我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用cv2.BORDER_REFLECT。
def linearToPolar(img, center=None, final_radius=None, initial_radius=None, phase_width=None, interpolation=cv2.INTER_AREA, maps=None, borderValue=0, borderMode=cv2.BORDER_REFLECT, **opts): ''' map a 2d (x,y) Cartesian array to a polar (r, phi) array using opencv.remap ''' if maps is None: mapY, mapX = linearToPolarMaps(img.shape[:2], center, final_radius, initial_radius, phase_width) else: mapY, mapX = maps o = {'interpolation': interpolation, 'borderValue': borderValue, 'borderMode': borderMode} o.update(opts) return cv2.remap(img, mapY, mapX, **o)
def polarToLinear(img, shape=None, center=None, maps=None, interpolation=cv2.INTER_AREA, borderValue=0, borderMode=cv2.BORDER_REFLECT, **opts): ''' map a 2d polar (r, phi) polar array to a Cartesian (x,y) array using opencv.remap ''' if maps is None: mapY, mapX = polarToLinearMaps(img.shape[:2], shape, center) else: mapY, mapX = maps o = {'interpolation': interpolation, 'borderValue': borderValue, 'borderMode': borderMode} o.update(opts) return cv2.remap(img, mapY, mapX, **o)
def get_mag_avg(img): img = np.sqrt(img) kernels = get_kernels() mag = np.zeros(img.shape, dtype='float32') for kernel_filter in kernels: gx = cv2.filter2D(np.float32(img), cv2.CV_32F, kernel_filter[1], borderType=cv2.BORDER_REFLECT) gy = cv2.filter2D(np.float32(img), cv2.CV_32F, kernel_filter[0], borderType=cv2.BORDER_REFLECT) mag += cv2.magnitude(gx, gy) mag /= len(kernels) return mag
def create_dataset(opt, mode): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), T.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2,0,1).astype(np.float32), torch.from_numpy, ]) train_transform = tnt.transform.compose([ T.RandomHorizontalFlip(), T.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), T.RandomCrop(32), convert, ]) ds = getattr(datasets, opt.dataset)(opt.data_root, train=mode, download=True) smode = 'train' if mode else 'test' ds = tnt.dataset.TensorDataset([ getattr(ds, smode+'_data'), getattr(ds, smode+'_labels')]) return ds.transform({0: train_transform if mode else convert})
def random_translate_img(img, xy_range, border_mode="constant"): if random.random() > xy_range.chance: return img import cv2 if not isinstance(img, list): img = [img] org_height, org_width = img[0].shape[:2] translate_x = random.randint(xy_range.x_min, xy_range.x_max) translate_y = random.randint(xy_range.y_min, xy_range.y_max) trans_matrix = numpy.float32([[1, 0, translate_x], [0, 1, translate_y]]) border_const = cv2.BORDER_CONSTANT if border_mode == "reflect": border_const = cv2.BORDER_REFLECT res = [] for img_inst in img: img_inst = cv2.warpAffine(img_inst, trans_matrix, (org_width, org_height), borderMode=border_const) res.append(img_inst) if len(res) == 1: res = res[0] xy_range.last_x = translate_x xy_range.last_y = translate_y return res
def create_dataset(opt, mode): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), T.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2, 0, 1).astype(np.float32), torch.from_numpy, ]) train_transform = tnt.transform.compose([ T.RandomHorizontalFlip(), T.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), T.RandomCrop(32), convert, ]) ds = getattr(datasets, opt.dataset)( opt.data_root, train=mode, download=True) smode = 'train' if mode else 'test' ds = tnt.dataset.TensorDataset([ getattr(ds, smode + '_data'), getattr(ds, smode + '_labels')]) return ds.transform({0: train_transform if mode else convert})
def test_box_filter_reflect(self): I = np.array(range(1, 50)).reshape(7, 7).astype(np.float32) r = 2 ret1 = cv.smooth.box_filter(I, r, normalize=True, border_type='reflect') ret2 = cv2.blur(I, (5,5), borderType=cv2.BORDER_REFLECT) self.assertTrue(np.array_equal(ret1, ret2))
def random_rotate(image): cols = image.shape[1] rows = image.shape[0] mean_color = np.mean(image, axis=(0, 1)) angle = random.uniform(0, 90) M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1) if random.randint(0, 1) == 1: dst = cv2.warpAffine(image, M, (cols, rows), borderValue=mean_color, borderMode=cv2.BORDER_REFLECT) else: dst = cv2.warpAffine(image, M, (cols, rows), borderValue=mean_color) return dst
def rotate(image, angle, interpolation=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REFLECT, borderValue=0): ''' angle [deg] ''' s0, s1 = image.shape image_center = (s0 - 1) / 2., (s1 - 1) / 2. rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0) result = cv2.warpAffine(image, rot_mat, image.shape, flags=interpolation, borderMode=borderMode, borderValue=borderValue) return result
def fastFilter(arr, ksize=30, every=None, resize=True, fn='median', interpolation=cv2.INTER_LANCZOS4, smoothksize=0, borderMode=cv2.BORDER_REFLECT): ''' fn['nanmean', 'mean', 'nanmedian', 'median'] a fast 2d filter for large kernel sizes that also works with nans the computation speed is increased because only 'every'nsth position within the median kernel is evaluated ''' if every is None: every = max(ksize//3, 1) else: assert ksize >= 3*every s0,s1 = arr.shape[:2] ss0 = s0//every every = s0//ss0 ss1 = s1//every out = np.full((ss0+1,ss1+1), np.nan) c = {'median':_calcMedian, 'nanmedian':_calcNanMedian, 'nanmean':_calcNanMean, 'mean':_calcMean, }[fn] ss0,ss1 = c(arr, out, ksize, every) out = out[:ss0,:ss1] if smoothksize: out = gaussian_filter(out, smoothksize) if not resize: return out return cv2.resize(out, arr.shape[:2][::-1], interpolation=interpolation)
def frame(image, top=2, bottom=2, left=2, right=2, borderType=cv.BORDER_CONSTANT, color=[255, 0, 0]): ''' add borders around :image: :param image: has to be in RBG color scheme. Use `convert_to_rgb` if it's in opencv BGR scheme. :param color: array representing an RGB color. :param borderType: Other options are: cv.BORDER_REFLECT, cv.BORDER_REFLECT_101, cv.BORDER_DEFAULT, cv.BORDER_REPLICATE, cv.BORDER_WRAP ''' return cv.copyMakeBorder(image, top, bottom, left, right, borderType, value=color)
def create_dataset(opt, mode,fold=0): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), lambda x: x / 255.0, # cvtransforms.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2, 0, 1).astype(np.float32), torch.from_numpy, ]) train_transform = tnt.transform.compose([ cvtransforms.RandomHorizontalFlip(), cvtransforms.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), cvtransforms.RandomCrop(96), convert, ]) smode = 'train' if mode else 'test' ds = getattr(datasets, opt.dataset)('.', split=smode, download=True) if mode: if fold>-1: folds_idx = [map(int, v.split(' ')[:-1]) for v in [line.replace('\n', '') for line in open('./stl10_binary/fold_indices.txt')]][fold] ds = tnt.dataset.TensorDataset([ getattr(ds, 'data').transpose(0, 2, 3, 1)[folds_idx], getattr(ds, 'labels')[folds_idx].tolist()]) else: ds = tnt.dataset.TensorDataset([ getattr(ds, 'data').transpose(0, 2, 3, 1), getattr(ds, 'labels').tolist()]) else: ds = tnt.dataset.TensorDataset([ getattr(ds, 'data').transpose(0, 2, 3, 1), getattr(ds, 'labels').tolist()]) return ds.transform({0: train_transform if mode else convert})
def __call__(self, image, *args): size=self.size if self.type=='constant': image = cv2.copyMakeBorder(image, size, size, size, size, cv2.BORDER_CONSTANT, value=self.constant_color) elif self.type=='reflect': image = cv2.copyMakeBorder(image, size, size, size, size, cv2.BORDER_REFLECT) elif self.type=='replicate': image = cv2.copyMakeBorder(image, size, size, size, size, cv2.BORDER_REPLICATE) if len(args): return (image, *args) else: return image
def create_iterator(opt, mode): if opt.dataset.startswith('CIFAR'): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), T.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2,0,1), torch.from_numpy, ]) train_transform = tnt.transform.compose([ T.RandomHorizontalFlip(), T.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), T.RandomCrop(32), convert, ]) ds = getattr(datasets, opt.dataset)(opt.dataroot, train=mode, download=True) smode = 'train' if mode else 'test' ds = tnt.dataset.TensorDataset([getattr(ds, smode + '_data'), getattr(ds, smode + '_labels')]) ds = ds.transform({0: train_transform if mode else convert}) return ds.parallel(batch_size=opt.batchSize, shuffle=mode, num_workers=opt.nthread, pin_memory=True) elif opt.dataset == 'ImageNet': def cvload(path): img = cv2.imread(path, cv2.IMREAD_COLOR) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) return img convert = tnt.transform.compose([ lambda x: x.astype(np.float32) / 255.0, T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), lambda x: x.transpose(2, 0, 1).astype(np.float32), torch.from_numpy, ]) print("| setting up data loader...") if mode: traindir = os.path.join(opt.dataroot, 'train') ds = datasets.ImageFolder(traindir, tnt.transform.compose([ T.RandomSizedCrop(224), T.RandomHorizontalFlip(), convert, ]), loader=cvload) else: valdir = os.path.join(opt.dataroot, 'val') ds = datasets.ImageFolder(valdir, tnt.transform.compose([ T.Scale(256), T.CenterCrop(224), convert, ]), loader=cvload) return torch.utils.data.DataLoader(ds, batch_size=opt.batchSize, shuffle=mode, num_workers=opt.nthread, pin_memory=False) else: raise ValueError('dataset not understood')
def get_orb_keypoints(bd, image_min, image_max): """ Computes the ORB key points Args: bd (2d array) image_min (int or float) image_max (int or float) """ # We want odd patch sizes. # if parameter_object.scales[-1] % 2 == 0: # patch_size = parameter_object.scales[-1] - 1 if bd.dtype != 'uint8': bd = np.uint8(rescale_intensity(bd, in_range=(image_min, image_max), out_range=(0, 255))) patch_size = 31 patch_size_d = patch_size * 3 # Initiate ORB detector orb = cv2.ORB_create(nfeatures=int(.25*(bd.shape[0]*bd.shape[1])), edgeThreshold=patch_size, scaleFactor=1.2, nlevels=8, patchSize=patch_size, WTA_K=4, scoreType=cv2.ORB_FAST_SCORE) # Add padding because ORB ignores edges. bd = cv2.copyMakeBorder(bd, patch_size_d, patch_size_d, patch_size_d, patch_size_d, cv2.BORDER_REFLECT) # Compute ORB keypoints key_points = orb.detectAndCompute(bd, None)[0] # img = cv2.drawKeypoints(np.uint8(ch_bd), key_points, np.uint8(ch_bd).copy()) return fill_key_points(np.float32(bd), key_points)[patch_size_d:-patch_size_d, patch_size_d:-patch_size_d]
def create_dataset(opt, mode): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), lambda x: x / 255.0, # cvtransforms.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2, 0, 1).astype(np.float32), torch.from_numpy, ]) train_transform = tnt.transform.compose([ cvtransforms.RandomHorizontalFlip(), cvtransforms.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), cvtransforms.RandomCrop(32), convert, ]) ds = getattr(datasets, opt.dataset)('.', train=mode, download=True) smode = 'train' if mode else 'test' if mode: from numpy.random import RandomState prng = RandomState(opt.seed) assert(opt.sampleSize%10==0) random_permute=prng.permutation(np.arange(0,5000))[0:opt.sampleSize/10] labels = np.array(getattr(ds,'train_labels')) data = getattr(ds,'train_data') classes=np.unique(labels) inds_all=np.array([],dtype='int32') for cl in classes: inds=np.where(np.array(labels)==cl)[0][random_permute] inds_all=np.r_[inds,inds_all] ds = tnt.dataset.TensorDataset([ data[inds_all,:].transpose(0, 2, 3, 1), labels[inds_all].tolist()]) else: ds = tnt.dataset.TensorDataset([ getattr(ds, smode + '_data').transpose(0, 2, 3, 1), getattr(ds, smode + '_labels')]) return ds.transform({0: train_transform if mode else convert})
