2b, x1b:x2b]
# update bbox
padw = x1a - x1b
padh = y1a - y1b
boxes = xywhn2xyxy(boxes, padw=padw, padh=padh)
labels4.append(boxes)
labels4 = np.concatenate(labels4, 0)
for x in labels4[:, 1:]:
np.clip(x, 0, s_mosaic, out=x) # clip coord
# draw result
draw_box(seg4, labels4, colors)
return img4, labels4，seg4
if __name__ == '__main__':
names = ['Pedestrian', 'Car', 'Truck_Bus']
im_files = ['camvid/images/0016E5_01440.png',
'camvid/images/0016E5_06600.png',
'camvid/images/0006R0_f00930.png',
'camvid/images/0006R0_f03390.png']
load_mosaic(im_files, name_color_dict)一 确定拼接中心点
二 放置
三 贴图
四 计算小图到大图上时所产生的偏移，用来计算mosaic增强后的标签的位置
五 进行mosaic的时候将四张图片整合到一起之后shape为[2img_size,2img_size]
六 对mosaic整合的图片进行随机旋转、平移、缩放、裁剪
七 resize为输入大小img_size
def random_perspective(im,
targets=(),
segments=(),
degrees=10,
translate=.1,
scale=.1,
shear=10,
perspective=0.0,
border=(0, 0)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
# targets = [cls, xyxy]
height = im.shape[0] + border[0] * 2 # shape(h,w,c)
width = im.shape[1] + border[1] * 2
# Center:平移改变旋转中心
C = np.eye(3)
C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
# Perspective：透视变换
P = np.eye(3)
P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
# Rotation and Scale，旋转和缩放
R = np.eye(3)
a = random.uniform(-degrees, degrees)
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(1 - scale, 1 + scale)
# s = 2 ** random.uniform(-scale, scale)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
# Shear：切变
S = np.eye(3)
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
# Translation：平移
T = np.eye(3)
T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
# Combined rotation matrix
M = T @ S @ R @ P @ C # 合并所有变换矩阵，从右到左做一次执行，先改变旋转中心，再透视变换，旋转和缩放，切变，最后平移
if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
if perspective:
im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
else: # affine
im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
# Visualize
# import matplotlib.pyplot as plt
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
# ax[0].imshow(im[:, :, ::-1]) # base
# ax[1].imshow(im2[:, :, ::-1]) # warped
# Transform label coordinates
n = len(targets)
if n:
use_segments = any(x.any() for x in segments)
new = np.zeros((n, 4))
if use_segments: # warp segments
segments = resample_segments(segments) # upsample
for i, segment in enumerate(segments):
xy = np.ones((len(segment), 3))
xy[:, :2] = segment
xy = xy @ M.T # transform
xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
# clip
new[i] = segment2box(xy, width, height)
else: # warp boxes
xy = np.ones((n * 4, 3))
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = xy @ M.T # transform
xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# clip
new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
# filter candidates
i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
targets = targets[i]
targets[:, 1:5] = new[i]
return im, targets补充：
在warpPerspective(）函数中，最后面两个参数 boarderMode，boarderValue声明在转换后的图片背景颜色。
其中：
**borderMode:**说明背景外插模式，可以选择的模式包括：
cv2.BORDER_CONSTANT
cv2.BORDER_REPLICATE
boardValue: 外插背景颜色，可以使用三元组（R,G,B）：0 ~ 255说明背景颜色。