博客
关于我
数据增强--对于目标检测(边框、图像)
阅读量:559 次
发布时间:2019-03-09

本文共 6607 字,大约阅读时间需要 22 分钟。

import torchfrom PIL import Image, ImageFont, ImageDrawfrom functools import reduceimport scipy.io as sciofrom PIL import Imageimport cv2 as cvimport numpy as npimport randomimport imutilsfrom imgaug import augmenters as iaadef horisontal_flip(images, targets):    images = torch.flip(images, [-1])    targets[:, 2] = 1 - targets[:, 2]    return images, targetsdef compose(*funcs):    """Compose arbitrarily many functions, evaluated left to right.    Reference: https://mathieularose.com/function-composition-in-python/    """    # return lambda x: reduce(lambda v, f: f(v), funcs, x)    if funcs:        return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)    else:        raise ValueError('Composition of empty sequence not supported.')def letterbox_image(image, size):    '''resize image with unchanged aspect ratio using padding'''    iw, ih = image.size    w, h = size    scale = min(w / iw, h / ih)    nw = int(iw * scale)    nh = int(ih * scale)    image = image.resize((nw, nh), Image.BICUBIC)    new_image = Image.new('RGB', size, (128, 128, 128))    new_image.paste(image, ((w - nw) // 2, (h - nh) // 2))    return new_imagedef rand(a=0, b=1):    return np.random.rand() * (b - a) + adef resize_3D_data(ima, size=(416, 416)):    dat = np.zeros([416, 416, 15])    for i in range(15):        slice = np.squeeze(ima[:, :, i])        re_slice = cv.resize(slice.astype('uint8'), size, interpolation=cv.INTER_AREA)        dat[:, :, i] = re_slice    return datdef random_crop(cell, boxes):    dx = random.randint(15, 20)    dy = random.randint(15, 20)    shape = cell.shape    nx = shape[0]    ny = shape[1]    boxes[:, [0, 2]] = boxes[:, [0, 2]] - dx    boxes[:, [1, 3]] = boxes[:, [1, 3]] - dy    new_cell = np.zeros_like(cell)    new_cell[0:int(nx - dx), 0:int(ny - dy)] = cell[dx:, dy:]    new_cell = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    new_cell = cv.cvtColor(np.asarray(new_cell), cv.COLOR_RGB2BGR)    boxes = np.where(boxes < 0, 0, boxes)    return new_cell, boxesdef random_noise(cell, boxes):    image = Image.fromarray(cell.astype('uint8')).convert('RGB')    image = cv.cvtColor(np.asarray(image), cv.COLOR_RGB2BGR)    seq = iaa.Sequential(        [            iaa.AdditiveGaussianNoise(scale=0.05 * 255),            iaa.LinearContrast((0.75, 1.5)),            iaa.GaussianBlur(sigma=(0, 4.0)),            iaa.Dropout(p=(0, 0.2)),            iaa.CoarseDropout(0.02, size_percent=0.5)        ], random_order=True    )    images_aug = seq.augment_images([image])[0]    new_cell = Image.fromarray(cv.cvtColor(images_aug, cv.COLOR_BGR2RGB))    return new_cell, boxesdef gray_level_crop(cell, boxes):    max_val = random.randint(80, 255) / 255    new_cell = cell * max_val    new_cell = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    new_cell = cv.cvtColor(np.asarray(new_cell), cv.COLOR_RGB2BGR)    return new_cell, boxesdef rotate_box(box, M, shape):    # print(box)    y1, x1, y2, x2 = box    p1 = np.array([x1, y1, 1]).reshape((3, 1))    p2 = np.array([x1, y2, 1]).reshape((3, 1))    p3 = np.array([x2, y2, 1]).reshape((3, 1))    p4 = np.array([x2, y1, 1]).reshape((3, 1))    p1 = np.matmul(M, p1)    p2 = np.matmul(M, p2)    p3 = np.matmul(M, p3)    p4 = np.matmul(M, p4)    x1 = np.min([p1[0, 0], p2[0, 0], p3[0, 0], p4[0, 0]])    x2 = np.max([p1[0, 0], p2[0, 0], p3[0, 0], p4[0, 0]])    y1 = np.min([p1[1, 0], p2[1, 0], p3[1, 0], p4[1, 0]])    y2 = np.max([p1[1, 0], p2[1, 0], p3[1, 0], p4[1, 0]])    if x1 < 0:        x1 = 0    if x1 > shape[1]:        x1 = shape[1] - 1    if x2 < 0:        x2 = 0    if x2 > shape[1]:        x2 = shape[1] - 1    if y1 < 0:        y1 = 0    if y1 > shape[0]:        y1 = shape[0] - 1    if y2 < 0:        y2 = 0    if y2 > shape[0]:        y2 = shape[0] - 1    box = [y1, x1, y2, x2]    # print(box)    # print('--------------')    return boxdef random_rotate(cell, boxes, angle=45):    (h, w) = cell.shape    (cX, cY) = (w // 2, h // 2)    new_cell = imutils.rotate_bound(cell.astype('uint8'), angle)    M = cv.getRotationMatrix2D((cX, cY), -angle, 1.0)    cos = np.abs(M[0, 0])    sin = np.abs(M[0, 1])    # compute the new bounding dimensions of the image    nW = int((h * sin) + (w * cos))    nH = int((h * cos) + (w * sin))    # adjust the rotation matrix to take into account translation    M[0, 2] += (nW / 2) - cX    M[1, 2] += (nH / 2) - cY    new_boxes = []    for i in range(len(boxes)):        new_boxes.append(rotate_box(boxes[i], M, new_cell.shape))    if len(new_boxes) > 0:        new_boxes = np.array(new_boxes)    new_cell = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    new_cell = cv.cvtColor(np.asarray(new_cell), cv.COLOR_RGB2BGR)    return new_cell, new_boxesdef random_argument(data, cell, boxes):    # crop region ...    if rand() < .5:        cell, boxes = random_crop(cell, boxes)    # add noise ...    if rand() < .5:        cell, boxes = random_noise(cell, boxes)    # add noise ...    if rand() < .5:        cell, boxes = gray_level_crop(cell, boxes)    # rotate ...    if rand() < .5:       cell, boxes = random_rotate(cell, boxes, random.randint(0, 90))    return cell, boxesdef pad_to_square(img, pad_value):    h, w, c = img.shape    dim_diff = np.abs(h - w)    # (upper / left) padding and (lower / right) padding    pad1, pad2 = dim_diff // 2, dim_diff - dim_diff // 2    # Determine padding    pad = ((0, 0), (pad1, pad2), (0, 0)) if w <= h else ((pad1, pad2), (0, 0), (0, 0))    # Add padding    img = np.pad(img, pad, "constant", constant_values=pad_value)    return img, padif __name__ == '__main__':    dat = scio.loadmat(        '/home/xuxu/Data/CTC_Signal_Datasets/Processed/20200616_cell_signalmark_39frames/Green/653802_650142_0_675.mat')    cell = dat['signal'].astype('uint8')    boxe = dat['rects']    print('boxe', boxe)    # image = Image.fromarray(cell.astype('uint8')).convert('RGB')    # image = cv.cvtColor(np.asarray(image), cv.COLOR_RGB2BGR)    # for x1, y1, x2, y2 in boxes:    #     cv.rectangle(image, (y1, x1), (y2, x2), (255, 255, 255), thickness=1)    # cv.imwrite('1.jpg', image)    new_cell, new_boxes = random_rotate(cell, boxe)    # new_cell, new_boxes = gray_level_crop(cell, boxe)    # print('new_boxes', new_boxes)    # new_boxes = np.where(new_boxes < 0, 0, new_boxes)    # print('new_boxes', new_boxes)    # image = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    # image = cv.cvtColor(np.asarray(image), cv.COLOR_RGB2BGR)    for x1, y1, x2, y2 in new_boxes:        cv.rectangle(new_cell, (int(y1), int(x1)), (int(y2), int(x2)), (255, 255, 255), thickness=1)    cv.imwrite('1.jpg', new_cell)

其中图像加噪使用的是imgaug库

转载地址:http://ewhsz.baihongyu.com/

你可能感兴趣的文章
MYSQL中TINYINT的取值范围
查看>>
MySQL中UPDATE语句的神奇技巧,让你操作数据库如虎添翼!
查看>>
Mysql中varchar类型数字排序不对踩坑记录
查看>>
MySQL中一条SQL语句到底是如何执行的呢?
查看>>
MySQL中你必须知道的10件事,1.5万字!
查看>>
MySQL中使用IN()查询到底走不走索引?
查看>>
Mysql中使用存储过程插入decimal和时间数据递增的模拟数据
查看>>
MySql中关于geometry类型的数据_空的时候如何插入处理_需用null_空字符串插入会报错_Cannot get geometry object from dat---MySql工作笔记003
查看>>
mysql中出现Incorrect DECIMAL value: '0' for column '' at row -1错误解决方案
查看>>
mysql中出现Unit mysql.service could not be found 的解决方法
查看>>
mysql中出现update-alternatives: 错误: 候选项路径 /etc/mysql/mysql.cnf 不存在 dpkg: 处理软件包 mysql-server-8.0的解决方法(全)
查看>>
Mysql中各类锁的机制图文详细解析(全)
查看>>