我有一个pandas数据框,用于保存图像ID,图像类和图像数据:
img_train.head(5)
ID index class data
0 10472 10472 0 [[[255, 255, 255, 0], [255, 255, 255, 0], [255...
1 7655 7655 0 [[[255, 255, 255, 0], [255, 255, 255, 0], [255...
2 6197 6197 0 [[[255, 255, 255, 0], [255, 255, 255, 0], [255...
3 9741 9741 0 [[[255, 255, 255, 0], [255, 255, 255, 0], [255...
4 9169 9169 0 [[[255, 255, 255, 0], [255, 255, 255, 0], [255...
我正在尝试将这些列中的每一列转换为numpy数组:
train_img_array = np.array([])
train_id_array = np.array([])
train_lab_array = np.array([])
count = 0
for index, row in img_train.iterrows():
imgid = row['ID']
imgclass = row['class']
imgdata = row['data']
#print(imgdata)
train_img_array = np.append(train_img_array, imgdata )
train_lab_array = np.append(train_lab_array, imgclass )
train_id_array = np.append(train_id_array, imgid )
但是,保存图像数据且属于“对象”类型的列未被转换为numpy数组中的相应行。例如,这是从原始数据帧处理58行后每个numpy数组的形状:
train_img_array.shape
train_lab_array.shape
train_id_array.shape
(93615200,)
(58,)
(58,)
我该如何解决这个问题?
答案 0 :(得分:-1)
我找到了这个问题的答案。这是非常直接的,我只是没有看到它开始。这就是我如何获取对象数据以及numpy数组(.values :))
train_img_array = np.array([])
train_id_array = np.array([])
train_lab_array = np.array([])
train_id_array = img_train['ID'].values
train_lab_array = img_train['class'].values
train_img_array =img_train['data'].values
#train_img_array = np.row_stack(img_train['data'])