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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import torchvision
import cv2
from core.inference import get_max_preds
def save_batch_image_with_joints(batch_image, batch_joints, batch_joints_vis,
file_name, nrow=8, padding=2):
'''
batch_image: [batch_size, channel, height, width]
batch_joints: [batch_size, num_joints, 3],
batch_joints_vis: [batch_size, num_joints, 1],
}
'''
grid = torchvision.utils.make_grid(batch_image, nrow, padding, True)
ndarr = grid.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()
ndarr = ndarr.copy()
nmaps = batch_image.size(0)
xmaps = min(nrow, nmaps)
ymaps = int(math.ceil(float(nmaps) / xmaps))
height = int(batch_image.size(2) + padding)
width = int(batch_image.size(3) + padding)
k = 0
for y in range(ymaps):
for x in range(xmaps):
if k >= nmaps:
break
joints = batch_joints[k]
joints_vis = batch_joints_vis[k]
for joint, joint_vis in zip(joints, joints_vis):
joint[0] = x * width + padding + joint[0]
joint[1] = y * height + padding + joint[1]
if joint_vis[0]:
cv2.circle(ndarr, (int(joint[0]), int(joint[1])), 2, [255, 0, 0], 2)
k = k + 1
cv2.imwrite(file_name, ndarr)
def save_batch_heatmaps(batch_image, batch_heatmaps, file_name,
normalize=True):
'''
batch_image: [batch_size, channel, height, width]
batch_heatmaps: ['batch_size, num_joints, height, width]
file_name: saved file name
'''
if normalize:
batch_image = batch_image.clone()
min = float(batch_image.min())
max = float(batch_image.max())
batch_image.add_(-min).div_(max - min + 1e-5)
batch_size = batch_heatmaps.size(0)
num_joints = batch_heatmaps.size(1)
heatmap_height = batch_heatmaps.size(2)
heatmap_width = batch_heatmaps.size(3)
grid_image = np.zeros((batch_size*heatmap_height,
(num_joints+1)*heatmap_width,
3),
dtype=np.uint8)
preds, maxvals = get_max_preds(batch_heatmaps.detach().cpu().numpy())
for i in range(batch_size):
image = batch_image[i].mul(255)\
.clamp(0, 255)\
.byte()\
.permute(1, 2, 0)\
.cpu().numpy()
heatmaps = batch_heatmaps[i].mul(255)\
.clamp(0, 255)\
.byte()\
.cpu().numpy()
resized_image = cv2.resize(image,
(int(heatmap_width), int(heatmap_height)))
height_begin = heatmap_height * i
height_end = heatmap_height * (i + 1)
for j in range(num_joints):
cv2.circle(resized_image,
(int(preds[i][j][0]), int(preds[i][j][1])),
1, [0, 0, 255], 1)
heatmap = heatmaps[j, :, :]
colored_heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
masked_image = colored_heatmap*0.7 + resized_image*0.3
cv2.circle(masked_image,
(int(preds[i][j][0]), int(preds[i][j][1])),
1, [0, 0, 255], 1)
width_begin = heatmap_width * (j+1)
width_end = heatmap_width * (j+2)
grid_image[height_begin:height_end, width_begin:width_end, :] = \
masked_image
# grid_image[height_begin:height_end, width_begin:width_end, :] = \
# colored_heatmap*0.7 + resized_image*0.3
grid_image[height_begin:height_end, 0:heatmap_width, :] = resized_image
cv2.imwrite(file_name, grid_image)
def save_debug_images(config, input, meta, target, joints_pred, output,
prefix):
if not config.DEBUG.DEBUG:
return
if config.DEBUG.SAVE_BATCH_IMAGES_GT:
save_batch_image_with_joints(
input, meta['joints'], meta['joints_vis'],
'{}_gt.jpg'.format(prefix)
)
if config.DEBUG.SAVE_BATCH_IMAGES_PRED:
save_batch_image_with_joints(
input, joints_pred, meta['joints_vis'],
'{}_pred.jpg'.format(prefix)
)
if config.DEBUG.SAVE_HEATMAPS_GT:
save_batch_heatmaps(
input, target, '{}_hm_gt.jpg'.format(prefix)
)
if config.DEBUG.SAVE_HEATMAPS_PRED:
save_batch_heatmaps(
input, output, '{}_hm_pred.jpg'.format(prefix)
)
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