数据集读取总结Image.open和cv2.imread
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2022-05-16 11:23:31
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数据集读取总结
生成可检索的地址字典集
data_path = listfilename
data_file_name = ["nyu_images/", "nyu_depths/", "nyu_hha/", "nyu_labels/"]
examples = []
example = {}
for data_dir in data_file_name:
img_dir = data_path + data_dir
file_names = os.listdir(img_dir)
for file_name in file_names:
if data_dir == "nyu_images/":
img_path = img_dir + file_name
img_id = file_name.split(".jpg")[0]
example["img_path"] = img_path
example["img_id"] = img_id
examples.append(example)
if data_dir == "nyu_depths/":
img_path = img_dir + file_name
example["depth_path"] = img_path
examples.append(example)
if data_dir == "nyu_hha/":
img_path = img_dir + file_name
example["hha_path"] = img_path
examples.append(example)
if data_dir == "nyu_labels/":
img_path = img_dir + file_name
example["label_path"] = img_path
examples.append(example)
num_examples = len(examples)
return examples,num_examples
批量读取数据集
def __getitem__(self, index):
#self.paths['images'][index]
#print (self.opt.scale,self.opt.flip,self.opt.crop,self.opt.colorjitter)
path=self.paths_dict[index]
# print ("print index===========%d",index)
# print(len(path))
# print(path.keys())
img_path=path["img_path"]
depth_path = path["depth_path"]
hha_path = path["hha_path"]
label_path = path["label_path"]
# img = cv2.imread(img_path, -1)
# img=np.asanyarray(Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)))
# depth =cv2.imread(depth_path,-1)
# depth = np.asanyarray(Image.fromarray(cv2.cvtColor(depth, cv2.COLOR_BGR2RGB)))
# HHA = cv2.imread(hha_path,-1)
# HHA = np.asanyarray(Image.fromarray(cv2.cvtColor(HHA, cv2.COLOR_BGR2RGB)))
# seg = cv2.imread(label_path, -1)
# seg = np.asanyarray(Image.fromarray(cv2.cvtColor(seg, cv2.COLOR_BGR2RGB)))
img = np.asarray(Image.open(img_path)).astype(np.uint8)
depth = np.asarray(Image.open(depth_path)).astype(np.float32)/120. # 1/10 * depth
HHA = np.asarray(Image.open(hha_path))
seg = np.asarray(Image.open(label_path)).astype(np.uint8)
params = get_params(self.opt, seg.shape)
depth_tensor_tranformed = transform(depth, params, normalize=False,istrain=self.opt.isTrain)
seg_tensor_tranformed = transform(seg, params, normalize=False,method='nearest',istrain=self.opt.isTrain)
if self.opt.inputmode == 'bgr-mean':
img_tensor_tranformed = transform(img, params, normalize=False, istrain=self.opt.isTrain, option=1)
HHA_tensor_tranformed = transform(HHA, params, normalize=False, istrain=self.opt.isTrain, option=2)
else:
img_tensor_tranformed = transform(img, params, istrain=self.opt.isTrain, option=1)
HHA_tensor_tranformed = transform(HHA, params, istrain=self.opt.isTrain, option=2)
# print img_tensor_tranformed
# print(np.unique(depth_tensor_tranformed.numpy()).shape)
# print img_tensor_tranformed.size()
return {'image':img_tensor_tranformed,
'depth':depth_tensor_tranformed,
'seg': seg_tensor_tranformed,
'HHA': HHA_tensor_tranformed,
'imgpath': label_path}
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