|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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) |
|
|
|
