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car-detection-bayes
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change of test batch image format from .jpg to .png, due to matplotlib bug (#817) 

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Piotr Skalski 2020-01-31 00:48:26 +01:00 committed by GitHub
parent 4b9d73f931
commit 20b0601fa7
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3 changed files with 9 additions and 7 deletions
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5
test.py
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@ -76,8 +76,9 @@ def test(cfg,
_, _, height, width = imgs.shape # batch size, channels, height, width _, _, height, width = imgs.shape # batch size, channels, height, width
# Plot images with bounding boxes # Plot images with bounding boxes
if batch_i == 0 and not os.path.exists('test_batch0.jpg'): if batch_i == 0 and not os.path.exists('test_batch0.png'):
plot_images(imgs=imgs, targets=targets, paths=paths, fname='test_batch0.jpg') plot_images(imgs=imgs, targets=targets, paths=paths, fname='test_batch0.png')
# Disable gradients # Disable gradients
with torch.no_grad(): with torch.no_grad():

4
train.py
View File

@ -73,7 +73,7 @@ def train():
nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
# Remove previous results # Remove previous results
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file): for f in glob.glob('*_batch*.png') + glob.glob(results_file):
os.remove(f) os.remove(f)
# Initialize model # Initialize model
@ -255,7 +255,7 @@ def train():
# Plot images with bounding boxes # Plot images with bounding boxes
if ni == 0: if ni == 0:
fname = 'train_batch%g.jpg' % i fname = 'train_batch%g.png' % i
plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname) plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
if tb_writer: if tb_writer:
tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC') tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')

7
utils/utils.py
View File

@ -911,7 +911,8 @@ def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
fig.savefig('comparison.png', dpi=200) fig.savefig('comparison.png', dpi=200)
def plot_images(imgs, targets, paths=None, fname='images.jpg'):
def plot_images(imgs, targets, paths=None, fname='images.png'):
# Plots training images overlaid with targets # Plots training images overlaid with targets
imgs = imgs.cpu().numpy() imgs = imgs.cpu().numpy()
targets = targets.cpu().numpy() targets = targets.cpu().numpy()
@ -947,13 +948,13 @@ def plot_test_txt(): # from utils.utils import *; plot_test()
ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
ax.set_aspect('equal') ax.set_aspect('equal')
fig.tight_layout() fig.tight_layout()
plt.savefig('hist2d.jpg', dpi=300) plt.savefig('hist2d.png', dpi=300)
fig, ax = plt.subplots(1, 2, figsize=(12, 6)) fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].hist(cx, bins=600) ax[0].hist(cx, bins=600)
ax[1].hist(cy, bins=600) ax[1].hist(cy, bins=600)
fig.tight_layout() fig.tight_layout()
plt.savefig('hist1d.jpg', dpi=200) plt.savefig('hist1d.png', dpi=200)
def plot_targets_txt(): # from utils.utils import *; plot_targets_txt() def plot_targets_txt(): # from utils.utils import *; plot_targets_txt()