1importnumpy as np2importimgaug as ia3importimageio4fromimgaugimportaugmenters as iaa5%matplotlib inline67BATCH_SIZE = 168NB_BATCHES = 1009image = imageio.imread("./pick1.jpg")10images = [np.copy(image)for_inrange(BATCH_SIZE)]1112keypoint =ia.KeypointsOnImage([13ia.Keypoint(x=...
1importimgaug.augmenters as iaa2ia.seed(3)34seq =iaa.Sequential([5iaa.Affine(translate_px={"x": (10, 30)}, rotate=(-10, 10)),6iaa.AddToHueAndSaturation((-50, 50))#color jitter, only affects the image7])8image_aug, kpsoi_aug = seq(image=img, keypoints=kpsoi)910ia...
1. 注意: 如果图像维度是[h,w,c],则要用 images_aug = seq.augment_image(images) ,也就是augment_image后面没有s,与之相反的是augment_images(有s)。 You provided a numpy array of shape (1200, 1920, 3) as input to augment_images(), which was interpreted as (N, H, W). The last dime...
augtmp = iaa.WithColorspace(to_colorspace="HSV") res = augtmp.augment_image(img) res_img["色彩空间"]=res augtmp = iaa.AddToHueAndSaturation((-20, 20), per_channel=True) res = augtmp.augment_image(img) res_img["随机色调和饱和度"]=res augtmp = iaa.Grayscale(alpha=1.0) res =...
Apply heavy augmentations to images (used to create the image at the very top of this readme):import imgaug as ia from imgaug import augmenters as iaa import numpy as np # random example images images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # Sometimes(...
ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples) )), # execute 0 to 5 of the following (less important) augmenters per image # don't execute all of them, as that would often be way too strong iaa.SomeOf((0, 5), [ sometimes(iaa....
import imgaug as ia from imgaug import augmenters as iaa import imageio import numpy as np ia.seed(1) image = ia.quokka(size=(128, 128), extract="square") #加载示例图像进来,大小为(128,128,3) #创建一个分割的图 segmap = np.zeros((128, 128), dtype=np.int32) segmap[28:71, 35:...
import imgaug as ia from imgaug import augmenters as iaa import imageio import numpy as np ia.seed(1) image = ia.quokka(size=(128, 128), extract="square") #加载示例图像进来,大小为(128,128,3) #创建一个分割的图 segmap = np.zeros((128, 128), dtype=np.int32) segmap[28:71, 35:...
image_aug = seq_det.augment_images([img])[0] bbs_aug = seq_det.augment_bounding_boxes([bbs])[0] before = bbs.bounding_boxes[0] after = bbs_aug.bounding_boxes[0] print("BB : (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)" % ( ...
image = cv2.imread('test.jpg'). 4.2 增强图像。 将读取的图像输入到增强序列中进行增强: python. augmented_image = seq.augment_image(image). 4.3 显示和保存增强后的图像。 可以使用OpenCV显示增强后的图像,并将其保存到本地: python. cv2.imshow('Original Image', image). cv2.imshow('Augmented Image...