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PaddleX
提交 93a582d1 编写于 作者: S sunyanfang01
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redraw the vdl

上级 91186090
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Showing with 272 addition and 75 deletion
docs/apis/visualize.md
浏览文件 @ 93a582d1
......@@ -169,8 +169,7 @@ NormLIME是利用一定数量的样本来出一个全局的解释。NormLIME会
## 数据预处理/增强过程可视化
```
paddlex.transforms.visualize(dataset,
index=0,
steps=3,
img_count=3,
save_dir='vdl_output')
```
对数据预处理/增强中间结果进行可视化。
......@@ -181,6 +180,5 @@ paddlex.transforms.visualize(dataset,
### 参数
>* **dataset** (paddlex.datasets): 数据集读取器。
>* **index** (int): 对数据集中的第index张图像进行可视化。默认为0
>* **steps** (int): 数据预处理/增强的次数。默认为3。
>* **img_count** (int): 需要进行数据预处理/增强的图像数目。默认为3。
>* **save_dir** (str): 日志保存的路径。默认为'vdl_output'。
\ No newline at end of file
paddlex/cv/transforms/cls_transforms.py
浏览文件 @ 93a582d1
......@@ -32,10 +32,8 @@ class ClsTransform:
class Compose(ClsTransform):
"""根据数据预处理/增强算子对输入数据进行操作。
所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
Args:
transforms (list): 数据预处理/增强算子。
Raises:
TypeError: 形参数据类型不满足需求。
ValueError: 数据长度不匹配。
......@@ -58,15 +56,11 @@ class Compose(ClsTransform):
"Elements in transforms should be defined in 'paddlex.cls.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
)
def __call__(self, im, label=None, vdl_writer=None, step=0):
def __call__(self, im, label=None):
"""
Args:
im (str/np.ndarray): 图像路径/图像np.ndarray数据。
label (int): 每张图像所对应的类别序号。
vdl_writer (visualdl.LogWriter): VisualDL存储器，日志信息将保存在其中。
当为None时，不对日志进行保存。默认为None。
step (int): 数据预处理的轮数，当vdl_writer不为None时有效。默认为0。
Returns:
tuple: 根据网络所需字段所组成的tuple；
字段由transforms中的最后一个数据预处理操作决定。
......@@ -82,11 +76,6 @@ class Compose(ClsTransform):
except:
raise TypeError('Can\'t read The image file {}!'.format(im))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if vdl_writer is not None:
vdl_writer.add_image(tag='0. origin image',
img=im,
step=step)
op_id = 1
for op in self.transforms:
if isinstance(op, ClsTransform):
outputs = op(im, label)
......@@ -100,12 +89,6 @@ class Compose(ClsTransform):
outputs = (im, )
if label is not None:
outputs = (im, label)
if vdl_writer is not None:
tag = str(op_id) + '. ' + op.__class__.__name__
vdl_writer.add_image(tag=tag,
img=im,
step=step)
op_id += 1
return outputs
def add_augmenters(self, augmenters):
......
paddlex/cv/transforms/det_transforms.py
浏览文件 @ 93a582d1
......@@ -41,10 +41,8 @@ class DetTransform:
class Compose(DetTransform):
"""根据数据预处理/增强列表对输入数据进行操作。
所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
Args:
transforms (list): 数据预处理/增强列表。
Raises:
TypeError: 形参数据类型不满足需求。
ValueError: 数据长度不匹配。
......@@ -70,7 +68,7 @@ class Compose(DetTransform):
"Elements in transforms should be defined in 'paddlex.det.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
)
def __call__(self, im, im_info=None, label_info=None, vdl_writer=None, step=0):
def __call__(self, im, im_info=None, label_info=None):
"""
Args:
im (str/np.ndarray): 图像路径/图像np.ndarray数据。
......@@ -94,10 +92,6 @@ class Compose(DetTransform):
其中n代表真实标注框的个数。
- difficult (np.ndarray): 每个真实标注框中的对象是否为难识别对象，形状为(n, 1)，
其中n代表真实标注框的个数。
vdl_writer (visualdl.LogWriter): VisualDL存储器，日志信息将保存在其中。
当为None时，不对日志进行保存。默认为None。
step (int): 数据预处理的轮数，当vdl_writer不为None时有效。默认为0。
Returns:
tuple: 根据网络所需字段所组成的tuple；
字段由transforms中的最后一个数据预处理操作决定。
......@@ -137,17 +131,12 @@ class Compose(DetTransform):
return (im, im_info)
else:
return (im, im_info, label_info)
outputs = decode_image(im, im_info, label_info)
im = outputs[0]
im_info = outputs[1]
if len(outputs) == 3:
label_info = outputs[2]
if vdl_writer is not None:
vdl_writer.add_image(tag='0. origin image',
img=im,
step=step)
op_id = 1
for op in self.transforms:
if im is None:
return None
......@@ -160,12 +149,6 @@ class Compose(DetTransform):
outputs = (im, im_info, label_info)
else:
outputs = (im, im_info)
if vdl_writer is not None:
tag = str(op_id) + '. ' + op.__class__.__name__
vdl_writer.add_image(tag=tag,
img=im,
step=step)
op_id += 1
return outputs
def add_augmenters(self, augmenters):
......@@ -827,7 +810,7 @@ class RandomExpand(DetTransform):
'gt_class' not in label_info:
raise TypeError('Cannot do RandomExpand! ' + \
'Becasuse gt_bbox/gt_class is not in label_info!')
if np.random.uniform(0., 1.) < self.prob:
if np.random.uniform(0., 1.) > self.prob:
return (im, im_info, label_info)
image_shape = im_info['image_shape']
......
paddlex/cv/transforms/seg_transforms.py
浏览文件 @ 93a582d1
......@@ -35,14 +35,11 @@ class SegTransform:
class Compose(SegTransform):
"""根据数据预处理/增强算子对输入数据进行操作。
所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
Args:
transforms (list): 数据预处理/增强算子。
Raises:
TypeError: transforms不是list对象
ValueError: transforms元素个数小于1。
"""
def __init__(self, transforms):
......@@ -61,8 +58,8 @@ class Compose(SegTransform):
raise Exception(
"Elements in transforms should be defined in 'paddlex.seg.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
)
def __call__(self, im, im_info=None, label=None, vdl_writer=None, step=0):
def __call__(self, im, im_info=None, label=None):
"""
Args:
im (str/np.ndarray): 图像路径/图像np.ndarray数据。
......@@ -71,13 +68,10 @@ class Compose(SegTransform):
图像在过resize前shape为(200, 300)， 过padding前shape为
(400, 600)
label (str/np.ndarray): 标注图像路径/标注图像np.ndarray数据。
vdl_writer (visualdl.LogWriter): VisualDL存储器，日志信息将保存在其中。
当为None时，不对日志进行保存。默认为None。
step (int): 数据预处理的轮数，当vdl_writer不为None时有效。默认为0。
Returns:
tuple: 根据网络所需字段所组成的tuple；字段由transforms中的最后一个数据预处理操作决定。
"""
if im_info is None:
im_info = list()
if isinstance(im, np.ndarray):
......@@ -95,11 +89,6 @@ class Compose(SegTransform):
if label is not None:
if not isinstance(label, np.ndarray):
label = np.asarray(Image.open(label))
if vdl_writer is not None:
vdl_writer.add_image(tag='0. origin image',
img=im,
step=step)
op_id = 1
for op in self.transforms:
if isinstance(op, SegTransform):
outputs = op(im, im_info, label)
......@@ -114,12 +103,6 @@ class Compose(SegTransform):
outputs = (im, im_info, label)
else:
outputs = (im, im_info)
if vdl_writer is not None:
tag = str(op_id) + '. ' + op.__class__.__name__
vdl_writer.add_image(tag=tag,
img=im,
step=step)
op_id += 1
return outputs
def add_augmenters(self, augmenters):
......
paddlex/cv/transforms/visualize.py
浏览文件 @ 93a582d1
......@@ -14,11 +14,248 @@
import os
import os.path as osp
import cv2
from PIL import Image
import numpy as np
from .imgaug_support import execute_imgaug
from .cls_transforms import ClsTransform
from .det_transforms import DetTransform
from .seg_transforms import SegTransform
import paddlex
from paddlex.cv.models.utils.visualize import get_color_map_list
def visualize(dataset, index=0, steps=3, save_dir='vdl_output'):
def _draw_rectangle_and_cname(img, xmin, ymin, xmax, ymax, cname, color):
""" 根据提供的标注信息，给图片描绘框体和类别显示
Args:
img: 图片路径
xmin: 检测框最小的x坐标
ymin: 检测框最小的y坐标
xmax: 检测框最大的x坐标
ymax: 检测框最大的y坐标
cname: 类别信息
color: 类别与颜色的对应信息
"""
# 描绘检测框
line_width = math.ceil(2 * max(img.shape[0:2]) / 600)
cv2.rectangle(
img,
pt1=(xmin, ymin),
pt2=(xmax, ymax),
color=color,
thickness=line_width)
return img
def cls_compose(im, label=None, transforms=None, vdl_writer=None, step=0):
"""
Args:
im (str/np.ndarray): 图像路径/图像np.ndarray数据。
label (int): 每张图像所对应的类别序号。
vdl_writer (visualdl.LogWriter): VisualDL存储器，日志信息将保存在其中。
当为None时，不对日志进行保存。默认为None。
step (int): 数据预处理的轮数，当vdl_writer不为None时有效。默认为0。
Returns:
tuple: 根据网络所需字段所组成的tuple；
字段由transforms中的最后一个数据预处理操作决定。
"""
if isinstance(im, np.ndarray):
if len(im.shape) != 3:
raise Exception(
"im should be 3-dimension, but now is {}-dimensions".
format(len(im.shape)))
else:
try:
im = cv2.imread(im).astype('float32')
except:
raise TypeError('Can\'t read The image file {}!'.format(im))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if vdl_writer is not None:
vdl_writer.add_image(tag='0. OriginalImange/' + str(step),
img=im,
step=0)
op_id = 1
for op in transforms:
if isinstance(op, ClsTransform):
if vdl_writer is not None and hasattr(op, 'prob'):
op.prob = 1.0
outputs = op(im, label)
im = outputs[0]
if len(outputs) == 2:
label = outputs[1]
else:
import imgaug.augmenters as iaa
if isinstance(op, iaa.Augmenter):
im = execute_imgaug(op, im)
outputs = (im, )
if label is not None:
outputs = (im, label)
if vdl_writer is not None:
tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step)
vdl_writer.add_image(tag=tag,
img=im,
step=0)
op_id += 1
def det_compose(im, im_info=None, label_info=None, transforms=None, vdl_writer=None, step=0,
labels=[], catid2color=None):
def decode_image(im_file, im_info, label_info):
if im_info is None:
im_info = dict()
if isinstance(im_file, np.ndarray):
if len(im_file.shape) != 3:
raise Exception(
"im should be 3-dimensions, but now is {}-dimensions".
format(len(im_file.shape)))
im = im_file
else:
try:
im = cv2.imread(im_file).astype('float32')
except:
raise TypeError('Can\'t read The image file {}!'.format(
im_file))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
# make default im_info with [h, w, 1]
im_info['im_resize_info'] = np.array(
[im.shape[0], im.shape[1], 1.], dtype=np.float32)
im_info['image_shape'] = np.array([im.shape[0],
im.shape[1]]).astype('int32')
use_mixup = False
for t in transforms:
if type(t).__name__ == 'MixupImage':
use_mixup = True
if not use_mixup:
if 'mixup' in im_info:
del im_info['mixup']
# decode mixup image
if 'mixup' in im_info:
im_info['mixup'] = \
decode_image(im_info['mixup'][0],
im_info['mixup'][1],
im_info['mixup'][2])
if label_info is None:
return (im, im_info)
else:
return (im, im_info, label_info)
outputs = decode_image(im, im_info, label_info)
im = outputs[0]
im_info = outputs[1]
if len(outputs) == 3:
label_info = outputs[2]
if vdl_writer is not None:
vdl_writer.add_image(tag='0. OriginalImange/' + str(step),
img=im,
step=0)
op_id = 1
bboxes = label_info['gt_bbox']
transforms = [None] + transforms
for op in transforms:
if im is None:
return None
if isinstance(op, DetTransform) or op is None:
if vdl_writer is not None and hasattr(op, 'prob'):
op.prob = 1.0
if op is not None:
outputs = op(im, im_info, label_info)
else:
outputs = (im, im_info, label_info)
im = outputs[0]
vdl_im = im
if vdl_writer is not None:
if isinstance(op, paddlex.cv.transforms.det_transforms.ResizeByShort):
scale = outputs[1]['im_resize_info'][2]
bboxes = bboxes * scale
elif isinstance(op, paddlex.cv.transforms.det_transforms.Resize):
h = outputs[1]['image_shape'][0]
w = outputs[1]['image_shape'][1]
target_size = op.target_size
if isinstance(target_size, int):
h_scale = float(target_size) / h
w_scale = float(target_size) / w
else:
h_scale = float(target_size[0]) / h
w_scale = float(target_size[1]) / w
bboxes[:,0] = bboxes[:,0] * w_scale
bboxes[:,1] = bboxes[:,1] * h_scale
bboxes[:,2] = bboxes[:,2] * w_scale
bboxes[:,3] = bboxes[:,3] * h_scale
else:
bboxes = outputs[2]['gt_bbox']
if not isinstance(op, paddlex.cv.transforms.det_transforms.RandomHorizontalFlip):
for i in range(bboxes.shape[0]):
bbox = bboxes[i]
cname = labels[outputs[2]['gt_class'][i][0]-1]
vdl_im = _draw_rectangle_and_cname(vdl_im,
int(bbox[0]),
int(bbox[1]),
int(bbox[2]),
int(bbox[3]),
cname,
catid2color[outputs[2]['gt_class'][i][0]-1])
if isinstance(op, paddlex.cv.transforms.det_transforms.Normalize):
vdl_im = im
else:
im = execute_imgaug(op, im)
if label_info is not None:
outputs = (im, im_info, label_info)
else:
outputs = (im, im_info)
vdl_im = im
if vdl_writer is not None:
tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step)
if op is None:
tag = str(op_id) + '. OriginalImangeWithBbox/' + str(step)
vdl_writer.add_image(tag=tag,
img=vdl_im,
step=0)
op_id += 1
def seg_compose(im, im_info=None, label=None, transforms=None, vdl_writer=None, step=0):
if im_info is None:
im_info = list()
if isinstance(im, np.ndarray):
if len(im.shape) != 3:
raise Exception(
"im should be 3-dimensions, but now is {}-dimensions".
format(len(im.shape)))
else:
try:
im = cv2.imread(im).astype('float32')
except:
raise ValueError('Can\'t read The image file {}!'.format(im))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if label is not None:
if not isinstance(label, np.ndarray):
label = np.asarray(Image.open(label))
if vdl_writer is not None:
vdl_writer.add_image(tag='0. OriginalImange' + '/' + str(step),
img=im,
step=0)
op_id = 1
for op in transforms:
if isinstance(op, SegTransform):
outputs = op(im, im_info, label)
im = outputs[0]
if len(outputs) >= 2:
im_info = outputs[1]
if len(outputs) == 3:
label = outputs[2]
else:
im = execute_imgaug(op, im)
if label is not None:
outputs = (im, im_info, label)
else:
outputs = (im, im_info)
if vdl_writer is not None:
tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step)
vdl_writer.add_image(tag=tag,
img=im,
step=0)
op_id += 1
def visualize(dataset, img_count=3, save_dir='vdl_output'):
'''对数据预处理/增强中间结果进行可视化。
可使用VisualDL查看中间结果：
1. VisualDL启动方式: visualdl --logdir vdl_output --port 8001
......@@ -27,25 +264,38 @@ def visualize(dataset, index=0, steps=3, save_dir='vdl_output'):
Args:
dataset (paddlex.datasets): 数据集读取器。
index (int): 对数据集中的第index张图像进行可视化。默认为0
steps (int): 数据预处理/增强的次数。默认为3。
img_count (int): 需要进行数据预处理/增强的图像数目。默认为3。
save_dir (str): 日志保存的路径。默认为'vdl_output'。
'''
if dataset.num_samples < img_count:
img_count = dataset.num_samples
transforms = dataset.transforms
if not osp.isdir(save_dir):
if osp.exists(save_dir):
os.remove(save_dir)
os.makedirs(save_dir)
for i, data in enumerate(dataset.iterator()):
if i == index:
break
from visualdl import LogWriter
vdl_save_dir = osp.join(save_dir, 'image_transforms')
vdl_writer = LogWriter(vdl_save_dir)
data.append(vdl_writer)
for s in range(steps):
if s != 0:
data.pop()
data.append(s)
transforms(*data)
\ No newline at end of file
for i, data in enumerate(dataset.iterator()):
if i == img_count:
break
data.append(transforms.transforms)
data.append(vdl_writer)
data.append(i)
if isinstance(transforms, ClsTransform):
cls_compose(*data)
elif isinstance(transforms, DetTransform):
labels = dataset.labels
color_map = get_color_map_list(len(labels) + 1)
catid2color = {}
for catid in range(len(labels)):
catid2color[catid] = color_map[catid + 1]
data.append(labels)
data.append(catid2color)
det_compose(*data)
elif isinstance(transforms, SegTransform):
seg_compose(*data)
else:
raise Exception('The transform must the subclass of \
ClsTransform or DetTransform or SegTransform!')
\ No newline at end of file
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