]
# prepare train, valid, annotation list
root_path = "./data/VOCdevkit/VOC2012/"
train_img_list, train_annotation_list, val_img_list, val_annotation_list = make_datapath_list(
root_path)
color_mean = (104, 117, 123)
input_size = 300
# transform = DataTransform(input_size, color_mean)
train_dataset = MyDataset(train_img_list,
train_annotation_list,
phase="train",
transform=DataTransform(input_size, color_mean),
anno_xml=Anno_xml(classes))
val_dataset = MyDataset(val_img_list,
val_annotation_list,
phase="val",
transform=DataTransform(input_size, color_mean),
anno_xml=Anno_xml(classes))
# print(train_dataset.__getitem__(1))
batch_size = 4
train_dataloader = data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=my_collate_fn)
val_dataloader = data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle",
"bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"]
# prepare train, valid, annotation list
root_path = "./data/VOCdevkit/VOC2012/"
train_img_list, train_annotation_list, val_img_list, val_annotation_list = make_datapath_list(root_path)
# read img
img_file_path = train_img_list[0]
img = cv2.imread(img_file_path) # Height, Width, Channel(BGR)
height, width, channels = img.shape
# annotation information
trans_anno = Anno_xml(classes)
anno_info_list = trans_anno(train_annotation_list[0], width, height)
# plot original image
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # mặc định của matplotlib là RGB
plt.show()
# prepare data transform
color_mean = (104, 117, 123)
input_size = 300
transform = DataTransform(input_size, color_mean)
# transform train img
phase = "train"
img_transformed, boxes, labels = transform(img, phase, anno_info_list[:,:4], anno_info_list[:, 4])
plt.imshow(cv2.cvtColor(img_transformed, cv2.COLOR_BGR2RGB)) # mặc định của matplotlib là RGB
if __name__ == "__main__":
classes = ['Car', 'Van', 'Truck', 'Pedestrian', 'Person_sitting',
'Cyclist', 'Tram', 'Misc', 'DontCare']
# prepare train, valid, annotation list
root_path = "../stereo_datasets/training"
train_img_list, train_annotation_list, val_img_list, val_annotation_list = make_datapath_list(root_path)
# prepare data transform
color_mean = (104, 117, 123)
input_size = 300
train_dataset = MyDataset(train_img_list, train_annotation_list, phase="train",
transform=DataTransform(input_size, color_mean), anno_xml=Anno_xml(classes))
val_dataset = MyDataset(val_img_list, val_annotation_list, phase="val",
transform=DataTransform(input_size, color_mean), anno_xml=Anno_xml(classes))
# print(len(train_dataset))
# print(train_dataset.__getitem__(1))
batch_size = 4
train_dataloader = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=my_collate_fn)
val_dataloader = data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, collate_fn=my_collate_fn)
dataloader_dict = {
"train": train_dataloader,
"val": val_dataloader
}