def __getitem__(self, index):
image, label = self._make_img_gt_point_pair(index)
seg = None
if self.cfg.NO_TRANS == False:
if 'seg' == self.cfg.TARGET_MODAL:
seg = Image.fromarray((color_label_np(
label_copy, ignore=self.ignore_label).astype(np.uint8)),
mode='RGB')
# seg = np.load(seg_path)
seg = Image.fromarray(seg.astype(np.uint8), mode='RGB')
sample = {'image': image, 'label': label, 'seg': seg}
else:
# print(image.size)
sample = {'image': image, 'label': label}
for key in list(sample.keys()):
if sample[key] is None:
sample.pop(key)
# if self.transform:
# sample = self.transform(sample)
# return sample
if self.split == "train":
return self.transform_tr(sample)
elif self.split == 'val':
return self.transform_val(sample)
def __getitem__(self, index):
example = self.examples[index]
img_path = example["img_path"]
# img = cv2.imread(img_path, -1) # (shape: (1024, 2048, 3))
image = Image.open(img_path).convert('RGB')
# image = F.resize(image, (320, 640))
# resize img without interpolation (want the image to still match
label_img_path = example["label_img_path"]
label_img = Image.open(label_img_path).convert(
'L') # (shape: (1024, 2048))
# label_img = F.resize(label_img, (320, 640), interpolation=Image.NEAREST)
label_img = convert(np.array(label_img))
# resize label_img without interpolation (want the resulting image to
# still only contain pixel values corresponding to an object class):
seg = Image.fromarray((color_label_np(label_img).astype(np.uint8)),
mode='RGB')
label = Image.fromarray(label_img.astype(np.uint8))
# depth = Image.open(depth_dir[idx]).convert('RGB')
depth = None
sample = {'image': image, 'depth': depth, 'label': label, 'seg': seg}
# sample = {'image': image, 'depth': depth, 'label': label}
if self.transform:
sample = self.transform(sample)
return sample
def __getitem__(self, idx):
if self.phase_train:
img_dir = self.img_dir_train
depth_dir = self.depth_dir_train
label_dir = self.label_dir_train
else:
img_dir = self.img_dir_test
depth_dir = self.depth_dir_test
label_dir = self.label_dir_test
# label = np.load(label_dir[idx])
image = Image.open(img_dir[idx]).convert('RGB')
# image_np = np.asarray(image)
# depth = Image.fromarray(np.uint8(color_label_np(np.load(label_dir[idx]))), mode='RGB')
# depth = Image.fromarray(color_label_np(np.uint8(np.load(label_dir[idx]))), mode='RGB')
# depth = Image.open(depth_dir[idx]).convert('RGB')
# label = np.uint8(np.maximum(_label, 0))
# label = Image.fromarray(label)
# label = Image.fromarray(np.uint8(np.load(label_dir[idx])))
# label_np = np.asarray(label)
_label = np.load(label_dir[idx])
_label_copy = _label.copy()
for k, v in self.id_to_trainid.items():
_label_copy[_label == k] = v
label = Image.fromarray(_label_copy.astype(np.uint8))
depth = Image.open(depth_dir[idx]).convert('RGB')
seg = Image.fromarray((color_label_np(_label_copy).astype(np.uint8)),
mode='RGB')
# # if len(label)==530:
# f = open('/home/lzy/ResNet_Backbone_segmentation/result','w')
# for i in range(len(label)):
# for j in range(len(label[0])):
# f.write(str(int(label[i][j]))+' ')
# f.write('\r')
# f.close()
# for i in range(1000000):
# print(i)
# cv2.imshow('label',label)
sample = {'image': image, 'depth': depth, 'label': label, 'seg': seg}
# sample = {'image': image, 'depth': depth, 'label': label}
if self.transform:
sample = self.transform(sample)
return sample
def __getitem__(self, index):
example = self.examples[index]
image_path = example["img_path"]
label_path = example["label_path"]
seg_path = example["seg_path"]
# image = np.load(img_path)
# label = np.load(label_path)
image = cv2.imread(
image_path,
cv2.IMREAD_COLOR) # BGR 3 channel ndarray wiht shape H * W * 3
image = cv2.cvtColor(
image, cv2.COLOR_BGR2RGB
) # convert cv2 read image from BGR order to RGB order
image = np.float32(image)
label = cv2.imread(label_path, cv2.IMREAD_GRAYSCALE) # GRAY 1 ch
# image = self.examples[index]["image"]
# label = self.examples[index]["label"]
label_copy = label.copy()
for k, v in self.id_to_trainid.items():
label_copy[label == k] = v
label = np.asarray(label_copy)
# image=cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
seg = None
if self.cfg.NO_TRANS == False:
if 'seg' == self.cfg.TARGET_MODAL:
seg = (color_label_np(label_copy,
ignore=self.ignore_label,
dataset='cityscapes').astype(np.uint8))
seg = cv2.cvtColor(np.asarray(seg), cv2.COLOR_RGB2BGR)
# seg=np.load(seg_path)
# seg = Image.fromarray(seg.astype(np.uint8),mode='RGB')
# seg = self.examples[index]["seg"]
sample = {'image': image, 'label': label, 'seg': seg}
else:
# print(image.size)
sample = {'image': image, 'label': label}
for key in list(sample.keys()):
if sample[key] is None:
sample.pop(key)
if self.transform:
sample = self.transform(sample)
return sample
def __getitem__(self, index):
image, label = self._make_img_gt_point_pair(index)
label_copy = label
seg = None
if self.cfg.NO_TRANS == False:
if 'seg' == self.cfg.TARGET_MODAL:
seg = Image.fromarray((color_label_np(label_copy, ignore=self.ignore_label).astype(np.uint8)), mode='RGB')
# seg = np.load(seg_path)
if 'lab' ==self.cfg.TARGET_MODAL:
seg=color.rgb2lab(image)
sample = {'image': image, 'label': label, 'seg': seg}
else:
# print(image.size)
sample = {'image': image, 'label': label}
for key in list(sample.keys()):
if sample[key] is None:
sample.pop(key)
return self.transform(sample)
def __getitem__(self, index):
example = self.examples[index]
# image = example["image"]
# label = example["label"]
image_path = example["image_path"]
label_path = example["label_path"]
image = cv2.imread(image_path, cv2.IMREAD_COLOR) #loadimage
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = np.float32(image)
label = cv2.imread(label_path, cv2.IMREAD_GRAYSCALE)
label = np.asarray(label)
label = label - 1 #
label_copy = label.copy()
label_copy[label == -1] = 255
label = label_copy
seg = None
if self.cfg.NO_TRANS == False:
if 'seg' == self.cfg.TARGET_MODAL:
seg = (color_label_np(label,
ignore=self.ignore_label,
dataset=ade20k).astype(np.uint8))
seg = cv2.cvtColor(np.asarray(seg), cv2.COLOR_RGB2BGR)
# seg=np.load(seg_path)
# seg = Image.fromarray(seg.astype(np.uint8),mode='RGB')
# seg = self.examples[index]["seg"]
sample = {'image': image, 'label': label, 'seg': seg}
else:
# print(image.size)
sample = {'image': image, 'label': label}
for key in list(sample.keys()):
if sample[key] is None:
sample.pop(key)
if self.transform:
sample = self.transform(sample)
return sample
