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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : visualizer.py
# Author : Zhaoyi Wan <wanzhaoyi@megvii.com>
# Date : 08.01.2019
# Last Modified Date: 02.12.2019
# Last Modified By : Minghui Liao
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 np.tile(x * 255., (3, 1, 1)).swapaxes(0, 2).swapaxes(1, 0).astype(np.uint8)
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)
# canvas = np.concatenate([canvas, canvas], axis=1)
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)
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