def func_per_iteration(self, data, device):
img = data['data']
label = data['label']
name = data['fn']
pred = self.sliding_eval(img, config.eval_crop_size,
config.eval_stride_rate, device)
# pred = self.whole_eval(img, None, None, device)
hist_tmp, labeled_tmp, correct_tmp = hist_info(config.num_classes,
pred, label)
results_dict = {
'hist': hist_tmp,
'labeled': labeled_tmp,
'correct': correct_tmp
}
if self.save_path is not None:
trans_labels = [
7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
31, 32, 33
]
label = np.zeros(pred.shape)
ids = np.unique(pred)
for id in ids:
label[np.where(pred == id)] = trans_labels[id]
fn = name.split('_')
fn = fn[0] + '_' + fn[1] + '_' + fn[2] + '.png'
cv2.imwrite(os.path.join(self.save_path, fn), label)
logger.info('Save the image ' + fn)
return results_dict
def func_per_iteration(self, data, device):
img = data['data']
label = data['label']
name = data['fn']
pred = self.sliding_eval(img,
config.eval_crop_size,
config.eval_stride_rate,
device=device)
hist_tmp, labeled_tmp, correct_tmp = hist_info(config.num_classes,
pred, label)
results_dict = {
'hist': hist_tmp,
'labeled': labeled_tmp,
'correct': correct_tmp
}
if self.save_path is not None:
fn = name + '.png'
cv2.imwrite(os.path.join(self.save_path, fn), pred)
logger.info('Save the image ' + fn)
if self.show_image:
colors = self.dataset.get_class_colors
image = img
clean = np.zeros(label.shape)
comp_img = show_img(colors, config.background, image, clean, label,
pred)
cv2.imshow('comp_image', comp_img)
cv2.waitKey(0)
return results_dict
def func_per_iteration(self, data, device, iter=None):
if self.config is not None: config = self.config
img = data['data']
label = data['label']
name = data['fn']
if len(config.eval_scale_array) == 1:
pred = self.whole_eval(img, None, device)
else:
pred = self.sliding_eval(img, config.eval_crop_size,
config.eval_stride_rate, device)
hist_tmp, labeled_tmp, correct_tmp = hist_info(config.num_classes,
pred, label)
results_dict = {
'hist': hist_tmp,
'labeled': labeled_tmp,
'correct': correct_tmp
}
if self.save_path is not None:
fn = name + '.png'
cv2.imwrite(os.path.join(self.save_path, fn), pred)
logger.info('Save the image ' + fn)
# tensorboard logger does not fit multiprocess
if self.logger is not None and iter is not None:
colors = self.dataset.get_class_colors()
image = img
clean = np.zeros(label.shape)
comp_img = show_img(colors, config.background, image, clean, label,
pred)
self.logger.add_image(
'vis', np.swapaxes(np.swapaxes(comp_img, 0, 2), 1, 2), iter)
print("self.show_prediction = ", self.show_prediction)
if self.show_image or self.show_prediction:
colors = self.dataset.get_class_colors()
image = img
clean = np.zeros(label.shape)
if self.show_image:
comp_img = show_img(colors, config.background, image, clean,
label, pred)
else:
comp_img = show_prediction(colors, config.background, image,
pred)
cv2.imwrite(
os.path.join(os.path.realpath('.'), self.config.save, "eval",
name + ".vis.png"), comp_img[:, :, ::-1])
# cv2.imwrite(name + ".png", comp_img[:,:,::-1])
return results_dict
def func_per_iteration(self, data, device):
img = data['data']
label = data['label']
hha = data['hha_img']
name = data['fn']
pred = self.sliding_eval_rgbdepth(img, hha, config.eval_crop_size,
config.eval_stride_rate, device)
hist_tmp, labeled_tmp, correct_tmp = hist_info(config.num_classes,
pred, label)
results_dict = {
'hist': hist_tmp,
'labeled': labeled_tmp,
'correct': correct_tmp
}
if self.save_path is not None:
ensure_dir(self.save_path)
ensure_dir(self.save_path + '_color')
fn = name + '.png'
'save colored result'
result_img = Image.fromarray(pred.astype(np.uint8), mode='P')
class_colors = get_class_colors()
palette_list = list(np.array(class_colors).flat)
if len(palette_list) < 768:
palette_list += [0] * (768 - len(palette_list))
result_img.putpalette(palette_list)
result_img.save(os.path.join(self.save_path + '_color', fn))
'save raw result'
cv2.imwrite(os.path.join(self.save_path, fn), pred)
logger.info('Save the image ' + fn)
if self.show_image:
colors = self.dataset.get_class_colors
image = img
clean = np.zeros(label.shape)
comp_img = show_img(colors, config.background, image, clean, label,
pred)
cv2.imshow('comp_image', comp_img)
cv2.waitKey(0)
return results_dict
def func_per_iteration(self, data, device):
img = data['data']
label = data['label']
name = data['fn']
img = cv2.resize(img, (config.image_width, config.image_height),
interpolation=cv2.INTER_LINEAR)
label = cv2.resize(label,
(config.image_width // config.gt_down_sampling,
config.image_height // config.gt_down_sampling),
interpolation=cv2.INTER_NEAREST)
pred = self.whole_eval(img,
(config.image_height // config.gt_down_sampling,
config.image_width // config.gt_down_sampling),
config.eval_stride_rate, device)
hist_tmp, labeled_tmp, correct_tmp = hist_info(config.num_classes,
pred, label)
results_dict = {
'hist': hist_tmp,
'labeled': labeled_tmp,
'correct': correct_tmp
}
if self.save_path is not None:
fn = name + '.png'
cv2.imwrite(os.path.join(self.save_path, fn), pred)
logger.info('Save the image ' + fn)
if self.show_image:
colors = self.dataset.get_class_colors
image = img
clean = np.zeros(label.shape)
comp_img = show_img(colors, config.background, image, clean, label,
pred)
cv2.imshow('comp_image', comp_img)
cv2.waitKey(0)
return results_dict
