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| import torch |
| import numpy as np |
| import cv2 |
|
|
| class Visualize: |
| @classmethod |
| def visualize(cls, x): |
| dimension = len(x.shape) |
| if dimension == 2: |
| pass |
| elif dimension == 3: |
| pass |
|
|
| @classmethod |
| def to_np(cls, x): |
| return x.cpu().data.numpy() |
|
|
| @classmethod |
| def visualize_weights(cls, tensor, format='HW', normalize=True): |
| if isinstance(tensor, torch.Tensor): |
| x = cls.to_np(tensor.permute(format.index('H'), format.index('W'))) |
| else: |
| x = tensor.transpose(format.index('H'), format.index('W')) |
| if normalize: |
| x = (x - x.min()) / (x.max() - x.min()) |
| |
| return cv2.applyColorMap((x * 255).astype(np.uint8), cv2.COLORMAP_JET) |
|
|
| @classmethod |
| def visualize_points(cls, image, tensor, radius=5, normalized=True): |
| if isinstance(tensor, torch.Tensor): |
| points = cls.to_np(tensor) |
| else: |
| points = tensor |
| if normalized: |
| points = points * image.shape[:2][::-1] |
| for i in range(points.shape[0]): |
| color = np.random.randint( |
| 0, 255, (3, ), dtype=np.uint8).astype(np.float) |
| image = cv2.circle(image, |
| tuple(points[i].astype(np.int32).tolist()), |
| radius, color, thickness=radius//2) |
| return image |
|
|
| @classmethod |
| def visualize_heatmap(cls, tensor, format='CHW'): |
| if isinstance(tensor, torch.Tensor): |
| x = cls.to_np(tensor.permute(format.index('H'), |
| format.index('W'), format.index('C'))) |
| else: |
| x = tensor.transpose( |
| format.index('H'), format.index('W'), format.index('C')) |
| canvas = np.zeros((x.shape[0], x.shape[1], 3), dtype=np.float) |
|
|
| for c in range(0, x.shape[-1]): |
| color = np.random.randint( |
| 0, 255, (3, ), dtype=np.uint8).astype(np.float) |
| canvas += np.tile(x[:, :, c], (3, 1, 1) |
| ).swapaxes(0, 2).swapaxes(1, 0) * color |
|
|
| canvas = canvas.astype(np.uint8) |
| return canvas |
|
|
| @classmethod |
| def visualize_classes(cls, x): |
| canvas = np.zeros((x.shape[0], x.shape[1], 3), dtype=np.uint8) |
| for c in range(int(x.max())): |
| color = np.random.randint( |
| 0, 255, (3, ), dtype=np.uint8).astype(np.float) |
| canvas[np.where(x == c)] = color |
| return canvas |
|
|
| @classmethod |
| def visualize_grid(cls, x, y, stride=16, color=(0, 0, 255), canvas=None): |
| h, w = x.shape |
| if canvas is None: |
| canvas = np.zeros((h, w, 3), dtype=np.uint8) |
| |
| i, j = 0, 0 |
| while i < w: |
| j = 0 |
| while j < h: |
| canvas = cv2.circle(canvas, (int(x[i, j] * w + 0.5), int(y[i, j] * h + 0.5)), radius=max(stride//4, 1), color=color, thickness=stride//8) |
| j += stride |
| i += stride |
| return canvas |
|
|
| @classmethod |
| def visualize_rect(cls, canvas, _rect, color=(0, 0, 255)): |
| rect = (_rect + 0.5).astype(np.int32) |
| return cv2.rectangle(canvas, (rect[0], rect[1]), (rect[2], rect[3]), color) |
|
|
