数据增强Mosaic
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，使得mini-batch大小不需要很大，那么一个GPU就可以达到比较好的效果。
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
主要代码：
def load_mosaic(self, index):
# loads images in a mosaic
s = self.img_size // 2
xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)] # mosaic center x, y
cls = int(self.images_path[index].split('/')[-2])
indices = []
for index_aother in range(index, len(self.images_path)):
if int(self.images_path[index_aother].split('/')[-2]) == cls:
indices.append(index_aother)
if len(indices) == 4:
break
if len(indices) < 4:
for index_aother in range(index):
if int(self.images_path[index_aother].split('/')[-2]) == cls:
indices.append(index_aother)
if len(indices) == 4:
break
for i, index in enumerate(indices):
# Load image
img, h, w = self.load_image(index)
# place img in img4
if i == 0: # top left
img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
elif i == 1: # top right
x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
elif i == 2: # bottom left
x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, max(xc, w), min(y2a - y1a, h)
elif i == 3: # bottom right
x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + 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] # img4[ymin:ymax, xmin:xmax]
# Augment
# img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)] # center crop (WARNING, requires box pruning)
img4 = random_affine(img4) # border to remove
label4 = [0] * 2
label4[cls] = 1
return img4, label4 def load_image(self, index):
# loads 1 image from dataset, returns img, original hw, resized hw
path = self.images_path[index]
img = cv2.imread(path) # BGR
assert img is not None, 'Image Not Found ' + path
h0, w0 = img.shape[:2] # orig hw
r = self.img_size / max(h0, w0) # resize image to img_size
print("r:", r)
if r != 1: # always resize down, only resize up if training with augmentation
interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR
img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
return img, img.shape[0], img.shape[1] # img, hw_original, hw_resizeddef random_affine(img, degrees=10, translate=.1, scale=.1, shear=10, border=0):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
height = img.shape[0] + border * 2
width = img.shape[1] + border * 2
# 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)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
# Translation
T = np.eye(3)
T[0, 2] = random.uniform(-translate, translate) * img.shape[0] + border # x translation (pixels)
T[1, 2] = random.uniform(-translate, translate) * img.shape[1] + border # y translation (pixels)
# 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)
# Combined rotation matrix
M = S @ T @ R # ORDER IS IMPORTANT HERE!!
if (border != 0) or (M != np.eye(3)).any(): # image changed
img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=(114, 114, 114))
return imggithub:
https://github.com/hobart07/trick/blob/master/%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA/mosaic.py