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# Modified from:
# https://github.com/anibali/pytorch-stacked-hourglass
# https://github.com/bearpaw/pytorch-pose
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import cv2
import torch
# import stacked_hourglass.datasets.utils_stanext as utils_stanext
# COLORS, labels = utils_stanext.load_keypoint_labels_and_colours()
COLORS = ['#d82400', '#d82400', '#d82400', '#fcfc00', '#fcfc00', '#fcfc00', '#48b455', '#48b455', '#48b455', '#0090aa', '#0090aa', '#0090aa', '#d848ff', '#d848ff', '#fc90aa', '#006caa', '#d89000', '#d89000', '#fc90aa', '#006caa', '#ededed', '#ededed', '#a9d08e', '#a9d08e']
RGB_MEAN = [0.4404, 0.4440, 0.4327]
RGB_STD = [0.2458, 0.2410, 0.2468]
def get_img_from_fig(fig, dpi=180):
buf = io.BytesIO()
fig.savefig(buf, format="png", dpi=dpi)
buf.seek(0)
img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
buf.close()
img = cv2.imdecode(img_arr, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
def save_input_image_with_keypoints(img, tpts, out_path='./test_input.png', colors=COLORS, rgb_mean=RGB_MEAN, rgb_std=RGB_STD, ratio_in_out=4., threshold=0.3, print_scores=False):
"""
img has shape (3, 256, 256) and is a torch tensor
pts has shape (20, 3) and is a torch tensor
-> this function is tested with the mpii dataset and the results look ok
"""
# reverse color normalization
for t, m, s in zip(img, rgb_mean, rgb_std): t.add_(m) # inverse to transforms.color_normalize()
img_np = img.detach().cpu().numpy().transpose(1, 2, 0)
# tpts_np = tpts.detach().cpu().numpy()
# plot image
fig, ax = plt.subplots()
plt.imshow(img_np) # plt.imshow(im)
plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)
plt.margins(0,0)
# plot all visible keypoints
#import pdb; pdb.set_trace()
for idx, (x, y, v) in enumerate(tpts):
if v > threshold:
x = int(x*ratio_in_out)
y = int(y*ratio_in_out)
plt.scatter([x], [y], c=[colors[idx]], marker="x", s=50)
if print_scores:
txt = '{:2.2f}'.format(v.item())
plt.annotate(txt, (x, y)) # , c=colors[idx])
plt.savefig(out_path, bbox_inches='tight', pad_inches=0)
plt.close()
return
def save_input_image(img, out_path, colors=COLORS, rgb_mean=RGB_MEAN, rgb_std=RGB_STD):
for t, m, s in zip(img, rgb_mean, rgb_std): t.add_(m) # inverse to transforms.color_normalize()
img_np = img.detach().cpu().numpy().transpose(1, 2, 0)
plt.imsave(out_path, img_np)
return
######################################################################
def get_bodypart_colors():
# body colors
n_body = 8
c = np.arange(1, n_body + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.gist_rainbow)
cmap.set_array([])
body_cols = []
for i in range(0, n_body):
body_cols.append(cmap.to_rgba(i + 1))
# head colors
n_blue = 5
c = np.arange(1, n_blue + 1)
norm = mpl.colors.Normalize(vmin=c.min()-1, vmax=c.max()+1)
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.Blues)
cmap.set_array([])
head_cols = []
for i in range(0, n_body):
head_cols.append(cmap.to_rgba(i + 1))
# torso colors
n_blue = 2
c = np.arange(1, n_blue + 1)
norm = mpl.colors.Normalize(vmin=c.min()-1, vmax=c.max()+1)
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.Greens)
cmap.set_array([])
torso_cols = []
for i in range(0, n_body):
torso_cols.append(cmap.to_rgba(i + 1))
return body_cols, head_cols, torso_cols
body_cols, head_cols, torso_cols = get_bodypart_colors()
tbp_dict = {'full_body': [0, 8],
'head': [8, 13],
'torso': [13, 15]}
def save_image_with_part_segmentation(partseg_big, seg_big, input_image_np, ind_img, out_path_seg=None, out_path_seg_overlay=None, thr=0.3):
soft_max = torch.nn.Softmax(dim=0)
# create dit with results
tbp_dict_res = {}
for ind_tbp, part in enumerate(['full_body', 'head', 'torso']):
partseg_tbp = partseg_big[:, tbp_dict[part][0]:tbp_dict[part][1], :, :]
segm_img_pred = soft_max((partseg_tbp[ind_img, :, :, :])) # [1, :, :]
m_v, m_i = segm_img_pred.max(axis=0)
tbp_dict_res[part] = {
'inds': tbp_dict[part],
'seg_probs': segm_img_pred,
'seg_max_inds': m_i,
'seg_max_values': m_v}
# create output_image
partseg_image = np.zeros((256, 256, 3))
for ind_sp in range(0, 5):
# partseg_image[tbp_dict_res['head']['seg_max_inds']==ind_sp, :] = head_cols[ind_sp][0:3]
mask_a = tbp_dict_res['full_body']['seg_max_inds']==1
mask_b = tbp_dict_res['head']['seg_max_inds']==ind_sp
partseg_image[mask_a*mask_b, :] = head_cols[ind_sp][0:3]
for ind_sp in range(0, 2):
# partseg_image[tbp_dict_res['torso']['seg_max_inds']==ind_sp, :] = torso_cols[ind_sp][0:3]
mask_a = tbp_dict_res['full_body']['seg_max_inds']==2
mask_b = tbp_dict_res['torso']['seg_max_inds']==ind_sp
partseg_image[mask_a*mask_b, :] = torso_cols[ind_sp][0:3]
for ind_sp in range(0, 8):
if (not ind_sp == 1) and (not ind_sp == 2): # head and torso
partseg_image[tbp_dict_res['full_body']['seg_max_inds']==ind_sp, :] = body_cols[ind_sp][0:3]
partseg_image[soft_max((seg_big[ind_img, :, :, :]))[1, :, :]<thr, :] = 0
# save images
if out_path_seg is not None:
plt.imsave(out_path_seg, partseg_image)
if out_path_seg_overlay is not None:
partseg_image[soft_max((seg_big[ind_img, :, :, :]))[1, :, :]<thr, :] = input_image_np[soft_max((seg_big[ind_img, :, :, :]))[1, :, :]<thr, :]
im_masked_partseg = cv2.addWeighted(input_image_np.astype(np.float32),0.5,partseg_image.astype(np.float32),0.5,0)
plt.imsave(out_path_seg_overlay, im_masked_partseg)
return
def save_image_with_part_segmentation_from_gt_annotation(partseg_annots, out_path, ind_img=0):
# partseg_annots: (bs, 3, 256, 256)
# import pdb; pdb.set_trace()
annots = partseg_annots[ind_img, :, :, :]
partseg_image = np.zeros((256, 256, 3))
for ind_sp in range(0, 8):
partseg_image[annots[0, :, :]==ind_sp, :] = body_cols[ind_sp][0:3]
for ind_sp in range(0, 5):
partseg_image[annots[1, :, :]==ind_sp, :] = head_cols[ind_sp][0:3]
for ind_sp in range(0, 2):
partseg_image[annots[2, :, :]==ind_sp, :] = torso_cols[ind_sp][0:3]
plt.imsave(out_path, partseg_image.astype(np.float32))
return
def save_image_from_prepared_partseg(partseg_init, out_path):
# partseg_init: (256, 256, 11)
# partseg_init = output_reproj['partseg_images_hg_nograd'][0, :, :, :].detach().cpu().numpy()
# out_path = '/ps/scratch/nrueegg/new_projects/Animals/dog_project/pytorch-stacked-hourglass/debugging_output/partseg_hg_0.png'
partseg = np.argmax(partseg_init, axis=2)
partseg_image = np.zeros((256, 256, 3))
for ind in range(partseg_init.shape[2]):
if ind == 0: # head
partseg_image[partseg==ind, :] = np.asarray(head_cols[0][0:3])
elif ind < 7:
partseg_image[partseg==ind, :] = np.asarray(body_cols[ind+1][0:3])
else: # 7 to 10
partseg_image[partseg==ind, :] = np.asarray(head_cols[ind-6][0:3])
partseg_image[partseg_init.sum(axis=2)==0, :] = 0
plt.imsave(out_path, partseg_image)
return
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