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+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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+#
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+# Licensed under the Apache License, Version 2.0 (the "License");
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+# you may not use this file except in compliance with the License.
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+# You may obtain a copy of the License at
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+#
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+# http://www.apache.org/licenses/LICENSE-2.0
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+#
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+# Unless required by applicable law or agreed to in writing, software
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+# distributed under the License is distributed on an "AS IS" BASIS,
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+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+# See the License for the specific language governing permissions and
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+# limitations under the License.
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+
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+import os
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+import os.path as osp
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+import cv2
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+from PIL import Image
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+import numpy as np
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+import math
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+from .imgaug_support import execute_imgaug
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+from .cls_transforms import ClsTransform
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+from .det_transforms import DetTransform
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+from .seg_transforms import SegTransform
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+import paddlex as pdx
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+from paddlex.cv.models.utils.visualize import get_color_map_list
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+
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+
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+def _draw_rectangle_and_cname(img, xmin, ymin, xmax, ymax, cname, color):
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+ """ 根据提供的标注信息，给图片描绘框体和类别显示
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+
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+ Args:
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+ img: 图片路径
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+ xmin: 检测框最小的x坐标
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+ ymin: 检测框最小的y坐标
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+ xmax: 检测框最大的x坐标
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+ ymax: 检测框最大的y坐标
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+ cname: 类别信息
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+ color: 类别与颜色的对应信息
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+ """
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+ # 描绘检测框
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+ line_width = math.ceil(2 * max(img.shape[0:2]) / 600)
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+ cv2.rectangle(
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+ img,
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+ pt1=(xmin, ymin),
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+ pt2=(xmax, ymax),
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+ color=color,
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+ thickness=line_width)
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+ return img
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+
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+def cls_compose(im, label=None, transforms=None, vdl_writer=None, step=0):
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+ """
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+ Args:
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+ im (str/np.ndarray): 图像路径/图像np.ndarray数据。
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+ label (int): 每张图像所对应的类别序号。
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+ vdl_writer (visualdl.LogWriter): VisualDL存储器，日志信息将保存在其中。
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+ 当为None时，不对日志进行保存。默认为None。
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+ step (int): 数据预处理的轮数，当vdl_writer不为None时有效。默认为0。
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+
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+ Returns:
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+ tuple: 根据网络所需字段所组成的tuple；
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+ 字段由transforms中的最后一个数据预处理操作决定。
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+ """
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+ if isinstance(im, np.ndarray):
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+ if len(im.shape) != 3:
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+ raise Exception(
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+ "im should be 3-dimension, but now is {}-dimensions".
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+ format(len(im.shape)))
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+ else:
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+ try:
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+ im = cv2.imread(im).astype('float32')
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+ except:
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+ raise TypeError('Can\'t read The image file {}!'.format(im))
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+ im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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+ if vdl_writer is not None:
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+ vdl_writer.add_image(tag='0. OriginalImange/' + str(step),
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+ img=im,
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+ step=0)
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+ op_id = 1
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+ for op in transforms:
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+ if isinstance(op, ClsTransform):
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+ if vdl_writer is not None and hasattr(op, 'prob'):
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+ op.prob = 1.0
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+ outputs = op(im, label)
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+ im = outputs[0]
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+ if len(outputs) == 2:
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+ label = outputs[1]
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+ if isinstance(op, pdx.cv.transforms.cls_transforms.Normalize):
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+ continue
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+ else:
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+ import imgaug.augmenters as iaa
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+ if isinstance(op, iaa.Augmenter):
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+ im = execute_imgaug(op, im)
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+ outputs = (im, )
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+ if label is not None:
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+ outputs = (im, label)
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+ if vdl_writer is not None:
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+ tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step)
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+ vdl_writer.add_image(tag=tag,
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+ img=im,
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+ step=0)
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+ op_id += 1
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+
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+def det_compose(im, im_info=None, label_info=None, transforms=None, vdl_writer=None, step=0,
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+ labels=[], catid2color=None):
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+ def decode_image(im_file, im_info, label_info):
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+ if im_info is None:
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+ im_info = dict()
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+ if isinstance(im_file, np.ndarray):
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+ if len(im_file.shape) != 3:
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+ raise Exception(
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+ "im should be 3-dimensions, but now is {}-dimensions".
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+ format(len(im_file.shape)))
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+ im = im_file
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+ else:
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+ try:
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+ im = cv2.imread(im_file).astype('float32')
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+ except:
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+ raise TypeError('Can\'t read The image file {}!'.format(
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+ im_file))
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+ im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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+ # make default im_info with [h, w, 1]
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+ im_info['im_resize_info'] = np.array(
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+ [im.shape[0], im.shape[1], 1.], dtype=np.float32)
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+ im_info['image_shape'] = np.array([im.shape[0],
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+ im.shape[1]]).astype('int32')
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+ use_mixup = False
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+ for t in transforms:
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+ if type(t).__name__ == 'MixupImage':
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+ use_mixup = True
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+ if not use_mixup:
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+ if 'mixup' in im_info:
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+ del im_info['mixup']
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+ # decode mixup image
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+ if 'mixup' in im_info:
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+ im_info['mixup'] = \
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+ decode_image(im_info['mixup'][0],
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+ im_info['mixup'][1],
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+ im_info['mixup'][2])
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+ if label_info is None:
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+ return (im, im_info)
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+ else:
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+ return (im, im_info, label_info)
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+
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+ outputs = decode_image(im, im_info, label_info)
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+ im = outputs[0]
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+ im_info = outputs[1]
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+ if len(outputs) == 3:
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+ label_info = outputs[2]
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+ if vdl_writer is not None:
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+ vdl_writer.add_image(tag='0. OriginalImange/' + str(step),
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+ img=im,
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+ step=0)
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+ op_id = 1
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+ bboxes = label_info['gt_bbox']
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+ transforms = [None] + transforms
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+ for op in transforms:
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+ if im is None:
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+ return None
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+ if isinstance(op, DetTransform) or op is None:
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+ if vdl_writer is not None and hasattr(op, 'prob'):
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+ op.prob = 1.0
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+ if op is not None:
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+ outputs = op(im, im_info, label_info)
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+ else:
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+ outputs = (im, im_info, label_info)
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+ im = outputs[0]
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+ vdl_im = im
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+ if vdl_writer is not None:
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+ if isinstance(op, pdx.cv.transforms.det_transforms.ResizeByShort):
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+ scale = outputs[1]['im_resize_info'][2]
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+ bboxes = bboxes * scale
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+ elif isinstance(op, pdx.cv.transforms.det_transforms.Resize):
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+ h = outputs[1]['image_shape'][0]
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+ w = outputs[1]['image_shape'][1]
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+ target_size = op.target_size
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+ if isinstance(target_size, int):
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+ h_scale = float(target_size) / h
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+ w_scale = float(target_size) / w
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+ else:
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+ h_scale = float(target_size[0]) / h
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+ w_scale = float(target_size[1]) / w
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+ bboxes[:,0] = bboxes[:,0] * w_scale
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+ bboxes[:,1] = bboxes[:,1] * h_scale
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+ bboxes[:,2] = bboxes[:,2] * w_scale
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+ bboxes[:,3] = bboxes[:,3] * h_scale
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+ else:
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+ bboxes = outputs[2]['gt_bbox']
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+ if not isinstance(op, pdx.cv.transforms.det_transforms.RandomHorizontalFlip):
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+ for i in range(bboxes.shape[0]):
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+ bbox = bboxes[i]
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+ cname = labels[outputs[2]['gt_class'][i][0]-1]
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+ vdl_im = _draw_rectangle_and_cname(vdl_im,
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+ int(bbox[0]),
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+ int(bbox[1]),
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+ int(bbox[2]),
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+ int(bbox[3]),
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+ cname,
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+ catid2color[outputs[2]['gt_class'][i][0]-1])
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+ if isinstance(op, pdx.cv.transforms.det_transforms.Normalize):
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+ continue
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+ else:
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+ im = execute_imgaug(op, im)
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+ if label_info is not None:
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+ outputs = (im, im_info, label_info)
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+ else:
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+ outputs = (im, im_info)
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+ vdl_im = im
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+ if vdl_writer is not None:
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+ tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step)
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+ if op is None:
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+ tag = str(op_id) + '. OriginalImangeWithGTBox/' + str(step)
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+ vdl_writer.add_image(tag=tag,
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+ img=vdl_im,
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+ step=0)
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+ op_id += 1
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+
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+def seg_compose(im, im_info=None, label=None, transforms=None, vdl_writer=None, step=0):
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+ if im_info is None:
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+ im_info = list()
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+ if isinstance(im, np.ndarray):
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+ if len(im.shape) != 3:
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+ raise Exception(
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+ "im should be 3-dimensions, but now is {}-dimensions".
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+ format(len(im.shape)))
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+ else:
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+ try:
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+ im = cv2.imread(im).astype('float32')
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+ except:
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+ raise ValueError('Can\'t read The image file {}!'.format(im))
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+ im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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+ if label is not None:
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+ if not isinstance(label, np.ndarray):
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+ label = np.asarray(Image.open(label))
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+ if vdl_writer is not None:
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+ vdl_writer.add_image(tag='0. OriginalImange' + '/' + str(step),
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+ img=im,
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+ step=0)
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+ op_id = 1
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+ for op in transforms:
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+ if isinstance(op, SegTransform):
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+ outputs = op(im, im_info, label)
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+ im = outputs[0]
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+ if len(outputs) >= 2:
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+ im_info = outputs[1]
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+ if len(outputs) == 3:
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+ label = outputs[2]
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+ if isinstance(op, pdx.cv.transforms.seg_transforms.Normalize):
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+ continue
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+ else:
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+ im = execute_imgaug(op, im)
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+ if label is not None:
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+ outputs = (im, im_info, label)
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+ else:
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+ outputs = (im, im_info)
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+ if vdl_writer is not None:
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+ tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step)
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+ vdl_writer.add_image(tag=tag,
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+ img=im,
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+ step=0)
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+ op_id += 1
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+
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+def visualize(dataset, img_count=3, save_dir='vdl_output'):
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+ '''对数据预处理/增强中间结果进行可视化。
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+ 可使用VisualDL查看中间结果：
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+ 1. VisualDL启动方式: visualdl --logdir vdl_output --port 8001
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+ 2. 浏览器打开 https://0.0.0.0:8001即可，
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+ 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP
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+
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+ Args:
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+ dataset (paddlex.datasets): 数据集读取器。
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+ img_count (int): 需要进行数据预处理/增强的图像数目。默认为3。
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+ save_dir (str): 日志保存的路径。默认为'vdl_output'。
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+ '''
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+ if dataset.num_samples < img_count:
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+ img_count = dataset.num_samples
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+ transforms = dataset.transforms
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+ if not osp.isdir(save_dir):
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+ if osp.exists(save_dir):
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+ os.remove(save_dir)
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+ os.makedirs(save_dir)
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+ from visualdl import LogWriter
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+ vdl_save_dir = osp.join(save_dir, 'image_transforms')
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+ vdl_writer = LogWriter(vdl_save_dir)
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+ for i, data in enumerate(dataset.iterator()):
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+ if i == img_count:
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+ break
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+ data.append(transforms.transforms)
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+ data.append(vdl_writer)
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+ data.append(i)
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+ if isinstance(transforms, ClsTransform):
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+ cls_compose(*data)
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+ elif isinstance(transforms, DetTransform):
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+ labels = dataset.labels
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+ color_map = get_color_map_list(len(labels) + 1)
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+ catid2color = {}
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+ for catid in range(len(labels)):
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+ catid2color[catid] = color_map[catid + 1]
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+ data.append(labels)
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+ data.append(catid2color)
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+ det_compose(*data)
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+ elif isinstance(transforms, SegTransform):
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+ seg_compose(*data)
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+ else:
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+ raise Exception('The transform must the subclass of \
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+ ClsTransform or DetTransform or SegTransform!')
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