目标检测|【yolov5 6.0 源码解析】---utils /augmentations.py 深度学习|人工智能|pytorch

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

【目标检测|【yolov5 6.0 源码解析】---utils /augmentations.py】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 ratiosdw /= 2# divide padding into 2 sides dh /= 2if 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=https://www.it610.com/article/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] ''' [ 10 -w/2 01 -h/2 001 ] ''' 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] ''' [100 rand10 rand01 ] ''' 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=https://www.it610.com/article/(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[:, 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

复制粘贴

def copy_paste(im, labels, segments, p=0.5): # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) n = len(segments) if p and n: h, w, c = im.shape# height, width, channels im_new = np.zeros(im.shape, np.uint8) for j in random.sample(range(n), k=round(p * n)): l, s = labels[j], segments[j] box = w - l[3], l[2], w - l[1], l[4] ioa = bbox_ioa(box, labels[:, 1:5])# intersection over area if (ioa < 0.30).all():# allow 30% obscuration of existing labels labels = np.concatenate((labels, [[l[0], *box]]), 0) segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)result = cv2.bitwise_and(src1=im, src2=im_new) result = cv2.flip(result, 1)# augment segments (flip left-right) i = result > 0# pixels to replace # i[:, :] = result.max(2).reshape(h, w, 1)# act over ch im[i] = result[i]# cv2.imwrite('debug.jpg', im)# debugreturn im, labels, segments

图片随机马赛克

def cutout(im, labels, p=0.5):# 随机马赛克 # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 if random.random() < p: h, w = im.shape[:2] scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # [0.5 0.25 0.25 0.125 0.125 0.0125 ... 0.03125]31 # image size fraction for s in scales: mask_h = random.randint(1, int(h * s))# create random masks mask_w = random.randint(1, int(w * s))# box 确定随机马赛克的位置 xmin = max(0, random.randint(0, w) - mask_w // 2) ymin = max(0, random.randint(0, h) - mask_h // 2) xmax = min(w, xmin + mask_w) ymax = min(h, ymin + mask_h)# apply random color mask 确定随机马赛克的马赛克像素 im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] # # return unobscured labels if len(labels) and s > 0.03: # box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) ioa = bbox_ioa(box, labels[:, 1:5])# intersection over area 计算马赛克位置与框位置的IOU 大小 labels = labels[ioa < 0.60]# remove >60% obscured labels # 保留下与马赛克框iou小于 0.6的，大于0.6的认为没有学习的必须 return labels

mixup

def mixup(im, labels, im2, labels2): # 两张图片像素点以不同权重融合 # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf r = np.random.beta(32.0, 32.0)# mixup ratio, alpha=beta=32.0 im = (im * r + im2 * (1 - r)).astype(np.uint8) # img 做融合 labels = np.concatenate((labels, labels2), 0) return im, labels

过滤图像

def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16):# box1(4,n), box2(4,n) # 过滤掉长宽小于阈值，面积图片占比小于阈值，框长宽比大于阈值的框 # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio # box1 图片，box2 框 w1, h1 = box1[2] - box1[0], box1[3] - box1[1] w2, h2 = box2[2] - box2[0], box2[3] - box2[1] ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))# aspect ratio 宽长比与长宽比的最大值 return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)# candidates