@@ -191,9 +191,9 @@ def test(data, | |||
# Plot images | |||
if plots and batch_i < 1: | |||
f = save_dir / ('test_batch%g_gt.jpg' % batch_i) # filename | |||
f = save_dir / f'test_batch{batch_i}_gt.jpg' # filename | |||
plot_images(img, targets, paths, str(f), names) # ground truth | |||
f = save_dir / ('test_batch%g_pred.jpg' % batch_i) | |||
f = save_dir / f'test_batch{batch_i}_pred.jpg' | |||
plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions | |||
# Compute statistics |
@@ -291,11 +291,11 @@ def train(hyp, opt, device, tb_writer=None): | |||
# Plot | |||
if ni < 3: | |||
f = str(log_dir / ('train_batch%g.jpg' % ni)) # filename | |||
f = str(log_dir / f'train_batch{ni}.jpg') # filename | |||
result = plot_images(images=imgs, targets=targets, paths=paths, fname=f) | |||
if tb_writer and result is not None: | |||
tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) | |||
# tb_writer.add_graph(model, imgs) # add model to tensorboard | |||
# if tb_writer and result is not None: | |||
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) | |||
# tb_writer.add_graph(model, imgs) # add model to tensorboard | |||
# end batch ------------------------------------------------------------------------------------------------ | |||
@@ -19,6 +19,7 @@ import numpy as np | |||
import torch | |||
import torch.nn as nn | |||
import yaml | |||
from PIL import Image | |||
from scipy.cluster.vq import kmeans | |||
from scipy.signal import butter, filtfilt | |||
from tqdm import tqdm | |||
@@ -1096,8 +1097,8 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max | |||
if fname is not None: | |||
mosaic = cv2.resize(mosaic, (int(ns * w * 0.5), int(ns * h * 0.5)), interpolation=cv2.INTER_AREA) | |||
cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) | |||
# cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save | |||
Image.fromarray(mosaic).save(fname) # PIL save | |||
return mosaic | |||