yolov5 6.0 源码解析---utils /augmentations.py
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yolov5 数据增强代码
主要有以下几种方式：
class Albumentations # 数据增强package,比pytorch 自带的transform 更丰富
def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5) # 图像增强方式，hgain 是色调，不同色调不同颜色，sgain是饱和度， vgain是亮度
def hist_equalize(im, clahe=True, bgr=False):# 采用自适应直方图均衡化做图像增强
def replicate(im, labels) #
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32) # 图像size扩充至指定大小
def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
border=(0, 0)) # 随机增强
def copy_paste(im, labels, segments, p=0.5)# 复制粘贴
def cutout(im, labels, p=0.5) # 裁剪
def mixup(im, labels, im2, labels2) # mixup
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16) # 框筛选
下面一个个来看图像增强的方式：
Albumentations 图像增强
class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self):
self.transform = None
try:
'''
albumentations --一个数据增强的package,比pytorch的transform丰富；详情
https://blog.csdn.net/cp1314971/article/details/106039800?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164015856916780261966386%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=164015856916780261966386&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~baidu_landing_v2~default-1-106039800.pc_search_es_clickV2&utm_term=import+albumentations+&spm=1018.2226.3001.4187
'''
import albumentations as A
check_version(A.__version__, '1.0.3') # version requirement
self.transform = A.Compose([
A.Blur(p=0.01), # 图像随机大小内核模糊输入图像
A.MedianBlur(p=0.01), # 图像随机模糊输入图像
A.ToGray(p=0.01), # 转成灰度图
A.CLAHE(p=0.01), #
A.RandomBrightnessContrast(p=0.0), # 随机亮度和对比度
A.RandomGamma(p=0.0), #
A.ImageCompression(quality_lower=75, p=0.0)], # 图像压缩
bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) #
logging.info(colorstr('albumentations: ') + ', '.join(f'x' for x in self.transform.transforms if x.p))
except ImportError: # package not installed, skip
pass
except Exception as e:
logging.info(colorstr('albumentations: ') + f'e')
def __call__(self, im, labels, p=1.0):
if self.transform and random.random() < p:
new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
return im, labels
hsv 色调-饱和度-亮度的图像增强
def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): # 做h-色调， s-饱和度， v-亮度上面的随机增强
# HSV color-space augmentation
if hgain or sgain or vgain:
r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains 生成3个[-1, 1)之间的随机数，分别与hsv相乘后+1 [0,2]之间
hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) # 将图像从BGR 转成HSV ，拆分
dtype = im.dtype # uint8
x = np.arange(0, 256, dtype=r.dtype) # [0, 1, 2,...,255]
lut_hue = ((x * r[0]) % 180).astype(dtype) # [0, 180)
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) # 将数组截断至[0, 255]
lut_val = np.clip(x * r[2], 0, 255).astype(dtype) # 将数组截断至[0, 255]
im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
# cv2.LUT lookup-table 查找表方式，即通过lut_hue 这个表对之前hue数值做修正，返回0-255对应位置的lut_hue值 具体： https://blog.csdn.net/Dontla/article/details/103963085
# cv2.merge 合并三个通道
cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
直方图均衡化增强
def hist_equalize(im, clahe=True, bgr=False): # 直方图均衡化增强 参考 https://www.cnblogs.com/my-love-is-python/p/10405811.html
# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) # 将图像从bgr转成YUV
if clahe:
c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
# cv2.createCLAHE 实例化自适应直方图均衡化函数 局部直方图均衡化 ，不会使得细节消失
# c.apply 进行自适应直方图均衡化
yuv[:, :, 0] = c.apply(yuv[:, :, 0])
else:
# cv2.equalizeHist 进行像素点的均衡化 ，即全局均衡化 ，使得整体亮度提升，但是局部会模糊
yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
图像框的平移复制增强
def replicate(im, labels): # 复制，实际上指的是框的平移
# Replicate labels
h, w = im.shape[:2] # 获取图像长宽
boxes = labels[:, 1:].astype(int) # 获取框的位置和大小
x1, y1, x2, y2 = boxes.T # 框的左右和上下位置
s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
x1b, y1b, x2b, y2b = boxes[i]
bh, bw = y2b - y1b, x2b - x1b
yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax]
labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
return im, labels
图像以letterbox缩放
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# 按比例缩放图片，并将其他部分填充，到resize图片的大小
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int): # 如果输入是一个数字，默认长宽相等
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) #
if not scaleup: # only scale down, do not scale up (for better val mAP) # 如果只缩小，不放大图片
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # 对图片按比例缩放后的长宽 (width, height)
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding 对缩放后的图像 需要填充的size
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding 取能被stride 整除的dw 和dh
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) # 先将图片按比例缩放到指定大小
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) # 上下位置
left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) # 左右位置
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
# cv2.copyMakeBorder 对im设置边界框
return im, ratio, (dw, dh)
旋转等变换（未更新完全，后续补充）
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=(.1, .1), scale=(.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 [w, h, c] -->[w/2 , h/2, c]
'''
[ 1 0 -w/2
0 1 -h/2
0 0 1
]
'''
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 [w, h, c] -->[w/2 , h/2, c]
'''
[ 1 0 0
rand 1 0
rand 0 1
]
'''
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
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
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
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 # order of operations (right to left) is IMPORTANT tf.matmul(A,C)=np.dot(A,C)= A@C
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)) # cv2.warpAffine()放射变换函数，可实现旋转，平移，缩放；变换后的平行线依旧平行
# cv2.warpAffine()放射变换函数，可实现旋转，平移，缩放；变换后的平行线依旧平行
# cv2.warpPerspective()透视变换函数，可保持直线不变形，但是平行线可能不再平行
# 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以上是关于yolov5 6.0 源码解析---utils /augmentations.py的主要内容，如果未能解决你的问题，请参考以下文章
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