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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
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