Python numpy.flip() Examples
The following are 30
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Example #1
Source File: utils.py From tf2-yolo3 with Apache License 2.0 | 8 votes |
def draw_labels(x, y, class_names=None): img = x.numpy() if img.ndim == 2 or img.shape[2] == 1: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) boxes, classes = tf.split(y, (4, 1), axis=-1) classes = classes[..., 0] wh = np.flip(img.shape[0:2]) min_wh = np.amin(wh) if min_wh <= 100: font_size = 0.5 else: font_size = 1 for i in range(len(boxes)): x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32)) x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32)) img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 1) if class_names: img = cv2.putText(img, class_names[classes[i]], x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, font_size, (0, 0, 255), 1) else: img = cv2.putText(img, str(classes[i]), x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1) return img
Example #2
Source File: utils.py From tf2-yolo3 with Apache License 2.0 | 8 votes |
def draw_outputs(img, outputs, class_names=None): boxes, objectness, classes = outputs #boxes, objectness, classes = boxes[0], objectness[0], classes[0] wh = np.flip(img.shape[0:2]) if img.ndim == 2 or img.shape[2] == 1: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) min_wh = np.amin(wh) if min_wh <= 100: font_size = 0.5 else: font_size = 1 for i in range(classes.shape[0]): x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32)) x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32)) img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 1) img = cv2.putText(img, '{}'.format(int(classes[i])), x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, font_size, (0, 0, 255), 1) return img
Example #3
Source File: time_align.py From scanorama with MIT License | 7 votes |
def time_align_visualize(alignments, time, y, namespace='time_align'): plt.figure() heat = np.flip(alignments + alignments.T + np.eye(alignments.shape[0]), axis=0) sns.heatmap(heat, cmap="YlGnBu", vmin=0, vmax=1) plt.savefig(namespace + '_heatmap.svg') G = nx.from_numpy_matrix(alignments) G = nx.maximum_spanning_tree(G) pos = {} for i in range(len(G.nodes)): pos[i] = np.array([time[i], y[i]]) mst_edges = set(nx.maximum_spanning_tree(G).edges()) weights = [ G[u][v]['weight'] if (not (u, v) in mst_edges) else 8 for u, v in G.edges() ] plt.figure() nx.draw(G, pos, edges=G.edges(), width=10) plt.ylim([-1, 1]) plt.savefig(namespace + '.svg')
Example #4
Source File: datagen.py From StyleGAN2-Tensorflow-2.0 with MIT License | 6 votes |
def __init__(self, folder, im_size, mss = (1024 ** 3), flip = True, verbose = True): self.folder = folder self.im_size = im_size self.segment_length = mss // (im_size * im_size * 3) self.flip = flip self.verbose = verbose self.segments = [] self.images = [] self.update = 0 if self.verbose: print("Importing images...") print("Maximum Segment Size: ", self.segment_length) try: os.mkdir("data/" + self.folder + "-npy-" + str(self.im_size)) except: self.load_from_npy(folder) return self.folder_to_npy(self.folder) self.load_from_npy(self.folder)
Example #5
Source File: inference.py From HorizonNet with MIT License | 6 votes |
def augment_undo(x_imgs_augmented, aug_type): x_imgs_augmented = x_imgs_augmented.cpu().numpy() sz = x_imgs_augmented.shape[0] // len(aug_type) x_imgs = [] for i, aug in enumerate(aug_type): x_img = x_imgs_augmented[i*sz : (i+1)*sz] if aug == 'flip': x_imgs.append(np.flip(x_img, axis=-1)) elif aug.startswith('rotate'): shift = int(aug.split()[-1]) x_imgs.append(np.roll(x_img, -shift, axis=-1)) elif aug == '': x_imgs.append(x_img) else: raise NotImplementedError() return np.array(x_imgs)
Example #6
Source File: randomized_argsort.py From pymoo with Apache License 2.0 | 6 votes |
def randomized_argsort(A, method="numpy", order='ascending'): if method == "numpy": P = np.random.permutation(len(A)) I = np.argsort(A[P], kind='quicksort') I = P[I] elif method == "quicksort": I = quicksort(A) else: raise Exception("Randomized sort method not known.") if order == 'ascending': return I elif order == 'descending': return np.flip(I, axis=0) else: raise Exception("Unknown sorting order: ascending or descending.")
Example #7
Source File: viz.py From signaltrain with GNU General Public License v3.0 | 6 votes |
def show_2d(array_2d, title="weights_layer", colormap=rainbow, flip=True): #print("weights_layer.shape = ",weights_layer.shape) if len(array_2d.shape) < 2: return img = np.clip(array_2d*255 ,-255,255).astype(np.uint8) # scale if flip: img = np.flip(np.transpose(img)) img = np.repeat(img[:,:,np.newaxis],3,axis=2) # add color channels img = cv2.applyColorMap(img, colormap) # rainbow: blue=low, red=high # see if it exists new_window = not check_window_exists(title) window = cv2.namedWindow(title,cv2.WINDOW_NORMAL) cv2.imshow(title, img) if new_window: cv2.resizeWindow(title, img.shape[1], img.shape[0]) # show what we've got #aspect = img.shape[0] / img.shape[1] #if aspect > 3: # cv2.resizeWindow(title, 200, 1024) # zoom in/out (can use two-finger-scroll to zoom in) #print(f"size for {title} = 1024, {int(1024/aspect*img.shape[0])}") #else: # cv2.resizeWindow(title, int(imWidth/2),int(imWidth/2)) # zoom in/out (can use two-finger-scroll to zoom in) # draw weights for all layers using model state dict
Example #8
Source File: dataset.py From HorizonNet with MIT License | 6 votes |
def __init__(self, root_dir, flip=False, rotate=False, gamma=False, stretch=False, p_base=0.96, max_stretch=2.0, normcor=False, return_cor=False, return_path=False): self.img_dir = os.path.join(root_dir, 'img') self.cor_dir = os.path.join(root_dir, 'label_cor') self.img_fnames = sorted([ fname for fname in os.listdir(self.img_dir) if fname.endswith('.jpg') or fname.endswith('.png') ]) self.txt_fnames = ['%s.txt' % fname[:-4] for fname in self.img_fnames] self.flip = flip self.rotate = rotate self.gamma = gamma self.stretch = stretch self.p_base = p_base self.max_stretch = max_stretch self.normcor = normcor self.return_cor = return_cor self.return_path = return_path self._check_dataset()
Example #9
Source File: algs.py From mabalgs with Apache License 2.0 | 6 votes |
def select(self): """ This method selects the best arm chosen by Thompsom Sampling. :return: Return selected arm number. Arm number returned is (n_arm - 1). Returns a list of arms by importance. The chosen arm is the index 0 of this list. """ rewards_0 = self.n_impressions - self.n_rewards rewards_0[rewards_0 <= 0] = 1 theta_value = np.random.beta(self.n_rewards, rewards_0) ranked_arms = np.flip(np.argsort(theta_value), axis=0) chosen_arm = ranked_arms[0] self.n_impressions[chosen_arm] += 1 return chosen_arm, ranked_arms
Example #10
Source File: utils.py From tensornets with MIT License | 6 votes |
def crop(img, crop_size, crop_loc=4, crop_grid=(3, 3)): if isinstance(crop_loc, list): imgs = np.zeros((img.shape[0], len(crop_loc), crop_size, crop_size, 3), np.float32) for (i, loc) in enumerate(crop_loc): r, c = crop_idx(img.shape[1:3], crop_size, loc, crop_grid) imgs[:, i] = img[:, r:r+crop_size, c:c+crop_size, :] return imgs elif crop_loc == np.prod(crop_grid) + 1: imgs = np.zeros((img.shape[0], crop_loc, crop_size, crop_size, 3), np.float32) r, c = crop_idx(img.shape[1:3], crop_size, 4, crop_grid) imgs[:, 0] = img[:, r:r+crop_size, c:c+crop_size, :] imgs[:, 1] = img[:, 0:crop_size, 0:crop_size, :] imgs[:, 2] = img[:, 0:crop_size, -crop_size:, :] imgs[:, 3] = img[:, -crop_size:, 0:crop_size, :] imgs[:, 4] = img[:, -crop_size:, -crop_size:, :] imgs[:, 5:] = np.flip(imgs[:, :5], axis=3) return imgs else: r, c = crop_idx(img.shape[1:3], crop_size, crop_loc, crop_grid) return img[:, r:r+crop_size, c:c+crop_size, :]
Example #11
Source File: datagen.py From StyleGAN2-Tensorflow-2.0 with MIT License | 6 votes |
def get_batch(self, num): if self.update > self.images.shape[0]: self.load_from_npy(self.folder) self.update = self.update + num idx = np.random.randint(0, self.images.shape[0] - 1, num) out = [] for i in idx: out.append(self.images[i]) if self.flip and random.random() < 0.5: out[-1] = np.flip(out[-1], 1) return np.array(out).astype('float32') / 255.0
Example #12
Source File: bigan.py From Keras-BiGAN with MIT License | 6 votes |
def __init__(self, steps = 1, lr = 0.0001, decay = 0.00001, silent = True): self.GAN = GAN(steps = steps, lr = lr, decay = decay) self.DisModel = self.GAN.DisModel() self.AdModel = self.GAN.AdModel() self.lastblip = time.clock() self.noise_level = 0 self.im = dataGenerator(directory, suffix = suff, flip = True) self.silent = silent #Train Generator to be in the middle, not all the way at real. Apparently works better?? self.ones = np.ones((BATCH_SIZE, 1), dtype=np.float32) self.zeros = np.zeros((BATCH_SIZE, 1), dtype=np.float32) self.nones = -self.ones
Example #13
Source File: utils.py From py360convert with MIT License | 6 votes |
def equirect_facetype(h, w): ''' 0F 1R 2B 3L 4U 5D ''' tp = np.roll(np.arange(4).repeat(w // 4)[None, :].repeat(h, 0), 3 * w // 8, 1) # Prepare ceil mask mask = np.zeros((h, w // 4), np.bool) idx = np.linspace(-np.pi, np.pi, w // 4) / 4 idx = h // 2 - np.round(np.arctan(np.cos(idx)) * h / np.pi).astype(int) for i, j in enumerate(idx): mask[:j, i] = 1 mask = np.roll(np.concatenate([mask] * 4, 1), 3 * w // 8, 1) tp[mask] = 4 tp[np.flip(mask, 0)] = 5 return tp.astype(np.int32)
Example #14
Source File: test_function_base.py From recruit with Apache License 2.0 | 6 votes |
def test_multiple_axes(self): a = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) assert_equal(np.flip(a, axis=()), a) b = np.array([[[5, 4], [7, 6]], [[1, 0], [3, 2]]]) assert_equal(np.flip(a, axis=(0, 2)), b) c = np.array([[[3, 2], [1, 0]], [[7, 6], [5, 4]]]) assert_equal(np.flip(a, axis=(1, 2)), c)
Example #15
Source File: BuildAdjacency.py From sparse-subspace-clustering-python with MIT License | 6 votes |
def BuildAdjacency(CMat, K): CMat = CMat.astype(float) CKSym = None N, _ = CMat.shape CAbs = np.absolute(CMat).astype(float) for i in range(0, N): c = CAbs[:, i] PInd = np.flip(np.argsort(c), 0) CAbs[:, i] = CAbs[:, i] / float(np.absolute(c[PInd[0]])) CSym = np.add(CAbs, CAbs.T).astype(float) if K != 0: Ind = np.flip(np.argsort(CSym, axis=0), 0) CK = np.zeros([N, N]).astype(float) for i in range(0, N): for j in range(0, K): CK[Ind[j, i], i] = CSym[Ind[j, i], i] / float(np.absolute(CSym[Ind[0, i], i])) CKSym = np.add(CK, CK.T) else: CKSym = CSym return CKSym
Example #16
Source File: CausalIntegration.py From pylops with GNU Lesser General Public License v3.0 | 5 votes |
def _rmatvec(self, x): if self.reshape: x = np.reshape(x, self.dims) if self.dir != -1: x = np.swapaxes(x, self.dir, -1) xflip = np.flip(x, axis=-1) if self.halfcurrent: y = self.sampling * (np.cumsum(xflip, axis=-1) - xflip/2.) else: y = self.sampling * np.cumsum(xflip, axis=-1) y = np.flip(y, axis=-1) if self.dir != -1: y = np.swapaxes(y, -1, self.dir) return y.ravel()
Example #17
Source File: test_function_base.py From lambda-packs with MIT License | 5 votes |
def test_basic_ud(self): a = get_mat(4) b = a[::-1, :] assert_equal(np.flip(a, 0), b) a = [[0, 1, 2], [3, 4, 5]] b = [[3, 4, 5], [0, 1, 2]] assert_equal(np.flip(a, 0), b)
Example #18
Source File: test_function_base.py From lambda-packs with MIT License | 5 votes |
def test_3d_swap_axis0(self): a = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) b = np.array([[[4, 5], [6, 7]], [[0, 1], [2, 3]]]) assert_equal(np.flip(a, 0), b)
Example #19
Source File: test_function_base.py From lambda-packs with MIT License | 5 votes |
def test_3d_swap_axis1(self): a = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) b = np.array([[[2, 3], [0, 1]], [[6, 7], [4, 5]]]) assert_equal(np.flip(a, 1), b)
Example #20
Source File: test_function_base.py From lambda-packs with MIT License | 5 votes |
def test_basic_lr(self): a = get_mat(4) b = a[:, ::-1] assert_equal(np.flip(a, 1), b) a = [[0, 1, 2], [3, 4, 5]] b = [[2, 1, 0], [5, 4, 3]] assert_equal(np.flip(a, 1), b)
Example #21
Source File: algs.py From mabalgs with Apache License 2.0 | 5 votes |
def select(self): """ This method selects the best arm chosen by UCB1. :return: Return selected arm number. Arm number returned is (n_arm - 1). Returns a list of arms by importance. The chosen arm is the index 0 of this list. """ arm_dont_usage = np.where(self.number_of_selections == 0)[0] if len(arm_dont_usage) > 0: self.number_of_selections[arm_dont_usage[0]] += 1 ranked_arms = list(range(len(self.number_of_selections))) if arm_dont_usage[0] != 0: ranked_arms = np.roll(ranked_arms, 1) first_element = ranked_arms[0] index_current = ranked_arms.tolist().index(arm_dont_usage[0]) ranked_arms[0] = arm_dont_usage[0] ranked_arms[index_current] = first_element return arm_dont_usage[0], ranked_arms average_reward = self.rewards / self.number_of_selections total_counts = np.sum(self.number_of_selections) ucb_values = self._factor_importance_each_arm( total_counts, self.number_of_selections, average_reward ) ranked_arms = np.flip(np.argsort(ucb_values), axis=0) chosen_arm = ranked_arms[0] self.number_of_selections[chosen_arm] += 1 return chosen_arm, ranked_arms
Example #22
Source File: Flip.py From pylops with GNU Lesser General Public License v3.0 | 5 votes |
def _matvec(self, x): if self.reshape: x = np.reshape(x, self.dims) y = np.flip(x, axis=self.dir) if self.reshape: y = np.ndarray.flatten(y) return y
Example #23
Source File: helpers.py From DEXTR-KerasTensorflow with GNU General Public License v3.0 | 5 votes |
def get_bbox(mask, points=None, pad=0, zero_pad=False): if points is not None: inds = np.flip(points.transpose(), axis=0) else: inds = np.where(mask > 0) if inds[0].shape[0] == 0: return None if zero_pad: x_min_bound = -np.inf y_min_bound = -np.inf x_max_bound = np.inf y_max_bound = np.inf else: x_min_bound = 0 y_min_bound = 0 x_max_bound = mask.shape[1] - 1 y_max_bound = mask.shape[0] - 1 x_min = max(inds[1].min() - pad, x_min_bound) y_min = max(inds[0].min() - pad, y_min_bound) x_max = min(inds[1].max() + pad, x_max_bound) y_max = min(inds[0].max() + pad, y_max_bound) return x_min, y_min, x_max, y_max
Example #24
Source File: cmath.py From ehtplot with GNU General Public License v3.0 | 5 votes |
def factor(Cp, softening=1.0, bitonic=True, diffuse=True, CpL=None, CpR=None, verbose=False): """Comput the factor required to perform several chroma operations""" S = Cp + softening s = S.copy() N = len(Cp) H = N//2 m = np.minimum(s[:H], np.flip(s[-H:])) if bitonic: # force half of Cp increase monotonically if m[H-1] > s[H]: m[H-1] = s[H] if verbose: print("Enforce bitonic at {}".format(s[H])) for i in range(H-1,0,-1): if m[i-1] > m[i]: m[i-1] = m[i] if verbose: print("Enforce bitonic at {}".format(m[i])) s[:+H] = m s[-H:] = np.flip(m) if CpL is not None: s[ 0] = CpL + softening if CpR is not None: s[-1] = CpR + softening if diffuse: # diffuse s using forward Euler for i in range(N): s[1:-1] += 0.5 * (s[2:] + s[:-2] - 2.0 * s[1:-1]) return s / S
Example #25
Source File: cmath.py From ehtplot with GNU General Public License v3.0 | 5 votes |
def interp(x, xp, yp): """Improve numpy's interp() function to allow decreasing `xp`""" if xp[0] < xp[-1]: return np.interp(x, xp, yp) else: return np.interp(x, np.flip(xp,0), np.flip(yp,0))
Example #26
Source File: video_transforms.py From deep-smoke-machine with BSD 3-Clause "New" or "Revised" License | 5 votes |
def __call__(self, imgs): """ Args: img (seq Images): seq Images to be flipped. Returns: seq Images: Randomly flipped seq images. """ if random.random() < self.p: # t x h x w return np.flip(imgs, axis=2).copy() return imgs
Example #27
Source File: stereo_selfsupervised.py From DSMnet with Apache License 2.0 | 5 votes |
def flip_lr_tensor(self, tensor): data_numpy = np.flip(tensor.cpu().numpy(), axis=-1).copy() return torch.from_numpy(data_numpy).type_as(tensor)
Example #28
Source File: img_rw.py From DSMnet with Apache License 2.0 | 5 votes |
def imwrite(fname, image): if(fname.find('.pfm') > 0): save_pfm(fname, image) else: image = np.flip(image, axis=2) # rgb --> bgr cv2.imwrite(fname, image)
Example #29
Source File: img_rw.py From DSMnet with Apache License 2.0 | 5 votes |
def imread(fname): if(fname.find('.pfm') > 0): return load_pfm(fname)[0] else: image = cv2.imread(fname) image = np.flip(image, axis=2) # bgr --> rgb return np.array(image)
Example #30
Source File: atlas.py From ibllib with MIT License | 5 votes |
def plot_sslice(self, ml_coordinate, volume='image', **kwargs): """ Imshow a sagittal slice :param: ml_coordinate (mm) :param: ax """ vol = self.label if volume == 'annotation' else self.image return self._plot_slice(vol[:, self.bc.x2i(ml_coordinate / 1e3), :].transpose(), extent=np.r_[self.bc.ylim * 1e3, np.flip(self.bc.zlim) * 1e3], **kwargs)