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Running
on
Zero
Running
on
Zero
File size: 7,291 Bytes
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import os
import random
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
import uuid
plt.rc('font', size=20)
colors = [
[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0],
[85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255],
[0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255],
[255, 0, 255], [255, 0, 170], [255, 0, 85], [255, 0, 0]]
def plot_results(support_img, query_img, support_kp, support_w, query_kp, query_w, skeleton,
initial_proposals, prediction, radius=6, out_dir='./heatmaps'):
img_names = [img.split("_")[0] for img in os.listdir(out_dir) if str_is_int(img.split("_")[0])]
if len(img_names) > 0:
name_idx = max([int(img_name) for img_name in img_names]) + 1
else:
name_idx = 0
h, w, c = support_img.shape
prediction = prediction[-1].cpu().numpy() * h
support_img = (support_img - np.min(support_img)) / (np.max(support_img) - np.min(support_img))
query_img = (query_img - np.min(query_img)) / (np.max(query_img) - np.min(query_img))
for id, (img, w, keypoint) in enumerate(zip([support_img, query_img],
[support_w, query_w],
[support_kp, prediction])):
f, axes = plt.subplots()
plt.imshow(img)
for k in range(keypoint.shape[0]):
if w[k] > 0:
kp = keypoint[k, :2]
c = (1, 0, 0, 0.75) if w[k] == 1 else (0, 0, 1, 0.6)
patch = plt.Circle(kp, radius, color=c)
axes.add_patch(patch)
axes.text(kp[0], kp[1], k)
plt.draw()
for l, limb in enumerate(skeleton):
kp = keypoint[:, :2]
if l > len(colors) - 1:
c = [x / 255 for x in random.sample(range(0, 255), 3)]
else:
c = [x / 255 for x in colors[l]]
if w[limb[0]] > 0 and w[limb[1]] > 0:
patch = plt.Line2D([kp[limb[0], 0], kp[limb[1], 0]],
[kp[limb[0], 1], kp[limb[1], 1]],
linewidth=6, color=c, alpha=0.6)
axes.add_artist(patch)
plt.axis('off') # command for hiding the axis.
name = 'support' if id == 0 else 'query'
plt.savefig(f'./{out_dir}/{str(name_idx)}_{str(name)}.png', bbox_inches='tight', pad_inches=0)
if id == 1:
plt.show()
plt.clf()
plt.close('all')
def plot_query_results(query_img, query_w, skeleton, prediction, radius=6, out_dir='./heatmaps'):
img_names = [img.split("_")[0] for img in os.listdir(out_dir) if str_is_int(img.split("_")[0])]
if len(img_names) > 0:
name_idx = max([int(img_name) for img_name in img_names]) + 1
else:
name_idx = 0
h, w, c = query_img.shape
prediction = prediction[-1].cpu().numpy() * h
query_img = (query_img - np.min(query_img)) / (np.max(query_img) - np.min(query_img))
for id, (img, w, keypoint) in enumerate(zip([query_img],
[query_w],
[prediction])):
f, axes = plt.subplots()
plt.imshow(img)
for k in range(keypoint.shape[0]):
if w[k] > 0:
kp = keypoint[k, :2]
c = (1, 0, 0, 0.75) if w[k] == 1 else (0, 0, 1, 0.6)
patch = plt.Circle(kp, radius, color=c)
axes.add_patch(patch)
axes.text(kp[0], kp[1], k)
plt.draw()
for l, limb in enumerate(skeleton):
kp = keypoint[:, :2]
if l > len(colors) - 1:
c = [x / 255 for x in random.sample(range(0, 255), 3)]
else:
c = [x / 255 for x in colors[l]]
if w[limb[0]] > 0 and w[limb[1]] > 0:
patch = plt.Line2D([kp[limb[0], 0], kp[limb[1], 0]],
[kp[limb[0], 1], kp[limb[1], 1]],
linewidth=6, color=c, alpha=0.6)
axes.add_artist(patch)
plt.axis('off') # command for hiding the axis.
plt.savefig(f'./{out_dir}/{str(name_idx)}_query_out.png', bbox_inches='tight', pad_inches=0)
plt.show()
plt.clf()
plt.close('all')
return name_idx
def plot_modified_query(query_img, query_w, skeleton, prediction, modified_prediction, radius=6, out_dir='./heatmaps'):
import math
img_names = [img.split("_")[0] for img in os.listdir(out_dir) if str_is_int(img.split("_")[0])]
if len(img_names) > 0:
name_idx = max([int(img_name) for img_name in img_names]) + 1
else:
name_idx = 0
h, w, c = query_img.shape
prediction = prediction * h
modified_prediction = modified_prediction * h
# support_img = (support_img - np.min(support_img)) / (np.max(support_img) - np.min(support_img))
query_img = (query_img - np.min(query_img)) / (np.max(query_img) - np.min(query_img))
# for id, (img, w, keypoint) in enumerate(zip([support_img, query_img],
# [support_w, query_w],
# [support_kp, prediction])):
for id, (img, w, keypoint, modified_keypoint) in enumerate(zip([query_img],
[query_w],
[prediction],
[modified_prediction])):
f, axes = plt.subplots()
plt.imshow(img)
for k in range(keypoint.shape[0]):
if w[k] > 0:
kp1 = keypoint[k, :2]
kp2 = modified_keypoint[k, :2]
dist = 20*math.dist(keypoint[k, :2], modified_keypoint[k, :2])/h
kp = (kp1+kp2)/2
# kp = keypoint[k, :2]
c = (1, 0, 0, 0.75) if w[k] == 1 else (0, 0, 1, 0.6)
patch = plt.Circle(kp, radius*dist, color=c)
axes.add_patch(patch)
axes.text(kp[0], kp[1], k)
plt.draw()
for l, limb in enumerate(skeleton):
kp1 = keypoint[:, :2]
kp2 = modified_keypoint[:, :2]
kp = (kp1 + kp2) / 2
if l > len(colors) - 1:
c = [x / 255 for x in random.sample(range(0, 255), 3)]
else:
c = [x / 255 for x in colors[l]]
if w[limb[0]] > 0 and w[limb[1]] > 0:
patch = plt.Line2D([kp[limb[0], 0], kp[limb[1], 0]],
[kp[limb[0], 1], kp[limb[1], 1]],
linewidth=6, color=c, alpha=0.6)
axes.add_artist(patch)
plt.axis('off') # command for hiding the axis.
# name = 'support' if id == 0 else 'query'
name = 'query'
plt.savefig(f'./{out_dir}/{str(name_idx)}_query_out.png', bbox_inches='tight', pad_inches=0)
plt.show()
plt.clf()
plt.close('all')
return name_idx
def str_is_int(s):
try:
int(s)
return True
except ValueError:
return False
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